A PDGFRB-CD44-MIF Crosstalk Promotes Cancer-Associated Cellular Phenotypes in ATRT

A PDGFRB-CD44-MIF Crosstalk Promotes Cancer-Associated Cellular Phenotypes in ATRT | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A PDGFRB-CD44-MIF Crosstalk Promotes Cancer-Associated Cellular Phenotypes in ATRT Yacine A. Choutri, Annie Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9707332/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Atypical teratoid/rhabdoid tumor (ATRT) is the most common brain tumor in children less than one-year-old. Previous studies have identified three epigenetic subgroups of ATRT: ATRT-SHH, ATRT-TYR, and ATRT-MYC. Interestingly, it was found that ATRT-TYR/MYC (mesenchymal) subgroups are sensitive to receptor-tyrosine kinase inhibitors (RTKIs), particularly those that inhibit the platelet-derived growth factor receptor B (PDGFRB), highlighting the importance of PDGF signaling in ATRTs. In addition, the ATRT-TYR/MYC subgroups show upregulation of the macrophage migration inhibitory factor (MIF), with dysregulation of the MIF signaling pathway, which was found to have immunosuppressive effects in other cancers. While PDGF and MIF pathways have been found to promote tumorigenesis of other cancers including glioma, their roles in ATRTs remain unknown. We hypothesized that PDGF and MIF signaling pathways contribute to maintaining malignant phenotypes in ATRT. Methods To test this hypothesis, our project aimed to characterize PDGF signaling and investigate PDGF-MIF crosstalk in ATRT, using CRISPR/Cas9 stable knockouts (KOs) of various PDGF receptor and ligands. Results We have shown that the PDGF pathway primarily promotes maintenance of malignant phenotypes in ATRT via modulation of the cell cycle. Furthermore, our studies reveal a plausible PDGFRB, MIF, and CD44 oncogenic signaling axis, that could modulate invasion and cellular phenotypic plasticity in ATRT, via regulation of neural stemness and epithelial-mesenchymal transition (EMT) marker expression. Conclusions Our collective study findings point to an important role for PDGFRB-MIF-CD44 signaling circuit as a potential therapeutic pathway for this very poor prognosis tumor. ATRT PDGFRB MIF CD44 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background ATRTs are classified as grade IV brain tumors by the World Heath Organization 1 , and represent only 3–5% of pediatric CNS tumors 2 , 3 . ATRT is highly malignant, clinically complex 4 , 5 , and fast-growing tumor 3 , that metastasizes in 30–50% of cases 4 . There is no definitive treatment for ATRT, and the 5-year survival rate is less than 35%. This makes ATRT one of the most lethal pediatric tumors 6 . ATRT has a low somatic mutational burden compared to other pediatric tumors 7 . It is characterized by bi-allelic loss of SMARCB1 , a gene located on chromosome 22 8 , or rarely loss of SMARCA4 , a gene located on chromosome 19 9 . The SMARCB1 protein is a component of the multi-protein SWI/SNF, ATP-dependent chromatin remodelling complex 10 . SMARCB1 loss in ATRT has been associated with an altered chromatin structure characterized by increased H3K27me3 repressive marks, which impairs cell differentiation and represses tumor suppressor gene expression 11 . DNA methylation analysis identified three epigenetic subgroups of ATRT: Sonic Hedgehog (SHH), Tyrosinase (TYR), and MYC. Notably, ATRT-SHH has neurogenic features, while ATRT-TYR/MYC have varying degrees of mesenchymal features 12 – 14 . Previous studies showed that cells from mesenchymal ATRT-TYR/MYC subgroup are sensitive to RTKIs 13 , 15 – 17 . Several RTKs could be important in ATRT, including PDGFRA, PDGFRB, ErbB2, and FGFR1 13,15,17 . Nevertheless, one RTK stands out as being the most differentially expressed between ATRT-TYR/MYC and ATRT-SHH subgroups, with a higher expression in TYR/MYC tumors. This is the platelet-derived growth factor B (PDGFRB) 13 . Torchia et al . 13 found phosphorylation of PDGFRB in ATRT primary samples. In addition, ATAC-seq analyses on primary ATRT show that ATRT-MYC tumors have an open chromatin configuration at the PDGFRB promoter, but not in primary SHH samples 13 . Together, these findings underscore potential importance of PDGFRB in ATRT; however, the mechanism responsible for its activation and its cellular effects on ATRT remain unexplored. PDGFRB is an RTK that can function as a homodimeric PDGFRB/B or heterodimeric PDGFRA/B receptor complexes 18 . PDGFR ligands are PDGF-A, -B, -C, and -D 18 . Among the downstream effects of PDGF signaling pathway are cell migration, proliferation, differentiation, and survival 18 . PDGF can mediate signaling via both paracrine and autocrine modes 19 . The physiologically normal and dominant fate of activated PDGFRB is degradation, proteasome or lysosome-mediated 20 , 21 . PDGFRB internalization is dependent on ubiquitination by E3 ubiquitin ligases, namely the Casitas b-lineage lymphoma (Cbl) family of E3 ubiquitin ligases: c-Cbl ( CBL ) and Cbl-b ( CBLB ) 20 , 21 . Under malignant conditions, such as loss-of-function mutations in phosphatases responsible for PDGFR dephosphorylation, PDGFR could be sorted from the early endosome back to the plasma membrane, a process known as PDGFR recycling, which is dependent on Protein Kinase C alpha (PKCα) 22,23 . The PDGF pathway is essential for normal functioning, including angiogenesis and tissue repair 24 . However, dysregulation of the PDGF pathway has been connected to tumorigenesis in several cancer types 24 . This includes PDGFR amplification in glioblastoma, activating point mutations in the gastrointestinal stromal tumor, and translocation in chronic myelomonocytic leukemia 24 . Oncogenic dysregulation of the PDGF pathway can manifest through mutational activation of PDGFR, ligand-independent activation of PDGFR, or constitutive expression of PDGF ligands 24 . Autocrine PDGF signaling has been found to promote the growth and proliferation of tumor cells in leukemia and high-grade glioma 19 . Targeting PDGFR has been of interest in cancer therapy, primarily using PDGFR antagonists, which prolonged remission and sensitized tumors to chemotherapy 25 . While the PDGF pathway has been explored in several tumor types, a similar effort has yet to be made in ATRT. RTKIs, such as dasatinib, are fairly well characterized at a mechanistic level and with respect to safety profiles 26 . Exploiting this therapeutic sensitivity is of interest in ATRT, as it offers a safer and more efficient treatment option for ATRT-TYR/MYC patients than the widely used chemotherapy/radiotherapy approach that can have long-term side effects on children, including causing brain damage with resulting learning disabilities 27 , 28 . The absence of an understanding of the role of PDGFRB in ATRT limits the exploitation of this treatment option. Meanwhile, SMARCB1 deletions in ATRT-TYR/MYC subgroups have generally been shown to encompass large regions extending beyond the SMARCB1 gene. These broad deletions have been shown to result in changes in gene expression not only in SMARCB1 , but also in other genes located near the SMARCB1 locus on chromosome 22. One specific gene stands out as having the highest expression following a broad SMARCB1 deletion as indicated by RNA-seq analyses, and this gene is the macrophage migration inhibitory factor ( MIF ) 13 , 29 . Best known for its role in the adaptive immune response, MIF has emerged as an important cytokine in cancer biology. The MIF signaling pathway has been connected to tumorigenesis in glioblastoma and medulloblastoma, as well as to a dysfunctional anti-tumor immunity in various cancers 30 – 32 . Exceptionally high expression of MIF suggests a role for the MIF protein in ATRT pathogenesis. MIF upregulation within ATRT tumor cells and within neighboring cells of the TME remain unexplored Several studies highlighted a potential connection between the PDGF and MIF pathways 33 , 34 . Specifically, a crosstalk between PDGFRB and CD44, MIF’s coreceptor, has been reported in normal and malignant conditions 35 , 36 . In addition, one specific CD44 isoform, CD44v6, is known to form heteroduplexes with RTKs (VEGFR2, c-MET, and EGFR) enhancing their activation 37 , 38 . Using ATRT-TYR/MYC cell lines BT12 and BT16, and their genetically edited sublines, we investigated how disruption of the PDGF signaling pathway impacts ATRT cancer-associated cellular phenotypes. Moreover, we investigated whether there is a connection between the PDGF and MIF pathways, potentially through CD44, that could impact ATRT malignancy. Methods Cell culture ATRT cell lines BT12 and BT16 were cultured in RPMI-1640 (Wisent, Cat#350-000 RL) supplemented with 10% FBS (Wisent, Cat#090-150- FBS). ATRT cell lines CHLA02, CHLA04, CHLA05, and CHLA06 were cultured in DMEM-F12 (Wisent, Cat# 319 − 085 CL) supplemented with 20 ng/mL FGF (R&D Systems, Cat#,233-FB), 20 ng/mL EGF (Stem Cell Technologies, Cat#78006) and 1xB27 supplement (Gibco, Cat#17504044). Renal malignant rhabdoid tumor (MRT) cell line G401 was cultured in McCoy’s 5A medium (Gibco, Cat# 16600082) supplemented with 20% FBS (Wisent, Cat#090-150-FBS). Normal human astrocytes (NHA), NIH-3T3, and Human embryonic kidney (HEK)293 cells were cultured in DMEM (Wisent, Cat# 319 − 205 CL) supplemented with 20% FBS (Wisent, Cat#090-150- FBS). Cells were tested for mycoplasma contamination using a Mycoplasma PCR Detection Kit (Applied Biological Materials, Cat#G238) per the manufacture’s protocol. Basal expression analysis was performed across multiple cell lines (BT12, BT16, CHLA06, CHLA02, CHLA04, and CHLA05); BT12 and BT16 were selected for subsequent experiments as they had robust expression of pathway components of interest (PDGFRB, MIF, and CD44), are among the most extensively characterized ATRT cell lines in the literature 12 , retain certain molecular characteristics consistent with patient-derived tumor data 13 , and demonstrated efficient lipofectamine-based transfection suitable for CRISPR/Cas9 editing experiments. (See Supplementary Table 1 for details about ATRT cell lines). Western blotting For all protein extractions, cells were grown in Opti-MEM serum-reduced medium (Gibco, Cat#31985062) overnight to minimize interference of serum or growth factors with PDGF pathway studies. Cells were detached using Versene dissociation reagent (Gibco, Cat#15040066). Whole-cell lysates were prepared using extraction buffer C (EBC) lysis buffer (pH = 8.0, Tris-HCL, NaCl, NP-40) supplemented with 1x protease and phosphatase inhibitor cocktail (Thermo Fisher, Cat#78440). Protein concentration was determined using a BCA protein assay (Thermo Scientific, Cat# 23225). For each sample, 25–120 µg/lane of protein, was loaded and separated on a 6–18% SDS-PAGE gel, depending on the abundance and size of protein of interest. Membranes were then developed with SuperSignal West Pico PLUS Chemiluminescent Substrate (Thermo Scientific, Cat#34580) and imaged using a Li-Cor Odyssey imager. When applicable, blots were stripped using Restore PLUS Western Blot Stripping Buffer per manufacturer’s protocol (Thermo Scientific, Cat#46430). Densitometry of blots, when applicable, was performed using ImageJ ( https://imagej.net/ij/ ). The number of technical replicates per biological replicate was one. (See Supplementary Fig. 5 for CD44 antibodies isoform-specificity validation). The following antibodies were used for immunoblotting: Table 1 Western blotting antibodies list Antibody Catalog number Concentration Primary antibodies α-Tubulin (monoclonal mouse) Cell Signaling Technology (CST) #3873 1:10,000 Beta 3 Tubulin (monoclonal rabbit) CST#5568 1:1,000 c-CBL (monoclonal rabbit) CST#2179 1:1,000 Cas9 ( S. pyogenes ) (monoclonal rabbit) CST#65832 1:1,000 CBL-b (polyclonal rabbit) Bethyl laboratories # A302-903A 1:1,000 CD44 (monoclonal rabbit) CST #37259 1:1,000 CD44v6 (monoclonal mouse) Invitrogen #33-6700 1:1,000 CD74 (monoclonal mouse) Invitrogen #14-0747-82 1:1,000 CDC25C (monoclonal rabbit) CST#4688 1:1,000 CDK2 (monoclonal rabbit) CST#2546 1:1,000 CDK4 (polyclonal rabbit) Santa Cruz Biotechnology #sc-260 1:500 CDK6 (polyclonal rabbit) Santa Cruz Biotechnology #sc-177 1:500 Claudin-1 (polyclonal rabbit) Invitrogen #51-9000 1:1,000 CSF-1R (polyclonal rabbit) CST#3152 1:1,000 Cyclin A2 (monoclonal mouse) CST#4656 1:1,000 Cyclin D2 (monoclonal rabbit) Santa Cruz Biotechnology #sc-452 1:1,000 Cyclin D3 (monoclonal rabbit) Santa Cruz Biotechnology #sc-182 1:1,000 Cyclin E1 (monoclonal rabbit) CST#20808 1:1,000 E-cadherin (monoclonal mouse) CST#14472 1:500 EFGR (monoclonal rabbit) CST#8339 1:1,000 ErbB2 (monoclonal rabbit) CST#4290 1:1,000 ErbB3 (monoclonal rabbit) CST#12708 1:1,000 MAP2 (monoclonal rabbit) CST#8707 1:1,000 MIF (monoclonal rabbit) CST #87501 1:500-1:1,000 MIF2 (DDT) (polyclonal rabbit) CUSABIO #CSB-PA221047-50UG 1:1,000 N-cadherin (monoclonal rabbit) CST#13116 1:1,000 Nestin (polyclonal rabbit) MilliporeSigma #ABD69 1:10,000 p16 (polyclonal rabbit) Proteintech #10883-1-AP 1:1000 p21(monoclonal mouse) BD Biosciences #556430 1:500 p27 (monoclonal mouse) BD Biosciences #610241 1:1,000 p44/42 MAPK (Erk1/2) (monoclonal mouse) CST#4696 1:1,000 p53 (polyclonal rabbit) Active Motif #61657 1:500 p62 (polyclonal mouse) Proteintech #66184-1-Ig 1:1,000 Pan-Cadherin (polyclonal rabbit) CST#4068 1:1,000 PDGFA (monoclonal mouse) Abcam #ab51868 1:1,000 PDGFB (polyclonal rabbit) Abcam #ab23914 1:1,000 PDGFC (polyclonal goat) R&D Systems#AF1560 1:1,000 Phospho-EGFR Tyr1018 (monoclonal rabbit) CST#4547 1:1,000 Phospho-ErbB3 Tyr1289 (monoclonal rabbit) CST#2842 1:1,000 Phospho-p44/42 MAPK (Erk1/2) Thr202/Tyr204 (monoclonal rabbit) CST#9101 1:1,000 Phospho-PDGFRB Tyr1009 (monoclonal rabbit) CST#3124 1:1,500-1:1,000 Phospho-PDGFRB Tyr1021 (polyclonal rabbit) R&D Systems #AF2316 1:400 Phospho-PDGFRB Tyr751 (monoclonal rabbit) CST#4549 1:500-1:1,000 Phospho-PI3 Kinase p85 Tyr458 (monoclonal rabbit) CST#4228 1:1,000 Phospho-Rb Ser807/811 (monoclonal rabbit) CST#8516 1:1,000 PI3 Kinase p85 (monoclonal rabbit) CST#4292 1:1,000 SOX2 (monoclonal rabbit) CST#3579 1:1,000 Total PDGFRA (monoclonal rabbit) CST#5241 1:1,000 Total PDGFRB (monoclonal rabbit) CST#3169 1:500-1:1,000 Total Rb (monoclonal mouse) CST#9309 1:1,000 TWIST1 (monoclonal rabbit) CST#69366 1:500-1:1,000 Vimentin (monoclonal rabbit) CST#5741 1:1,000 Secondary antibodies Horseradish peroxidase (HRP)-conjugated donkey anti-goat IgG Invitrogen#A15999 1:10,000 HRP-conjugated goat anti-rabbit IgG CST#7074 1:3,000–1:5,000 HRP-conjugated horse anti-mouse IgG CST#7076 1:3,000–1:5,000 PDGF stimulation Cells were serum-starved (BT12 and BT16) or growth factor-deprived (CHLA05, and CHLA06) by growing them in Opti-MEM serum-reduced medium (Gibco, Cat#31985062) overnight (approximately 16 hours). Cells were then stimulated with 10–50 ng/mL PDGF-BB (PeproTech #100-14B), PDGF-CC, or PDGF-DD (StemCell Technologies, Cat#78168, 78222), for 5 minutes to 1 hour, depending on the experiment. PDGFRB phosphorylation was assessed following ligand stimulation at a single optimized timepoint. Stimulation duration was optimized by testing timepoints between 5 and 15 minutes (data not shown); 5 minutes was selected for all subsequent experiments. Receptor recycling and degradation were assessed following ligand stimulation at a single optimized timepoint. Stimulation duration was optimized by testing timepoints of 30, 45, and 60 minutes (data not shown); 60 minutes was selected for all subsequent experiments. RT-qPCR Total RNA was extracted using a PuroSPIN Total RNA Purification Kit (Luna Nanotech Cat#NK051-50). cDNA synthesis and qPCR were performed using the Luna Universal One-Step RT-qPCR Kit (New England Biolabs, Cat#E3005). The ΔCq or ΔΔCq method was used to calculate mRNA abundance. A list of gene-specific qPCR primers is provided in the table below. Unless otherwise noted, qPCR primers were designed using the Integrated DNA Technologies (IDT) PCR & qPCR primer design tool ( https://www.idtdna.com/pages/tools/primerquest ). Primer specificity was confirmed in silico using the UCSC In-Silico PCR online tool ( https://genome.ucsc.edu/cgi-bin/hgPcr ). The number of technical replicates per biological replicate was 2–3. Table 2 qRT-PCR primers list Gene Forward primer (5’ to 3’) Reverse primer (5’ to 3’) ACTB ACCTTCTACAATGAGCTGCGT ATAGCACAGCCTGGATAGCAA CBL CGC CAT GTT CCT TCC ACT AA GAG AAG CTG CCT GGT CTA TTA C CBLB AGA GAC CCA GTA GAG GAA GAT G CAA GAC CGA ACA GGA GGT TT CD44* AAG ACA TCT ACC CCA GCA GGT AGC AGG GAT TCT GTC CD44v6* TCC AGG CAA CTC CTA GTA GTA C CAG CTG TCC CTG TTG TCG AAT G CD74 CAG ACT TGA CTG TTC CCA TAC A AGA GCC GTA AGT CCC ATA GA CDH1 GTC ATT GAG CCT GGC AAT TTA G GTT GAG ACT CCT CCA TTC CTT C CDH2 GGA TGA AAC GCC GGG ATA AA TCT TCT TCT CCT CCA CCT TCT T CLDN1 CCA GTT AGA AGA GGT AGT GTG AAT CAG CCA GCT GAG CAA ATA AAG GAPDH TTCTCCATGGTGGTGAAGACG ATTCCATGGCACCGTCAAGG MAP2 GAT AGA AGG GCA ACA GAG CTA AA GCC AGA CTC AAC ACC CAT AAA MIF CGGACAGGGTCTACATCAACTA TCTTAGGCGAAGGTGGAGTT NES CCA TAG AGG GCA AAG TGG TAA G CCA GAG ACT TCA GGG TTT CTT T PDGFA GGACAGTGCGACGGTATTT GGAGAAACACAAAGCCAGAAAC PDGFB TGTAGATGGTGACCTGGGTATC GGGTGGAGGTAGAGAGATGAAA PDGFC GCCACTGGACCTGCTTAAT CTAAGTCCAACTGCCATCTCTC PDGFD CCCTGCTGTCCTACTGTTTAAT CACAAAGGAGGCAGAGAGAAA PDGFRA CAGGTATTT CTG GGA GGT TCT G CAT GGC AAG ACT CCA TCT CTA C PDGFRB GCTCACCATCATCTCCCTTATC CTCACAGACTCAATCACCTTCC PRKCA CCA TCC GCT CCA CAC TAA AT GAT CCC AGT CCC AGA TTT CTA C SNAI1 CCA CGA GGT GTG ACT AAC TAT G ACC AAA CAG GAG GCT GAA ATA SOX2 GCC CTG CAG TAC AAC TCC AT GAC TTG ACC ACC GAA CCC AT TUBB3 CGA AGC CAG CAG TGT CTA AA GGA GGA CGA GGC CAT AAA TAC TWIST1 AGG CAT CAC TAT GGA CTT TCT C GGC CAG TTT GAT CCC AGT AT VIM CAG CTT TCA AGT GCC TTT CTG CTT GTA GGA GTG TCG GTT GTT ZEB1 CTT CTC ACA CTC TGG GTC TTA TTC CGT TCT TCC GCT TCT CTC TTA C * CD44 and CD44v6 primers were adapted from Yamao et al. (1998) 39 . siRNA-mediated knockdown To knockdown PDGFB , CBL , and PRKCA , siRNAs targeting each gene were used from Horizon Discovery, Dharmacon ON-TARGETplus Human siRNA: J-011749-05-0002; J-003004-09-0002; and J-003523-16-0002, respectively. To knockdown CBLB , siRNA targeting CBLB from Thermo Scientific (Cat# AM16708) was used. siRNAs were transfected at a concentration of 50–100 nM/transfection using Lipofectamine 3000 transfection reagent (Invitrogen, CAT#L3000001). Non-targeting siRNA (Horizon Discovery, ON-TARGETplus Non-targeting Control siRNAs, D-001810-01-05) was used at 50–100 nM/transfection. Transfection was performed according to manufacturer’s protocol in a 6-cm dish format. Cells were serum starved overnight before transfection and harvested 48–72 hours post-transfection for protein or RNA extraction. Proteasome and lysosome inhibition Cells were grown in Opti-MEM serum-reduced medium (Gibco, Cat#31985062) overnight (approximately 16 hours), then treated with either 1–10 µM MG132 (Selleckchem, Cat#S2619), 10 µM Bafilomycin A1 (Selleckchem, Cat#S1413), 100 µg/mL cycloheximide (Selleckchem, Cat#S7418), or 0.01–0.1% DMSO (Sigma-Aldrich, Cat#D2650) diluted in Opti-MEM for 2–16 hours. Following treatment, cells were collected for protein extraction. Cell cycle analysis Cell cycle analysis was performed on fixed and stained BT12 and BT16 KO cells, derived from the same passage (P15). Briefly, cells were detached using Versene dissociation reagent (Gibco, Cat#15040066). Two million cells were then washed twice with PBS (Wisent, Cat#311 − 013 CL), filtered through a 40 µm cell strainer (Fisher Scientific, Cat#22-363-547), and fixed with ice-cold 80% ethanol overnight at -20⁰C. Following fixation, cells were washed with PBS twice, then treated with 500 µL of 200 µg/mL RNase A/DNAse and protease-free (Thermo Scientific, Cat#EN0531) diluted in PBS for 1 hour at 37⁰C. Next, 100 µg/mL Propidium Iodide (PI) (Thermo Scientific, Cat#P3566) diluted in 500 µL PBS was added, to a final concentration of 50 µg/mL PI. Cells were filtered with a 40 µm cell strainer and stained overnight at 4⁰C. The flow cytometry runs were carried out by Eve Coulter at SickKids-UHN Flow Cytometry Core Facility. Cells were gated to exclude debris and doublets, and cell cycle phases distribution analysis was performed using https://floreada.io/ . The number of technical replicates per biological replicate was one. (See Supplementary Fig. 6 for an example of gating steps). CRISPR/Cas9-mediated generation of stable knockout (KO) cell lines Single-guide RNAs (sgRNA) against genes of interest were designed using the IDT CRISPR Cas9 guide RNA design checker ( https://www.idtdna.com/site/order/designtool/index/CRISPR_SEQUENCE ). A list of optimized and selected gRNAs used in the successful transfections is provided in this table: Table 3 CRISPR gRNAs list Gene Sense oligonucleotide (5’ to 3’) Complementary oligonucleotide (5’ to 3’) CD44 for CD44v6 KO* gRNA1: TGATATTCTTCTCACAGTCC gRNA2: AGGAATTGTCACGAGATGTT gRNA1: GG ACT GTG AGA AGA ATA TCA gRNA2: AA CAT CTC GTG ACA ATT CCT CD44* gRNA1: GAATACACCTGCAAAGCGGC gRNA2: AAGGGCACGTGGTGATTCCC gRNA1: GCCGCTTTGCAGGTGTATTC gRNA2: GGGAATCACCACGTGCCCTT MIF TTGGTGTTTACGATGAACAT ATGTTCATCGTAAACACCAA Non-targeting gRNA (NT gRNA) GTATTACTGATATTGGTGGG CCCACCAATATCAGTAATAC PDGFB CATCAAAGGAGCGGATCGAG CTCGATCCGCTCCTTTGATG PDGFC CTGGTTCAAGATATCGAATA TATTCGATATCTTGAACCAG PDGFRB GTCCCCTATGATCACCAACG CGTTGGTGATCATAGGGGAC * CD44 isoform-specific KO gRNAs were adapted from Lobo, S. et al. (2020) 40 gRNAs were cloned into the pSpCas9(BB)-2A-Puro (PX459) V2.0 vector (Addgene, Cat#62988) according to the Zhang lab target sequence cloning protocol 41 . Successful cloning was confirmed by Sanger sequencing, using U6 forward primer 5'-GAG GGC CTA TTT CCC ATG ATT CC-3' immediately upstream of the cloning site. Sequencing was performed by the Centre for Applied Genomics at SickKids. BT12 and BT16 cells were maintained in RPMI-1640 (Wisent, Cat#350-000 RL) supplemented with 10% FBS (Wisent, Cat#090-150- FBS). Cells (P10) were seeded in 6-well plates and transfected at 70–80% confluency. Prior to transfection, cells were serum-starved for 2–16 hours. Lipofectamine 3000 transfection reagent (Invitrogen, CAT#L3000001) was used according to the manufacturer’s protocol. Seventy-two hours post-transfection, cell culture medium was replaced with fresh medium containing 5 µg/mL of Puromycin (Gibco, Cat# A1113803) for three weeks. Following selection, cells were maintained in 10% FBS RPMI-1640 containing 1–5 µg/mL Puromycin. Pooled cell populations were expanded without clonal selection. All subsequent experiments were therefore performed using heterogeneous pooled KO populations rather than isogenic clonal lines. Genomic DNA was extracted from cells using a Genomic DNA Mini Kit (Geneaid, Cat#GB300). The regions flanking gRNA target sites were amplified by PCR (New England Biolabs, Cat#M0494), using primers listed in the table below. Primers were designed using the Primer Wizard tool ( Benchling.com online platform). Table 4 TIDE analysis primers list Gene Forward primer (5’ to 3’) Reverse primer (5’ to 3’) CD44 for CD44v6* ATC AGT GGC CTG TTT CCT TG TTT GGC TCT GTG TGA ACT GC CD44* TCC CCA CTT AAG CTG AGC TCC A TGC AAT GCT CAG GAG GCA GTG T MIF ACA TTG GCC GCG TTC ATG TCG T GGG TCT CCT GGT CCT TCT GCC A PDGFB TGA CCA GGG CTC CAG GGA ACA G CTG CCT GTC CGT CTC CCT GTG A PDGFC GTA TTG AAT ATA TCA CTG ATG A TAT TGA CCA TTC AAG CTT TTT A PDGFRB CCA CCG TGG GCT TCC TCC CTA A GAA GCT TGT GCC CTC ACC GAC C PCR products were also subjected to T7 Endonuclease I assays per manufacturer’s protocol (New England Biolabs, Cat#M0302). PCR products were sequenced using Sanger sequencing. Results were analyzed using the TIDE web tool ( https://tide.nki.nl/ ) to assess insertion/deletion (indels) distribution and frequency. Protein lysates from KO cells were analyzed by western blot to confirm the loss of protein expression. For KO validation data, please see Supplementary Fig. 1–4 and Supplementary Table 2. *CD44 KO sequencing primers were adapted from Lobo, S. et al. (2020) 40 . CD44 isoform-specific KO generation To generate CD44s KO, a gRNA against exon 3 of CD44 gene was used. CD44v6 KO was generated as described in Lobo, S. et al. (2020) 40 . CD44v6 KO was validated by PCR size reduction, TIDE analysis, and western blot confirmation of CD44v6 protein loss (see Supplementary Fig. 2p-s; Supplementary Fig. 4p-s). Zygosity of the deletion was not determined. Cell invasion/migration assay BT12 and BT16 cells were serum-starved overnight prior to seeding of 50,000 cells/chamber on Corning FluoroBlok 24-Multiwell Insert Systems PET Membranes (Corning Life Sciences, Cat# 351152) in 400 µL Opti-MEM on the apical side. RPMI-1640 supplemented with 30%FBS was used as chemoattractant for migration assays. For invasion assay, the PET membranes were pre-coated with 100 µL of 200 µg/mL Matrigel (Corning Life Sciences, Cat#354234) for 2 hours at 37°C prior to the assay. Cells were incubated for 24 hours at 37°C. Inserts were stained using 10 µM Calcein AM (diluted in PBS/DMSO) (Thermo Scientific, Cat#C1430) for 1 hour at 37°C. Fluorescence was then measured using a SpectraMax Gemini EM fluorescence plate reader (Ex/Em 494/517nm). The number of technical replicates per biological replicate was two. (See Supplementary Fig. 7 for validation of the invasion/migration assay). Colony formation assay BT12 and BT16 cells were seeded at 500 cells/well in a 6-well plate and grown for about 2 weeks (or until ~ 300 colonies were observed in wells under NT gRNA control conditions), with regular medium change every 3–4 days. Cells were washed with PBS, fixed in 1:7 acetic acid:methanol (Fisher Scientific, Cat#A38-500, #A412-4) for 5 minutes, then stained for 1 hour with 0.5% crystal violet (BioShop, Cat#CRY422). Plates were washed 5 times with water, then air-dried for 24–48 hours. Plates were then imaged and colonies counted using the Optronix GelCount Colony Counter. The number of technical replicates per biological replicate was three. Cell proliferation assay BT12 and BT16 cells were seeded at 2,000 cells/well in 96-well plates in 100 µL/well RPMI-1640 supplemented with 10%FBS. On days of fluorescence measurements, media was changed to 100 µL OptiMEM/well, prior to adding 10 µL AlamarBlue (Invitrogen, Cat#DAL1025). Cells were incubated for 4 hours at 37°C, then fluorescence measured using a SpectraMax Gemini EM fluorescence plate reader (Ex/Em 570/585nm). The number of technical replicates per biological replicate was 4–8. Statistical analysis All experiments were performed with at least three independent biological replicates, unless otherwise noted. A two-tailed Student’s t-test or a One-way ANOVA (Dunnett’s correction for multiple comparisons) test was used for statistical analysis. The significance cut-off was p < 0.05. Results PDGF receptors and ligands are differentially enriched in ATRT cell lines Basal expression of PDGF receptors and ligands was assessed by western blot and qRT-PCR across ATRT cell lines: ATRT-TYR/MYC (BT12, BT16, and CHLA06) and ATRT-SHH (CHLA02, CHLA04, and CHLA05). PDGFRA expression was elevated in ATRT-SHH relative to ATRT-TYR/MYC lines, with CHLA05 showing the highest PDGFRA mRNA levels (Fig. 1 a). Conversely, PDGFRB was preferentially expressed in ATRT-TYR/MYC lines, though CHLA05 also expressed notable levels (Fig. 1 a). ATRT cell lines additionally expressed multiple RTKs, including EGFR and ErbB4, as well as several PDGF ligands (Fig. 1 b, Supplementary Fig. 7a)). Among these, PDGFA was highly expressed in CHLA02, while PDGFB , the primary PDGFRB ligand binding all PDGF receptors, was enriched in BT12 and BT16 (Fig. 1 b). PDGFC and PDGFD showed preferential expression in BT16 and ATRT-SHH lines, respectively (Fig. 1 b). PDGFRB is constitutively active in BT12 and BT16 cells To characterize PDGF pathway activity in ATRT cell lines, phosphorylation status of PDGFRB was assessed. BT12 and BT16 constitutively expressed phospho-PDGFRB without PDGF stimulation; while CHLA05 and CHLA06 only expressed phospho-PDGFRB with external PDGF treatment (Fig. 1 d, e). Furthermore, all ATRT cell lines tested showed activation of downstream PDGF signaling, including active MAPK, PI3K/Akt, and PLCγ pathways (Fig. 1 c). To better understand the effect of PDGF ligand expression on PDGFRB signaling in ATRT cell lines, cells were treated with three different ligands (PDGF-BB, PDGF-CC, and PDGF-DD). PDGF-BB resulted in diminished PDGFRB expression notably in BT12 and BT16, suggesting that PDGF-BB may promote PDGFRB degradation (Fig. 1 d). Similar to PDGF-BB stimulation, PDGF-CC treatment resulted in decreased PDGFRB expression in BT12 and BT16 cells (Supplementary Fig. 7b). In contrast, PDGF-DD stimulation was associated with an increase (in BT12 and CHLA05) or no significant change in PDGFRB levels (in BT16 and CHLA06), suggesting it may promote PDGFRB accumulation (Fig. 1 e). In summary, the effect of PDGF on PDGFRB expression and phosphorylation appears to be cell line-dependent and ligand-specific. a. Expression of PDGFRA (left) and PDGFRB (right) protein (by western blot) and mRNA (by qRT-PCR), in ATRT cell lines. ATRT-SHH cell lines are labelled in red; ATRT-TYR/MYC cell lines are labelled in blue; Normal human astrocytes (NHA), labelled in green, is a non-cancer cell line that was included as a negative control for PDGFRA and a positive control for PDGFRB expression in qRT-PCR b. Left: Expression of PDGF ligand protein in ATRT cell lines. Human recombinant PDGF ligands were included as a positive control for western blot (10 ng per lane). Right: PDGFA, PDGFB, PDGFC , and PDGFD mRNA expression (by qRT-PCR in ATRT) cell lines. NHA mRNA was included for comparison. Western blot shown (in a, b ) is representative of 3 independent biological replicates performed in technical singlets; quantification across replicates was not performed. qRT-PCR data represent the mean ± SEM of n = 3 independent biological replicates performed in technical duplicates or triplicates, with a minimum of 6 data points shown per condition. c. PI3K/Akt, MAPK, and PLCγ protein expression. Stimulated (+ PDGF-BB/+PDGF-DD) and non-stimulated NIH-3T3 cells were included as a negative/positive control for phospho-protein expression. Effects of PDGF-BB ( d ) and PDGF-DD ( e ) (0,10, 20, and 30 ng/mL for 5 minutes) on PDGFRB and phospho-PDGFRB expression in BT12, BT16, CHLA06, and CHLA05 by western blot (top). Corresponding densitometry quantification of total PDGFRB (bottom). Western blot shown is representative of 2–3 independent biological replicates. Error bars represent ± SEM (n = 2–3 independent biological replicates performed in technical singlets; 2–3 data points per condition). One-way ANOVA with Dunnett’s post-hoc test * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001. PDGF-BB drives an autocrine loop leading to PDGFRB degradation in BT12 and BT16 cells Diminished PDGFRB levels in response to PDGF-BB stimulation, co-expression of PDGF-BB and PDGFRB, as well as constitutively active PDGFRB in BT12 and BT16 cells, suggested PDGFRB-PDGFB autocrine signaling may lead to downregulation and degradation of PDGFRB in ATRTs. Indeed, using siRNA-mediated knockdown, we saw an increase in total PDGFRB protein accumulation, but no change in PDGFRB mRNA, in cells transfected with PDGFB siRNA (Fig. 2 a, b). Surprisingly, however, there was no considerable change in phospho-PDGFRB levels seen (Fig. 2 a). Together, this indicates that PDGFB can promote degradation of PDGFRB and that PDGFRB phosphorylation in BT12 and BT16 is not solely ligand-dependent. To confirm the existence of the autocrine loop, we also targeted PDGFRB ubiquitination by knocking down CBL and CBLB , the two major ubiquitin ligases responsible for PDGFRB ubiquitination 32 . CBLB knockdown effects were consistent across both cell lines, where PDGF-BB stimulation failed to cause a decrease in PDGFRB in the CBLB knockdown condition (Fig. 2 g-j). This suggests that CBLB may be essential for PDGFRB ubiquitination and subsequent degradation. CBLB knockdown also caused a significant ( p = 0.000008; p = 0.0284, respectively) increase in PDGFRB mRNA in BT12 and BT16 (Fig. 2 g-j). Consequently, the PDGFRB-PDGFB autocrine loop could downregulate PDGFRB protein and mRNA. In BT16 cells, CBL knockdown attenuated the effects of PDGF-BB stimulation on PDGFRB, with no significant reduction in PDGFRB levels, in contrast to control condition, where PDGFRB levels were significantly ( p = 0.0122) reduced post-stimulation (Fig. 2 e, f). However, in BT12 cells, CBL knockdown did not affect the ligand-induced decrease in PDGFRB levels indicating these cells could be less dependent on CBL -mediated PDGFRB ubiquitination (Fig. 2 c, d). Next, we tested whether prevention of PDGFRB degradation using MG132, a proteasome inhibitor, and Bafilomycin-A1, a lysosome inhibitor, could disrupt the autocrine loop. MG132 treatment attenuated the PDGF-BB-induced downregulation of PDGFRB in BT12 and BT16, suggesting that proteasome inhibition disrupts autocrine degradation (Fig. 2 k, m). Bafilomycin A-1 treatment was only effective in decreasing PDGFRB degradation in BT12 but not in BT16, suggesting proteasome-mediated degradation is the primary mode of PDGFRB degradation in BT16 cells (Fig. 2 l, n). Overall, these experiments reveal an autocrine PDGFRB-PDGFB loop in BT12 and BT16 cells that has retained some elements of physiologic control, with negative feedback on PDGFRB protein and mRNA. Taken together, these data indicate that PDGF pathway activation can result from ligand-induced autocrine signaling in BT12 and BT16 cells. PDGF-DD promotes PKCα-mediated PDGFRB accumulation in BT12 and BT16 cells PDGF-DD stimulation was associated with maintenance or increase of PDGFRB levels despite no change in PDGFRB mRNA (Fig. 2 t, v); suggesting that the effect occurs post-transcriptionally. To determine whether this reflected increased de novo protein synthesis, BT12 and BT16 cells were treated with cycloheximide (CHX), which did not abrogate PDGF-DD-induced increase in PDGFRB levels (Fig. 2 s, u); suggesting the increase is not due to translational upregulation. Knockdown of PRKCA , which encodes PKCα, essential for PDGFRB recycling, prevented PDGF-DD-induced increase in PDGFRB levels, plausibly implicating receptor recycling in the observed effect (Fig. 2 o-r). In addition, PRKCA knockdown caused an increase in PDGFRB transcription, suggesting that the cells could compensate for the absence of recycling by producing more PDGFRB receptors (Fig. 3 p, r). Together, these results suggest that PDGF-DD may promote PDGFRB recycling via PKCα, potentially contributing to sustained PDGF signaling. a. Effects of PDGFB knockdown on PDGFRB and phospho-PDGFRB expression in BT12 and BT16 cells by western blot, and corresponding densitometry quantification. PDGFB siRNA ( –) corresponds to cells transfected with non-targeting (NT) siRNA; PDGFB siRNA ( +) corresponds to cells transfected with PDGFB siRNA. b. qRT-PCR validation of PDGFB knockdown efficiency and its effect on PDGFRB mRNA levels in BT12 and BT16. Effects of CBL knockdown on PDGFRB in BT12 (c) and BT16 (e) by western blot, and corresponding densitometry quantification. Cells were transfected with NT siRNA or CBL siRNA; (+) indicates PDGF-BB stimulation (30 ng/mL for 1 hour); (-) indicates no PDGF-BB stimulation. The gradient on the western blot indicates higher contrast. d, f. qRT-PCR validation of CBL knockdown, and its effects on CBLB and PDGFRB in BT12 (d) and BT16 (f) . g, i. Effects of CBLB knockdown on PDGFRB expression in BT12 (g) and BT16 (i) by western blot, and corresponding densitometry quantification. Cells were transfected with NT siRNA or CBLB siRNA; (+) indicates PDGF-BB stimulation (30 ng/mL for 1 hour); (-) indicates no PDGF-BB stimulation. h, j. qRT-PCR validation of CBLB knockdown, and its effects on CBL and PDGFRB in BT12 (h) and BT16 (j) . k, m. Effects of MG132 treatment on PDGFRB by western blot, quantified by densitometry in BT12 (k) and BT16 (m) . Cells were treated with MG132 or with DMSO, followed by PDGF-BB stimulation (30 ng/mL for 1 hour) (+) or no stimulation (-). p21 protein levels were assessed to confirm the efficiency of MG132 treatment. l, n. Effects of Bafilomycin A1 treatment on PDGFRB by western blot, quantified by densitometry in BT12 (l) and BT16 (n) . Cells were treated with Bafilomycin A1 or with DMSO, followed by PDGF-BB stimulation (30 ng/mL for 1 hour) (+) or no stimulation (-). p62 protein levels were assessed to confirm the efficiency of Bafilomycin A1 treatment. o, q. Effects of PRKCA knockdown on PDGFRB and phospho-PDGFRB expression in BT12 (o) and BT16 (q) cells by western blot, and corresponding densitometry quantification. Cells were transfected with NT siRNA or PRCKA siRNA; (+) indicates PDGF-DD stimulation (50 ng/mL for 1 hour); (-) indicates no PDGF-DD stimulation. p, r. qRT-PCR validation of PRKCA knockdown, and its effects on PDGFRB in BT12 (p) and BT16 (r) . s, u. PDGFRB protein levels in cycloheximide (CHX)-treated and DMSO-treated BT12 (s) and BT16 (u) cells by western blot. Cells were pretreated with 100 µg/mL CHX or 0.1% DMSO for 2 hours, followed by PDGF-DD stimulation (50 ng/mL for 1 hour) (+) or no stimulation (-). Right: corresponding densitometry analysis. In BT12, one biological replicate in the CHX condition had ~ 25.8 relative PDGFRB:Tubulin ratio and was excluded from the analysis using the ROUT method (Q = 1%). Pan-Akt and p21 (short-lived proteins) were assessed to confirm the efficiency of CHX treatment. t, v. PDGFRB mRNA levels by qRT-PCR in DMSO-pretreated (0.1% DMSO for 2 hours) BT12 (t) and BT16 (v) PDGF-DD-stimulated cells (50 ng/mL for 1 hour) relative to non-stimulated cells. Error bars represent ± SEM. Western blot shown is representative of 3 independent biological replicates performed in technical singlets (3 data points per condition). qRT-PCR data represent 3 independent biological replicates performed in technical duplicates (minimum 6 data points per condition). Two-tailed Student’s t-test * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001. MIF is highly expressed in ATRT-TYR/MYC cell lines MIF is the highest expressed gene located near SMARCB1 in ATRTs with broad SMARCB1 deletions 13 . Interestingly, multiple reports have linked the PDGF and MIF pathways, notably through the intermediate of MIF’s co-receptor CD44, that can also form a complex with PDGFRB 33 , 34 , 42 . Given this, we sought to investigate whether PDGF and MIF pathways are connected in ATRT, and whether this connection has any implications for malignancy. All tested lines expressed MIF, except for CHLA05, where MIF protein was below detection limits (Fig. 3 a). ATRT-TYR/MYC cell lines BT12, BT16, and CHLA06 had higher MIF expression relative to ATRT-SHH cell lines, with BT12 having the highest expression (Fig. 3 a). For comparison, MIF expression in these cell lines was similar to or even higher than seen in HEK293, a kidney cell line known to express very high levels of MIF. Additionally, ATRT cell lines also expressed MIF-2 (also known as D-Dopachrome Tautomerase, DDT), a member of the MIF protein family, that is structurally and functionally homologous to MIF, as well as CD74, MIF’s receptor (Fig. 3 a, b). After confirming MIF expression in ATRT cell lines, we sought to answer the original question about the connection between PDGFRB and MIF. To achieve this, we used stable KOs BT12 and BT16 cells (see Supplementary Figs. 2 and 4 for validation of KOs). In BT12 and BT16, we found that PDGFRB KO caused an increase in MIF protein and mRNA levels (Fig. 3 e-h; Supplementary Fig. 7d); conversely, MIF KO caused an increase in PDGFRB protein and mRNA expression in BT12 cells, while only PDGFRB protein expression was increased in BT16 cells with MIF KO (Fig. 3 g, h; Supplementary Fig. 7d). These findings suggest PDGFRB and MIF reciprocally inhibit each other’s expression, potentially as part of a regulatory crosstalk. PDGFRB and MIF influence CD44 expression Next, we sought to test whether PDGFRB and MIF signaling impact CD44, which functions on both pathways. Indeed, CD44 is a MIF coreceptor known to form heteroduplexes with PDGFRB. The strongest link between the PDGF and MIF pathways reported in the literature is the PDGFRB-CD44 connection. First, we assessed basal expression of two CD44 isoforms: CD44s which is the ubiquitously expressed isoform, and CD44v6, an isoform known to be upregulated in cancer and to interact with several RTKs. All ATRT cell lines tested (BT12, BT16, CHLA02, and CHLA06) expressed CD44s, except for CHLA05 (Fig. 3 c, d). CD44s expression was highest in BT12 and CHLA06 relative to the other lines. Interestingly, some ATRT cell lines, notably BT12 and CHLA06, expressed high levels of CD44v6, an isoform not expressed in the normal brain (Fig. 3 c, d). Next, we assessed whether the PDGFRB and MIF regulatory connection described above can influence CD44s and CD44v6 expression in BT12 and BT16 PDGFRB KO and MIF KO cell lines. In both lines, we observed PDGFB and PDGFRB KOs resulted in a substantial reduction of CD44s expression (Fig. 3 i-l). A nearly complete loss of expression was seen in BT16 cells (Fig. 3 i-l). Consequently, PDGFB and PDGFRB may play a role in driving CD44s expression. The effects of PDGFRB KO and MIF KO on CD44v6 expression were more heterogeneous. In BT12, MIF KO led to a significant ( p < 0.0001) reduction in CD44v6, while PDGFRB KO led to an increase in CD44v6 protein, without a significant change in its mRNA (Fig. 3 i, j). Consequently, MIF can represent an important regulator of CD44v6 expression (Supplementary Fig. 7e). Surprisingly, in BT16, MIF KO correlated with an increase in both CD44s and CD44v6 (Fig. 3 k, l). Taking together, these data suggest that PDGFRB and MIF influence CD44 expression in BT12 and BT16. While PDGFRB seems important for driving CD44 transcription in both cell lines, the effect of MIF on CD44 is cell line-dependent, which as a ligand-receptor pair may be subject to differential feedback regulation depending on baseline receptor expression and ligand availability in each cell line. CD44 influences expression of PDGFRB and MIF Next, we sought to determine whether CD44 influences expression of PDGFRB and MIF. The observed effects of CD44s and CD44v6 KOs on PDGFRB and MIF were consistent across BT12 and BT16. First, CD44s KO and CD44v6 KO correlated with an increase in MIF (Fig. 3 m-p). Second, CD44s KO led to a reduction in PDGFRB protein, but not mRNA levels (Fig. 3 m-o), indicating that CD44s could promote PDGFRB protein stability. Third, CD44v6 KO caused a significant ( p < 0.0001; p = 0.0057, in BT12 and BT16, respectively) increase in PDGFRB, with almost 25-fold increase in mRNA relative to the NT gRNA control (Fig. 3 m, n, p). Interestingly, in BT12, CD44v6 KO also caused an increase in expression of several other RTKs, including EGFR (Supplementary Fig. 7c). In summary, these data indicate that CD44s and CD44v6 influence PDGFRB and MIF expression, and that this effect is consistent across BT12 and BT16. In addition, data show that PDGFRB and MIF influence CD44 expression, with the specific effect being cell line-dependent. Collectively, these data suggest a regulatory PDGFRB-CD44-MIF crosstalk. a. Expression of MIF protein, and its functional homologue MIF2 by western blot (left) and MIF mRNA by qRT-PCR (right) in ATRT cell lines. HEK293 was included as a positive control for MIF expression. b. Expression of CD74, MIF’s canonical receptor, by western blot. c, d. Expression of CD44, MIF’s co-receptor, and its isoform CD44v6. For western blot, two different antibodies were used: a standard isoform-specific (CD44s) antibody that does not cross-react with other isoforms; a CD44v6-specific antibody that does not cross-react with CD44s (see Supplementary Fig. 5). For qRT-PCR, two pairs of primers were used: CD44 primers, which detect all CD44 isoforms, including CD44s and CD44v6 (to our knowledge, it is not possible to design qRT-PCR primers specific to CD44s); CD44v6 primers that detect CD44v6 and CD44v10 isoforms mRNA (see Table 2 ). e, g. PDGFRB and MIF protein expression in PDGFRB KO and MIF KO by western blot and corresponding densitometry analysis. f, h. PDGFRB mRNA expression in MIF KO, and MIF mRNA expression in PDGFRB KO by qRT-PCR. i. Effects of PDGFB KO, PDGFRB KO, and MIF KO on CD44s and CD44v6 expression in BT12 by western blot. (n = 2) j. and by qRT-PCR (One-way ANOVA with Dunnett’s post-hoc test). k, l. Effects of PDGFB KO, PDGFC KO, PDGFRB KO, MIF KO, and PDGFRB/MIF DKO on CD44s and CD44v6 expression in BT16 by western blot and qRT-PCR (One-way ANOVA with Dunnett’s post-hoc test). m. Effects of BT12 CD44s KO and CD44v6 KO on PDGFRB and MIF expression by western blot (and corresponding densitometry), and n. qRT-PCR. o, p. Effects of BT16 CD44s KO and CD44v6 KO on PDGFRB and MIF expression by western blot and qRT-PCR. (Two-tailed Student’s t-test). Western blot shown is representative of 2–3 independent biological replicates performed in technical singlets (2–3 data points per condition). qRT-PCR data represent mean ± SEM of 3 independent biological replicates performed in technical duplicates (minimum 6 data points per condition). * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001. The PDGFRB-CD44-MIF crosstalk regulates cell cycle progression and proliferation in BT12 and BT16 cells The observation that CD44s promotes stability of PDGFRB is an indicator that this crosstalk could well contribute to ATRT malignancy. To further investigate this possibility, we assessed effects of the PDGFRB-CD44-MIF crosstalk on cell proliferation, cell cycle, colony formation, and invasion/migration, using CRISPR/Cas9-mediated knockout (KO) in BT12 and BT16 cell lines. Although either PDGF ligand or receptor KO had no significant effects on BT12 cell growth, PDGFRB/PDGFB double knock-out (DKO) correlated with significantly ( p = 0.0100; p = 0.0004; p = 0.0045, on days 4, 7, and 10, respectively) reduced cell proliferation relative to non-targeting gRNA (NT gRNA) controls, indicating that a PDGFRB-PDGFB autocrine loop could be important for cell proliferation (Fig. 4 a). PDGFRB/MIF DKO, CD44s KO, and CD44v6 KO all reduced BT12 proliferation (Fig. 4 a). DKO of PDGFRB and MIF resulted in a robust decrease in cell proliferation, with a significant ( p = 0.0279) reduction starting as early as day 3 in BT12 PDGFRB/MIF DKO relative to NT gRNA cells (Fig. 4 a). Furthermore, cell cycle analyses showed a modest, but consistent, increase in the percentage of cells in the G2/M phase in BT12 subject to PDGFB KO (15.2±0.5%), PDGFRB KO (15.3±1.8%), and PDGFRB/PDGFB DKO (15.9±0.1%) cells relative to NT gRNA (9.8±0.5%), representing an average 57% increase (Fig. 4 b; Supplementary Fig. 7f). Consistently, these KO cell lines showed an increase in G2/M protein accumulation (Cyclin A2 and CDC25C) (Fig. 4 c; Supplementary Fig. 7g). In contrast, BT12 MIF KO (5.8 ± 0.8%) and PDGFRB/MIF DKO (5.4 ± 1.0%) were accompanied by a reduction in the G2/M population (relative to 9.8 ± 0.5% in NT gRNA cells) (Fig. 4 b; Supplementary Fig. 7f). In addition, MIF KO induced a significant ( p = 0.0035) increase in TP53 , a G1/S transition checkpoint activator (Fig. 4 c). KO of CD44v6 in BT12 cells caused a significant ( p = 0.0043) reduction in the G0/G1 population (43.3 ± 1.6% relative to 60.9 ± 3.6%, which represents ~ 29% decrease) and increase in the G2/M population (26.4 ± 1.5%; ~2.7-fold increase in the cell population), suggesting G2/M arrest (Fig. 4 b). CD44s KO did not cause a significant change in the cell cycle distribution (Supplementary Fig. 7f). In BT16, none of the PDGF KOs significantly affected cell proliferation, while PDGFRB/MIF DKO, CD44s KO, and CD44v6 KO reduced it (Fig. 4 d). Furthermore, PDGFB KO had no effects on the cell cycle in BT16 cells, while both PDGFC KO (6.0±0.2%) and PDGFRB KO (0.5±0.2%) led to a decrease in G2/M population relative to NT gRNA (7.9±0.4%), with the PDGFRB KO also causing an increase in the G0/G1 population (81.1±2.7% relative to 66.9±1.1%) (Fig. 4 e; Supplementary Fig. 7h). This corresponded to a decrease in Cyclin A2 in PDGFC KO and PDGFRB KO, and an increase in Cyclin D3, a G1 marker, in the same cell lines (Fig. 4 f; Supplementary Fig. 7i). Interestingly, DKO of PDGFRB/PDGFB in BT16 cells caused a mild increase in the S-phase population (28.0±0.1% relative to 25.1±0.6%) (Supplementary Fig. 7h). Meanwhile, BT16 MIF KO led to a significant ( p = 0.0026) increase in G2/M population (13.0 ± 0.5% relative to 7.9 ± 0.4% in NT gRNA cells, which represents ~ 65% increase); which contrasts with a G2/M decrease seen in PDGFRB KO (Fig. 4 e). Similar to BT12, MIF KO in BT16 also correlated with an increase in p53, despite the phenotype differences in cell cycle distribution between BT12 MIF KO and BT16 MIF KO cells (Fig. 4 f). Interestingly, there were no significant changes in cell cycle distribution in the PDGFRB/MIF DKO (Supplementary Fig. 4h). BT16 CD44s KO resulted in a significant ( p = 0.0375) increase in S-phase cells (30.7 ± 1.7% relative to 25.1 ± 0.6%, representing ~ 22% increase), accompanied by a reduction in the G2/M marker Cyclin A2 (Fig. 4 f; Supplementary Fig. 7h). BT16 CD44v6 KO showed a significant ( p = 0.0198) reduction of G2/M cells (1.8 ± 1.5%, representing ~ 77% decrease), with a simultaneous increase in the G0/G1 population (83.2 ± 7.4% relative to 66.9 ± 1.1%, representing ~ 25% increase) (Fig. 4 e). The differences between BT12 and BT16 may reflect baseline variation in cell cycle distribution and checkpoint regulatory protein expression. We next investigated the effects of PDGFRB-CD44-MIF crosstalk on colony formation as well as cell migration and invasion. We observed that in relation to controls cells with NT gRNA, BT12 and BT16 cells with PDGFB KO, PDGFRB/PDGFB DKO, MIF KO, PDGFRB/MIF DKO, and CD44 KOs, but not PDGFRB KO, exhibited significant ( p < 0.01) reduction in colony formation, suggesting a predominant ligand-mediated change in BT12 and BT16 clonogenicity (Fig. 5 a, b; Supplementary Fig. 8a, b). Notably, colony formation in BT16 cells with ligand and receptor DKO was reduced to a much greater extent than that in corresponding BT12 cells (Fig. 5 a, b). Effects on cell migration and invasion showed some variability between BT12 and BT16 cell lines, which may reflect differences in baseline invasive capacity. We found PDGFRB KO caused a significant ( p = 0.0003) reduction in BT12 cell migration but not invasion (Fig. 5 c; Supplementary Fig. 8c) while PDGFRB KO reduced ( p = 0.0245) BT16 cell invasion but not migration (Fig. 5 d; Supplementary Fig. 8c). In contrast, MIF KO significantly ( p = 0.0157; p = 0.0095, respectively) impaired cell invasion in both BT12 and BT16 (Fig. 5 c, d). Additionally, CD44s KO and CD44v6 KO in BT12 led to a significant ( p = 0.0011; p = 0.0047, respectively) reduction in cell invasion, without affecting cell migration (Fig. 5 c; Supplementary Fig. 8c). These observations suggest that KOs in the PDGFRB-CD44-MIF crosstalk components correlated with a decrease in cell proliferation, cell cycle perturbations, reduced clonogenicity, and cell motility. a, d. Cell proliferation in PDGFB KO, PDGFC KO, PDGFRB KO, PDGFRB/PDGFB DKO, MIF KO, PDGFRB/MIF DKO, CD44s KO, and CD44v6 KO relative to NT gRNA in BT12 (a) and BT16 (d) by measured AlamarBlue assay (Em/Ex 570/585 nm) at indicated timepoints. Cell proliferation data represent mean ± SEM of 3 independent biological replicates performed in technical quadruplicates (12 data points per condition; individual data points omitted for clarity) (One-way ANOVA with Dunnett’s post-hoc test). b, e. Cell cycle distribution in PDGFRB KO, PDGFRB/PDGFB DKO, MIF KO, and CD44v6 KO relative to NT gRNA BT12 (b) and BT16 (e) measured by PI staining by flow cytometry. Cell cycle data represent mean ± SEM of 3 independent biological replicates performed in technical singlets (3 data points per condition) (One-way ANOVA with Dunnett’s post-hoc test). c, f. Left: Cell cycle markers protein expression in BT12 (c) and BT16 (f) NT gRNA, PDGFB KO, PDGFRB KO, MIF KO, PDGFRB/PDGFB DKO, PDGFRB/MIF DKO, CD44s KO, and CD44v6 KO by western blot. Markers were grouped by cell cycle phase/transition. Right: p53 expression in MIF KO relative to NT gRNA. (c) qRT-PCR data represent mean ± SEM of 3 independent biological replicates performed in technical duplicates (6 data points) (f) Western blot shown is representative of 2 independent biological replicates performed in technical singlets (2 data points) (Two-tailed Student’s t-test). * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001. a, b. Colony count in PDGFB KO, PDGFC KO, PDGFRB KO, PDGFRB/PDGFB DKO, MIF KO, PDGFRB/MIF DKO, CD44s KO, and CD44v6 KO relative to NT gRNA. Colony formation data represent mean ± SEM of 3 independent biological replicates performed in technical triplicates (minimum 9 data points per condition). The images represent one of the replicates (One-way ANOVA with Dunnett’s post-hoc test). c, d. Cell invasion/migration in PDGFB KO (BT12), PDGFRB KO, MIF KO, CD44s KO (BT12), and CD44v6 KO (BT12) relative to NT gRNA measured by Matrigel/FluroBlok assay (Em/Ex 494/517 nm). Cell invasion and migration data represent mean ± SEM of 3 independent biological replicates performed in technical duplicates (6 data points per condition; except for BT12 CCD44 KOs migration assay: 2 biological replicates and 4 data points) ( (c) One-way ANOVA with Dunnett’s post-hoc test; (c, d) Two-tailed Student’s t-test). * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001. The PDGFRB-CD44-MIF crosstalk regulates neural stemness and EMT marker expression Given the effect of KOs in the PDGFRB-CD44-MIF crosstalk components on cellular homeostasis, we tested whether this crosstalk may influence cellular plasticity in ATRT. We focused on two major cellular phenotypes that vary across ATRT tumors - neural differentiation and EMT status. We first investigated the effects of PDGFRB-CD44-MIF crosstalk on neural stemness marker expression in BT12 and BT16 by assessing expression of SOX2 and NES , and early neural differentiation markers ( MAP2 and TUBB3 ) by western blot and qRT-PCR. We observed that KOs of PDGFB, PDGFRB, and MIF in BT12 led to a significant ( p < 0.0001) reduction in SOX2 , a marker of neural stem cells (Fig. 6 a; Supplementary Fig. 8d). In addition, PDGFRB KO and MIF KO correlated with a significant ( p < 0.0001; p = 0.0023, respectively) increase in NES , which is associated with neural progenitor cells (Fig. 6 a). PDGFRB KO also correlated with an increase ( p = 0.0060) in MAP2 , a neuronal lineage commitment marker, while MIF KO induced increased ( p = 0.0091) expression of TUBB3 , a neuronal cytoskeleton marker (Fig. 6 a). Similarly, both CD44s KO and CD44v6 KO resulted in a significant ( p = 0.0136; p = 0.0254) reduction in NES and SOX2 ( p = 0.0013; p = 0.0006), with CD44s KO correlating with an increase ( p < 0.0001) in MAP2 , and CD44v6 KO correlating with an increase ( p < 0.0001) in TUBB3 (Fig. 6 b). In BT16 KOs of PDGFRB, MIF and, CD44 produced slightly different patterns of neural and stemness markers (Fig. 6 c). PDGFRB KO correlated with increased ( p = 0.0434) NES and reduced ( p < 0.0001) MAP2 expression, while MIF KO led to a significant ( p < 0.0001) reduction in SOX2 and NES , and a significant ( p = 0.0098) increase in MAP2 (Fig. 6 c). Both CD44s KO and CD44v6 KO resulted in a reduction in SOX2 , an increase in NES , with CD44v6 KO also causing an increase in MAP2 (Fig. 6 d, e). These differences seen in BT12 and BT16 KO lines could reflect either differences in PDGF signaling or differences in cell states of the two lines (Fig. 6 a, c). Overall, results from BT12 and BT16 suggest that PDGFRB-CD44-MIF crosstalk may influence features of stemness and neural plasticity; KOs of components of this axis correlated with changes in neural differentiation indicators, including neural progenitor, neuronal lineage commitment, and neuronal cytoskeleton formation. Effects of the PDGFRB-CD44-MIF crosstalk on EMT EMT could be one of the factors driving ATRT inter-tumor heterogeneity. For instance, ATRT-TYR/MYC tumors exhibit mesenchymal features, whereas ATRT-SHH is neurogenic. The mechanism underlying these differences remain unknown. To this end, we assessed effects of PDGFRB-CD44-MIF crosstalk on EMT. We first determined mesenchymal (N-cadherin, CDH2 ; Beta-catenin; Vimentin, VIM ; and Twist-1, TWIST1 ) and epithelial (E-cadherin, CDH1; and Claudin-1, CLDN1 ) marker expression in ATRT cell lines. BT16 was the only cell line that exhibited a defined mesenchymal phenotype, characterized by expression of N-cadherin, Vimentin, and Beta-catenin, and the absence of E-cadherin and Claudin-1 (Fig. 6 f). The other cell lines had a hybrid epithelial/mesenchymal (EM) phenotype. Notably, BT12, which expressed Claudin-1 and Beta-catenin, lacked detectable expression of other mesenchymal or epithelial markers (Fig. 6 f). CHLA02 and CHLA06 expressed E-cadherin and Vimentin (Fig. 6 f). TWIST-1, a master regulator of EMT, was only detectable at the protein level in CHLA05 (Fig. 6 f). Overall, these data support that EMT status is complex in ATRT, characterized by hybrid expression of both mesenchymal and epithelial markers. Consistent with western blot data, BT16 seems to have a well-defined mesenchymal phenotype, characterized by a high expression of CDH2 and VIM (Fig. 6 f). In contrast, BT12 seems to have a hybrid EM phenotype, with high expression of CLDN1 and low CDH2 (Fig. 6 f). Next, we assessed the effects of genetically manipulating PDGFRB-CD44-MIF crosstalk on EMT status of these cells. In BT12, PDGFRB KO led to a significant ( p < 0.0001) reduction in TWIST1 and a significant ( p = 0.0204) increase in CDH1 (Fig. 6 g). Interestingly, PDGFRB KO also correlated with an increase ( p = 0.0087) in CDH2 (Fig. 6 g). MIF KO in BT12 provoked an opposite effect to the changes induced by PDGFRB KO: it resulted in a decrease ( p < 0.0001) in epithelial marker expression ( CDH1 and CLDN1) , with loss of detectable Claudin-1 protein; and it led to increased ( p = 0.0002; p = 0.0201; p = 0.0003) accumulation of mesenchymal markers TWIST1 , CDH2 , and VIM (Fig. 6 g). KO of CD44s KO in BT12 resulted in a significant ( p = 0.0009) reduction of TWIST1 expression and an increase ( p < 0.0001) in CDH2 , whereas CD44v6 KO led to a significant ( p < 0.0001) increase in CDH1 (Fig. 6 h; Supplementary Fig. 8e) and a remarkable change in morphology, transitioning from a spindle cell to a cobblestone morphology, characteristic of epithelial cells (Fig. 6 i). In BT16, PDGFRB KO led to a reduction ( p < 0.0001) in TWIST1 and an increase ( p = 0.0040) in CDH2 (Fig. 6 j). Interestingly, PDGFRB KO also led to an increase (p < 0.0001) in ZEB1 , a transcription factor involved in EMT. BT16 MIF KO resulted in a significant increase in CDH1 , and a reduction in TWIST1, ZEB1 , and VIM ( p = 0.0138; p = 0.0001; p = 0.0106), which contrasts with the changes observed in BT12 MIF KO (Fig. 6 j). BT16 CD44s KO and CD44v6 KO correlated with a significant ( p = 0.0005; p < 0.0001) increase in TWSIT1 , with CD44s KO inducing almost a 10-fold increase in TWIST1 mRNA level relative to NT gRNA control (Fig. 6 k). Furthermore, CD44s KO cells appeared to have a spindle-like, elongated morphology, characteristic of mesenchymal cells, and more fibroblastic relative to the NT gRNA (Fig. 6 l). This finding is consistent with the increase in mesenchymal markers observed by qRT-PCR. Whereas CD44v6 KO cells had a rounder, less elongated morphology, indicative of reduced mesenchymal characteristics (Fig. 6 l). Together, these results indicate that KOs in the PDGFRB-CD44-MIF crosstalk could impact the expression of mesenchymal and epithelial markers, and that the exact effect is gene-specific and cell line-dependent. Baseline expression of EMT-associated markers differed between cell lines (Fig. 6 f), which may contribute to the differential effects of KO conditions on EMT marker expression observed in each line. Notably, across both BT12 and BT16, PDGFRB loss correlated with a decrease in mesenchymal markers. a-e. Neural stemness ( SOX2 and NES ) and differentiation marker ( TUBB3 and MAP2 ) expression in PDGFRB DKO, MIF KO, CD44s KO, and CD44v6 KO relative to NT gRNA BT12 (a, b) and BT16 (c-e) by western blot and qRT-PCR. In BT16, one MIF KO biological replicate values for MAP2 (131.2 and 131.4) were excluded using the ROUT method (Q = 1%) (( a, c-e ) Two-tailed Student’s t-test; (b) One-way ANOVA with Dunnett’s post-hoc test). f. Expression of mesenchymal (N-cadherin, Beta Catenin, Vimentin, and Twist1) and epithelial markers (E-cadherin and Claudin-1) in ATRT cell lines by western blot (left) and by qRT-PCR in BT12 and BT16 cells (right). g, j. EMT marker expression in BT12 (g) and BT16 (j) PDGFRB KO and MIF KO relative to NT gRNA cells by western blot (left) and qRT-PCR (right). For BT12 western blot, only Vimentin and Claudin-1 had detectable protein expression among the selected markers. For BT16 western blot, only N-cadherin and Vimentin had detectable protein expression among the selected markers (Two-tailed Student’s t-test). h, k. EMT marker expression in BT12 (h) and BT16 (k) CD44s KO and CD44v6 KO relative to NT gRNA by qRT-PCR. One-way ANOVA with Dunnett’s post-hoc test; (k) Two-tailed Student’s t-test). i, l. Representative phase-contrast micrographs of BT12 (i) and BT16 (l) NT gRNA (left), CD44s KO (center), and CD44v6 KO (right). (i) Top panels provide a wide-field view of cell distribution and density at 10x objective magnification. The bottom panels show a high-magnification (20x) view, highlighting individual cell morphology details; (l) The panels provide a wide-field view of cell distribution and density at 20x objective magnification. Scale bars represent 100 µm; dimensions estimated based on a mean cell diameter of 12 µm. qRT-PCR data represent mean ± SEM of 3 independent biological replicates performed in technical duplicates (minimum 6 data points per condition). * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001. Discussion Previous studies on ATRT established sensitivity to RTKIs, specifically those that inhibit PDGFRB 13 , 15 – 17 . However, it remained unclear whether PDGFRB critically contributes to ATRT tumorigenesis, similar to its role in brain tumors with dysregulated PDGF signaling, such as glioblastoma. Our data indicated high-level expression of both PDGFRA and PDGFRB in ATRTs, which differs from other brain tumors where aberrant PDGF pathway activation is generally associated with high expression of only PDGFRA 43 . Conversely, PDGFRB expression has been shown to be restricted to stromal cells, such as pericytes 43 . Co-expression of PDGFRA and PDGFRB in some ATRT cell lines suggests that they could express heterodimeric PDGFRA-B, which is uncommon for brain tumor cells 23 . Our observation of expression of multiple RTKs, including some constitutively active ones, suggest ATRT cell lines heavily depend on RTK signaling. Furthermore, this co-expression could indicate signaling redundancy, where cells could compensate if one signaling pathway is impaired or compromised. Our study also revealed that beyond PDGFs expression in TYR/MYC subgroup cell lines, multiple other RTKs are activated in other ATRT cell lines. CHLA02, for example, is an ATRT-SHH cell line that co-expresses EGFR, ErbB3, and ErbB4, with constitutively active EGFR and ErbB3. Our study elucidated that the PDGF pathway has two modes of signaling in ATRT. First, autocrine as seen in BT12 and BT16 cells, which induces low-level chronic activation of PDGFRB. This type of signaling exists in other brain tumors, as seen with EGFR autocrine signaling in glioblastoma 44 , 45 , and has been associated with acquired therapy resistance 46 , 47 . Notably, our study also revealed all tested ATRT cell lines (BT12, BT16, CHLA05, and CHLA06) exhibited paracrine PDGF signaling, and evidence of a plausible paracrine PDGFD-induced PDGFRB recycling in BT12 and BT16 cells. Indeed, PDGF-DD stimulation in BT12 and BT16 appears to promote PKCα-mediated PDGFRB accumulation, potentially through receptor recycling, a phenomenon that is described for PDGFRA in glioblastoma 48 . Thus, use of RTKIs that target PDGFRB, for example, could alleviate a negative feedback loop resulting in enhanced PDGFRB transcription and protein synthesis, and eventually to reactivation of the PDGF pathway in ATRTs. The recycling mechanism represents yet another potential driver of RTKI therapy resistance as receptor recycling could replenish functional PDGFRB to the plasma membrane, overriding the drug’s effect. The proposed autocrine and paracrine signaling models would benefit from additional experimental validation using approaches that provide spatial and temporal resolution of receptor dynamics. Specifically, these findings were assessed at a single optimized timepoint; future time-course analyses would provide more comprehensive insight into receptor degradation kinetics. Moreover, a deeper characterization of these signaling modes in vivo is essential for understanding how autocrine or paracrine signaling uncovered in our study may drive RTKI therapy resistance in ATRT. By using CRISPR/Cas9 stable KO cell lines (PDGFB KO, PDGFRB KO, and PDGFRB/PDGFB DKO), we systematically examined the functional impact of various PDGF signaling pathway components on ATRT cellular phenotypes. Similar to studies of PDGF in other tumors, including glioblastoma 49 , our data revealed that PDGF signaling contributes to proliferative signaling in BT12 and BT16. The strongest effect of PDGF signaling was observed on ATRT cell growth in clonogenic assays, suggesting a role in sustaining proliferative potential. In addition to enhanced clonogenicity, our study revealed that PDGFRB is important for cell motility in BT12 and BT16, which is consistent with what was reported for many brain tumors including medulloblastoma and glioblastoma 50 . Previous studies indicated that MIF can be upregulated in ATRT, specifically ATRT-TYR/MYC 13 . However, the role of MIF in ATRT has remained unknown, and our project has helped to elucidate its functions. MIF is highly expressed in BT12, BT16, and could be connected to the PDGF pathway via the intermediate of CD44 (Fig. 7 ). While the precise mechanism by which PDGFRB, CD44, and MIF interact is cell line-dependent and will require further experimentation to define across ATRT subtypes/cell lines, a consistent effect that was shared by both BT12 and BT16 is that PDGFRB appears to drive CD44s expression, which in turn is essential for MIF signal transduction. This further highlights the potential role of PDGFRB as a primary oncogenic driver of this crosstalk. Reciprocally, CD44s could promote PDGFRB protein stability, which is consistent with what was reported about PDGFRB-CD44 interaction in other cancer cells 42 . In contrast, CD44v6 loss correlate with increased PDGFRB and MIF expression, which could be a compensatory mechanism. The reciprocal downregulation between PDGFRB and MIF could serve as a regulatory mechanism to maintain balanced expression of CD44v6; very high CD44v6 levels may be toxic to cells, by inducing replication or metabolic stress, as reported in a study of esophageal squamous cell carcinoma 51 . Nevertheless, this requires further experimental validation in ATRT. As this proposed PDGFRB-MIF crosstalk is inferred solely from expression changes in individual KO contexts, the role of CD44 as a mediator remains to be more robustly tested. Formal establishment of this relationship would require combinatorial KOs (such as PDGFRB/CD44 DKO) and rescue experiments to distinguish direct mediation from parallel regulatory responses. Moreover, these findings should be interpreted in the context of the pooled non-clonal experimental system employed (for example, the residual CD44v6 isoform expression observed in BT12 CD44s KO). Furthermore, the use of only two cell lines, BT12 and BT16, represents a limitation of the current model, as findings may not fully capture subgroup-specific biology across all ATRT subtypes. Validation in additional cell lines representing ATRT-SHH and primary patient-derived cultures would strengthen the generalizability of this proposed crosstalk. KOs in the PDGFRB-CD44-MIF crosstalk components significantly impaired cell proliferation, cell cycle, colony formation, and invasion/migration, suggesting that this crosstalk can be malignant. Furthermore, our data suggest that the PDGFRB-CD44-MIF crosstalk regulates cell states in BT12 and BT16 cells. The PDGF pathway could promote EMT in BT12 and BT16 cells, in line with the findings from other tumors 52 , 53 . PDGFRB appears to drive mesenchymal markers expression, nevertheless, its loss also led to a significant increase in CDH2 . Although CDH2 is a mesenchymal marker in carcinomas, brain tumors, such as glioblastoma, commonly maintain high levels of CDH2 while reversing the mesenchymal phenotype, and rarely express CDH1 54 . Consequently, an increase in CDH2 in PDGFRB KO does not contradict the partial loss of mesenchymal phenotype. MIF plays a distinct role, where it can promote MET in BT12, and EMT in BT16. MIF’s effect on EMT is context dependent: in glioblastoma, where MIF expression is acute and induced by hypoxia, MIF has been found to promote EMT 55 ; in gastrointestinal cancer, where MIF expression is chronic, it promotes MET, specifically through the CD74 intracellular domain (CD74-ICD), which is formed upon MIF binding 56 . The TME could be important in determining whether MIF signaling resembles that of glioblastoma or gastrointestinal tumors, and it may explain differences in MIF’s impact on EMT in BT12 and BT16. Specifically, this may reflect differences in MIF signaling modes, analogous to the distinct PDGF signaling modes previously described in these cells. Like MIF, CD44s also seems to play a dual role, in BT12, it can promote EMT; in BT16, CD44s can inhibit mesenchymal marker gene expression, especially TWIST1 . One possible explanation is that because BT16 has a pronounced basal mesenchymal phenotype, CD44s could prevent the cells from undergoing further EMT, thereby reducing the risk of acquiring an extreme mesenchymal phenotype. Cells that undergo extreme EMT lose invasive capacities and can even become quiescent 57 . Finally, CD44v6 KO stood out for its considerable effect on cell morphology, prompting both BT12 and BT16 to acquire a more epithelial phenotype, and thus CD44v6 loss can be central in driving MET in these cells. However, the markers assessed represent a limited subset of the EMT program, and a more comprehensive characterization incorporating additional regulators as well as brain tumor-relevant mediators including YAP/TAZ and STAT3, would be required to better define EMT status in this context. Overall, the findings suggest that PDGFRB-CD44-MIF crosstalk is associated with the expression of neural stemness-associated markers and a less differentiated cell state. In the absence of MIF, CD44s, and CD44v6 in BT12 and BT16 cells, a partial neural differentiation is seen. This is consistent with a reported role for CD44 as a stemness marker in cancer cells 58 . Though, stemness assessment was limited to a select panel of markers, and a more comprehensive characterization incorporating in vivo limiting dilution assays will be needed to determine whether the observed changes in stemness-associated markers correspond to an altered tumor-initiating capacity. Furthermore, a limitation of using pooled, non-clonal KO cell populations rather than isogenic clonal lines is that it introduces heterogeneity in editing efficiency within the population. As a result, observed phenotypes may reflect a mixture of fully edited, partially edited, and potentially unedited cells, which could influence the magnitude of the observed effects. Future studies using validated isogenic clonal lines would strengthen the mechanistic interpretation of the findings presented here. Conclusion In summary, the PDGFRB-CD44-MIF crosstalk appears to facilitate cellular maintenance of an optimal EM and stemness phenotype associated with growth and invasion. It should also be noted that EMT and stemness represent highly plastic and reversible cell states that exist along a spectrum rather than as discrete categories. The expression changes observed here could reflect a partial or hybrid state transition, and bulk expression analysis (western blot, qRT-PCR, and RNA-seq) cannot resolve the heterogeneity of cell states within a cell population. Single-cell RNA-seq analysis would be required to deconvolute cellular heterogeneity and to model the dynamic and continuous nature of these cell states. However, the observed variability in responses between BT12 and BT16 cell lines highlights both the complexity of ATRT biology and the inherent limitation of drawing broad conclusions from a restricted number of models. Accordingly, the findings presented in this paper should be interpreted as hypothesis-generating observations that provide preliminary evidence for the biological relevance of the studied PDGF signaling modes and PDGFRB-CD44-MIF crosstalk in ATRT, rather than definitive mechanistic conclusions. Indeed, two cell lines are insufficient to fully capture the molecular and phenotypic inter- and intra-tumoral heterogeneity of ATRT. Future studies incorporating additional ATRT cell lines representing all three molecular subgroups, as well as primary patient-derived cultures, would provide a more comprehensive assessment of the study’s findings relevance and generalizability across the heterogeneous ATRT landscape. Abbreviations ACTB Actin cytoskeletal beta AKT AKR Thymoma/ Protein kinase B ANOVA Analysis of variance ATAC-seq Assay for transposase-accessible chromatin with high-throughput sequencing ATP Adenosine triphosphate ATRT-MYC Atypical teratoid rhabdoid tumor-myelocytomatosis ATRT-SHH Atypical teratoid rhabdoid tumor-sonic hedgehog ATRT-TYR Atypical teratoid rhabdoid tumor-tyrosinase ATRT Atypical teratoid rhabdoid tumor BAF47 Brahma-related gene-1-associated factor 47 BT12 Brain tumor 12 BT16 Brain tumor 16 CBL Casitas B-lineage lymphoma CBLB Casitas B-lineage lymphoma proto-oncogene B CD44 Cluster of differentiation 44 CD44s Standard cluster of differentiation 44 CD44v6 Cluster of differentiation 44 variable exon 6 CD74 Cluster of differentiation 74 CDC Cell division cycle CDH Calcium-dependent adhesion CDK Cyclin-dependent kinase CDKN Cyclin-dependent kinase inhibitor ChIP-seq Chromatin immunoprecipitation sequencing CHLA Children’s Hospital Los Angeles CLDN1 Claudin-1 CNS Central nervous system CRISPR/Cas9 Clustered regularly interspaced short palindromic repeats-associated protein 9 CXCR C-X-C Motif chemokine receptor DDT D-dopachrome tautomerase DKO Double knockout DMSO Dimethyl sulfoxide DNA Deoxyribonucleic acid EBTs Embryonal brain tumors ECM Extracellular matrix EGFR Epidermal growth factor receptor EMT Epithelial-mesenchymal transition ERBB Erythroblastic oncogene B receptor ESRP Epithelial splicing regulatory protein ETMRs Embryonal tumors with multilayered rosettes FACS Fluorescence-activated cell sorting FBS Fetal bovine serum FGFR1 Fibroblast growth factor receptor 1 G0; G1; G2 Gap phase 0; 1; 2 GAPDH Glyceraldehyde-3-phosphate dehydrogenase GFP Green fluorescent protein gRNA Guide RNA H3K27me3 Histone H3 lysine 27 trimethylation HEK293 Human embryonic kidney 293 ICD Intracellular domain IHC Immunohistochemistry INI1 Integrase interactor 1 KO Knockout M (as in G2/M) Mitosis MAP2 Microtubule associated protein 2 MAPK Mitogen-activated protein kinase MCM Minichromosome Maintenance MET Mesenchymal-epithelial transition MIF Macrophage migration inhibitory factor mRNA Messenger ribonucleic acid MT Mesenchymal transition MYC Myelocytomatosis gene NES Nestin NHA Normal human astrocytes NT Non-targeting PDGF Platelet-derived growth factor subunit A PDGFR Platelet-derived growth factor receptor PI3K Phosphoinositide 3-kinase PLC-γ Phospholipase C, gamma 1 PRC2 Polycomb repressive complex 2 PRKCA Protein kinase C alpha qRT-PCR Quantitative reverse transcription polymerase chain reaction Rab4a Ras-related protein 4a RFU Relative fluorescence units RNA-seq Ribonucleic acid sequencing RNA Ribonucleic acid RTK Receptor tyrosine kinase RTKI Receptor tyrosine kinase inhibitor scRNA-seq Single-cell RNA sequencing SEM Standard error of the mean SMARCA4 SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily A, member 4 SMARCB1 SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily B, member 1 SNAI1/2 Snail family transcriptional repressor 1/2 SNF5 Sucrose nonfermenting, yeast, factor 5 SOX2 SRY-box transcription factor 2 SWI/SNF SWItch/Sucrose non-fermentable TGFB Transforming growth factor beta TIDE Tracking of insertions and deletions TME Tumor microenvironment TUBB3 Tubulin beta class III VIM Vimentin WHO World health organization ZEB1/2 Zinc finger E-box-binding homeobox 1/2 Declarations Ethics approval, Consent to participate, and Consent to publish declarations Not applicable. Availability of data and materials Data supporting the findings of this study are available within the article and its Supplementary Information files, or from the corresponding author upon reasonable request. Competing interests The authors declare no competing interests. Funding This work was supported by a grant from the Canadian Institutes of Health Research (CIHR). Authors’ contributions Y.A.C: Conceptualization, Methodology, Validation, Formal Analysis, Investigation, Data Curation, Writing - Original Draft, Visualization. A.H.: Conceptualization, Resources, Writing - Review & Editing, Supervision, Project Administration, Funding Acquisition. All authors reviewed and approved the final manuscript. References Louis, D. N. et al. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro. Oncol. 23 , 1231 (2021). Tekautz, T. M. et al. 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PLoS One 9 , e98656 (2014). Lu, W. & Kang, Y. Epithelial-Mesenchymal Plasticity in Cancer Progression and Metastasis. Dev. Cell 49 , 361–374 (2019). Li, W. et al. Unraveling the roles of CD44/CD24 and ALDH1 as cancer stem cell markers in tumorigenesis and metastasis. Sci. Rep. 7 , 1–15 (2017). Additional Declarations No competing interests reported. Supplementary Files Tables.docx Supplementarymaterials.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9707332","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":640073639,"identity":"2257fe19-1e3b-4e5a-9ca9-b4130990ab93","order_by":0,"name":"Yacine A. Choutri","email":"data:image/png;base64,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","orcid":"","institution":"University of Toronto","correspondingAuthor":true,"prefix":"","firstName":"Yacine","middleName":"A.","lastName":"Choutri","suffix":""},{"id":640073641,"identity":"d09fbbee-8083-400d-82c0-8232ea60e6d6","order_by":1,"name":"Annie Huang","email":"","orcid":"","institution":"University of Toronto","correspondingAuthor":false,"prefix":"","firstName":"Annie","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2026-05-13 19:24:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9707332/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9707332/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109341401,"identity":"f07bd278-56e1-4786-9243-9a7b7aaf8f1b","added_by":"auto","created_at":"2026-05-15 18:54:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":5181590,"visible":true,"origin":"","legend":"\u003cp\u003ePDGFRB is constitutively active in BT12 and BT16 cells\u003c/p\u003e\n\u003cp\u003e· \u003cstrong\u003ea.\u003c/strong\u003eExpression of PDGFRA (left) and PDGFRB (right) protein (by western blot) and mRNA (by qRT-PCR), in ATRT cell lines. ATRT-SHH cell lines are labelled in red; ATRT-TYR/MYC cell lines are labelled in blue; Normal human astrocytes (NHA), labelled in green, is a non-cancer cell line that was included as a negative control for \u003cem\u003ePDGFRA\u003c/em\u003e and a positive control for \u003cem\u003ePDGFRB\u003c/em\u003e expression in qRT-PCR\u003c/p\u003e\n\u003cp\u003e· \u003cstrong\u003eb.\u003c/strong\u003eLeft: Expression of PDGF ligand protein in ATRT cell lines. Human recombinant PDGF ligands were included as a positive control for western blot (10 ng per lane). Right: \u003cem\u003ePDGFA, PDGFB, PDGFC\u003c/em\u003e, and \u003cem\u003ePDGFD\u003c/em\u003e mRNA expression (by qRT-PCR in ATRT) cell lines. NHA mRNA was included for comparison.\u003c/p\u003e\n\u003cp\u003eWestern blot shown (in \u003cstrong\u003ea, b\u003c/strong\u003e) is representative of 3 independent biological replicates performed in technical singlets; quantification across replicates was not performed. qRT-PCR data represent the mean ± SEM of n=3 independent biological replicates performed in technical duplicates or triplicates, with a minimum of 6 data points shown per condition.\u003c/p\u003e\n\u003cp\u003e· \u003cstrong\u003ec.\u003c/strong\u003ePI3K/Akt, MAPK, and PLCg protein expression. Stimulated (+PDGF-BB/+PDGF-DD) and non-stimulated NIH-3T3 cells were included as a negative/positive control for phospho-protein expression.\u003c/p\u003e\n\u003cp\u003e· Effects of PDGF-BB (\u003cstrong\u003ed\u003c/strong\u003e) and PDGF-DD (\u003cstrong\u003ee\u003c/strong\u003e) (0,10, 20, and 30 ng/mL for 5 minutes) on PDGFRB and phospho-PDGFRB expression in BT12, BT16, CHLA06, and CHLA05 by western blot (top). Corresponding densitometry quantification of total PDGFRB (bottom). Western blot shown is representative of 2-3 independent biological replicates.\u003c/p\u003e\n\u003cp\u003eError bars represent ± SEM (n=2-3 independent biological replicates performed in technical singlets; 2-3 data points per condition). One-way ANOVA with Dunnett’s post-hoc test *\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, ****\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9707332/v1/6c7e8c440974e0c63ef595d4.png"},{"id":109405564,"identity":"503661dd-1270-48a2-8a7a-056926763961","added_by":"auto","created_at":"2026-05-17 13:19:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":10455972,"visible":true,"origin":"","legend":"\u003cp\u003ePDGFRB-PDGFB autocrine signaling in BT12 and BT16 cell lines\u003c/p\u003e\n\u003cp\u003e· \u003cstrong\u003ea.\u003c/strong\u003eEffects of \u003cem\u003ePDGFB \u003c/em\u003eknockdown on PDGFRB and phospho-PDGFRB expression in BT12 and BT16 cells by western blot, and corresponding densitometry quantification. \u003cem\u003ePDGFB\u003c/em\u003e siRNA (\u003cstrong\u003e–)\u003c/strong\u003e corresponds to cells transfected with non-targeting (NT) siRNA; \u003cem\u003ePDGFB\u003c/em\u003e siRNA (\u003cstrong\u003e+)\u003c/strong\u003e corresponds to cells transfected with \u003cem\u003ePDGFB\u003c/em\u003e siRNA.\u003c/p\u003e\n\u003cp\u003e· \u003cstrong\u003eb.\u003c/strong\u003eqRT-PCR validation of\u003cem\u003e PDGFB\u003c/em\u003e knockdown efficiency and its effect on \u003cem\u003ePDGFRB\u003c/em\u003emRNA levels in BT12 and BT16.\u003c/p\u003e\n\u003cp\u003e· Effects of \u003cem\u003eCBL \u003c/em\u003eknockdown on PDGFRB in BT12 \u003cstrong\u003e(c)\u003c/strong\u003e and BT16 \u003cstrong\u003e(e)\u003c/strong\u003e by western blot, and corresponding densitometry quantification. Cells were transfected with NT siRNA or \u003cem\u003eCBL\u003c/em\u003e siRNA; (+) indicates PDGF-BB stimulation (30 ng/mL for 1 hour); (-) indicates no PDGF-BB stimulation. The gradient on the western blot indicates higher contrast.\u003c/p\u003e\n\u003cp\u003e· \u003cstrong\u003ed, f. \u003c/strong\u003eqRT-PCR validation of \u003cem\u003eCBL\u003c/em\u003e knockdown, and its effects on \u003cem\u003eCBLB\u003c/em\u003e and \u003cem\u003ePDGFRB\u003c/em\u003e in BT12 \u003cstrong\u003e(d)\u003c/strong\u003e and BT16 \u003cstrong\u003e(f)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e· \u003cstrong\u003eg, i.\u003c/strong\u003e Effects of \u003cem\u003eCBLB\u003c/em\u003e knockdown on PDGFRB expression in BT12 \u003cstrong\u003e(g)\u003c/strong\u003e and BT16 \u003cstrong\u003e(i)\u003c/strong\u003e by western blot, and corresponding densitometry quantification. Cells were transfected with NT siRNA or \u003cem\u003eCBLB\u003c/em\u003esiRNA; (+) indicates PDGF-BB stimulation (30 ng/mL for 1 hour); (-) indicates no PDGF-BB stimulation.\u003c/p\u003e\n\u003cp\u003e· \u003cstrong\u003eh, j.\u003c/strong\u003e qRT-PCR validation of \u003cem\u003eCBLB\u003c/em\u003e knockdown, and its effects on \u003cem\u003eCBL\u003c/em\u003e and \u003cem\u003ePDGFRB \u003c/em\u003ein BT12 \u003cstrong\u003e(h)\u003c/strong\u003e and BT16 \u003cstrong\u003e(j)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e· \u003cstrong\u003ek, m.\u003c/strong\u003e Effects of MG132 treatment on PDGFRB by western blot, quantified by densitometry in BT12 \u003cstrong\u003e(k) \u003c/strong\u003eand BT16 \u003cstrong\u003e(m)\u003c/strong\u003e. Cells were treated with MG132 or with DMSO, followed by PDGF-BB stimulation (30 ng/mL for 1 hour) (+) or no stimulation (-). p21 protein levels were assessed to confirm the efficiency of MG132 treatment.\u003c/p\u003e\n\u003cp\u003e· \u003cstrong\u003el, n.\u003c/strong\u003e Effects of Bafilomycin A1 treatment on PDGFRB by western blot, quantified by densitometry in BT12 \u003cstrong\u003e(l)\u003c/strong\u003e and BT16 \u003cstrong\u003e(n)\u003c/strong\u003e. Cells were treated with Bafilomycin A1 or with DMSO, followed by PDGF-BB stimulation (30 ng/mL for 1 hour) (+) or no stimulation (-). p62 protein levels were assessed to confirm the efficiency of Bafilomycin A1 treatment.\u003c/p\u003e\n\u003cp\u003e· \u003cstrong\u003eo, q.\u003c/strong\u003e Effects of \u003cem\u003ePRKCA\u003c/em\u003e knockdown on PDGFRB and phospho-PDGFRB expression in BT12 \u003cstrong\u003e(o)\u003c/strong\u003e and BT16 \u003cstrong\u003e(q)\u003c/strong\u003e cells by western blot, and corresponding densitometry quantification. Cells were transfected with NT siRNA or \u003cem\u003ePRCKA \u003c/em\u003esiRNA; (+) indicates PDGF-DD stimulation (50 ng/mL for 1 hour); (-) indicates no PDGF-DD stimulation.\u003c/p\u003e\n\u003cp\u003e· \u003cstrong\u003ep, r.\u003c/strong\u003e qRT-PCR validation of \u003cem\u003ePRKCA\u003c/em\u003e knockdown, and its effects on \u003cem\u003ePDGFRB \u003c/em\u003ein BT12 \u003cstrong\u003e(p)\u003c/strong\u003e and BT16 \u003cstrong\u003e(r)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e· \u003cstrong\u003es, u.\u003c/strong\u003e PDGFRB protein levels in cycloheximide (CHX)-treated and DMSO-treated BT12 \u003cstrong\u003e(s)\u003c/strong\u003e and BT16 \u003cstrong\u003e(u)\u003c/strong\u003ecells by western blot. Cells were pretreated with 100 µg/mL CHX or 0.1% DMSO for 2 hours, followed by PDGF-DD stimulation (50 ng/mL for 1 hour) (+) or no stimulation (-). Right: corresponding densitometry analysis. In BT12, one biological replicate in the CHX condition had ~25.8 relative PDGFRB:Tubulin ratio and was excluded from the analysis using the ROUT method (Q=1%). Pan-Akt and p21 (short-lived proteins) were assessed to confirm the efficiency of CHX treatment.\u003c/p\u003e\n\u003cp\u003e· \u003cstrong\u003et, v.\u003c/strong\u003e \u003cem\u003ePDGFRB\u003c/em\u003e mRNA levels by qRT-PCR in DMSO-pretreated (0.1% DMSO for 2 hours) BT12 \u003cstrong\u003e(t)\u003c/strong\u003eand BT16 \u003cstrong\u003e(v)\u003c/strong\u003e PDGF-DD-stimulated cells (50 ng/mL for 1 hour) relative to non-stimulated cells.\u003c/p\u003e\n\u003cp\u003eError bars represent ± SEM. Western blot shown is representative of 3 independent biological replicates performed in technical singlets (3 data points per condition). qRT-PCR data represent 3 independent biological replicates performed in technical duplicates (minimum 6 data points per condition). Two-tailed Student’s t-test *\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, ****\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9707332/v1/a5926b7041233c21f98b7201.png"},{"id":109341403,"identity":"82e10037-216a-4cb1-b3dd-73deedf73fad","added_by":"auto","created_at":"2026-05-15 18:54:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3418541,"visible":true,"origin":"","legend":"\u003cp\u003eThe PDGFRB-CD44-MIF regulatory crosstalk\u003c/p\u003e\n\u003cp\u003e• \u003cstrong\u003ea.\u003c/strong\u003e Expression of MIF protein, and its functional homologue MIF2 by western blot (left) and \u003cem\u003eMIF\u003c/em\u003e mRNA by qRT-PCR (right) in ATRT cell lines. HEK293 was included as a positive control for MIF expression.\u003c/p\u003e\n\u003cp\u003e• \u003cstrong\u003eb.\u003c/strong\u003e Expression of CD74, MIF’s canonical receptor, by western blot.\u003c/p\u003e\n\u003cp\u003e• \u003cstrong\u003ec, d.\u003c/strong\u003e Expression of CD44, MIF’s co-receptor, and its isoform CD44v6. For western blot, two different antibodies were used: a standard isoform-specific (CD44s) antibody that does not cross-react with other isoforms; a CD44v6-specific antibody that does not cross-react with CD44s (see Supplementary Fig. 5). For qRT-PCR, two pairs of primers were used: CD44 primers, which detect all CD44 isoforms, including CD44s and CD44v6 (to our knowledge, it is not possible to design qRT-PCR primers specific to CD44s); CD44v6 primers that detect CD44v6 and CD44v10 isoforms mRNA (see Table 2).\u003c/p\u003e\n\u003cp\u003e• \u003cstrong\u003ee, g.\u003c/strong\u003e PDGFRB and MIF protein expression in PDGFRB KO and MIF KO by western blot and corresponding densitometry analysis.\u003c/p\u003e\n\u003cp\u003e• \u003cstrong\u003ef, h.\u003c/strong\u003e \u003cem\u003ePDGFRB\u003c/em\u003e mRNA expression in MIF KO, and \u003cem\u003eMIF\u003c/em\u003e mRNA expression in PDGFRB KO by qRT-PCR.\u003c/p\u003e\n\u003cp\u003e• \u003cstrong\u003ei. \u003c/strong\u003eEffects of PDGFB KO, PDGFRB KO, and MIF KO on CD44s and CD44v6 expression in BT12 by western blot. (n=2) \u003cstrong\u003ej. \u003c/strong\u003eand by qRT-PCR (One-way ANOVA with Dunnett’s post-hoc test).\u003c/p\u003e\n\u003cp\u003e• \u003cstrong\u003ek, l.\u003c/strong\u003e Effects of PDGFB KO, PDGFC KO, PDGFRB KO, MIF KO, and PDGFRB/MIF DKO on CD44s and CD44v6 expression in BT16 by western blot and qRT-PCR (One-way ANOVA with Dunnett’s post-hoc test).\u003c/p\u003e\n\u003cp\u003e• \u003cstrong\u003em.\u003c/strong\u003e Effects of BT12 CD44s KO and CD44v6 KO on PDGFRB and MIF expression by western blot (and corresponding densitometry), and \u003cstrong\u003en.\u003c/strong\u003eqRT-PCR.\u003c/p\u003e\n\u003cp\u003e• \u003cstrong\u003eo, p.\u003c/strong\u003e Effects of BT16 CD44s KO and CD44v6 KO on PDGFRB and MIF expression by western blot and qRT-PCR. (Two-tailed Student’s t-test).\u003c/p\u003e\n\u003cp\u003eWestern blot shown is representative of 2-3 independent biological replicates performed in technical singlets (2-3 data points per condition). qRT-PCR data represent mean ± SEM of 3 independent biological replicates performed in technical duplicates (minimum 6 data points per condition). *\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, ****\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9707332/v1/aa0d6947968b46adf5c3352c.png"},{"id":109405434,"identity":"471b91e1-c7a7-4d11-b2db-a8204d40e224","added_by":"auto","created_at":"2026-05-17 13:18:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":5796632,"visible":true,"origin":"","legend":"\u003cp\u003eThe PDGFRB-CD44-MIF crosstalk promotes cell proliferation in BT12 and BT16 cells\u003c/p\u003e\n\u003cp\u003e• \u003cstrong\u003ea, d.\u003c/strong\u003e Cell proliferation in PDGFB KO, PDGFC KO, PDGFRB KO, PDGFRB/PDGFB DKO, MIF KO, PDGFRB/MIF DKO, CD44s KO, and CD44v6 KO relative to NT gRNA in BT12 \u003cstrong\u003e(a) \u003c/strong\u003eand BT16 \u003cstrong\u003e(d) \u003c/strong\u003eby measured AlamarBlue assay (Em/Ex 570/585 nm) at indicated timepoints. Cell proliferation data represent mean ± SEM of 3 independent biological replicates performed in technical quadruplicates (12 data points per condition; individual data points omitted for clarity) (One-way ANOVA with Dunnett’s post-hoc test).\u003c/p\u003e\n\u003cp\u003e• \u003cstrong\u003eb, e.\u003c/strong\u003e Cell cycle distribution in PDGFRB KO, PDGFRB/PDGFB DKO, MIF KO, and CD44v6 KO relative to NT gRNA BT12 \u003cstrong\u003e(b) \u003c/strong\u003eand BT16 \u003cstrong\u003e(e)\u003c/strong\u003emeasured by PI staining by flow cytometry. Cell cycle data represent mean ± SEM of 3 independent biological replicates performed in technical singlets (3 data points per condition) (One-way ANOVA with Dunnett’s post-hoc test).\u003c/p\u003e\n\u003cp\u003e• \u003cstrong\u003ec, f.\u003c/strong\u003e Left: Cell cycle markers protein expression in BT12 \u003cstrong\u003e(c)\u003c/strong\u003e and BT16 \u003cstrong\u003e(f)\u003c/strong\u003e NT gRNA, PDGFB KO, PDGFRB KO, MIF KO, PDGFRB/PDGFB DKO, PDGFRB/MIF DKO, CD44s KO, and CD44v6 KO by western blot. Markers were grouped by cell cycle phase/transition. Right: p53 expression in MIF KO relative to NT gRNA. \u003cstrong\u003e(c)\u003c/strong\u003e qRT-PCR data represent mean ± SEM of 3 independent biological replicates performed in technical duplicates (6 data points) \u003cstrong\u003e(f) \u003c/strong\u003eWestern blot shown is representative of 2 independent biological replicates performed in technical singlets (2 data points) (Two-tailed Student’s t-test).\u003c/p\u003e\n\u003cp\u003e*\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, ****\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9707332/v1/a8b476bd21e6fbff2cab4003.png"},{"id":109341405,"identity":"f9acb9c9-6992-4164-bcc7-7cdc0045ef73","added_by":"auto","created_at":"2026-05-15 18:54:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3317427,"visible":true,"origin":"","legend":"\u003cp\u003eThe PDGFRB-CD44-MIF promotes colony formation and cell motility in BT12 and BT16 cells\u003c/p\u003e\n\u003cp\u003e• \u003cstrong\u003ea, b.\u003c/strong\u003e Colony count in PDGFB KO, PDGFC KO, PDGFRB KO, PDGFRB/PDGFB DKO, MIF KO, PDGFRB/MIF DKO, CD44s KO, and CD44v6 KO relative to NT gRNA. Colony formation data represent mean ± SEM of 3 independent biological replicates performed in technical triplicates (minimum 9 data points per condition). The images represent one of the replicates (One-way ANOVA with Dunnett’s post-hoc test).\u003c/p\u003e\n\u003cp\u003e• \u003cstrong\u003ec, d. \u003c/strong\u003eCell invasion/migration in PDGFB KO (BT12), PDGFRB KO, MIF KO, CD44s KO (BT12), and CD44v6 KO (BT12) relative to NT gRNA measured by Matrigel/FluroBlok assay (Em/Ex 494/517 nm). Cell invasion and migration data represent mean ± SEM of 3 independent biological replicates performed in technical duplicates (6 data points per condition; except for BT12 CCD44 KOs migration assay: 2 biological replicates and 4 data points)(\u003cstrong\u003e(c) \u003c/strong\u003eOne-way ANOVA with Dunnett’s post-hoc test; \u003cstrong\u003e(c, d)\u003c/strong\u003eTwo-tailed Student’s t-test).\u003c/p\u003e\n\u003cp\u003e*\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, ****\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-9707332/v1/892eca9706a1864e79ec1fb8.png"},{"id":109341406,"identity":"df12153c-a287-4d48-88d1-cba3833e2462","added_by":"auto","created_at":"2026-05-15 18:54:54","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":34460996,"visible":true,"origin":"","legend":"\u003cp\u003eThe PDGFRB-CD44-MIF regulates EMT status in BT12 and BT16 cells\u003c/p\u003e\n\u003cp\u003e• \u003cstrong\u003ea-e.\u003c/strong\u003e Neural stemness (\u003cem\u003eSOX2\u003c/em\u003eand \u003cem\u003eNES\u003c/em\u003e) and differentiation marker (\u003cem\u003eTUBB3\u003c/em\u003e and \u003cem\u003eMAP2\u003c/em\u003e) expression in PDGFRB DKO, MIF KO, CD44s KO, and CD44v6 KO relative to NT gRNA BT12 \u003cstrong\u003e(a, b)\u003c/strong\u003e and BT16 \u003cstrong\u003e(c-e) \u003c/strong\u003eby western blot and qRT-PCR. In BT16, one MIF KO biological replicate values for \u003cem\u003eMAP2\u003c/em\u003e (131.2 and 131.4) were excluded using the ROUT method (Q=1%) ((\u003cstrong\u003ea, c-e\u003c/strong\u003e) Two-tailed Student’s t-test; \u003cstrong\u003e(b)\u003c/strong\u003e One-way ANOVA with Dunnett’s post-hoc test).\u003c/p\u003e\n\u003cp\u003e• \u003cstrong\u003ef.\u003c/strong\u003e Expression of mesenchymal (N-cadherin, Beta Catenin, Vimentin, and Twist1) and epithelial markers (E-cadherin and Claudin-1) in ATRT cell lines by western blot (left) and by qRT-PCR in BT12 and BT16 cells (right).\u003c/p\u003e\n\u003cp\u003e• \u003cstrong\u003eg, j.\u003c/strong\u003e EMT marker expression in BT12 \u003cstrong\u003e(g)\u003c/strong\u003e and BT16 \u003cstrong\u003e(j)\u003c/strong\u003e PDGFRB KO and MIF KO relative to NT gRNA cells by western blot (left) and qRT-PCR (right). For BT12 western blot, only Vimentin and Claudin-1 had detectable protein expression among the selected markers. For BT16 western blot, only N-cadherin and Vimentin had detectable protein expression among the selected markers (Two-tailed Student’s t-test).\u003c/p\u003e\n\u003cp\u003e• \u003cstrong\u003eh, k.\u003c/strong\u003e EMT marker expression in BT12 \u003cstrong\u003e(h)\u003c/strong\u003e and BT16 \u003cstrong\u003e(k)\u003c/strong\u003e CD44s KO and CD44v6 KO relative to NT gRNA by qRT-PCR. One-way ANOVA with Dunnett’s post-hoc test; \u003cstrong\u003e(k)\u003c/strong\u003e Two-tailed Student’s t-test).\u003c/p\u003e\n\u003cp\u003e• \u003cstrong\u003ei, l.\u003c/strong\u003e Representative phase-contrast micrographs of BT12 \u003cstrong\u003e(i) \u003c/strong\u003eand BT16 \u003cstrong\u003e(l)\u003c/strong\u003e NT gRNA (left), CD44s KO (center), and CD44v6 KO (right). \u003cstrong\u003e(i)\u003c/strong\u003e Top panels provide a wide-field view of cell distribution and density at 10x objective magnification. The bottom panels show a high-magnification (20x) view, highlighting individual cell morphology details; \u003cstrong\u003e(l)\u003c/strong\u003e The panels provide a wide-field view of cell distribution and density at 20x objective magnification. Scale bars represent 100 µm; dimensions estimated based on a mean cell diameter of 12 µm.\u003c/p\u003e\n\u003cp\u003eqRT-PCR data represent mean ± SEM of 3 independent biological replicates performed in technical duplicates (minimum 6 data points per condition). *\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, ****\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-9707332/v1/cdfb99dc0e733dc646c4b721.png"},{"id":109341407,"identity":"02b4e4f8-e2e6-4ae3-a71e-6081ee85de45","added_by":"auto","created_at":"2026-05-15 18:54:55","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":156285,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA proposed model of the PDGFRB-CD44-MIF regulatory crosstalk\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-9707332/v1/d158edbb1dbf11d478b4b697.png"},{"id":109341436,"identity":"c35fd590-55e9-4bdb-9917-a8b708cbd6bd","added_by":"auto","created_at":"2026-05-15 18:55:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":82501971,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9707332/v1/ec87e122-790d-42b7-8813-c49db812f4f3.pdf"},{"id":109341400,"identity":"13ec64a5-591d-48f3-b40c-19d62319b3dc","added_by":"auto","created_at":"2026-05-15 18:54:54","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":46191,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-9707332/v1/dfc927f8c840cc55658dfe40.docx"},{"id":109341402,"identity":"e5311b58-041e-4432-bc30-265ea2569999","added_by":"auto","created_at":"2026-05-15 18:54:54","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":12912072,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-9707332/v1/405b048412f9c94b984a040e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A PDGFRB-CD44-MIF Crosstalk Promotes Cancer-Associated Cellular Phenotypes in ATRT","fulltext":[{"header":"Background","content":"\u003cp\u003eATRTs are classified as grade IV brain tumors by the World Heath Organization\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, and represent only 3–5% of pediatric CNS tumors\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. ATRT is highly malignant, clinically complex\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, and fast-growing tumor\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, that metastasizes in 30–50% of cases\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. There is no definitive treatment for ATRT, and the 5-year survival rate is less than 35%. This makes ATRT one of the most lethal pediatric tumors\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. ATRT has a low somatic mutational burden compared to other pediatric tumors\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. It is characterized by bi-allelic loss of \u003cem\u003eSMARCB1\u003c/em\u003e, a gene located on chromosome 22\u003csup\u003e8\u003c/sup\u003e, or rarely loss of \u003cem\u003eSMARCA4\u003c/em\u003e, a gene located on chromosome 19\u003csup\u003e9\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe SMARCB1 protein is a component of the multi-protein SWI/SNF, ATP-dependent chromatin remodelling complex\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eSMARCB1\u003c/em\u003e loss in ATRT has been associated with an altered chromatin structure characterized by increased H3K27me3 repressive marks, which impairs cell differentiation and represses tumor suppressor gene expression\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. DNA methylation analysis identified three epigenetic subgroups of ATRT: Sonic Hedgehog (SHH), Tyrosinase (TYR), and MYC. Notably, ATRT-SHH has neurogenic features, while ATRT-TYR/MYC have varying degrees of mesenchymal features\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePrevious studies showed that cells from mesenchymal ATRT-TYR/MYC subgroup are sensitive to RTKIs\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Several RTKs could be important in ATRT, including PDGFRA, PDGFRB, ErbB2, and FGFR1\u003csup\u003e13,15,17\u003c/sup\u003e. Nevertheless, one RTK stands out as being the most differentially expressed between ATRT-TYR/MYC and ATRT-SHH subgroups, with a higher expression in TYR/MYC tumors. This is the platelet-derived growth factor B (PDGFRB)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Torchia \u003cem\u003eet al\u003c/em\u003e.\u003csup\u003e13\u003c/sup\u003e found phosphorylation of PDGFRB in ATRT primary samples. In addition, ATAC-seq analyses on primary ATRT show that ATRT-MYC tumors have an open chromatin configuration at the \u003cem\u003ePDGFRB\u003c/em\u003e promoter, but not in primary SHH samples\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Together, these findings underscore potential importance of PDGFRB in ATRT; however, the mechanism responsible for its activation and its cellular effects on ATRT remain unexplored.\u003c/p\u003e \u003cp\u003ePDGFRB is an RTK that can function as a homodimeric PDGFRB/B or heterodimeric PDGFRA/B receptor complexes\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. PDGFR ligands are PDGF-A, -B, -C, and -D\u003csup\u003e18\u003c/sup\u003e. Among the downstream effects of PDGF signaling pathway are cell migration, proliferation, differentiation, and survival\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. PDGF can mediate signaling via both paracrine and autocrine modes\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The physiologically normal and dominant fate of activated PDGFRB is degradation, proteasome or lysosome-mediated\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. PDGFRB internalization is dependent on ubiquitination by E3 ubiquitin ligases, namely the Casitas b-lineage lymphoma (Cbl) family of E3 ubiquitin ligases: c-Cbl (\u003cem\u003eCBL\u003c/em\u003e) and Cbl-b (\u003cem\u003eCBLB\u003c/em\u003e)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Under malignant conditions, such as loss-of-function mutations in phosphatases responsible for PDGFR dephosphorylation, PDGFR could be sorted from the early endosome back to the plasma membrane, a process known as PDGFR recycling, which is dependent on Protein Kinase C alpha (PKCα)\u003csup\u003e22,23\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe PDGF pathway is essential for normal functioning, including angiogenesis and tissue repair\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. However, dysregulation of the PDGF pathway has been connected to tumorigenesis in several cancer types\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. This includes PDGFR amplification in glioblastoma, activating point mutations in the gastrointestinal stromal tumor, and translocation in chronic myelomonocytic leukemia\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Oncogenic dysregulation of the PDGF pathway can manifest through mutational activation of PDGFR, ligand-independent activation of PDGFR, or constitutive expression of PDGF ligands\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Autocrine PDGF signaling has been found to promote the growth and proliferation of tumor cells in leukemia and high-grade glioma\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Targeting PDGFR has been of interest in cancer therapy, primarily using PDGFR antagonists, which prolonged remission and sensitized tumors to chemotherapy\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhile the PDGF pathway has been explored in several tumor types, a similar effort has yet to be made in ATRT. RTKIs, such as dasatinib, are fairly well characterized at a mechanistic level and with respect to safety profiles\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Exploiting this therapeutic sensitivity is of interest in ATRT, as it offers a safer and more efficient treatment option for ATRT-TYR/MYC patients than the widely used chemotherapy/radiotherapy approach that can have long-term side effects on children, including causing brain damage with resulting learning disabilities\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. The absence of an understanding of the role of PDGFRB in ATRT limits the exploitation of this treatment option.\u003c/p\u003e \u003cp\u003eMeanwhile, \u003cem\u003eSMARCB1\u003c/em\u003e deletions in ATRT-TYR/MYC subgroups have generally been shown to encompass large regions extending beyond the \u003cem\u003eSMARCB1\u003c/em\u003e gene. These broad deletions have been shown to result in changes in gene expression not only in \u003cem\u003eSMARCB1\u003c/em\u003e, but also in other genes located near the \u003cem\u003eSMARCB1\u003c/em\u003e locus on chromosome 22. One specific gene stands out as having the highest expression following a broad \u003cem\u003eSMARCB1\u003c/em\u003e deletion as indicated by RNA-seq analyses, and this gene is the macrophage migration inhibitory factor (\u003cem\u003eMIF\u003c/em\u003e)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Best known for its role in the adaptive immune response, MIF has emerged as an important cytokine in cancer biology. The MIF signaling pathway has been connected to tumorigenesis in glioblastoma and medulloblastoma, as well as to a dysfunctional anti-tumor immunity in various cancers\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Exceptionally high expression of \u003cem\u003eMIF\u003c/em\u003e suggests a role for the MIF protein in ATRT pathogenesis. MIF upregulation within ATRT tumor cells and within neighboring cells of the TME remain unexplored\u003c/p\u003e \u003cp\u003eSeveral studies highlighted a potential connection between the PDGF and MIF pathways\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Specifically, a crosstalk between PDGFRB and CD44, MIF’s coreceptor, has been reported in normal and malignant conditions\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. In addition, one specific CD44 isoform, CD44v6, is known to form heteroduplexes with RTKs (VEGFR2, c-MET, and EGFR) enhancing their activation \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eUsing ATRT-TYR/MYC cell lines BT12 and BT16, and their genetically edited sublines, we investigated how disruption of the PDGF signaling pathway impacts ATRT cancer-associated cellular phenotypes. Moreover, we investigated whether there is a connection between the PDGF and MIF pathways, potentially through CD44, that could impact ATRT malignancy.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003eCell culture\u003c/p\u003e\u003cp\u003eATRT cell lines BT12 and BT16 were cultured in RPMI-1640 (Wisent, Cat#350-000 RL) supplemented with 10% FBS (Wisent, Cat#090-150- FBS). ATRT cell lines CHLA02, CHLA04, CHLA05, and CHLA06 were cultured in DMEM-F12 (Wisent, Cat# 319 − 085 CL) supplemented with 20 ng/mL FGF (R\u0026amp;D Systems, Cat#,233-FB), 20 ng/mL EGF (Stem Cell Technologies, Cat#78006) and 1xB27 supplement (Gibco, Cat#17504044). Renal malignant rhabdoid tumor (MRT) cell line G401 was cultured in McCoy’s 5A medium (Gibco, Cat# 16600082) supplemented with 20% FBS (Wisent, Cat#090-150-FBS). Normal human astrocytes (NHA), NIH-3T3, and Human embryonic kidney (HEK)293 cells were cultured in DMEM (Wisent, Cat# 319 − 205 CL) supplemented with 20% FBS (Wisent, Cat#090-150- FBS). Cells were tested for mycoplasma contamination using a Mycoplasma PCR Detection Kit (Applied Biological Materials, Cat#G238) per the manufacture’s protocol.\u003c/p\u003e\u003cp\u003eBasal expression analysis was performed across multiple cell lines (BT12, BT16, CHLA06, CHLA02, CHLA04, and CHLA05); BT12 and BT16 were selected for subsequent experiments as they had robust expression of pathway components of interest (PDGFRB, MIF, and CD44), are among the most extensively characterized ATRT cell lines in the literature\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, retain certain molecular characteristics consistent with patient-derived tumor data\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, and demonstrated efficient lipofectamine-based transfection suitable for CRISPR/Cas9 editing experiments. (See Supplementary Table\u0026nbsp;1 for details about ATRT cell lines).\u003c/p\u003e\u003cp\u003eWestern blotting\u003c/p\u003e\u003cp\u003eFor all protein extractions, cells were grown in Opti-MEM serum-reduced medium (Gibco, Cat#31985062) overnight to minimize interference of serum or growth factors with PDGF pathway studies. Cells were detached using Versene dissociation reagent (Gibco, Cat#15040066). Whole-cell lysates were prepared using extraction buffer C (EBC) lysis buffer (pH = 8.0, Tris-HCL, NaCl, NP-40) supplemented with 1x protease and phosphatase inhibitor cocktail (Thermo Fisher, Cat#78440). Protein concentration was determined using a BCA protein assay (Thermo Scientific, Cat# 23225). For each sample, 25–120 µg/lane of protein, was loaded and separated on a 6–18% SDS-PAGE gel, depending on the abundance and size of protein of interest. Membranes were then developed with SuperSignal West Pico PLUS Chemiluminescent Substrate (Thermo Scientific, Cat#34580) and imaged using a Li-Cor Odyssey imager. When applicable, blots were stripped using Restore PLUS Western Blot Stripping Buffer per manufacturer’s protocol (Thermo Scientific, Cat#46430). Densitometry of blots, when applicable, was performed using ImageJ (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://imagej.net/ij/\u003c/span\u003e\u003cspan class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The number of technical replicates per biological replicate was one. (See Supplementary Fig.\u0026nbsp;5 for CD44 antibodies isoform-specificity validation).\u003c/p\u003e\u003cp\u003eThe following antibodies were used for immunoblotting:\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab1\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWestern blotting antibodies list\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eAntibody\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eCatalog number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eConcentration\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\"\u003e \u003cp\u003ePrimary antibodies\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eα-Tubulin (monoclonal mouse)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCell Signaling Technology (CST) #3873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:10,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eBeta 3 Tubulin (monoclonal rabbit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCST#5568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ec-CBL (monoclonal rabbit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCST#2179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCas9 (\u003cem\u003eS. pyogenes\u003c/em\u003e) (monoclonal rabbit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCST#65832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCBL-b (polyclonal rabbit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eBethyl laboratories # A302-903A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCD44 (monoclonal rabbit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCST #37259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCD44v6 (monoclonal mouse)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eInvitrogen #33-6700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCD74 (monoclonal mouse)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eInvitrogen #14-0747-82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCDC25C (monoclonal rabbit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCST#4688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCDK2 (monoclonal rabbit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCST#2546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCDK4 (polyclonal rabbit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSanta Cruz Biotechnology #sc-260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCDK6 (polyclonal rabbit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSanta Cruz Biotechnology #sc-177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eClaudin-1 (polyclonal rabbit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eInvitrogen #51-9000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCSF-1R (polyclonal rabbit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCST#3152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCyclin A2 (monoclonal mouse)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCST#4656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCyclin D2 (monoclonal rabbit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSanta Cruz Biotechnology #sc-452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCyclin D3 (monoclonal rabbit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSanta Cruz Biotechnology #sc-182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCyclin E1 (monoclonal rabbit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCST#20808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eE-cadherin (monoclonal mouse)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCST#14472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eEFGR (monoclonal rabbit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCST#8339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eErbB2 (monoclonal rabbit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCST#4290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eErbB3 (monoclonal rabbit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCST#12708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMAP2 (monoclonal rabbit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCST#8707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMIF (monoclonal rabbit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCST #87501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:500-1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMIF2 (DDT) (polyclonal rabbit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCUSABIO #CSB-PA221047-50UG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eN-cadherin (monoclonal rabbit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCST#13116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNestin (polyclonal rabbit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMilliporeSigma #ABD69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:10,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ep16 (polyclonal rabbit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eProteintech #10883-1-AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:1000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ep21(monoclonal mouse)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eBD Biosciences #556430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ep27 (monoclonal mouse)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eBD Biosciences #610241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ep44/42 MAPK (Erk1/2) (monoclonal mouse)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCST#4696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ep53 (polyclonal rabbit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eActive Motif #61657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ep62 (polyclonal mouse)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eProteintech #66184-1-Ig\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePan-Cadherin (polyclonal rabbit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCST#4068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePDGFA (monoclonal mouse)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAbcam #ab51868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePDGFB (polyclonal rabbit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAbcam #ab23914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePDGFC (polyclonal goat)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eR\u0026amp;D Systems#AF1560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePhospho-EGFR Tyr1018 (monoclonal rabbit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCST#4547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePhospho-ErbB3 Tyr1289 (monoclonal rabbit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCST#2842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePhospho-p44/42 MAPK (Erk1/2) Thr202/Tyr204 (monoclonal rabbit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCST#9101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePhospho-PDGFRB Tyr1009 (monoclonal rabbit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCST#3124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:1,500-1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePhospho-PDGFRB Tyr1021 (polyclonal rabbit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eR\u0026amp;D Systems #AF2316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePhospho-PDGFRB Tyr751 (monoclonal rabbit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCST#4549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:500-1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePhospho-PI3 Kinase p85 Tyr458 (monoclonal rabbit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCST#4228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePhospho-Rb Ser807/811 (monoclonal rabbit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCST#8516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePI3 Kinase p85 (monoclonal rabbit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCST#4292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSOX2 (monoclonal rabbit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCST#3579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eTotal PDGFRA (monoclonal rabbit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCST#5241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eTotal PDGFRB (monoclonal rabbit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCST#3169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:500-1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eTotal Rb (monoclonal mouse)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCST#9309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eTWIST1 (monoclonal rabbit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCST#69366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:500-1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eVimentin (monoclonal rabbit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCST#5741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eSecondary antibodies\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eHorseradish peroxidase (HRP)-conjugated donkey anti-goat IgG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eInvitrogen#A15999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:10,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eHRP-conjugated goat anti-rabbit IgG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCST#7074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:3,000–1:5,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eHRP-conjugated horse anti-mouse IgG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCST#7076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1:3,000–1:5,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003ch2\u003ePDGF stimulation\u003c/h2\u003e\u003cp\u003eCells were serum-starved (BT12 and BT16) or growth factor-deprived (CHLA05, and CHLA06) by growing them in Opti-MEM serum-reduced medium (Gibco, Cat#31985062) overnight (approximately 16 hours). Cells were then stimulated with 10–50 ng/mL PDGF-BB (PeproTech #100-14B), PDGF-CC, or PDGF-DD (StemCell Technologies, Cat#78168, 78222), for 5 minutes to 1 hour, depending on the experiment. PDGFRB phosphorylation was assessed following ligand stimulation at a single optimized timepoint. Stimulation duration was optimized by testing timepoints between 5 and 15 minutes (data not shown); 5 minutes was selected for all subsequent experiments. Receptor recycling and degradation were assessed following ligand stimulation at a single optimized timepoint. Stimulation duration was optimized by testing timepoints of 30, 45, and 60 minutes (data not shown); 60 minutes was selected for all subsequent experiments.\u003c/p\u003e\u003cp\u003eRT-qPCR\u003c/p\u003e\u003cp\u003eTotal RNA was extracted using a PuroSPIN Total RNA Purification Kit (Luna Nanotech Cat#NK051-50). cDNA synthesis and qPCR were performed using the Luna Universal One-Step RT-qPCR Kit (New England Biolabs, Cat#E3005). The ΔCq or ΔΔCq method was used to calculate mRNA abundance. A list of gene-specific qPCR primers is provided in the table below. Unless otherwise noted, qPCR primers were designed using the Integrated DNA Technologies (IDT) PCR \u0026amp; qPCR primer design tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.idtdna.com/pages/tools/primerquest\u003c/span\u003e\u003cspan class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Primer specificity was confirmed \u003cem\u003ein silico\u003c/em\u003e using the UCSC \u003cem\u003eIn-Silico\u003c/em\u003e PCR online tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://genome.ucsc.edu/cgi-bin/hgPcr\u003c/span\u003e\u003cspan class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The number of technical replicates per biological replicate was 2–3.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab2\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eqRT-PCR primers list\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eForward primer (5’ to 3’)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eReverse primer (5’ to 3’)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003eACTB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eACCTTCTACAATGAGCTGCGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eATAGCACAGCCTGGATAGCAA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003eCBL\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCGC CAT GTT CCT TCC ACT AA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGAG AAG CTG CCT GGT CTA TTA C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003eCBLB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAGA GAC CCA GTA GAG GAA GAT G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCAA GAC CGA ACA GGA GGT TT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003eCD44*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAAG ACA TCT ACC CCA GCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGGT AGC AGG GAT TCT GTC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003eCD44v6*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eTCC AGG CAA CTC CTA GTA GTA C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCAG CTG TCC CTG TTG TCG AAT G\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003eCD74\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCAG ACT TGA CTG TTC CCA TAC A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAGA GCC GTA AGT CCC ATA GA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003eCDH1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGTC ATT GAG CCT GGC AAT TTA G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGTT GAG ACT CCT CCA TTC CTT C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003eCDH2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGGA TGA AAC GCC GGG ATA AA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eTCT TCT TCT CCT CCA CCT TCT T\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003eCLDN1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCCA GTT AGA AGA GGT AGT GTG AAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCAG CCA GCT GAG CAA ATA AAG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003eGAPDH\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eTTCTCCATGGTGGTGAAGACG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eATTCCATGGCACCGTCAAGG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003eMAP2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGAT AGA AGG GCA ACA GAG CTA AA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGCC AGA CTC AAC ACC CAT AAA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003eMIF\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCGGACAGGGTCTACATCAACTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eTCTTAGGCGAAGGTGGAGTT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003eNES\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCCA TAG AGG GCA AAG TGG TAA G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCCA GAG ACT TCA GGG TTT CTT T\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003ePDGFA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGGACAGTGCGACGGTATTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGGAGAAACACAAAGCCAGAAAC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003ePDGFB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eTGTAGATGGTGACCTGGGTATC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGGGTGGAGGTAGAGAGATGAAA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003ePDGFC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGCCACTGGACCTGCTTAAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCTAAGTCCAACTGCCATCTCTC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003ePDGFD\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCCCTGCTGTCCTACTGTTTAAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCACAAAGGAGGCAGAGAGAAA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003ePDGFRA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCAGGTATTT CTG GGA GGT TCT G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCAT GGC AAG ACT CCA TCT CTA C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003ePDGFRB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGCTCACCATCATCTCCCTTATC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCTCACAGACTCAATCACCTTCC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003ePRKCA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCCA TCC GCT CCA CAC TAA AT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGAT CCC AGT CCC AGA TTT CTA C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003eSNAI1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCCA CGA GGT GTG ACT AAC TAT G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eACC AAA CAG GAG GCT GAA ATA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003eSOX2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGCC CTG CAG TAC AAC TCC AT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGAC TTG ACC ACC GAA CCC AT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003eTUBB3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCGA AGC CAG CAG TGT CTA AA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGGA GGA CGA GGC CAT AAA TAC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003eTWIST1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAGG CAT CAC TAT GGA CTT TCT C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGGC CAG TTT GAT CCC AGT AT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003eVIM\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCAG CTT TCA AGT GCC TTT CTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCTT GTA GGA GTG TCG GTT GTT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003eZEB1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCTT CTC ACA CTC TGG GTC TTA TTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCGT TCT TCC GCT TCT CTC TTA C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003e*\u003cem\u003eCD44\u003c/em\u003e and \u003cem\u003eCD44v6\u003c/em\u003e primers were adapted from Yamao \u003cem\u003eet al.\u003c/em\u003e (1998)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003esiRNA-mediated knockdown\u003c/p\u003e\u003cp\u003eTo knockdown \u003cem\u003ePDGFB\u003c/em\u003e, \u003cem\u003eCBL\u003c/em\u003e, and \u003cem\u003ePRKCA\u003c/em\u003e, siRNAs targeting each gene were used from Horizon Discovery, Dharmacon ON-TARGETplus Human siRNA: J-011749-05-0002; J-003004-09-0002; and J-003523-16-0002, respectively. To knockdown \u003cem\u003eCBLB\u003c/em\u003e, siRNA targeting \u003cem\u003eCBLB\u003c/em\u003e from Thermo Scientific (Cat# AM16708) was used. siRNAs were transfected at a concentration of 50–100 nM/transfection using Lipofectamine 3000 transfection reagent (Invitrogen, CAT#L3000001). Non-targeting siRNA (Horizon Discovery, ON-TARGETplus Non-targeting Control siRNAs, D-001810-01-05) was used at 50–100 nM/transfection. Transfection was performed according to manufacturer’s protocol in a 6-cm dish format. Cells were serum starved overnight before transfection and harvested 48–72 hours post-transfection for protein or RNA extraction.\u003c/p\u003e\u003cp\u003eProteasome and lysosome inhibition\u003c/p\u003e\u003cp\u003eCells were grown in Opti-MEM serum-reduced medium (Gibco, Cat#31985062) overnight (approximately 16 hours), then treated with either 1–10 µM MG132 (Selleckchem, Cat#S2619), 10 µM Bafilomycin A1 (Selleckchem, Cat#S1413), 100 µg/mL cycloheximide (Selleckchem, Cat#S7418), or 0.01–0.1% DMSO (Sigma-Aldrich, Cat#D2650) diluted in Opti-MEM for 2–16 hours. Following treatment, cells were collected for protein extraction.\u003c/p\u003e\u003cp\u003eCell cycle analysis\u003c/p\u003e\u003cp\u003eCell cycle analysis was performed on fixed and stained BT12 and BT16 KO cells, derived from the same passage (P15). Briefly, cells were detached using Versene dissociation reagent (Gibco, Cat#15040066). Two million cells were then washed twice with PBS (Wisent, Cat#311 − 013 CL), filtered through a 40 µm cell strainer (Fisher Scientific, Cat#22-363-547), and fixed with ice-cold 80% ethanol overnight at -20⁰C. Following fixation, cells were washed with PBS twice, then treated with 500 µL of 200 µg/mL RNase A/DNAse and protease-free (Thermo Scientific, Cat#EN0531) diluted in PBS for 1 hour at 37⁰C. Next, 100 µg/mL Propidium Iodide (PI) (Thermo Scientific, Cat#P3566) diluted in 500 µL PBS was added, to a final concentration of 50 µg/mL PI. Cells were filtered with a 40 µm cell strainer and stained overnight at 4⁰C. The flow cytometry runs were carried out by Eve Coulter at SickKids-UHN Flow Cytometry Core Facility. Cells were gated to exclude debris and doublets, and cell cycle phases distribution analysis was performed using \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://floreada.io/\u003c/span\u003e\u003cspan class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The number of technical replicates per biological replicate was one. (See Supplementary Fig.\u0026nbsp;6 for an example of gating steps).\u003c/p\u003e\u003cp\u003eCRISPR/Cas9-mediated generation of stable knockout (KO) cell lines\u003c/p\u003e\u003cp\u003eSingle-guide RNAs (sgRNA) against genes of interest were designed using the IDT CRISPR Cas9 guide RNA design checker (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.idtdna.com/site/order/designtool/index/CRISPR_SEQUENCE\u003c/span\u003e\u003cspan class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A list of optimized and selected gRNAs used in the successful transfections is provided in this table:\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab3\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCRISPR gRNAs list\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eSense oligonucleotide (5’ to 3’)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eComplementary oligonucleotide (5’ to 3’)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003eCD44\u003c/em\u003e for CD44v6 KO*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003egRNA1: TGATATTCTTCTCACAGTCC\u003c/p\u003e \u003cp\u003egRNA2: AGGAATTGTCACGAGATGTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003egRNA1: GG ACT GTG AGA AGA ATA TCA\u003c/p\u003e \u003cp\u003egRNA2: AA CAT CTC GTG ACA ATT CCT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003eCD44*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003egRNA1: GAATACACCTGCAAAGCGGC\u003c/p\u003e \u003cp\u003egRNA2: AAGGGCACGTGGTGATTCCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003egRNA1: GCCGCTTTGCAGGTGTATTC\u003c/p\u003e \u003cp\u003egRNA2: GGGAATCACCACGTGCCCTT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003eMIF\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eTTGGTGTTTACGATGAACAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eATGTTCATCGTAAACACCAA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNon-targeting gRNA (NT gRNA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGTATTACTGATATTGGTGGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCCCACCAATATCAGTAATAC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003ePDGFB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCATCAAAGGAGCGGATCGAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCTCGATCCGCTCCTTTGATG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003ePDGFC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCTGGTTCAAGATATCGAATA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eTATTCGATATCTTGAACCAG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003ePDGFRB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGTCCCCTATGATCACCAACG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCGTTGGTGATCATAGGGGAC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e*\u003cem\u003eCD44\u003c/em\u003e isoform-specific KO gRNAs were adapted from Lobo, S. \u003cem\u003eet al.\u003c/em\u003e (2020)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003egRNAs were cloned into the pSpCas9(BB)-2A-Puro (PX459) V2.0 vector (Addgene, Cat#62988) according to the Zhang lab target sequence cloning protocol\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Successful cloning was confirmed by Sanger sequencing, using U6 forward primer 5'-GAG GGC CTA TTT CCC ATG ATT CC-3' immediately upstream of the cloning site. Sequencing was performed by the Centre for Applied Genomics at SickKids.\u003c/p\u003e\u003cp\u003eBT12 and BT16 cells were maintained in RPMI-1640 (Wisent, Cat#350-000 RL) supplemented with 10% FBS (Wisent, Cat#090-150- FBS). Cells (P10) were seeded in 6-well plates and transfected at 70–80% confluency. Prior to transfection, cells were serum-starved for 2–16 hours. Lipofectamine 3000 transfection reagent (Invitrogen, CAT#L3000001) was used according to the manufacturer’s protocol. Seventy-two hours post-transfection, cell culture medium was replaced with fresh medium containing 5 µg/mL of Puromycin (Gibco, Cat# A1113803) for three weeks. Following selection, cells were maintained in 10% FBS RPMI-1640 containing 1–5 µg/mL Puromycin. Pooled cell populations were expanded without clonal selection. All subsequent experiments were therefore performed using heterogeneous pooled KO populations rather than isogenic clonal lines.\u003c/p\u003e\u003cp\u003eGenomic DNA was extracted from cells using a Genomic DNA Mini Kit (Geneaid, Cat#GB300). The regions flanking gRNA target sites were amplified by PCR (New England Biolabs, Cat#M0494), using primers listed in the table below. Primers were designed using the Primer Wizard tool (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eBenchling.com\u003c/span\u003e online platform).\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab4\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTIDE analysis primers list\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eForward primer (5’ to 3’)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eReverse primer (5’ to 3’)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003eCD44\u003c/em\u003e for CD44v6*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eATC AGT GGC CTG TTT CCT TG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eTTT GGC TCT GTG TGA ACT GC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003eCD44*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eTCC CCA CTT AAG CTG AGC TCC A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eTGC AAT GCT CAG GAG GCA GTG T\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003eMIF\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eACA TTG GCC GCG TTC ATG TCG T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGGG TCT CCT GGT CCT TCT GCC A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003ePDGFB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eTGA CCA GGG CTC CAG GGA ACA G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCTG CCT GTC CGT CTC CCT GTG A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003ePDGFC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGTA TTG AAT ATA TCA CTG ATG A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eTAT TGA CCA TTC AAG CTT TTT A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003ePDGFRB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCCA CCG TGG GCT TCC TCC CTA A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGAA GCT TGT GCC CTC ACC GAC C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003ePCR products were also subjected to T7 Endonuclease I assays per manufacturer’s protocol (New England Biolabs, Cat#M0302). PCR products were sequenced using Sanger sequencing. Results were analyzed using the TIDE web tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tide.nki.nl/\u003c/span\u003e\u003cspan class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to assess insertion/deletion (indels) distribution and frequency. Protein lysates from KO cells were analyzed by western blot to confirm the loss of protein expression. For KO validation data, please see Supplementary Fig.\u0026nbsp;1–4 and Supplementary Table\u0026nbsp;2.\u003c/p\u003e\u003cp\u003e*CD44 KO sequencing primers were adapted from Lobo, S. \u003cem\u003eet al.\u003c/em\u003e (2020)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCD44 isoform-specific KO generation\u003c/p\u003e\u003cp\u003eTo generate CD44s KO, a gRNA against exon 3 of CD44 gene was used. CD44v6 KO was generated as described in Lobo, S. \u003cem\u003eet al.\u003c/em\u003e (2020)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. CD44v6 KO was validated by PCR size reduction, TIDE analysis, and western blot confirmation of CD44v6 protein loss (see Supplementary Fig.\u0026nbsp;2p-s; Supplementary Fig.\u0026nbsp;4p-s). Zygosity of the deletion was not determined.\u003c/p\u003e\u003cp\u003eCell invasion/migration assay\u003c/p\u003e\u003cp\u003eBT12 and BT16 cells were serum-starved overnight prior to seeding of 50,000 cells/chamber on Corning FluoroBlok 24-Multiwell Insert Systems PET Membranes (Corning Life Sciences, Cat# 351152) in 400 µL Opti-MEM on the apical side. RPMI-1640 supplemented with 30%FBS was used as chemoattractant for migration assays. For invasion assay, the PET membranes were pre-coated with 100 µL of 200 µg/mL Matrigel (Corning Life Sciences, Cat#354234) for 2 hours at 37°C prior to the assay. Cells were incubated for 24 hours at 37°C. Inserts were stained using 10 µM Calcein AM (diluted in PBS/DMSO) (Thermo Scientific, Cat#C1430) for 1 hour at 37°C. Fluorescence was then measured using a SpectraMax Gemini EM fluorescence plate reader (Ex/Em 494/517nm). The number of technical replicates per biological replicate was two. (See Supplementary Fig.\u0026nbsp;7 for validation of the invasion/migration assay).\u003c/p\u003e\u003cp\u003eColony formation assay\u003c/p\u003e\u003cp\u003eBT12 and BT16 cells were seeded at 500 cells/well in a 6-well plate and grown for about 2 weeks (or until ~ 300 colonies were observed in wells under NT gRNA control conditions), with regular medium change every 3–4 days. Cells were washed with PBS, fixed in 1:7 acetic acid:methanol (Fisher Scientific, Cat#A38-500, #A412-4) for 5 minutes, then stained for 1 hour with 0.5% crystal violet (BioShop, Cat#CRY422). Plates were washed 5 times with water, then air-dried for 24–48 hours. Plates were then imaged and colonies counted using the Optronix GelCount Colony Counter. The number of technical replicates per biological replicate was three.\u003c/p\u003e\u003cp\u003eCell proliferation assay\u003c/p\u003e\u003cp\u003eBT12 and BT16 cells were seeded at 2,000 cells/well in 96-well plates in 100 µL/well RPMI-1640 supplemented with 10%FBS. On days of fluorescence measurements, media was changed to 100 µL OptiMEM/well, prior to adding 10 µL AlamarBlue (Invitrogen, Cat#DAL1025). Cells were incubated for 4 hours at 37°C, then fluorescence measured using a SpectraMax Gemini EM fluorescence plate reader (Ex/Em 570/585nm). The number of technical replicates per biological replicate was 4–8.\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll experiments were performed with at least three independent biological replicates, unless otherwise noted. A two-tailed Student’s t-test or a One-way ANOVA (Dunnett’s correction for multiple comparisons) test was used for statistical analysis. The significance cut-off was \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003ePDGF receptors and ligands are differentially enriched in ATRT cell lines\u003c/p\u003e \u003cp\u003eBasal expression of PDGF receptors and ligands was assessed by western blot and qRT-PCR across ATRT cell lines: ATRT-TYR/MYC (BT12, BT16, and CHLA06) and ATRT-SHH (CHLA02, CHLA04, and CHLA05). PDGFRA expression was elevated in ATRT-SHH relative to ATRT-TYR/MYC lines, with CHLA05 showing the highest \u003cem\u003ePDGFRA\u003c/em\u003e mRNA levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Conversely, PDGFRB was preferentially expressed in ATRT-TYR/MYC lines, though CHLA05 also expressed notable levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). ATRT cell lines additionally expressed multiple RTKs, including EGFR and ErbB4, as well as several PDGF ligands (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, Supplementary Fig.\u0026nbsp;7a)). Among these, \u003cem\u003ePDGFA\u003c/em\u003e was highly expressed in CHLA02, while \u003cem\u003ePDGFB\u003c/em\u003e, the primary PDGFRB ligand binding all PDGF receptors, was enriched in BT12 and BT16 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). \u003cem\u003ePDGFC\u003c/em\u003e and \u003cem\u003ePDGFD\u003c/em\u003e showed preferential expression in BT16 and ATRT-SHH lines, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003ePDGFRB is constitutively active in BT12 and BT16 cells\u003c/p\u003e \u003cp\u003eTo characterize PDGF pathway activity in ATRT cell lines, phosphorylation status of PDGFRB was assessed. BT12 and BT16 constitutively expressed phospho-PDGFRB without PDGF stimulation; while CHLA05 and CHLA06 only expressed phospho-PDGFRB with external PDGF treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed, e). Furthermore, all ATRT cell lines tested showed activation of downstream PDGF signaling, including active MAPK, PI3K/Akt, and PLCγ pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). To better understand the effect of PDGF ligand expression on PDGFRB signaling in ATRT cell lines, cells were treated with three different ligands (PDGF-BB, PDGF-CC, and PDGF-DD). PDGF-BB resulted in diminished PDGFRB expression notably in BT12 and BT16, suggesting that PDGF-BB may promote PDGFRB degradation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). Similar to PDGF-BB stimulation, PDGF-CC treatment resulted in decreased PDGFRB expression in BT12 and BT16 cells (Supplementary Fig.\u0026nbsp;7b). In contrast, PDGF-DD stimulation was associated with an increase (in BT12 and CHLA05) or no significant change in PDGFRB levels (in BT16 and CHLA06), suggesting it may promote PDGFRB accumulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). In summary, the effect of PDGF on PDGFRB expression and phosphorylation appears to be cell line-dependent and ligand-specific.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ea.\u003c/b\u003e Expression of PDGFRA (left) and PDGFRB (right) protein (by western blot) and mRNA (by qRT-PCR), in ATRT cell lines. ATRT-SHH cell lines are labelled in red; ATRT-TYR/MYC cell lines are labelled in blue; Normal human astrocytes (NHA), labelled in green, is a non-cancer cell line that was included as a negative control for \u003cem\u003ePDGFRA\u003c/em\u003e and a positive control for \u003cem\u003ePDGFRB\u003c/em\u003e expression in qRT-PCR\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eb.\u003c/b\u003e Left: Expression of PDGF ligand protein in ATRT cell lines. Human recombinant PDGF ligands were included as a positive control for western blot (10 ng per lane). Right: \u003cem\u003ePDGFA, PDGFB, PDGFC\u003c/em\u003e, and \u003cem\u003ePDGFD\u003c/em\u003e mRNA expression (by qRT-PCR in ATRT) cell lines. NHA mRNA was included for comparison.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWestern blot shown (in \u003cb\u003ea, b\u003c/b\u003e) is representative of 3 independent biological replicates performed in technical singlets; quantification across replicates was not performed. qRT-PCR data represent the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM of n\u0026thinsp;=\u0026thinsp;3 independent biological replicates performed in technical duplicates or triplicates, with a minimum of 6 data points shown per condition.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ec.\u003c/b\u003e PI3K/Akt, MAPK, and PLCγ protein expression. Stimulated (+\u0026thinsp;PDGF-BB/+PDGF-DD) and non-stimulated NIH-3T3 cells were included as a negative/positive control for phospho-protein expression.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEffects of PDGF-BB (\u003cb\u003ed\u003c/b\u003e) and PDGF-DD (\u003cb\u003ee\u003c/b\u003e) (0,10, 20, and 30 ng/mL for 5 minutes) on PDGFRB and phospho-PDGFRB expression in BT12, BT16, CHLA06, and CHLA05 by western blot (top). Corresponding densitometry quantification of total PDGFRB (bottom). Western blot shown is representative of 2\u0026ndash;3 independent biological replicates.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eError bars represent\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM (n\u0026thinsp;=\u0026thinsp;2\u0026ndash;3 independent biological replicates performed in technical singlets; 2\u0026ndash;3 data points per condition). One-way ANOVA with Dunnett\u0026rsquo;s post-hoc test *\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ****\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003ePDGF-BB drives an autocrine loop leading to PDGFRB degradation in BT12 and BT16 cells\u003c/p\u003e \u003cp\u003eDiminished PDGFRB levels in response to PDGF-BB stimulation, co-expression of PDGF-BB and PDGFRB, as well as constitutively active PDGFRB in BT12 and BT16 cells, suggested PDGFRB-PDGFB autocrine signaling may lead to downregulation and degradation of PDGFRB in ATRTs. Indeed, using siRNA-mediated knockdown, we saw an increase in total PDGFRB protein accumulation, but no change in \u003cem\u003ePDGFRB\u003c/em\u003e mRNA, in cells transfected with \u003cem\u003ePDGFB\u003c/em\u003e siRNA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, b). Surprisingly, however, there was no considerable change in phospho-PDGFRB levels seen (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Together, this indicates that PDGFB can promote degradation of PDGFRB and that PDGFRB phosphorylation in BT12 and BT16 is not solely ligand-dependent.\u003c/p\u003e \u003cp\u003eTo confirm the existence of the autocrine loop, we also targeted PDGFRB ubiquitination by knocking down \u003cem\u003eCBL\u003c/em\u003e and \u003cem\u003eCBLB\u003c/em\u003e, the two major ubiquitin ligases responsible for PDGFRB ubiquitination\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eCBLB\u003c/em\u003e knockdown effects were consistent across both cell lines, where PDGF-BB stimulation failed to cause a decrease in PDGFRB in the \u003cem\u003eCBLB\u003c/em\u003e knockdown condition (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg-j). This suggests that \u003cem\u003eCBLB\u003c/em\u003e may be essential for PDGFRB ubiquitination and subsequent degradation. \u003cem\u003eCBLB\u003c/em\u003e knockdown also caused a significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000008; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0284, respectively) increase in \u003cem\u003ePDGFRB\u003c/em\u003e mRNA in BT12 and BT16 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg-j). Consequently, the PDGFRB-PDGFB autocrine loop could downregulate PDGFRB protein and mRNA. In BT16 cells, \u003cem\u003eCBL\u003c/em\u003e knockdown attenuated the effects of PDGF-BB stimulation on PDGFRB, with no significant reduction in PDGFRB levels, in contrast to control condition, where PDGFRB levels were significantly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0122) reduced post-stimulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee, f). However, in BT12 cells, \u003cem\u003eCBL\u003c/em\u003e knockdown did not affect the ligand-induced decrease in PDGFRB levels indicating these cells could be less dependent on \u003cem\u003eCBL\u003c/em\u003e-mediated PDGFRB ubiquitination (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec, d).\u003c/p\u003e \u003cp\u003eNext, we tested whether prevention of PDGFRB degradation using MG132, a proteasome inhibitor, and Bafilomycin-A1, a lysosome inhibitor, could disrupt the autocrine loop. MG132 treatment attenuated the PDGF-BB-induced downregulation of PDGFRB in BT12 and BT16, suggesting that proteasome inhibition disrupts autocrine degradation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ek, m). Bafilomycin A-1 treatment was only effective in decreasing PDGFRB degradation in BT12 but not in BT16, suggesting proteasome-mediated degradation is the primary mode of PDGFRB degradation in BT16 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003el, n).\u003c/p\u003e \u003cp\u003eOverall, these experiments reveal an autocrine PDGFRB-PDGFB loop in BT12 and BT16 cells that has retained some elements of physiologic control, with negative feedback on PDGFRB protein and mRNA. Taken together, these data indicate that PDGF pathway activation can result from ligand-induced autocrine signaling in BT12 and BT16 cells.\u003c/p\u003e \u003cp\u003ePDGF-DD promotes PKCα-mediated PDGFRB accumulation in BT12 and BT16 cells\u003c/p\u003e \u003cp\u003ePDGF-DD stimulation was associated with maintenance or increase of PDGFRB levels despite no change in PDGFRB mRNA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003et, v); suggesting that the effect occurs post-transcriptionally. To determine whether this reflected increased \u003cem\u003ede novo\u003c/em\u003e protein synthesis, BT12 and BT16 cells were treated with cycloheximide (CHX), which did not abrogate PDGF-DD-induced increase in PDGFRB levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003es, u); suggesting the increase is not due to translational upregulation. Knockdown of \u003cem\u003ePRKCA\u003c/em\u003e, which encodes PKCα, essential for PDGFRB recycling, prevented PDGF-DD-induced increase in PDGFRB levels, plausibly implicating receptor recycling in the observed effect (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eo-r). In addition, \u003cem\u003ePRKCA\u003c/em\u003e knockdown caused an increase in \u003cem\u003ePDGFRB\u003c/em\u003e transcription, suggesting that the cells could compensate for the absence of recycling by producing more PDGFRB receptors (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ep, r). Together, these results suggest that PDGF-DD may promote PDGFRB recycling via PKCα, potentially contributing to sustained PDGF signaling.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ea.\u003c/b\u003e Effects of \u003cem\u003ePDGFB\u003c/em\u003e knockdown on PDGFRB and phospho-PDGFRB expression in BT12 and BT16 cells by western blot, and corresponding densitometry quantification. \u003cem\u003ePDGFB\u003c/em\u003e siRNA (\u003cb\u003e\u0026ndash;)\u003c/b\u003e corresponds to cells transfected with non-targeting (NT) siRNA; \u003cem\u003ePDGFB\u003c/em\u003e siRNA (\u003cb\u003e+)\u003c/b\u003e corresponds to cells transfected with \u003cem\u003ePDGFB\u003c/em\u003e siRNA.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eb.\u003c/b\u003e qRT-PCR validation of \u003cem\u003ePDGFB\u003c/em\u003e knockdown efficiency and its effect on \u003cem\u003ePDGFRB\u003c/em\u003e mRNA levels in BT12 and BT16.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEffects of \u003cem\u003eCBL\u003c/em\u003e knockdown on PDGFRB in BT12 \u003cb\u003e(c)\u003c/b\u003e and BT16 \u003cb\u003e(e)\u003c/b\u003e by western blot, and corresponding densitometry quantification. Cells were transfected with NT siRNA or \u003cem\u003eCBL\u003c/em\u003e siRNA; (+) indicates PDGF-BB stimulation (30 ng/mL for 1 hour); (-) indicates no PDGF-BB stimulation. The gradient on the western blot indicates higher contrast.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ed, f.\u003c/b\u003e qRT-PCR validation of \u003cem\u003eCBL\u003c/em\u003e knockdown, and its effects on \u003cem\u003eCBLB\u003c/em\u003e and \u003cem\u003ePDGFRB\u003c/em\u003e in BT12 \u003cb\u003e(d)\u003c/b\u003e and BT16 \u003cb\u003e(f)\u003c/b\u003e.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eg, i.\u003c/b\u003e Effects of \u003cem\u003eCBLB\u003c/em\u003e knockdown on PDGFRB expression in BT12 \u003cb\u003e(g)\u003c/b\u003e and BT16 \u003cb\u003e(i)\u003c/b\u003e by western blot, and corresponding densitometry quantification. Cells were transfected with NT siRNA or \u003cem\u003eCBLB\u003c/em\u003e siRNA; (+) indicates PDGF-BB stimulation (30 ng/mL for 1 hour); (-) indicates no PDGF-BB stimulation.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eh, j.\u003c/b\u003e qRT-PCR validation of \u003cem\u003eCBLB\u003c/em\u003e knockdown, and its effects on \u003cem\u003eCBL\u003c/em\u003e and \u003cem\u003ePDGFRB\u003c/em\u003e in BT12 \u003cb\u003e(h)\u003c/b\u003e and BT16 \u003cb\u003e(j)\u003c/b\u003e.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ek, m.\u003c/b\u003e Effects of MG132 treatment on PDGFRB by western blot, quantified by densitometry in BT12 \u003cb\u003e(k)\u003c/b\u003e and BT16 \u003cb\u003e(m)\u003c/b\u003e. Cells were treated with MG132 or with DMSO, followed by PDGF-BB stimulation (30 ng/mL for 1 hour) (+) or no stimulation (-). p21 protein levels were assessed to confirm the efficiency of MG132 treatment.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003el, n.\u003c/b\u003e Effects of Bafilomycin A1 treatment on PDGFRB by western blot, quantified by densitometry in BT12 \u003cb\u003e(l)\u003c/b\u003e and BT16 \u003cb\u003e(n)\u003c/b\u003e. Cells were treated with Bafilomycin A1 or with DMSO, followed by PDGF-BB stimulation (30 ng/mL for 1 hour) (+) or no stimulation (-). p62 protein levels were assessed to confirm the efficiency of Bafilomycin A1 treatment.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eo, q.\u003c/b\u003e Effects of \u003cem\u003ePRKCA\u003c/em\u003e knockdown on PDGFRB and phospho-PDGFRB expression in BT12 \u003cb\u003e(o)\u003c/b\u003e and BT16 \u003cb\u003e(q)\u003c/b\u003e cells by western blot, and corresponding densitometry quantification. Cells were transfected with NT siRNA or \u003cem\u003ePRCKA\u003c/em\u003e siRNA; (+) indicates PDGF-DD stimulation (50 ng/mL for 1 hour); (-) indicates no PDGF-DD stimulation.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ep, r.\u003c/b\u003e qRT-PCR validation of \u003cem\u003ePRKCA\u003c/em\u003e knockdown, and its effects on \u003cem\u003ePDGFRB\u003c/em\u003e in BT12 \u003cb\u003e(p)\u003c/b\u003e and BT16 \u003cb\u003e(r)\u003c/b\u003e.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003es, u.\u003c/b\u003e PDGFRB protein levels in cycloheximide (CHX)-treated and DMSO-treated BT12 \u003cb\u003e(s)\u003c/b\u003e and BT16 \u003cb\u003e(u)\u003c/b\u003e cells by western blot. Cells were pretreated with 100 \u0026micro;g/mL CHX or 0.1% DMSO for 2 hours, followed by PDGF-DD stimulation (50 ng/mL for 1 hour) (+) or no stimulation (-). Right: corresponding densitometry analysis. In BT12, one biological replicate in the CHX condition had\u0026thinsp;~\u0026thinsp;25.8 relative PDGFRB:Tubulin ratio and was excluded from the analysis using the ROUT method (Q\u0026thinsp;=\u0026thinsp;1%). Pan-Akt and p21 (short-lived proteins) were assessed to confirm the efficiency of CHX treatment.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003et, v.\u003c/b\u003e \u003cem\u003ePDGFRB\u003c/em\u003e mRNA levels by qRT-PCR in DMSO-pretreated (0.1% DMSO for 2 hours) BT12 \u003cb\u003e(t)\u003c/b\u003e and BT16 \u003cb\u003e(v)\u003c/b\u003e PDGF-DD-stimulated cells (50 ng/mL for 1 hour) relative to non-stimulated cells.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eError bars represent\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM. Western blot shown is representative of 3 independent biological replicates performed in technical singlets (3 data points per condition). qRT-PCR data represent 3 independent biological replicates performed in technical duplicates (minimum 6 data points per condition). Two-tailed Student\u0026rsquo;s t-test *\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ****\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eMIF is highly expressed in ATRT-TYR/MYC cell lines\u003c/p\u003e \u003cp\u003eMIF is the highest expressed gene located near \u003cem\u003eSMARCB1\u003c/em\u003e in ATRTs with broad \u003cem\u003eSMARCB1\u003c/em\u003e deletions\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Interestingly, multiple reports have linked the PDGF and MIF pathways, notably through the intermediate of MIF\u0026rsquo;s co-receptor CD44, that can also form a complex with PDGFRB\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Given this, we sought to investigate whether PDGF and MIF pathways are connected in ATRT, and whether this connection has any implications for malignancy. All tested lines expressed MIF, except for CHLA05, where MIF protein was below detection limits (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). ATRT-TYR/MYC cell lines BT12, BT16, and CHLA06 had higher MIF expression relative to ATRT-SHH cell lines, with BT12 having the highest expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). For comparison, MIF expression in these cell lines was similar to or even higher than seen in HEK293, a kidney cell line known to express very high levels of MIF. Additionally, ATRT cell lines also expressed MIF-2 (also known as D-Dopachrome Tautomerase, DDT), a member of the MIF protein family, that is structurally and functionally homologous to MIF, as well as CD74, MIF\u0026rsquo;s receptor (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, b).\u003c/p\u003e \u003cp\u003eAfter confirming MIF expression in ATRT cell lines, we sought to answer the original question about the connection between PDGFRB and MIF. To achieve this, we used stable KOs BT12 and BT16 cells (see Supplementary Figs.\u0026nbsp;2 and 4 for validation of KOs). In BT12 and BT16, we found that PDGFRB KO caused an increase in MIF protein and mRNA levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee-h; Supplementary Fig.\u0026nbsp;7d); conversely, MIF KO caused an increase in PDGFRB protein and mRNA expression in BT12 cells, while only PDGFRB protein expression was increased in BT16 cells with MIF KO (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eg, h; Supplementary Fig.\u0026nbsp;7d). These findings suggest PDGFRB and MIF reciprocally inhibit each other\u0026rsquo;s expression, potentially as part of a regulatory crosstalk.\u003c/p\u003e \u003cp\u003ePDGFRB and MIF influence CD44 expression\u003c/p\u003e \u003cp\u003eNext, we sought to test whether PDGFRB and MIF signaling impact CD44, which functions on both pathways. Indeed, CD44 is a MIF coreceptor known to form heteroduplexes with PDGFRB. The strongest link between the PDGF and MIF pathways reported in the literature is the PDGFRB-CD44 connection. First, we assessed basal expression of two CD44 isoforms: CD44s which is the ubiquitously expressed isoform, and CD44v6, an isoform known to be upregulated in cancer and to interact with several RTKs. All ATRT cell lines tested (BT12, BT16, CHLA02, and CHLA06) expressed CD44s, except for CHLA05 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, d). CD44s expression was highest in BT12 and CHLA06 relative to the other lines. Interestingly, some ATRT cell lines, notably BT12 and CHLA06, expressed high levels of CD44v6, an isoform not expressed in the normal brain (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, d).\u003c/p\u003e \u003cp\u003eNext, we assessed whether the PDGFRB and MIF regulatory connection described above can influence CD44s and CD44v6 expression in BT12 and BT16 PDGFRB KO and MIF KO cell lines. In both lines, we observed PDGFB and PDGFRB KOs resulted in a substantial reduction of CD44s expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ei-l). A nearly complete loss of expression was seen in BT16 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ei-l). Consequently, PDGFB and PDGFRB may play a role in driving CD44s expression. The effects of PDGFRB KO and MIF KO on CD44v6 expression were more heterogeneous. In BT12, MIF KO led to a significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) reduction in CD44v6, while PDGFRB KO led to an increase in CD44v6 protein, without a significant change in its mRNA (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ei, j). Consequently, MIF can represent an important regulator of CD44v6 expression (Supplementary Fig.\u0026nbsp;7e). Surprisingly, in BT16, MIF KO correlated with an increase in both CD44s and CD44v6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ek, l).\u003c/p\u003e \u003cp\u003eTaking together, these data suggest that PDGFRB and MIF influence CD44 expression in BT12 and BT16. While PDGFRB seems important for driving CD44 transcription in both cell lines, the effect of MIF on CD44 is cell line-dependent, which as a ligand-receptor pair may be subject to differential feedback regulation depending on baseline receptor expression and ligand availability in each cell line.\u003c/p\u003e \u003cp\u003eCD44 influences expression of PDGFRB and MIF\u003c/p\u003e \u003cp\u003eNext, we sought to determine whether CD44 influences expression of PDGFRB and MIF. The observed effects of CD44s and CD44v6 KOs on PDGFRB and MIF were consistent across BT12 and BT16. First, CD44s KO and CD44v6 KO correlated with an increase in MIF (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003em-p). Second, CD44s KO led to a reduction in PDGFRB protein, but not mRNA levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003em-o), indicating that CD44s could promote PDGFRB protein stability. Third, CD44v6 KO caused a significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0057, in BT12 and BT16, respectively) increase in PDGFRB, with almost 25-fold increase in mRNA relative to the NT gRNA control (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003em, n, p). Interestingly, in BT12, CD44v6 KO also caused an increase in expression of several other RTKs, including EGFR (Supplementary Fig.\u0026nbsp;7c).\u003c/p\u003e \u003cp\u003eIn summary, these data indicate that CD44s and CD44v6 influence PDGFRB and MIF expression, and that this effect is consistent across BT12 and BT16. In addition, data show that PDGFRB and MIF influence CD44 expression, with the specific effect being cell line-dependent. Collectively, these data suggest a regulatory PDGFRB-CD44-MIF crosstalk.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ea.\u003c/b\u003e Expression of MIF protein, and its functional homologue MIF2 by western blot (left) and \u003cem\u003eMIF\u003c/em\u003e mRNA by qRT-PCR (right) in ATRT cell lines. HEK293 was included as a positive control for MIF expression.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eb.\u003c/b\u003e Expression of CD74, MIF\u0026rsquo;s canonical receptor, by western blot.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ec, d.\u003c/b\u003e Expression of CD44, MIF\u0026rsquo;s co-receptor, and its isoform CD44v6. For western blot, two different antibodies were used: a standard isoform-specific (CD44s) antibody that does not cross-react with other isoforms; a CD44v6-specific antibody that does not cross-react with CD44s (see Supplementary Fig.\u0026nbsp;5). For qRT-PCR, two pairs of primers were used: CD44 primers, which detect all CD44 isoforms, including CD44s and CD44v6 (to our knowledge, it is not possible to design qRT-PCR primers specific to CD44s); CD44v6 primers that detect CD44v6 and CD44v10 isoforms mRNA (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ee, g.\u003c/b\u003e PDGFRB and MIF protein expression in PDGFRB KO and MIF KO by western blot and corresponding densitometry analysis.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ef, h.\u003c/b\u003e \u003cem\u003ePDGFRB\u003c/em\u003e mRNA expression in MIF KO, and \u003cem\u003eMIF\u003c/em\u003e mRNA expression in PDGFRB KO by qRT-PCR.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ei.\u003c/b\u003e Effects of PDGFB KO, PDGFRB KO, and MIF KO on CD44s and CD44v6 expression in BT12 by western blot. (n\u0026thinsp;=\u0026thinsp;2) \u003cb\u003ej.\u003c/b\u003e and by qRT-PCR (One-way ANOVA with Dunnett\u0026rsquo;s post-hoc test).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ek, l.\u003c/b\u003e Effects of PDGFB KO, PDGFC KO, PDGFRB KO, MIF KO, and PDGFRB/MIF DKO on CD44s and CD44v6 expression in BT16 by western blot and qRT-PCR (One-way ANOVA with Dunnett\u0026rsquo;s post-hoc test).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003em.\u003c/b\u003e Effects of BT12 CD44s KO and CD44v6 KO on PDGFRB and MIF expression by western blot (and corresponding densitometry), and \u003cb\u003en.\u003c/b\u003e qRT-PCR.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eo, p.\u003c/b\u003e Effects of BT16 CD44s KO and CD44v6 KO on PDGFRB and MIF expression by western blot and qRT-PCR. (Two-tailed Student\u0026rsquo;s t-test).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWestern blot shown is representative of 2\u0026ndash;3 independent biological replicates performed in technical singlets (2\u0026ndash;3 data points per condition). qRT-PCR data represent mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM of 3 independent biological replicates performed in technical duplicates (minimum 6 data points per condition). *\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ****\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe PDGFRB-CD44-MIF crosstalk regulates cell cycle progression and proliferation in BT12 and BT16 cells\u003c/p\u003e \u003cp\u003eThe observation that CD44s promotes stability of PDGFRB is an indicator that this crosstalk could well contribute to ATRT malignancy. To further investigate this possibility, we assessed effects of the PDGFRB-CD44-MIF crosstalk on cell proliferation, cell cycle, colony formation, and invasion/migration, using CRISPR/Cas9-mediated knockout (KO) in BT12 and BT16 cell lines.\u003c/p\u003e \u003cp\u003eAlthough either PDGF ligand or receptor KO had no significant effects on BT12 cell growth, PDGFRB/PDGFB double knock-out (DKO) correlated with significantly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0100; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0004; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0045, on days 4, 7, and 10, respectively) reduced cell proliferation relative to non-targeting gRNA (NT gRNA) controls, indicating that a PDGFRB-PDGFB autocrine loop could be important for cell proliferation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). PDGFRB/MIF DKO, CD44s KO, and CD44v6 KO all reduced BT12 proliferation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). DKO of PDGFRB and MIF resulted in a robust decrease in cell proliferation, with a significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0279) reduction starting as early as day 3 in BT12 PDGFRB/MIF DKO relative to NT gRNA cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Furthermore, cell cycle analyses showed a modest, but consistent, increase in the percentage of cells in the G2/M phase in BT12 subject to PDGFB KO (15.2\u0026plusmn;0.5%), PDGFRB KO (15.3\u0026plusmn;1.8%), and PDGFRB/PDGFB DKO (15.9\u0026plusmn;0.1%) cells relative to NT gRNA (9.8\u0026plusmn;0.5%), representing an average 57% increase (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb; Supplementary Fig.\u0026nbsp;7f). Consistently, these KO cell lines showed an increase in G2/M protein accumulation (Cyclin A2 and CDC25C) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec; Supplementary Fig.\u0026nbsp;7g). In contrast, BT12 MIF KO (5.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8%) and PDGFRB/MIF DKO (5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0%) were accompanied by a reduction in the G2/M population (relative to 9.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5% in NT gRNA cells) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb; Supplementary Fig.\u0026nbsp;7f). In addition, MIF KO induced a significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0035) increase in \u003cem\u003eTP53\u003c/em\u003e, a G1/S transition checkpoint activator (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). KO of CD44v6 in BT12 cells caused a significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0043) reduction in the G0/G1 population (43.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6% relative to 60.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6%, which represents\u0026thinsp;~\u0026thinsp;29% decrease) and increase in the G2/M population (26.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5%; ~2.7-fold increase in the cell population), suggesting G2/M arrest (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). CD44s KO did not cause a significant change in the cell cycle distribution (Supplementary Fig.\u0026nbsp;7f).\u003c/p\u003e \u003cp\u003eIn BT16, none of the PDGF KOs significantly affected cell proliferation, while PDGFRB/MIF DKO, CD44s KO, and CD44v6 KO reduced it (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). Furthermore, PDGFB KO had no effects on the cell cycle in BT16 cells, while both PDGFC KO (6.0\u0026plusmn;0.2%) and PDGFRB KO (0.5\u0026plusmn;0.2%) led to a decrease in G2/M population relative to NT gRNA (7.9\u0026plusmn;0.4%), with the PDGFRB KO also causing an increase in the G0/G1 population (81.1\u0026plusmn;2.7% relative to 66.9\u0026plusmn;1.1%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee; Supplementary Fig.\u0026nbsp;7h). This corresponded to a decrease in Cyclin A2 in PDGFC KO and PDGFRB KO, and an increase in Cyclin D3, a G1 marker, in the same cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef; Supplementary Fig.\u0026nbsp;7i). Interestingly, DKO of PDGFRB/PDGFB in BT16 cells caused a mild increase in the S-phase population (28.0\u0026plusmn;0.1% relative to 25.1\u0026plusmn;0.6%) (Supplementary Fig.\u0026nbsp;7h). Meanwhile, BT16 MIF KO led to a significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0026) increase in G2/M population (13.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5% relative to 7.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4% in NT gRNA cells, which represents\u0026thinsp;~\u0026thinsp;65% increase); which contrasts with a G2/M decrease seen in PDGFRB KO (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee). Similar to BT12, MIF KO in BT16 also correlated with an increase in p53, despite the phenotype differences in cell cycle distribution between BT12 MIF KO and BT16 MIF KO cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef). Interestingly, there were no significant changes in cell cycle distribution in the PDGFRB/MIF DKO (Supplementary Fig.\u0026nbsp;4h). BT16 CD44s KO resulted in a significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0375) increase in S-phase cells (30.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7% relative to 25.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6%, representing\u0026thinsp;~\u0026thinsp;22% increase), accompanied by a reduction in the G2/M marker Cyclin A2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef; Supplementary Fig.\u0026nbsp;7h). BT16 CD44v6 KO showed a significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0198) reduction of G2/M cells (1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5%, representing\u0026thinsp;~\u0026thinsp;77% decrease), with a simultaneous increase in the G0/G1 population (83.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4% relative to 66.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1%, representing\u0026thinsp;~\u0026thinsp;25% increase) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee). The differences between BT12 and BT16 may reflect baseline variation in cell cycle distribution and checkpoint regulatory protein expression.\u003c/p\u003e \u003cp\u003eWe next investigated the effects of PDGFRB-CD44-MIF crosstalk on colony formation as well as cell migration and invasion. We observed that in relation to controls cells with NT gRNA, BT12 and BT16 cells with PDGFB KO, PDGFRB/PDGFB DKO, MIF KO, PDGFRB/MIF DKO, and CD44 KOs, but not PDGFRB KO, exhibited significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) reduction in colony formation, suggesting a predominant ligand-mediated change in BT12 and BT16 clonogenicity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, b; Supplementary Fig.\u0026nbsp;8a, b). Notably, colony formation in BT16 cells with ligand and receptor DKO was reduced to a much greater extent than that in corresponding BT12 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, b). Effects on cell migration and invasion showed some variability between BT12 and BT16 cell lines, which may reflect differences in baseline invasive capacity. We found PDGFRB KO caused a significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0003) reduction in BT12 cell migration but not invasion (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec; Supplementary Fig.\u0026nbsp;8c) while PDGFRB KO reduced (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0245) BT16 cell invasion but not migration (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed; Supplementary Fig.\u0026nbsp;8c). In contrast, MIF KO significantly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0157; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0095, respectively) impaired cell invasion in both BT12 and BT16 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec, d). Additionally, CD44s KO and CD44v6 KO in BT12 led to a significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0011; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0047, respectively) reduction in cell invasion, without affecting cell migration (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec; Supplementary Fig.\u0026nbsp;8c).\u003c/p\u003e \u003cp\u003eThese observations suggest that KOs in the PDGFRB-CD44-MIF crosstalk components correlated with a decrease in cell proliferation, cell cycle perturbations, reduced clonogenicity, and cell motility.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ea, d.\u003c/b\u003e Cell proliferation in PDGFB KO, PDGFC KO, PDGFRB KO, PDGFRB/PDGFB DKO, MIF KO, PDGFRB/MIF DKO, CD44s KO, and CD44v6 KO relative to NT gRNA in BT12 \u003cb\u003e(a)\u003c/b\u003e and BT16 \u003cb\u003e(d)\u003c/b\u003e by measured AlamarBlue assay (Em/Ex 570/585 nm) at indicated timepoints. Cell proliferation data represent mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM of 3 independent biological replicates performed in technical quadruplicates (12 data points per condition; individual data points omitted for clarity) (One-way ANOVA with Dunnett\u0026rsquo;s post-hoc test).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eb, e.\u003c/b\u003e Cell cycle distribution in PDGFRB KO, PDGFRB/PDGFB DKO, MIF KO, and CD44v6 KO relative to NT gRNA BT12 \u003cb\u003e(b)\u003c/b\u003e and BT16 \u003cb\u003e(e)\u003c/b\u003e measured by PI staining by flow cytometry. Cell cycle data represent mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM of 3 independent biological replicates performed in technical singlets (3 data points per condition) (One-way ANOVA with Dunnett\u0026rsquo;s post-hoc test).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ec, f.\u003c/b\u003e Left: Cell cycle markers protein expression in BT12 \u003cb\u003e(c)\u003c/b\u003e and BT16 \u003cb\u003e(f)\u003c/b\u003e NT gRNA, PDGFB KO, PDGFRB KO, MIF KO, PDGFRB/PDGFB DKO, PDGFRB/MIF DKO, CD44s KO, and CD44v6 KO by western blot. Markers were grouped by cell cycle phase/transition. Right: p53 expression in MIF KO relative to NT gRNA. \u003cb\u003e(c)\u003c/b\u003e qRT-PCR data represent mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM of 3 independent biological replicates performed in technical duplicates (6 data points) \u003cb\u003e(f)\u003c/b\u003e Western blot shown is representative of 2 independent biological replicates performed in technical singlets (2 data points) (Two-tailed Student\u0026rsquo;s t-test).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e*\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ****\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ea, b.\u003c/b\u003e Colony count in PDGFB KO, PDGFC KO, PDGFRB KO, PDGFRB/PDGFB DKO, MIF KO, PDGFRB/MIF DKO, CD44s KO, and CD44v6 KO relative to NT gRNA. Colony formation data represent mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM of 3 independent biological replicates performed in technical triplicates (minimum 9 data points per condition). The images represent one of the replicates (One-way ANOVA with Dunnett\u0026rsquo;s post-hoc test).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ec, d.\u003c/b\u003e Cell invasion/migration in PDGFB KO (BT12), PDGFRB KO, MIF KO, CD44s KO (BT12), and CD44v6 KO (BT12) relative to NT gRNA measured by Matrigel/FluroBlok assay (Em/Ex 494/517 nm). Cell invasion and migration data represent mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM of 3 independent biological replicates performed in technical duplicates (6 data points per condition; except for BT12 CCD44 KOs migration assay: 2 biological replicates and 4 data points) (\u003cb\u003e(c)\u003c/b\u003e One-way ANOVA with Dunnett\u0026rsquo;s post-hoc test; \u003cb\u003e(c, d)\u003c/b\u003e Two-tailed Student\u0026rsquo;s t-test).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e*\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ****\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe PDGFRB-CD44-MIF crosstalk regulates neural stemness and EMT marker expression\u003c/p\u003e \u003cp\u003eGiven the effect of KOs in the PDGFRB-CD44-MIF crosstalk components on cellular homeostasis, we tested whether this crosstalk may influence cellular plasticity in ATRT. We focused on two major cellular phenotypes that vary across ATRT tumors - neural differentiation and EMT status.\u003c/p\u003e \u003cp\u003eWe first investigated the effects of PDGFRB-CD44-MIF crosstalk on neural stemness marker expression in BT12 and BT16 by assessing expression of \u003cem\u003eSOX2\u003c/em\u003e and \u003cem\u003eNES\u003c/em\u003e, and early neural differentiation markers (\u003cem\u003eMAP2\u003c/em\u003e and \u003cem\u003eTUBB3\u003c/em\u003e) by western blot and qRT-PCR.\u003c/p\u003e \u003cp\u003eWe observed that KOs of PDGFB, PDGFRB, and MIF in BT12 led to a significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) reduction in \u003cem\u003eSOX2\u003c/em\u003e, a marker of neural stem cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea; Supplementary Fig.\u0026nbsp;8d). In addition, PDGFRB KO and MIF KO correlated with a significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0023, respectively) increase in \u003cem\u003eNES\u003c/em\u003e, which is associated with neural progenitor cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). PDGFRB KO also correlated with an increase (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0060) in \u003cem\u003eMAP2\u003c/em\u003e, a neuronal lineage commitment marker, while MIF KO induced increased (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0091) expression of \u003cem\u003eTUBB3\u003c/em\u003e, a neuronal cytoskeleton marker (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). Similarly, both CD44s KO and CD44v6 KO resulted in a significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0136; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0254) reduction in \u003cem\u003eNES\u003c/em\u003e and \u003cem\u003eSOX2\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0013; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0006), with CD44s KO correlating with an increase (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) in \u003cem\u003eMAP2\u003c/em\u003e, and CD44v6 KO correlating with an increase (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) in \u003cem\u003eTUBB3\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eIn BT16 KOs of PDGFRB, MIF and, CD44 produced slightly different patterns of neural and stemness markers (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). PDGFRB KO correlated with increased (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0434) \u003cem\u003eNES\u003c/em\u003e and reduced (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) \u003cem\u003eMAP2\u003c/em\u003e expression, while MIF KO led to a significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) reduction in \u003cem\u003eSOX2\u003c/em\u003e and \u003cem\u003eNES\u003c/em\u003e, and a significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0098) increase in \u003cem\u003eMAP2\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). Both CD44s KO and CD44v6 KO resulted in a reduction in \u003cem\u003eSOX2\u003c/em\u003e, an increase in \u003cem\u003eNES\u003c/em\u003e, with CD44v6 KO also causing an increase in \u003cem\u003eMAP2\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed, e). These differences seen in BT12 and BT16 KO lines could reflect either differences in PDGF signaling or differences in cell states of the two lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea, c).\u003c/p\u003e \u003cp\u003eOverall, results from BT12 and BT16 suggest that PDGFRB-CD44-MIF crosstalk may influence features of stemness and neural plasticity; KOs of components of this axis correlated with changes in neural differentiation indicators, including neural progenitor, neuronal lineage commitment, and neuronal cytoskeleton formation.\u003c/p\u003e \u003cp\u003eEffects of the PDGFRB-CD44-MIF crosstalk on EMT\u003c/p\u003e \u003cp\u003eEMT could be one of the factors driving ATRT inter-tumor heterogeneity. For instance, ATRT-TYR/MYC tumors exhibit mesenchymal features, whereas ATRT-SHH is neurogenic. The mechanism underlying these differences remain unknown. To this end, we assessed effects of PDGFRB-CD44-MIF crosstalk on EMT. We first determined mesenchymal (N-cadherin, \u003cem\u003eCDH2\u003c/em\u003e; Beta-catenin; Vimentin, \u003cem\u003eVIM\u003c/em\u003e; and Twist-1, \u003cem\u003eTWIST1\u003c/em\u003e) and epithelial (E-cadherin, \u003cem\u003eCDH1;\u003c/em\u003e and Claudin-1, \u003cem\u003eCLDN1\u003c/em\u003e) marker expression in ATRT cell lines. BT16 was the only cell line that exhibited a defined mesenchymal phenotype, characterized by expression of N-cadherin, Vimentin, and Beta-catenin, and the absence of E-cadherin and Claudin-1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ef). The other cell lines had a hybrid epithelial/mesenchymal (EM) phenotype. Notably, BT12, which expressed Claudin-1 and Beta-catenin, lacked detectable expression of other mesenchymal or epithelial markers (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ef). CHLA02 and CHLA06 expressed E-cadherin and Vimentin (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ef). TWIST-1, a master regulator of EMT, was only detectable at the protein level in CHLA05 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ef). Overall, these data support that EMT status is complex in ATRT, characterized by hybrid expression of both mesenchymal and epithelial markers. Consistent with western blot data, BT16 seems to have a well-defined mesenchymal phenotype, characterized by a high expression of \u003cem\u003eCDH2\u003c/em\u003e and \u003cem\u003eVIM\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ef). In contrast, BT12 seems to have a hybrid EM phenotype, with high expression of \u003cem\u003eCLDN1\u003c/em\u003e and low \u003cem\u003eCDH2\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ef).\u003c/p\u003e \u003cp\u003eNext, we assessed the effects of genetically manipulating PDGFRB-CD44-MIF crosstalk on EMT status of these cells. In BT12, PDGFRB KO led to a significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) reduction in \u003cem\u003eTWIST1\u003c/em\u003e and a significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0204) increase in \u003cem\u003eCDH1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eg). Interestingly, PDGFRB KO also correlated with an increase (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0087) in \u003cem\u003eCDH2\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eg). MIF KO in BT12 provoked an opposite effect to the changes induced by PDGFRB KO: it resulted in a decrease (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) in epithelial marker expression (\u003cem\u003eCDH1\u003c/em\u003e and \u003cem\u003eCLDN1)\u003c/em\u003e, with loss of detectable Claudin-1 protein; and it led to increased (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0002; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0201; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0003) accumulation of mesenchymal markers \u003cem\u003eTWIST1\u003c/em\u003e, \u003cem\u003eCDH2\u003c/em\u003e, and \u003cem\u003eVIM\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eg). KO of CD44s KO in BT12 resulted in a significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0009) reduction of \u003cem\u003eTWIST1\u003c/em\u003e expression and an increase (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) in \u003cem\u003eCDH2\u003c/em\u003e, whereas CD44v6 KO led to a significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) increase in \u003cem\u003eCDH1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eh; Supplementary Fig.\u0026nbsp;8e) and a remarkable change in morphology, transitioning from a spindle cell to a cobblestone morphology, characteristic of epithelial cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ei).\u003c/p\u003e \u003cp\u003eIn BT16, PDGFRB KO led to a reduction (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) in \u003cem\u003eTWIST1\u003c/em\u003e and an increase (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0040) in \u003cem\u003eCDH2\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ej). Interestingly, PDGFRB KO also led to an increase \u003cem\u003e(p\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) in \u003cem\u003eZEB1\u003c/em\u003e, a transcription factor involved in EMT. BT16 MIF KO resulted in a significant increase in \u003cem\u003eCDH1\u003c/em\u003e, and a reduction in \u003cem\u003eTWIST1, ZEB1\u003c/em\u003e, and \u003cem\u003eVIM\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0138; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0001; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0106), which contrasts with the changes observed in BT12 MIF KO (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ej). BT16 CD44s KO and CD44v6 KO correlated with a significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0005; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) increase in \u003cem\u003eTWSIT1\u003c/em\u003e, with CD44s KO inducing almost a 10-fold increase in \u003cem\u003eTWIST1\u003c/em\u003e mRNA level relative to NT gRNA control (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ek). Furthermore, CD44s KO cells appeared to have a spindle-like, elongated morphology, characteristic of mesenchymal cells, and more fibroblastic relative to the NT gRNA (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003el). This finding is consistent with the increase in mesenchymal markers observed by qRT-PCR. Whereas CD44v6 KO cells had a rounder, less elongated morphology, indicative of reduced mesenchymal characteristics (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003el).\u003c/p\u003e \u003cp\u003eTogether, these results indicate that KOs in the PDGFRB-CD44-MIF crosstalk could impact the expression of mesenchymal and epithelial markers, and that the exact effect is gene-specific and cell line-dependent. Baseline expression of EMT-associated markers differed between cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ef), which may contribute to the differential effects of KO conditions on EMT marker expression observed in each line. Notably, across both BT12 and BT16, PDGFRB loss correlated with a decrease in mesenchymal markers.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ea-e.\u003c/b\u003e Neural stemness (\u003cem\u003eSOX2\u003c/em\u003e and \u003cem\u003eNES\u003c/em\u003e) and differentiation marker (\u003cem\u003eTUBB3\u003c/em\u003e and \u003cem\u003eMAP2\u003c/em\u003e) expression in PDGFRB DKO, MIF KO, CD44s KO, and CD44v6 KO relative to NT gRNA BT12 \u003cb\u003e(a, b)\u003c/b\u003e and BT16 \u003cb\u003e(c-e)\u003c/b\u003e by western blot and qRT-PCR. In BT16, one MIF KO biological replicate values for \u003cem\u003eMAP2\u003c/em\u003e (131.2 and 131.4) were excluded using the ROUT method (Q\u0026thinsp;=\u0026thinsp;1%) ((\u003cb\u003ea, c-e\u003c/b\u003e) Two-tailed Student\u0026rsquo;s t-test; \u003cb\u003e(b)\u003c/b\u003e One-way ANOVA with Dunnett\u0026rsquo;s post-hoc test).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ef.\u003c/b\u003e Expression of mesenchymal (N-cadherin, Beta Catenin, Vimentin, and Twist1) and epithelial markers (E-cadherin and Claudin-1) in ATRT cell lines by western blot (left) and by qRT-PCR in BT12 and BT16 cells (right).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eg, j.\u003c/b\u003e EMT marker expression in BT12 \u003cb\u003e(g)\u003c/b\u003e and BT16 \u003cb\u003e(j)\u003c/b\u003e PDGFRB KO and MIF KO relative to NT gRNA cells by western blot (left) and qRT-PCR (right). For BT12 western blot, only Vimentin and Claudin-1 had detectable protein expression among the selected markers. For BT16 western blot, only N-cadherin and Vimentin had detectable protein expression among the selected markers (Two-tailed Student\u0026rsquo;s t-test).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eh, k.\u003c/b\u003e EMT marker expression in BT12 \u003cb\u003e(h)\u003c/b\u003e and BT16 \u003cb\u003e(k)\u003c/b\u003e CD44s KO and CD44v6 KO relative to NT gRNA by qRT-PCR. One-way ANOVA with Dunnett\u0026rsquo;s post-hoc test; \u003cb\u003e(k)\u003c/b\u003e Two-tailed Student\u0026rsquo;s t-test).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ei, l.\u003c/b\u003e Representative phase-contrast micrographs of BT12 \u003cb\u003e(i)\u003c/b\u003e and BT16 \u003cb\u003e(l)\u003c/b\u003e NT gRNA (left), CD44s KO (center), and CD44v6 KO (right). \u003cb\u003e(i)\u003c/b\u003e Top panels provide a wide-field view of cell distribution and density at 10x objective magnification. The bottom panels show a high-magnification (20x) view, highlighting individual cell morphology details; \u003cb\u003e(l)\u003c/b\u003e The panels provide a wide-field view of cell distribution and density at 20x objective magnification. Scale bars represent 100 \u0026micro;m; dimensions estimated based on a mean cell diameter of 12 \u0026micro;m.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eqRT-PCR data represent mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM of 3 independent biological replicates performed in technical duplicates (minimum 6 data points per condition). *\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ****\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePrevious studies on ATRT established sensitivity to RTKIs, specifically those that inhibit PDGFRB\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. However, it remained unclear whether PDGFRB critically contributes to ATRT tumorigenesis, similar to its role in brain tumors with dysregulated PDGF signaling, such as glioblastoma. Our data indicated high-level expression of both PDGFRA and PDGFRB in ATRTs, which differs from other brain tumors where aberrant PDGF pathway activation is generally associated with high expression of only PDGFRA\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Conversely, PDGFRB expression has been shown to be restricted to stromal cells, such as pericytes \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Co-expression of PDGFRA and PDGFRB in some ATRT cell lines suggests that they could express heterodimeric PDGFRA-B, which is uncommon for brain tumor cells\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Our observation of expression of multiple RTKs, including some constitutively active ones, suggest ATRT cell lines heavily depend on RTK signaling. Furthermore, this co-expression could indicate signaling redundancy, where cells could compensate if one signaling pathway is impaired or compromised. Our study also revealed that beyond PDGFs expression in TYR/MYC subgroup cell lines, multiple other RTKs are activated in other ATRT cell lines. CHLA02, for example, is an ATRT-SHH cell line that co-expresses EGFR, ErbB3, and ErbB4, with constitutively active EGFR and ErbB3.\u003c/p\u003e \u003cp\u003eOur study elucidated that the PDGF pathway has two modes of signaling in ATRT. First, autocrine as seen in BT12 and BT16 cells, which induces low-level chronic activation of PDGFRB. This type of signaling exists in other brain tumors, as seen with EGFR autocrine signaling in glioblastoma\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, and has been associated with acquired therapy resistance\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Notably, our study also revealed all tested ATRT cell lines (BT12, BT16, CHLA05, and CHLA06) exhibited paracrine PDGF signaling, and evidence of a plausible paracrine PDGFD-induced PDGFRB recycling in BT12 and BT16 cells. Indeed, PDGF-DD stimulation in BT12 and BT16 appears to promote PKCα-mediated PDGFRB accumulation, potentially through receptor recycling, a phenomenon that is described for PDGFRA in glioblastoma\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Thus, use of RTKIs that target PDGFRB, for example, could alleviate a negative feedback loop resulting in enhanced PDGFRB transcription and protein synthesis, and eventually to reactivation of the PDGF pathway in ATRTs. The recycling mechanism represents yet another potential driver of RTKI therapy resistance as receptor recycling could replenish functional PDGFRB to the plasma membrane, overriding the drug\u0026rsquo;s effect. The proposed autocrine and paracrine signaling models would benefit from additional experimental validation using approaches that provide spatial and temporal resolution of receptor dynamics. Specifically, these findings were assessed at a single optimized timepoint; future time-course analyses would provide more comprehensive insight into receptor degradation kinetics. Moreover, a deeper characterization of these signaling modes \u003cem\u003ein vivo\u003c/em\u003e is essential for understanding how autocrine or paracrine signaling uncovered in our study may drive RTKI therapy resistance in ATRT.\u003c/p\u003e \u003cp\u003eBy using CRISPR/Cas9 stable KO cell lines (PDGFB KO, PDGFRB KO, and PDGFRB/PDGFB DKO), we systematically examined the functional impact of various PDGF signaling pathway components on ATRT cellular phenotypes. Similar to studies of PDGF in other tumors, including glioblastoma\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, our data revealed that PDGF signaling contributes to proliferative signaling in BT12 and BT16. The strongest effect of PDGF signaling was observed on ATRT cell growth in clonogenic assays, suggesting a role in sustaining proliferative potential. In addition to enhanced clonogenicity, our study revealed that PDGFRB is important for cell motility in BT12 and BT16, which is consistent with what was reported for many brain tumors including medulloblastoma and glioblastoma\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePrevious studies indicated that MIF can be upregulated in ATRT, specifically ATRT-TYR/MYC\u003csup\u003e13\u003c/sup\u003e. However, the role of MIF in ATRT has remained unknown, and our project has helped to elucidate its functions. MIF is highly expressed in BT12, BT16, and could be connected to the PDGF pathway via the intermediate of CD44 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). While the precise mechanism by which PDGFRB, CD44, and MIF interact is cell line-dependent and will require further experimentation to define across ATRT subtypes/cell lines, a consistent effect that was shared by both BT12 and BT16 is that PDGFRB appears to drive CD44s expression, which in turn is essential for MIF signal transduction. This further highlights the potential role of PDGFRB as a primary oncogenic driver of this crosstalk. Reciprocally, CD44s could promote PDGFRB protein stability, which is consistent with what was reported about PDGFRB-CD44 interaction in other cancer cells\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. In contrast, CD44v6 loss correlate with increased PDGFRB and MIF expression, which could be a compensatory mechanism. The reciprocal downregulation between PDGFRB and MIF could serve as a regulatory mechanism to maintain balanced expression of CD44v6; very high CD44v6 levels may be toxic to cells, by inducing replication or metabolic stress, as reported in a study of esophageal squamous cell carcinoma\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Nevertheless, this requires further experimental validation in ATRT. As this proposed PDGFRB-MIF crosstalk is inferred solely from expression changes in individual KO contexts, the role of CD44 as a mediator remains to be more robustly tested. Formal establishment of this relationship would require combinatorial KOs (such as PDGFRB/CD44 DKO) and rescue experiments to distinguish direct mediation from parallel regulatory responses. Moreover, these findings should be interpreted in the context of the pooled non-clonal experimental system employed (for example, the residual CD44v6 isoform expression observed in BT12 CD44s KO). Furthermore, the use of only two cell lines, BT12 and BT16, represents a limitation of the current model, as findings may not fully capture subgroup-specific biology across all ATRT subtypes. Validation in additional cell lines representing ATRT-SHH and primary patient-derived cultures would strengthen the generalizability of this proposed crosstalk.\u003c/p\u003e \u003cp\u003eKOs in the PDGFRB-CD44-MIF crosstalk components significantly impaired cell proliferation, cell cycle, colony formation, and invasion/migration, suggesting that this crosstalk can be malignant. Furthermore, our data suggest that the PDGFRB-CD44-MIF crosstalk regulates cell states in BT12 and BT16 cells. The PDGF pathway could promote EMT in BT12 and BT16 cells, in line with the findings from other tumors\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. PDGFRB appears to drive mesenchymal markers expression, nevertheless, its loss also led to a significant increase in \u003cem\u003eCDH2\u003c/em\u003e. Although \u003cem\u003eCDH2\u003c/em\u003e is a mesenchymal marker in carcinomas, brain tumors, such as glioblastoma, commonly maintain high levels of \u003cem\u003eCDH2\u003c/em\u003e while reversing the mesenchymal phenotype, and rarely express \u003cem\u003eCDH1\u003c/em\u003e\u003csup\u003e54\u003c/sup\u003e. Consequently, an increase in \u003cem\u003eCDH2\u003c/em\u003e in PDGFRB KO does not contradict the partial loss of mesenchymal phenotype. MIF plays a distinct role, where it can promote MET in BT12, and EMT in BT16. MIF\u0026rsquo;s effect on EMT is context dependent: in glioblastoma, where MIF expression is acute and induced by hypoxia, MIF has been found to promote EMT\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e; in gastrointestinal cancer, where MIF expression is chronic, it promotes MET, specifically through the CD74 intracellular domain (CD74-ICD), which is formed upon MIF binding\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. The TME could be important in determining whether MIF signaling resembles that of glioblastoma or gastrointestinal tumors, and it may explain differences in MIF\u0026rsquo;s impact on EMT in BT12 and BT16. Specifically, this may reflect differences in MIF signaling modes, analogous to the distinct PDGF signaling modes previously described in these cells. Like MIF, CD44s also seems to play a dual role, in BT12, it can promote EMT; in BT16, CD44s can inhibit mesenchymal marker gene expression, especially \u003cem\u003eTWIST1\u003c/em\u003e. One possible explanation is that because BT16 has a pronounced basal mesenchymal phenotype, CD44s could prevent the cells from undergoing further EMT, thereby reducing the risk of acquiring an extreme mesenchymal phenotype. Cells that undergo extreme EMT lose invasive capacities and can even become quiescent\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Finally, CD44v6 KO stood out for its considerable effect on cell morphology, prompting both BT12 and BT16 to acquire a more epithelial phenotype, and thus CD44v6 loss can be central in driving MET in these cells. However, the markers assessed represent a limited subset of the EMT program, and a more comprehensive characterization incorporating additional regulators as well as brain tumor-relevant mediators including YAP/TAZ and STAT3, would be required to better define EMT status in this context.\u003c/p\u003e \u003cp\u003eOverall, the findings suggest that PDGFRB-CD44-MIF crosstalk is associated with the expression of neural stemness-associated markers and a less differentiated cell state. In the absence of MIF, CD44s, and CD44v6 in BT12 and BT16 cells, a partial neural differentiation is seen. This is consistent with a reported role for CD44 as a stemness marker in cancer cells\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Though, stemness assessment was limited to a select panel of markers, and a more comprehensive characterization incorporating \u003cem\u003ein vivo\u003c/em\u003e limiting dilution assays will be needed to determine whether the observed changes in stemness-associated markers correspond to an altered tumor-initiating capacity. Furthermore, a limitation of using pooled, non-clonal KO cell populations rather than isogenic clonal lines is that it introduces heterogeneity in editing efficiency within the population. As a result, observed phenotypes may reflect a mixture of fully edited, partially edited, and potentially unedited cells, which could influence the magnitude of the observed effects. Future studies using validated isogenic clonal lines would strengthen the mechanistic interpretation of the findings presented here.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, the PDGFRB-CD44-MIF crosstalk appears to facilitate cellular maintenance of an optimal EM and stemness phenotype associated with growth and invasion. It should also be noted that EMT and stemness represent highly plastic and reversible cell states that exist along a spectrum rather than as discrete categories. The expression changes observed here could reflect a partial or hybrid state transition, and bulk expression analysis (western blot, qRT-PCR, and RNA-seq) cannot resolve the heterogeneity of cell states within a cell population. Single-cell RNA-seq analysis would be required to deconvolute cellular heterogeneity and to model the dynamic and continuous nature of these cell states.\u003c/p\u003e \u003cp\u003eHowever, the observed variability in responses between BT12 and BT16 cell lines highlights both the complexity of ATRT biology and the inherent limitation of drawing broad conclusions from a restricted number of models. Accordingly, the findings presented in this paper should be interpreted as hypothesis-generating observations that provide preliminary evidence for the biological relevance of the studied PDGF signaling modes and PDGFRB-CD44-MIF crosstalk in ATRT, rather than definitive mechanistic conclusions. Indeed, two cell lines are insufficient to fully capture the molecular and phenotypic inter- and intra-tumoral heterogeneity of ATRT. Future studies incorporating additional ATRT cell lines representing all three molecular subgroups, as well as primary patient-derived cultures, would provide a more comprehensive assessment of the study\u0026rsquo;s findings relevance and generalizability across the heterogeneous ATRT landscape.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eACTB\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eActin cytoskeletal beta\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eAKT\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAKR Thymoma/ Protein kinase B\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eANOVA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnalysis of variance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eATAC-seq\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAssay for transposase-accessible chromatin with high-throughput sequencing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eATP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdenosine triphosphate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eATRT-MYC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAtypical teratoid rhabdoid tumor-myelocytomatosis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eATRT-SHH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAtypical teratoid rhabdoid tumor-sonic hedgehog\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eATRT-TYR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAtypical teratoid rhabdoid tumor-tyrosinase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eATRT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAtypical teratoid rhabdoid tumor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eBAF47\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBrahma-related gene-1-associated factor 47\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBT12\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBrain tumor 12\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBT16\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBrain tumor 16\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eCBL\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCasitas B-lineage lymphoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eCBLB\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCasitas B-lineage lymphoma proto-oncogene B\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eCD44\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCluster of differentiation 44\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eCD44s\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard cluster of differentiation 44\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eCD44v6\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCluster of differentiation 44 variable exon 6\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eCD74\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCluster of differentiation 74\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eCDC\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCell division cycle\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eCDH\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCalcium-dependent adhesion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eCDK\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCyclin-dependent kinase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eCDKN\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCyclin-dependent kinase inhibitor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eChIP-seq\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChromatin immunoprecipitation sequencing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCHLA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChildren\u0026rsquo;s Hospital Los Angeles\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eCLDN1\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eClaudin-1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCNS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCentral nervous system\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCRISPR/Cas9\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eClustered regularly interspaced short palindromic repeats-associated protein 9\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eCXCR\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eC-X-C Motif chemokine receptor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eDDT\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eD-dopachrome tautomerase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDKO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDouble knockout\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDMSO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDimethyl sulfoxide\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDeoxyribonucleic acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEBTs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEmbryonal brain tumors\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eECM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExtracellular matrix\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eEGFR\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEpidermal growth factor receptor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEMT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEpithelial-mesenchymal transition\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eERBB\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eErythroblastic oncogene B receptor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eESRP\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEpithelial splicing regulatory protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eETMRs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEmbryonal tumors with multilayered rosettes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFACS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFluorescence-activated cell sorting\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFBS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFetal bovine serum\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eFGFR1\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFibroblast growth factor receptor 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eG0; G1; G2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGap phase 0; 1; 2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eGAPDH\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlyceraldehyde-3-phosphate dehydrogenase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGFP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGreen fluorescent protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003egRNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGuide RNA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eH3K27me3\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHistone H3 lysine 27 trimethylation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHEK293\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHuman embryonic kidney 293\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntracellular domain\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIHC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eImmunohistochemistry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eINI1\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntegrase interactor 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKnockout\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eM (as in G2/M)\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMitosis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eMAP2\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMicrotubule associated protein 2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eMAPK\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMitogen-activated protein kinase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eMCM\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMinichromosome Maintenance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMET\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMesenchymal-epithelial transition\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eMIF\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMacrophage migration inhibitory factor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003emRNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMessenger ribonucleic acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMesenchymal transition\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eMYC\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMyelocytomatosis gene\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eNES\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNestin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNHA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNormal human astrocytes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNon-targeting\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003ePDGF\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePlatelet-derived growth factor subunit A\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003ePDGFR\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePlatelet-derived growth factor receptor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003ePI3K\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePhosphoinositide 3-kinase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003ePLC-γ\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePhospholipase C, gamma 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePRC2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePolycomb repressive complex 2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003ePRKCA\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProtein kinase C alpha\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eqRT-PCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eQuantitative reverse transcription polymerase chain reaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eRab4a\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRas-related protein 4a\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRFU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRelative fluorescence units\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRNA-seq\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRibonucleic acid sequencing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRibonucleic acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRTK\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceptor tyrosine kinase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRTKI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceptor tyrosine kinase inhibitor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003escRNA-seq\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSingle-cell RNA sequencing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSEM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard error of the mean\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eSMARCA4\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily A, member 4\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eSMARCB1\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily B, member 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eSNAI1/2\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSnail family transcriptional repressor 1/2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eSNF5\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSucrose nonfermenting, yeast, factor 5\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eSOX2\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSRY-box transcription factor 2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSWI/SNF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSWItch/Sucrose non-fermentable\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eTGFB\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTransforming growth factor beta\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTIDE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTracking of insertions and deletions\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTME\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTumor microenvironment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eTUBB3\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTubulin beta class III\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eVIM\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVimentin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld health organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eZEB1/2\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eZinc finger E-box-binding homeobox 1/2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval, Consent to participate, and Consent to publish declarations\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eData supporting the findings of this study are available within the article and its Supplementary Information files, or from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThis work was supported by a grant from the Canadian Institutes of Health Research (CIHR).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eY.A.C: Conceptualization, Methodology, Validation, Formal Analysis, Investigation, Data Curation, Writing - Original Draft, Visualization. A.H.: Conceptualization, Resources, Writing - Review \u0026amp; Editing, Supervision, Project Administration, Funding Acquisition. All authors reviewed and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLouis, D. N. \u003cem\u003eet al.\u003c/em\u003e The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. \u003cem\u003eNeuro. Oncol.\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 1231 (2021).\u003c/li\u003e\n\u003cli\u003eTekautz, T. M. \u003cem\u003eet al.\u003c/em\u003e Atypical teratoid/rhabdoid tumors (ATRT): improved survival in children 3 years of age and older with radiation therapy and high-dose alkylator-based chemotherapy. \u003cem\u003eJ. Clin. Oncol.\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 1491\u0026ndash;1499 (2005).\u003c/li\u003e\n\u003cli\u003eHilden, J. 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Rep.\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 1\u0026ndash;15 (2017).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"ATRT, PDGFRB, MIF, CD44","lastPublishedDoi":"10.21203/rs.3.rs-9707332/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9707332/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAtypical teratoid/rhabdoid tumor (ATRT) is the most common brain tumor in children less than one-year-old. Previous studies have identified three epigenetic subgroups of ATRT: ATRT-SHH, ATRT-TYR, and ATRT-MYC. Interestingly, it was found that ATRT-TYR/MYC (mesenchymal) subgroups are sensitive to receptor-tyrosine kinase inhibitors (RTKIs), particularly those that inhibit the platelet-derived growth factor receptor B (PDGFRB), highlighting the importance of PDGF signaling in ATRTs. In addition, the ATRT-TYR/MYC subgroups show upregulation of the macrophage migration inhibitory factor (MIF), with dysregulation of the MIF signaling pathway, which was found to have immunosuppressive effects in other cancers. While PDGF and MIF pathways have been found to promote tumorigenesis of other cancers including glioma, their roles in ATRTs remain unknown. We hypothesized that PDGF and MIF signaling pathways contribute to maintaining malignant phenotypes in ATRT.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eTo test this hypothesis, our project aimed to characterize PDGF signaling and investigate PDGF-MIF crosstalk in ATRT, using CRISPR/Cas9 stable knockouts (KOs) of various PDGF receptor and ligands.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe have shown that the PDGF pathway primarily promotes maintenance of malignant phenotypes in ATRT via modulation of the cell cycle. Furthermore, our studies reveal a plausible PDGFRB, MIF, and CD44 oncogenic signaling axis, that could modulate invasion and cellular phenotypic plasticity in ATRT, via regulation of neural stemness and epithelial-mesenchymal transition (EMT) marker expression.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur collective study findings point to an important role for PDGFRB-MIF-CD44 signaling circuit as a potential therapeutic pathway for this very poor prognosis tumor.\u003c/p\u003e","manuscriptTitle":"A PDGFRB-CD44-MIF Crosstalk Promotes Cancer-Associated Cellular Phenotypes in ATRT","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-15 09:41:35","doi":"10.21203/rs.3.rs-9707332/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7aca2b01-c3bb-4007-8751-b9d79ea49415","owner":[],"postedDate":"May 15th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-15T18:46:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-15T10:49:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-15T10:49:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2026-05-13T19:11:11+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T18:54:41+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-15 09:41:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9707332","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9707332","identity":"rs-9707332","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}