Establishment and characterization of multiple myeloma cell lines from a single patient’s disease course immortalizes clonal heterogeneity

Establishment and characterization of multiple myeloma cell lines from a single patient’s disease course immortalizes clonal heterogeneity | 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 Article Establishment and characterization of multiple myeloma cell lines from a single patient’s disease course immortalizes clonal heterogeneity P. Leif Bergsagel, Julia E. Wiedmeier-Nutor, Daniel Riggs, Caleb Stein, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9003051/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Multiple myeloma (MM) is an incurable plasma cell neoplasm characterized by diverse and complex genetic abnormalities, including translocations of immunoglobulin (Ig) genes and a hyperdiploid (HRD) karyotype. Much of our current understanding of MM genetics, epigenetics, pathogenesis and response/resistance to myeloma directed therapies originated from studies on human MM-derived cell lines (HMCL). In this study, we conducted an integrated multi-omics analysis in five hyperdiploid cell lines established from serial samples from a single relapsed/refractory hyperdiploid MM patient, enabling detailed characterization of the genetic landscape and molecular alterations through disease progression. Biological sciences/Cancer/Cancer genomics Biological sciences/Genetics/Cancer genomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Multiple myeloma (MM) is a plasma cell neoplasm, representing 10% of all hematologic malignancies. Despite improvements in MM treatments, the prognosis is variable, due to the heterogeneous nature of the disease. Genetic aberrations are common in MM and can be classified into primary and secondary events 1 . Primary genetic events include chromosome translocations involving immunoglobulin genes (mostly IgH, cut also IgK and IgL) with multiple partners and gains of multiple odd numbered chromosomes (hyperdiploidy, HRD), found in approximately half of patients, each of which are primarily mutually exclusive. Secondary genetic events arise in sub-clonal cells and can lead to progression and therapy resistance 2 , 3 . Some common secondary aberrations include rearrangements of the MYC locus and loss of chromosome 13q. MYC is a transcription factor that orchestrates and promotes numerous aspects of cell growth and is a key driver in oncogenesis in MM 4 . The presence of MYC structural variations (SV) are common in newly diagnosed MM (42%) but very rare in the early phase of monoclonal gammopathy of undetermined significance (MGUS), suggesting it plays a prominent role in tumor progression 5 . These MYC SVs involve chromosomal structural changes that allow immunoglobulin enhancers/super-enhancers access to the MYC locus, leading to its dysregulation 6 . In addition to translocations, amplification of the MYC locus can occur in MM 5 6 7 . Monosomy of chromosome 13 results in loss of one copy of MIR15A/MIR16-1 which has been shown to accelerate disease progression in mice 8 . In addition biallelic loss of RB1 at 13q14 is associated with aggressive disease progression in patients with MM 8 9 . Much of our current understanding of MM genetics, epigenetics, pathogenesis and response or resistance to treatment has come from studying human MM-derived cell lines (HMCL). Interestingly, cell lines with hyperdiploid cytogenetics are rare. In addition, most HMCL were developed before lenalidomide, pomalidomide, and carfilzomib were approved for treatment of MM. In this study we use microarrays, next generation sequencing (NGS), and mass spectrometry-based proteomics to characterize a set of cell lines established from serial samples from a relapsed/refractory MM patient with recurrent, malignant, pleural effusions. In this series, a duplication telomeric to MYC emerged in the second cell line and was associated with increased MYC expression but was subsequently lost in the final cell line obtained shortly before the patient’s death. An RB1 deletion, which was initially monoallelic in the first cell line, progressed to a large biallelic deletion in the final cell line, as the patient’s disease progressed. We also identified large FAM46C and TRAF3 deletions, several single nucleotide variants (SNVs), and altered gene expression in several other oncogenes and tumor suppressor genes. Further, proteomic analysis of three of the cell lines identified dysregulation of several proteins of interest, including increased abundance of MYC, CDKN2A, MTOR, and cancer testes antigens, and loss of RB1. This series of established cell lines enabled detailed characterization of molecular changes and underlying genetics of MM through disease progression. Materials/Subjects and Methods Collection of patient information: Institutional review board approval and patient consent was obtained prior to clinical and patient sample collection (IRB 919-04). Cell culture Seven serial samples were collected from the patient’s recurrent pleural effusions at relapse (MC1286PE1-PE7). Only MC1286PE1, PE2, PE3, PE5 and PE7 survived in vitro . All cell lines were grown in RPMI-1640 medium supplemented with 10% fetal calf serum. Array comparative genomic hybridization (aCGH) Preparation of samples for aCGH was completed as previously described 10 . Labeled DNA was hybridized to 244A Human Genome CGH Microarrays (Agilent) according to the manufacturer's suggestion. All samples were viewed IGV. The copy number of each sample was normalized to two copies (log2(tumor/normal)). IGV defaults to a blue-to-red scale that corresponds to copy numbers from -1.5 to 1.5 (IGV). Mate pair libraries Mate pair library and sequencing were carried out as previously described 6 . Briefly, the Illumina Mate Pair Library Preparation Kit was used for library construction following the manufacturer’s instructions. Two samples (MC1286PE2 and MC1286PE5) were run on one lane of an Illumina HiSeq 2000 with 50bp reads. BAM files were analyzed in search for clusters of discordant read pairs. Breakpoints supported by more than five discordant read pairs were identified. Custom capture Methods for custom capture have been previously described 5 . Briefly, a targeted capture approach was used to sequence the regions adjacent to the immunoglobulin enhancers (IGH3’RR1 and 2, IGK, and IGL) as well as MYC , RB1, TRAF3, and TENT5A/FAM46C and 80 other genes previously described 5 . Ion Torrent next generation sequencing Methods for Ion Torrent NGS have been previously described 11 , 12 . Briefly, 20 ng of DNA were used to prepare 200 bp libraries. Template preparation and enrichment of DNA libraries was done on the Ion OneTouch2 and Ion OneTouch ES automated system. Samples were barcoded, pooled, and sequenced on Ion 318v2 chips using the Ion Sequencing 200 Kit v2. The generated sequencing data were analyzed using the Ion Reported Software v1.6, then visualized and manually reviewed using the Integrative Genomics Viewer (IGV). Linked-read 10X genomics sequencing was performed on MC1286PE5 to confirm Mate Pair SVs and was conducted following the manufacturer's protocol. Whole genome sequencing Whole-genome sequencing (WGS) was performed at the Translational Genomics Research Institute (TGen, Phoenix, AZ) following institutional standard operating procedures for high-quality genomic DNA analysis. Genomic DNA was extracted from bone marrow mononuclear cells or purified plasma cells using the Qiagen AllPrep DNA/RNA Kit , and DNA quality and quantity were assessed by Qubit fluorometry and Agilent TapeStation . Sequencing libraries were prepared using the Illumina TruSeq DNA PCR-Free Library Preparation Kit according to the manufacturer’s protocol. Libraries were quantified by qPCR and pooled equimolarly. Paired-end sequencing (2 × 150 bp reads) was performed on the Illumina NovaSeq 6000 platform, targeting an average depth of 60× for tumor samples and 30× for matched normal controls. Base calling and demultiplexing were performed using Illumina bcl2fastq software (v2.20), and raw FASTQ files were subjected to downstream analysis. Reads were aligned to the GRCh38/hg38 human reference genome using BWA-MEM (v0.7.17). Alignment files were processed with Picard Tools to mark duplicates and GATK (v4.4.0) for base quality recalibration. Copy number alterations were inferred with CNVkit. RNA-seq processing and analysis Reads per kilobase million (RPKM) across expressed genes in 70 cell lines previously run were normalized by conditional quantile normalization method to achieve similar count distributions across samples and to remove GC-content bias 13 . A uniform manifold approximation and projection (umap) visualization of the top principal components of highly expressed protein coding genes revealed distinctions and similarities in the expression profiles of all cell lines. Downstream Analyses Annotated variants were integrated with RNA-seq and copy-number data where available to identify driver mutations, structural rearrangements, and mutational signatures. Recurrent alterations were visualized using IGV and summarized with R (v4.3). Allele-specific expression of MYC RNA cDNA, prepared by standard methods from RNA isolated from the five established MC1286 cells lines, was Sanger sequenced and the relative expression of the alleles determined. Immunomodulatory treatment (IMiD) of MC1286 cells lines Cell lines were treated for three days with Pomalidomide (200 nM) to determine effects on cell proliferation. Proliferation was measured by live cell count of trypan blue-stained cells with a hemocytometer. The proliferation index was defined as cell proliferation of treated culture (final cell number minus inoculum number) normalized to the proliferation of the untreated control. Quantitative proteomic analysis of MC1286 cell lines The cell line pellets were processed for label-free quantitative proteomic approach as described before 14 for cell lines MC1286PE1, MC1286PE3, and MC1286PE7. In brief, protein extraction was performed, and it was followed by overnight trypsin digestion to generate peptides. Digested peptides were cleaned using C 18 stage-tips, lyophilized and stored at -80°C until mass spectrometry analysis. 200 ng of peptides from each cell line was subjected to LC-MS/MS analysis in triplicate injections. The LC-MS/MS analysis was performed using an Orbitrap Astral mass spectrometer (Thermo Scientific, San Jose, USA) coupled to a Thermo Vanquish Neo HPLC system operated in data independent acquisition mode. Protein identification and quantitation was performed using DIA-NN software (v2.3) using an in silico spectral library generated from human UniProt protein database. False discovery rate was controlled to 1% at protein level. Protein normalization was performed within DIA-NN software using global normalization strategy. Data analysis was performed using Perseus computational platform (v 2.1.5). Proteins detected in at least two replicates were retained following log transformation, and missing values were imputed. Differential expression analysis was performed using two sample student’s T test comparing MC1286PE3 and MC1286PE7 cell lines with MC1286PE1. Results Patient Information and establishment of MC1286 cell lines The patient was a 73-year-old female with IgG kappa MM with plasmacytomas (Supplemental Figure 1 ). At diagnosis, bone marrow biopsy FISH revealed trisomies 3 and 7 and monosomies 13 and 14. MM treatment, response and sample collection for establishment of five MC1286 cell lines are detailed in Figure 1A . MC1286PE1T was a pleural fluid sample collected following relapse from autologous stem cell transplant. Subsequent samples were collected after relapsing on successive myeloma directed therapy combination regimens. All samples for cell culture were obtained from the patient’s recurrent, malignant, pleural effusion. WGS analysis of the MC1286 cell lines revealed a hyperdiploid (HRD) state (gain of chromosomes 3, 7 and monosomy 13, 14, Figure 1B ), consistent with the patient’s bone marrow FISH results at diagnosis. In addition, WGS identified gain 1q, del(1p), and partial gain of chromosomes 5, 9, 11, 19, and 21. Interestingly, there were alterations and differences in the trisomies characteristics of hyperdiploidy in the five cell lines, although it is clear from the copy number changes that both major subclones are present in the original pleural fluid sample (PE1T) ( Figure 1B ). For example, there was a partial gain of chromosome 7 in MC1286PE3, complete gain of chromosome 7 in MC1286PE5 and MC1286PE7. There was also loss of partial gain of chromosome 9, chromosome 11, and chromosome 19 in MC1286PE7. Emergence of an RB1 biallelic deletion and other structural variants in MC1286 cell lines is associated with disease progression WGS revealed one copy of RB1 at relapse/progression following autologous stem cell transplant (MC1286PE1). In contrast, a biallelic focal RB1 deletion at the 3’ end of the gene (approximately 12 kb, covering exons 24 - 27) was identified in subsequent cell lines (MC1286PE2, MC1286PE3, and MC1286PE5, Supplemental Figure 2A ). MC1286PE7 had a larger biallelic deletion that removed all but the last exon (exon 27) ( Supplemental Figure 2A) . RB1 protein was detected only in MC1286PE1 by unbiased global proteomics analysis and was not detected in MC1286PE3 and MC1286PE7 cell lines, consistent with genetic findings. We also identified partial biallelic deletions of the terminal end of TRAF3 (approximately 150 kb, covering exons 11 and 12) and monoallelic deletions of TENT5C (approximately 60 kb, covering exon 2) in all cell lines ( Supplemental Figure 2B ). TRAF3 is a NFkB negative regulator whose common deletion/mutation in MM patients induces constitutive NFkB signaling and 3’ deletion of TRAF3 induces protein degradation 15 16 . TENT5C (which encodes the protein FAM46C), is a poly(A) RNA polymerase that enhances mRNA stability and gene expression, and variants are common in smoldering myeloma (SMM) and MGUS 5 7 17 . While TRAF3 protein was not identified, FAM46C protein abundance remained unchanged in proteomic analysis of MC1286 cell lines. Identification of a duplication telomeric of MYC MC1286 cell lines established from the pleural effusions collected after subsequent, sequential combination therapies (MC1286PE2, MC1286PE3, MC1286PE5, see Figure 1A for details regarding therapies) were found to have a 420 kb bp duplication located about half a megabase telomeric of MYC. Both mate pair and linked-read sequencing detected the duplication, but unexpectedly not custom capture ( Supplemental Figure 3A ). To delineate the boundaries of the duplication, oligonucleotides flanking the approximate breakpoint were used to amplify the region by PCR. Sanger sequencing identified the precise breakpoint which indicated that the duplicated region spanned position 129,207,090 to 129,628,867 ( Supplemental Figure 4A ). Breakpoint PCR analysis identified the duplication in cell lines MC1286PE2, MC1286PE3, and MC1286PE5, but not in MC1286PE1 or MC1286PE7 ( Supplemental Figure 4B ). Duplications telomeric of MYC are associated with increased MYC expression To distinguish expression of the two MYC alleles, a heterozygous MYC single nucleotide variant (SNV) in the promoter region (c.1350G>A) was used. Linked-read sequencing established that the MYC c.1350A allele was linked to the telomeric duplication ( Figure 2A, B and D ). Sequencing of bulk cDNA showed that the MYC c.1350A allele was preferentially transcribed only in MC1286PE2, MC1286PE3, and MC1286PE5 cell lines, while both were equally expressed in MC1286PE1 and MC1286PE7 ( Figure 2C ). These results directly demonstrate the cis -enhancing activity of the telomeric duplication on the adjacent MYC gene. In line with these findings, MYC protein abundance was mildly higher in MC1286PE3 (0.52 log 2 fold-change) and also in MC1286PE7 compared to MC1286PE1 (0.7 log 2 fold-change). Resistance to IMiD therapy correlated with patient disease refractory to IMiD therapy Immunomodulatory (IMiD) agents (Lenalidomide and Pomalidomide) have been used for MM treatment for years. One important aspect of their anti-tumor activity is that they direct degradation of the enhancer-binding transcription factors Ikaros and Ailos which are involved in the dysregulation of MYC . MC1286 cell lines were treated with Pomalidomide for three days to determine the effects on cell proliferation. We found that IMiD resistant cell lines correlated with the patient’s disease progression while on IMiD therapy (MC1286PE1 and MC1286PE3) ( Supplemental Table 1 ). IMiD sensitive cell lines (MC1286PE2 and MC1286PE7) were collected when the patient was not on IMiD therapy. In cell lines with increased MYC expression (MC1286PE2 and MC1286PE3), only MC1286PE2 showed sensitivity to IMiD therapy ( Supplemental Table 1 ). Identification of significant single nucleotide variants (SNVs) Significant SNVs were found in NRAS , TENT5A , ACTG1 , TLR4 , IFNGR2 , IDH3A , and DUSP2 in all cell lines( Figure 3A ). While all other significant SNVs were relatively stable with disease progression, DUSP2 c.388+5G>A variant allele frequency increased as the patient’s disease progressed. Despite this increase, the variant was predicted to have minimal impact on splicing by SpliceAI and was not associated with alternative or abnormal splice isoforms. A summary of SVs and SNVs are summarized in Figure 3B . Variability between MC1286 cell lines and other HMCLs Principal component analysis (PCA) of RNA-seq data from MC1286 cell lines, approximately 70 other HMCLs, and 825 samples from the publicly available CoMMpass cohort (NCT01454297) revealed that the MC1286 cell lines clustered closest to samples without IgH translocations and without trisomy of chromosome 11, that have frequent monosomy 13 and 14 that we have called nHRD2 ( Figure 4A and Supplemental Table 2 ) 8 18 . Despite distinct clustering of MC1286 cell lines compared to other HMCLs, we found divergent expression of genes commonly dysregulated in MM between MC1286 cell lines ( Figure 4B ). MC1286 cell lines progressively decreased expression of RB1 from MC1286PE1 to MC1286PE7, consistent with the telomeric biallelic and complete biallelic RB1 deletion in MC1286PE2, MC1286PE3, MC1286PE5, and MC1286PE7, respectively. In MC1286PE7, the very low expression of RB1 was associated with decreased CCND2 expression. We found relative increased expression of MYC in cell lines with the duplication telomeric to MYC (MC1286PE2, MC1286PE3, and MC1286PE5, Figure 4B ). The NFkB index (NFkBi) was higher in cell lines without MYC SV (MC1286PE1 and MC1286PE7), consistent with previously reported results, which showed an inverse relationship between high MYC expression and NFkBi 5 . High expression of IL6 was found in MC1286PE2, MC1286PE3, and MC1286PE5 but plummeted in MC1286PE7. Interestingly, decreased IL6 expression was associated with increased pSTAT3 suggesting that this cell line’s activation of STAT3 was independent of IL6 signaling ( Supplemental Figure 5 ). Osteopontin ( SPP1 ), a bone matrix glycoprotein involved in angiogenesis, cell survival and tumor progression, was highly expressed in MC1286PE7 19 . Overall, CRBN in all cell lines was low relative to other HMCLs other than MC1286PE7. Proteomic analysis showed mildly decreased CRBN protein in MC1286PE3 and MC1286PE7 lines (-0.2 log 2 fold change and -0.2 log 2 fold change, respectively). This correlated with pomalidomide sensitivity in vitro ( Supplemental Table 1 ). There was decreased expression of TP53 in MC1286PE3, MC1286PE5, and MC1286PE7. Mass spectrometry-based proteomics reveals novel proteins in MC1286 cell lines A total of 7,590 proteins were quantified in at least two replicates in proteomic analysis. Differential expression analysis identified more significantly altered proteins in MC1286PE3 and MC1286PE7 compared to MC1286PE1. We identified < 50 significantly differentially expressed proteins between MC1286PE3 and MC1286PE7 and a sample correlation heat map showed higher Pearson correlation coefficients among these two cell lines ( Supplemental Figure 6A and 6B ). Differential expression analysis of the two cell lines compared to MC1286PE1 indeed showed similar altered protein expression signatures ( Figure 5 ). This makes sense as these cell lines were obtained at closer time points compared to MC1286PE1, suggesting that the major proteomic changes occurred earlier in disease progression. Interestingly, differential abundance identified decreased CDK6 in MC1286PE3 and MC1286PE7 compared to MC1286PE1, which is consistent with loss of RB1 expression ( Figure 5 ) 20 . We also found expression of CDKN2A and CDKN1A in MC1286PE3 and MC1286PE7, but not MC1286PE1, both of which may be an attempt by the cell lines to compensate for the loss of RB1 20 21 . We found increased MYC expression in MC1286PE3 and MC1286PE7, despite the loss of the duplication telomeric to MYC in MC1286PE7. We also found increased expression of MTOR, CD40, and CD320, and SMYD3 in MC1286PE3 and MC1286PE7 compared to MC1286PE1, all of which have been described in MM and may promote proliferation and/or MM cell survival 22 23 24 25 26 27 28 . We found high expression of two cancer testes antigens, PAGE2 and GAGE12F 29 . Cancer testes antigens are expressed in late stage MM and may be linked to poorer outcomes 30 31 32 33 . Interestingly, GSEA analysis showed enrichment of cholesterol synthesis pathways ( Supplemental Figure 6 ). FADS2 and LRPAP1, proteins linked to the cholesterol/lipid pathway, were both overexpressed in MC1286PE3 and MC1286PE7 compared to MC1286PE1. FADS2 has been shown to be important for tumor cell lipid metabolism and is increased in most cancers, associated with poor survival outcomes 34 . LRPAP1 has been identified as a B cell receptor (BCR) antigen in mantle cell lymphoma (in both patient samples and cell lines), associated with proliferation by BCR pathway activation 35 . A recent study showed the prognostic value of cholesterol metabolism-related genes in MM 36 . Discussion The genetic diversity of a large number of cell lines derived from patients with MM have been an extremely valuable tool to understand the mechanisms and pathogenesis of the disease. Most existing models represent a single snapshot of the disease and do not capture the temporal dynamics of tumor evolution. In this study, we describe a series of five novel HMCLs derived from serial pleural effusion samples collected from a single patient with relapsed/refractory hyperdiploid MM, allowing us to comprehensively examine clonal evolution and molecular adaptation over the course of disease progression. By integrating genomic, transcriptomic, and proteomic profiling, we show key oncogenic events, including progressive RB1 loss and MYC dysregulation, emerging and evolving through disease progression, highlighting the dynamic complexity of MM biology. We found a progressive evolution of RB1 loss, progressing from monoallelic deletion to focal biallelic loss, and ultimately a large biallelic deletion of the entire gene. This coincided with loss of RB1 protein expression, consistent with previous observations linking biallelic RB1 loss to poor prognosis. This also provided evidence that biallelic RB1 loss may act as a late-stage driver event. In parallel, we identified a duplication telomeric to MYC that arose during intermediate stages of disease progression, associated with allele specific increased relative MYC expression. This duplication was subsequently lost in the terminal cell line, despite persistent MYC overexpression, suggesting that MYC activation may be maintained through other mechanisms during clonal evolution. Proteomic analysis revealed consistent upregulation of proteins involved in mTOR signaling, lipid and cholesterol metabolism, and cancer-testis antigen expression in later-stage cell lines, changes that may be associated with advanced and treatment refractory MM. Interestingly, despite originating from a hyperdiploid state, the serial MC1286 HMCLs exhibited dynamic changes in chromosomal copy number during disease progression, including loss and/or acquisition of partial or whole chromosomes. These findings suggest that hyperdiploidy in MM may not be a static genomic state, but rather dynamic and subject to positive and negative selection over time. Certain chromosomal gains may confer a selective advantage under specific biological contexts or therapeutic pressures, while others may be neutral or detrimental for the myeloma cell through disease progression. This study has several limitations. First, although the serial MC1286 HMCLs provide a unique longitudinal model of disease evolution, we lacked bone marrow biopsy samples at identical time points, limiting our ability to directly assess how the molecular profile of the pleural-derived HMCLs compared to the bone marrow. As a result, some observed molecular features may reflect site-specific evolution or adaptation. In addition, while these cell lines preserve many clinically relevant features for the patient’s disease, we cannot exclude the possibility that additional molecular changes arose during the process of immortalization and in vitro adaptation. Nonetheless, the molecular and evolutionary trends observed through this multi-omic approach, with the concordant clinical disease course, supports our model for studying late-stage MM through progression and therapy resistance. In addition, the molecular profiling results presented here are consistent with previous studies in MM showing that not all mutations in a given tumor are conserved over time 10 37 38 39 . Declarations Acknowledgments: We would like to acknowledge the patient who consented for this study. This work was supported in part by a grant from the NCI CPTAC program (U01CA271410) to AP, RF, and PLB. Author Contributions JEWN was responsible for summarizing patient data, extracting and analyzing data, interpreting results, writing the manuscript, and creating final figures. DLR was responsible for extracting and analyzing data, interpreting results, and contributed to writing the manuscript. CKS was responsible for extracting and analyzing data and interpreting results. YWA was responsible for extracting and analyzing data. MC generated the cell lines and obtained their genomic profiling. EB was responsible for overseeing the project and PLB was responsible for study design and overseeing the project. Data Availability Statement : Data available on request to corresponding author. Competing Interests: There are no competing financial interests in relation to the work described. References Kuehl, W. M. & Bergsagel, P. L. Early genetic events provide the basis for a clinical classification of multiple myeloma. Hematology Am Soc Hematol Educ Program , 346-352 (2005). https://doi.org/10.1182/asheducation-2005.1.346 Corre, J. et al. Multiple myeloma clonal evolution in homogeneously treated patients. Leukemia 32 , 2636-2647 (2018). https://doi.org/10.1038/s41375-018-0153-6 Kuehl, W. M. & Bergsagel, P. L. Molecular pathogenesis of multiple myeloma and its premalignant precursor. J Clin Invest 122 , 3456-3463 (2012). https://doi.org/10.1172/JCI61188 Jovanović, K. K. et al. Targeting MYC in multiple myeloma. Leukemia 32 , 1295-1306 (2018). https://doi.org/10.1038/s41375-018-0036-x Misund, K. et al. MYC dysregulation in the progression of multiple myeloma. Leukemia 34 , 322-326 (2020). https://doi.org/10.1038/s41375-019-0543-4 Affer, M. et al. Promiscuous MYC locus rearrangements hijack enhancers but mostly super-enhancers to dysregulate MYC expression in multiple myeloma. Leukemia 28 , 1725-1735 (2014). https://doi.org/10.1038/leu.2014.70 Maura, F. et al. Genomic landscape and chronological reconstruction of driver events in multiple myeloma. Nat Commun 10 , 3835 (2019). https://doi.org/10.1038/s41467-019-11680-1 Chesi, M. et al. Monosomic Loss of MIR15A/MIR16-1 Is a Driver of Multiple Myeloma Proliferation and Disease Progression. Blood Cancer Discovery 1 , 68-81 (2020). https://doi.org/10.1158/0008-5472.Bcd-19-0068 Binder, M. et al. Prognostic implications of abnormalities of chromosome 13 and the presence of multiple cytogenetic high-risk abnormalities in newly diagnosed multiple myeloma. Blood Cancer Journal 7 , e600-e600 (2017). https://doi.org/10.1038/bcj.2017.83 Keats, J. J. et al. Clonal competition with alternating dominance in multiple myeloma. Blood 120 , 1067-1076 (2012). https://doi.org/10.1182/blood-2012-01-405985 Kortüm, K. M. et al. Targeted sequencing of refractory myeloma reveals a high incidence of mutations in CRBN and Ras pathway genes. Blood 128 , 1226-1233 (2016). https://doi.org/10.1182/blood-2016-02-698092 Kortuem, K. M. et al. Panel sequencing for clinically oriented variant screening and copy number detection in 142 untreated multiple myeloma patients. Blood Cancer J 6 , e397 (2016). https://doi.org/10.1038/bcj.2016.1 Hansen, K. D., Irizarry, R. A. & Wu, Z. Removing technical variability in RNA-seq data using conditional quantile normalization. Biostatistics 13 , 204-216 (2012). https://doi.org/10.1093/biostatistics/kxr054 Mangalaparthi, K. K. et al. Molecular Characterization of Esophageal Squamous Cell Carcinoma Using Quantitative Proteomics. Cancers (Basel) 15 (2023). https://doi.org/10.3390/cancers15133302 Häcker, H., Tseng, P.-H. & Karin, M. Expanding TRAF function: TRAF3 as a tri-faced immune regulator. Nature Reviews Immunology 11 , 457-468 (2011). https://doi.org/10.1038/nri2998 Keats, J. J. et al. Promiscuous mutations activate the noncanonical NF-kappaB pathway in multiple myeloma. Cancer Cell 12 , 131-144 (2007). https://doi.org/10.1016/j.ccr.2007.07.003 Kortuem, K. M. et al. Panel sequencing for clinically oriented variant screening and copy number detection in 142 untreated multiple myeloma patients. Blood Cancer Journal 6 , e397-e397 (2016). https://doi.org/10.1038/bcj.2016.1 de Leval, L. et al. Genomic profiling for clinical decision making in lymphoid neoplasms. Blood 140 , 2193-2227 (2022). https://doi.org/10.1182/blood.2022015854 Standal, T. et al. Osteopontin is an adhesive factor for myeloma cells and is found in increased levels in plasma from patients with multiple myeloma. Haematologica 89 , 174-182 (2004). Nevins, J. R. The Rb/E2F pathway and cancer. Human Molecular Genetics 10 , 699-703 (2001). https://doi.org/10.1093/hmg/10.7.699 Engeland, K. Cell cycle regulation: p53-p21-RB signaling. Cell Death & Differentiation 29 , 946-960 (2022). https://doi.org/10.1038/s41418-022-00988-z Wang, Y. et al. Targeting mTOR signaling pathways in multiple myeloma: biology and implication for therapy. Cell Communication and Signaling 22 , 320 (2024). https://doi.org/10.1186/s12964-024-01699-3 Tai, Y.-T. et al. CD40 induces human multiple myeloma cell migration via phosphatidylinositol 3–kinase/AKT/NF-κB signaling. Blood 101 , 2762-2769 (2003). https://doi.org/10.1182/blood-2002-09-2813 Han, T.-S., Kim, D.-S., Son, M.-Y. & Cho, H.-S. SMYD family in cancer: epigenetic regulation and molecular mechanisms of cancer proliferation, metastasis, and drug resistance. Experimental & Molecular Medicine 56 , 2325-2336 (2024). https://doi.org/10.1038/s12276-024-01326-8 Fasano, C. et al. Identifying novel SMYD3 interactors on the trail of cancer hallmarks. Computational and Structural Biotechnology Journal 20 , 1860-1875 (2022). https://doi.org/https://doi.org/10.1016/j.csbj.2022.03.037 Garofano, F. et al. Identification of CD320, SLC44A1 and TNFRSF13B as potential novel therapeutic targets for CAR T-cell therapy in multiple myeloma. Frontiers in Medicine Volume 12 - 2025 (2026). https://doi.org/10.3389/fmed.2025.1737919 Shu, L. et al. CD320 Regulates Bone Marrow Angiogenesis in Multiple Myeloma Via HGF-ERK Axis. Blood 140 , 12466-12467 (2022). https://doi.org/10.1182/blood-2022-169581 Garofano, F. et al. MM-055: Identification of CD320, CD92, and CD267 as Potential Novel Therapeutic Targets for CAR-T Therapy in Multiple Myeloma. Clinical Lymphoma Myeloma and Leukemia 25 , S891 (2025). https://doi.org/https://doi.org/10.1016/S2152-2650(25)02548-0 Yilmaz-Ozcan, S. et al. Epigenetic Mechanisms Underlying the Dynamic Expression of Cancer-Testis Genes, PAGE2, -2B and SPANX-B, during Mesenchymal-to-Epithelial Transition. PLOS ONE 9 , e107905 (2014). https://doi.org/10.1371/journal.pone.0107905 Ohlstrom, D. et al. Longitudinal multi-omic profiling uncovers immune escape and predictors of response in multiple myeloma. bioRxiv , 2025.2005.2027.656392 (2025). https://doi.org/10.1101/2025.05.27.656392 Atanackovic, D. et al. Cancer-testis antigens are commonly expressed in multiple myeloma and induce systemic immunity following allogeneic stem cell transplantation. Blood 109 , 1103-1112 (2006). https://doi.org/10.1182/blood-2006-04-014480 Jungbluth, A. A. et al. The cancer-testis antigens CT7 (MAGE-C1) and MAGE-A3/6 are commonly expressed in multiple myeloma and correlate with plasma-cell proliferation. Blood 106 , 167-174 (2005). https://doi.org/10.1182/blood-2004-12-4931 van Duin, M. et al. Cancer testis antigens in newly diagnosed and relapse multiple myeloma: prognostic markers and potential targets for immunotherapy. Haematologica 96 , 1662-1669 (2011). https://doi.org/10.3324/haematol.2010.037978 Chen, E., Wang, C., Lv, H. & Yu, J. The role of fatty acid desaturase 2 in multiple tumor types revealed by bulk and single-cell transcriptomes. Lipids in Health and Disease 22 , 25 (2023). https://doi.org/10.1186/s12944-023-01789-0 Thurner, L. et al. LRPAP1 is a frequent proliferation-inducing antigen of BCRs of mantle cell lymphomas and can be used for specific therapeutic targeting. Leukemia 33 , 148-158 (2019). https://doi.org/10.1038/s41375-018-0182-1 Zhao, N. et al. Identification of a cholesterol metabolism-related prognostic signature for multiple myeloma. Sci Rep 13 , 19395 (2023). https://doi.org/10.1038/s41598-023-46426-z Landau, H. J. et al. Accelerated single cell seeding in relapsed multiple myeloma. Nature Communications 11 , 3617 (2020). https://doi.org/10.1038/s41467-020-17459-z Rasche, L. et al. The spatio-temporal evolution of multiple myeloma from baseline to relapse-refractory states. Nature Communications 13 , 4517 (2022). https://doi.org/10.1038/s41467-022-32145-y Maura, F. et al. Temporal genomic dynamics shape clinical trajectory in multiple myeloma. Nature Genetics 57 , 2203-2214 (2025). https://doi.org/10.1038/s41588-025-02292-1 Additional Declarations There is NO conflict of interest to disclose. Supplementary Files SupplementalTable2DifferentialanalysisMC1283celllines102525.xlsx Supplemental Table 2 20251202supplementaryfigures.pptx Supplemental Figure 1, Supplemental Figure 2, Supplemental Figure 3, Supplemental Figure 4, Supplemental Figure 5, Supplemental Figure 6, Supplemental Figure 7, Supplemental Table 1 Cite Share Download PDF Status: Under Review Version 1 posted Review # 3 received at journal 16 Apr, 2026 Reviewer # 3 agreed at journal 14 Apr, 2026 Reviewer # 2 agreed at journal 13 Mar, 2026 Reviewer # 1 agreed at journal 05 Mar, 2026 Reviewers invited by journal 05 Mar, 2026 Editor assigned by journal 05 Mar, 2026 Submission checks completed at journal 05 Mar, 2026 First submitted to journal 03 Mar, 2026 Unknown event 02 Mar, 2026 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-9003051","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":601383194,"identity":"50f58861-3b7e-48eb-af07-299b733a0187","order_by":0,"name":"P. Leif Bergsagel","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIie3OsUrDQBzH8f9xcFmMccykr5BSaCktvojLvwTOxVs71bYSOJfUOZO+gr5BQiFZJK6BDgaEglu7lFssvYIFl6S6Cb3vdD+4D/wBTKZ/mLd/nAKdAPZ635P9gjAgmnD+VwJ8dpi0renHpxrenmtyV5b4duVEfgzLwaySdMKs1T1Js6YmgYc4F1HBkUR5NfEKzprA0r4EIt2+motJceNRW9aQ9wVrqE061uReIebiaUe+6kjBaGnLIerDJCDG4nlHSA3phJxR+yFuSEoCF9EXL68LTML8upK0rZSu1Hp04VhBslJ4KR4zPynVoFtJdMwF0GfQ/T5DiOv+6+gSYPRjO4eAyWQyHVtbcK9arE2gje8AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-1523-7388","institution":"Mayo Clinic in Arizona","correspondingAuthor":true,"prefix":"","firstName":"P.","middleName":"Leif","lastName":"Bergsagel","suffix":""},{"id":601383195,"identity":"2a9430a6-f055-4159-9dad-2375b8656ce0","order_by":1,"name":"Julia E. Wiedmeier-Nutor","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Julia","middleName":"E.","lastName":"Wiedmeier-Nutor","suffix":""},{"id":601383196,"identity":"3b6cabb7-e283-4324-a5d0-c41e40667f0c","order_by":2,"name":"Daniel Riggs","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Riggs","suffix":""},{"id":601383197,"identity":"7d889126-e040-4eb1-a5a4-4ddcd19169ba","order_by":3,"name":"Caleb Stein","email":"","orcid":"https://orcid.org/0000-0001-9561-140X","institution":"Mayo Clinic, USA","correspondingAuthor":false,"prefix":"","firstName":"Caleb","middleName":"","lastName":"Stein","suffix":""},{"id":601383198,"identity":"4956ab76-d274-4051-8697-f0c7c1da46ce","order_by":4,"name":"Kiran Mangalaparthi","email":"","orcid":"https://orcid.org/0000-0002-5791-727X","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Kiran","middleName":"","lastName":"Mangalaparthi","suffix":""},{"id":601383199,"identity":"7d939d92-56be-4db5-9aa0-46cb8e8d0231","order_by":5,"name":"Richard Kandasamy","email":"","orcid":"https://orcid.org/0000-0002-0894-1222","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"","lastName":"Kandasamy","suffix":""},{"id":601383200,"identity":"e0ef612e-273e-4341-aa9e-346ffef86507","order_by":6,"name":"Sochilt Brown","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Sochilt","middleName":"","lastName":"Brown","suffix":""},{"id":601383201,"identity":"357005f4-5079-4021-9ec9-085f880c0191","order_by":7,"name":"Karl Franke","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Karl","middleName":"","lastName":"Franke","suffix":""},{"id":601383202,"identity":"f70dee2f-dfd9-4ff6-ba27-ad6d110bf864","order_by":8,"name":"Yuliza Shotts","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Yuliza","middleName":"","lastName":"Shotts","suffix":""},{"id":601383203,"identity":"b3540306-dfdf-4a52-8a74-bea53550298b","order_by":9,"name":"Juana Fiueroa Aguirre","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Juana","middleName":"Fiueroa","lastName":"Aguirre","suffix":""},{"id":601383204,"identity":"f1d357cc-e414-4532-b15d-579c126c80b0","order_by":10,"name":"Yan Asmann","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Asmann","suffix":""},{"id":601383205,"identity":"b784cbea-838d-4e44-8626-c6083d6128d2","order_by":11,"name":"Esteban Braggio","email":"","orcid":"https://orcid.org/0000-0003-3860-4830","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Esteban","middleName":"","lastName":"Braggio","suffix":""},{"id":601383206,"identity":"9d09133e-4ad6-4a17-836a-797f6d292914","order_by":12,"name":"Rafael Fonseca","email":"","orcid":"","institution":"Mayo Clinic in Arizona","correspondingAuthor":false,"prefix":"","firstName":"Rafael","middleName":"","lastName":"Fonseca","suffix":""},{"id":601383207,"identity":"adcb980c-9b9b-4014-96fe-5580b7db9c6a","order_by":13,"name":"Akhilesh Pandey","email":"","orcid":"https://orcid.org/0000-0001-9943-6127","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Akhilesh","middleName":"","lastName":"Pandey","suffix":""},{"id":601383208,"identity":"e4f56488-1280-4be8-940a-9d569ef8d60a","order_by":14,"name":"Marta chesi","email":"","orcid":"https://orcid.org/0000-0002-4024-8225","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Marta","middleName":"","lastName":"chesi","suffix":""}],"badges":[],"createdAt":"2026-03-01 17:15:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9003051/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9003051/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104470597,"identity":"5d22f222-9df8-47c8-b2fe-b377e1c7f085","added_by":"auto","created_at":"2026-03-12 07:22:14","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":184743,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Slide1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9003051/v1/a931df7ee2e5bd3263793934.jpg"},{"id":104780391,"identity":"1e572361-b0fe-49b3-8657-1ab4e57755b5","added_by":"auto","created_at":"2026-03-17 07:52:45","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":160998,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Slide2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9003051/v1/01be3bdbb69bea7cbc6c3672.jpg"},{"id":104470601,"identity":"07e1b6b4-00ea-4939-b738-856d02b71ed9","added_by":"auto","created_at":"2026-03-12 07:22:14","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":149772,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Slide3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9003051/v1/5aeb4aad11ef053249140183.jpg"},{"id":104470603,"identity":"40521c43-ad66-446c-ad22-10f62197233d","added_by":"auto","created_at":"2026-03-12 07:22:15","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":138231,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Slide4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9003051/v1/36b6ec805b3d0269b7647549.jpg"},{"id":104780752,"identity":"bc8c5d1e-35ad-45e1-a9da-990000295554","added_by":"auto","created_at":"2026-03-17 07:53:47","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":136362,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Slide5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9003051/v1/eba71bd0d0bcf774cdfcaf51.jpg"},{"id":104470599,"identity":"6b70f236-452b-4d05-88ad-70c7ccb5668c","added_by":"auto","created_at":"2026-03-12 07:22:14","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3405617,"visible":true,"origin":"","legend":"Supplemental Table 2","description":"","filename":"SupplementalTable2DifferentialanalysisMC1283celllines102525.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9003051/v1/7ceece7d0c1c2b6f5aea82f7.xlsx"},{"id":104470604,"identity":"9c4651c4-6335-454b-add2-f3a81cf64210","added_by":"auto","created_at":"2026-03-12 07:22:15","extension":"pptx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":12382500,"visible":true,"origin":"","legend":"Supplemental Figure 1, Supplemental Figure 2, Supplemental Figure 3, Supplemental Figure 4, Supplemental Figure 5, Supplemental Figure 6, Supplemental Figure 7, Supplemental Table 1","description":"","filename":"20251202supplementaryfigures.pptx","url":"https://assets-eu.researchsquare.com/files/rs-9003051/v1/dadd15a074974195c4350140.pptx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"Establishment and characterization of multiple myeloma cell lines from a single patient’s disease course immortalizes clonal heterogeneity","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMultiple myeloma (MM) is a plasma cell neoplasm, representing 10% of all hematologic malignancies. Despite improvements in MM treatments, the prognosis is variable, due to the heterogeneous nature of the disease. Genetic aberrations are common in MM and can be classified into primary and secondary events \u003csup\u003e1\u003c/sup\u003e. Primary genetic events include chromosome translocations involving immunoglobulin genes (mostly IgH, cut also IgK and IgL) with multiple partners and gains of multiple odd numbered chromosomes (hyperdiploidy, HRD), found in approximately half of patients, each of which are primarily mutually exclusive. Secondary genetic events arise in sub-clonal cells and can lead to progression and therapy resistance \u003csup\u003e2\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e3\u003c/sup\u003e. \u003c/p\u003e\n\u003cp\u003eSome common secondary aberrations include rearrangements of the \u003cem\u003eMYC\u003c/em\u003e locus and loss of chromosome 13q. MYC is a transcription factor that orchestrates and promotes numerous aspects of cell growth and is a key driver in oncogenesis in MM \u003csup\u003e4\u003c/sup\u003e. The presence of \u003cem\u003eMYC\u003c/em\u003e structural variations (SV) are common in newly diagnosed MM (42%) but very rare in the early phase of monoclonal gammopathy of undetermined significance (MGUS), suggesting it plays a prominent role in tumor progression \u003csup\u003e5\u003c/sup\u003e. These \u003cem\u003eMYC\u003c/em\u003e SVs involve chromosomal structural changes that allow immunoglobulin enhancers/super-enhancers access to the \u003cem\u003eMYC\u003c/em\u003e locus, leading to its dysregulation \u003csup\u003e6\u003c/sup\u003e. In addition to translocations, amplification of the \u003cem\u003eMYC\u003c/em\u003e locus can occur in MM \u003csup\u003e5\u003c/sup\u003e \u003csup\u003e6\u003c/sup\u003e \u003csup\u003e7\u003c/sup\u003e. Monosomy of chromosome 13 results in loss of one copy of MIR15A/MIR16-1 which has been shown to accelerate disease progression in mice \u003csup\u003e8\u003c/sup\u003e. In addition biallelic loss of \u003cem\u003eRB1\u003c/em\u003e at 13q14 is associated with aggressive disease progression in patients with MM \u003csup\u003e8\u003c/sup\u003e \u003csup\u003e9\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eMuch of our current understanding of MM genetics, epigenetics, pathogenesis and response or resistance to treatment has come from studying human MM-derived cell lines (HMCL). Interestingly, cell lines with hyperdiploid cytogenetics are rare. In addition, most HMCL were developed before lenalidomide, pomalidomide, and carfilzomib were approved for treatment of MM. In this study we use microarrays, next generation sequencing (NGS), and mass spectrometry-based proteomics to characterize a set of cell lines established from serial samples from a relapsed/refractory MM patient with recurrent, malignant, pleural effusions. In this series, a duplication telomeric to\u003cem\u003e MYC\u003c/em\u003e emerged in the second cell line and was associated with increased MYC expression but was subsequently lost in the final cell line obtained shortly before the patient’s death. An \u003cem\u003eRB1\u003c/em\u003e deletion, which was initially monoallelic in the first cell line, progressed to a large biallelic deletion in the final cell line, as the patient’s disease progressed. We also identified large \u003cem\u003eFAM46C\u003c/em\u003e and \u003cem\u003eTRAF3\u003c/em\u003e deletions, several single nucleotide variants (SNVs), and altered gene expression in several other oncogenes and tumor suppressor genes. Further, proteomic analysis of three of the cell lines identified dysregulation of several proteins of interest, including increased abundance of MYC, CDKN2A, MTOR, and cancer testes antigens, and loss of RB1. This series of established cell lines enabled detailed characterization of molecular changes and underlying genetics of MM through disease progression.\u003c/p\u003e"},{"header":"Materials/Subjects and Methods","content":"\u003cp\u003e\u003cem\u003eCollection of patient information:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eInstitutional review board approval and patient consent was obtained prior to clinical and patient sample collection (IRB 919-04).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCell culture\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSeven serial samples were collected from the patient’s recurrent pleural effusions at relapse (MC1286PE1-PE7). Only MC1286PE1, PE2, PE3, PE5 and PE7 survived \u003cem\u003ein vitro\u003c/em\u003e. All cell lines were grown in RPMI-1640 medium supplemented with 10% fetal calf serum. \u003c/p\u003e\n\u003cp\u003e\u003cem\u003eArray comparative genomic hybridization (aCGH)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePreparation of samples for aCGH was completed as previously described \u003csup\u003e10\u003c/sup\u003e. Labeled DNA was hybridized to 244A Human Genome CGH Microarrays (Agilent) according to the manufacturer's suggestion. All samples were viewed IGV. The copy number of each sample was normalized to two copies (log2(tumor/normal)). IGV defaults to a blue-to-red scale that corresponds to copy numbers from -1.5 to 1.5 (IGV).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMate pair libraries\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMate pair library and sequencing were carried out as previously described \u003csup\u003e6\u003c/sup\u003e. Briefly, the Illumina Mate Pair Library Preparation Kit was used for library construction following the manufacturer’s instructions. Two samples (MC1286PE2 and MC1286PE5) were run on one lane of an Illumina HiSeq 2000 with 50bp reads. BAM files were analyzed in search for clusters of discordant read pairs. Breakpoints supported by more than five discordant read pairs were identified. \u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCustom capture\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMethods for custom capture have been previously described \u003csup\u003e5\u003c/sup\u003e. Briefly, a targeted capture approach was used to sequence the regions adjacent to the immunoglobulin enhancers (IGH3’RR1 and 2, IGK, and IGL) as well as \u003cem\u003eMYC\u003c/em\u003e, \u003cem\u003eRB1, TRAF3, \u003c/em\u003eand \u003cem\u003eTENT5A/FAM46C \u003c/em\u003eand 80 other genes previously described \u003csup\u003e5\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIon Torrent next generation sequencing \u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMethods for Ion Torrent NGS have been previously described \u003csup\u003e11\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e12\u003c/sup\u003e. Briefly, 20 ng of DNA were used to prepare 200 bp libraries. Template preparation and enrichment of DNA libraries was done on the Ion OneTouch2 and Ion OneTouch ES automated system. Samples were barcoded, pooled, and sequenced on Ion 318v2 chips using the Ion Sequencing 200 Kit v2. The generated sequencing data were analyzed using the Ion Reported Software v1.6, then visualized and manually reviewed using the Integrative Genomics Viewer (IGV). Linked-read 10X genomics sequencing was performed on MC1286PE5 to confirm Mate Pair SVs and was conducted following the manufacturer's protocol. \u003c/p\u003e\n\u003cp\u003e\u003cem\u003eWhole genome sequencing \u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWhole-genome sequencing (WGS) was performed at the \u003cstrong\u003eTranslational Genomics Research Institute (TGen, Phoenix, AZ)\u003c/strong\u003e following institutional standard operating procedures for high-quality genomic DNA analysis. Genomic DNA was extracted from bone marrow mononuclear cells or purified plasma cells using the \u003cstrong\u003eQiagen AllPrep DNA/RNA Kit\u003c/strong\u003e, and DNA quality and quantity were assessed by \u003cstrong\u003eQubit fluorometry\u003c/strong\u003e and \u003cstrong\u003eAgilent TapeStation\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eSequencing libraries were prepared using the Illumina TruSeq DNA PCR-Free Library Preparation Kit according to the manufacturer’s protocol. Libraries were quantified by qPCR and pooled equimolarly. Paired-end sequencing (2 × 150 bp reads) was performed on the Illumina NovaSeq 6000 platform, targeting an average depth of 60× for tumor samples and 30× for matched normal controls. Base calling and demultiplexing were performed using Illumina bcl2fastq software (v2.20), and raw FASTQ files were subjected to downstream analysis.\u003c/p\u003e\n\u003cp\u003eReads were aligned to the GRCh38/hg38 human reference genome using BWA-MEM (v0.7.17). Alignment files were processed with Picard Tools to mark duplicates and GATK (v4.4.0) for base quality recalibration. Copy number alterations were inferred with CNVkit. \u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRNA-seq processing and analysis \u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eReads per kilobase million (RPKM) across expressed genes in 70 cell lines previously run were normalized by conditional quantile normalization method to achieve similar count distributions across samples and to remove GC-content bias \u003csup\u003e13\u003c/sup\u003e. A uniform manifold approximation and projection (umap) visualization of the top principal components of highly expressed protein coding genes revealed distinctions and similarities in the expression profiles of all cell lines. \u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDownstream Analyses\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAnnotated variants were integrated with RNA-seq and copy-number data where available to identify driver mutations, structural rearrangements, and mutational signatures. Recurrent alterations were visualized using IGV and summarized with R (v4.3).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAllele-specific expression of MYC RNA\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ecDNA, prepared by standard methods from RNA isolated from the five established MC1286 cells lines, was Sanger sequenced and the relative expression of the alleles determined.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eImmunomodulatory treatment (IMiD) of MC1286 cells lines \u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCell lines were treated for three days with Pomalidomide (200 nM) to determine effects on cell proliferation. Proliferation was measured by live cell count of trypan blue-stained cells with a hemocytometer. The proliferation index was defined as cell proliferation of treated culture (final cell number minus inoculum number) normalized to the proliferation of the untreated control. \u003c/p\u003e\n\u003cp\u003e\u003cem\u003eQuantitative proteomic analysis of MC1286 cell lines\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe cell line pellets were processed for label-free quantitative proteomic approach as described before \u003csup\u003e14\u003c/sup\u003e for cell lines MC1286PE1, MC1286PE3, and MC1286PE7. In brief, protein extraction was performed, and it was followed by overnight trypsin digestion to generate peptides. Digested peptides were cleaned using C\u003csub\u003e18\u003c/sub\u003e stage-tips, lyophilized and stored at -80°C until mass spectrometry analysis. 200 ng of peptides from each cell line was subjected to LC-MS/MS analysis in triplicate injections. \u003c/p\u003e\n\u003cp\u003eThe LC-MS/MS analysis was performed using an Orbitrap Astral mass spectrometer (Thermo Scientific, San Jose, USA) coupled to a Thermo Vanquish Neo HPLC system operated in data independent acquisition mode. Protein identification and quantitation was performed using DIA-NN software (v2.3) using an in silico spectral library generated from human UniProt protein database. False discovery rate was controlled to 1% at protein level. Protein normalization was performed within DIA-NN software using global normalization strategy. \u003c/p\u003e\n\u003cp\u003eData analysis was performed using Perseus computational platform (v 2.1.5). Proteins detected in at least two replicates were retained following log transformation, and missing values were imputed. Differential expression analysis was performed using two sample student’s T test comparing MC1286PE3 and MC1286PE7 cell lines with MC1286PE1.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003ePatient Information and establishment of MC1286 cell lines \u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe patient was a 73-year-old female with IgG kappa MM with plasmacytomas \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(Supplemental Figure 1\u003c/strong\u003e). At diagnosis, bone marrow biopsy FISH revealed trisomies 3 and 7 and monosomies 13 and 14. MM treatment, response and sample collection for establishment of five MC1286 cell lines are detailed in \u003cstrong\u003eFigure 1A\u003c/strong\u003e. MC1286PE1T was a pleural fluid sample collected following relapse from autologous stem cell transplant. Subsequent samples were collected after relapsing on successive myeloma directed therapy combination regimens. All samples for cell culture were obtained from the patient’s recurrent, malignant, pleural effusion. WGS analysis of the MC1286 cell lines revealed a hyperdiploid (HRD) state (gain of chromosomes 3, 7 and monosomy 13, 14, \u003cstrong\u003eFigure 1B\u003c/strong\u003e), consistent with the patient’s bone marrow FISH results at diagnosis. In addition, WGS identified gain 1q, del(1p), and partial gain of chromosomes 5, 9, 11, 19, and 21. Interestingly, there were alterations and differences in the trisomies characteristics of hyperdiploidy in the five cell lines, although it is clear from the copy number changes that both major subclones are present in the original pleural fluid sample (PE1T) (\u003cstrong\u003eFigure 1B\u003c/strong\u003e). For example, there was a partial gain of chromosome 7 in MC1286PE3, complete gain of chromosome 7 in MC1286PE5 and MC1286PE7. There was also loss of partial gain of chromosome 9, chromosome 11, and chromosome 19 in MC1286PE7. \u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEmergence of an RB1 biallelic deletion and other structural variants in MC1286 cell lines is associated with disease progression\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWGS revealed one copy of \u003cem\u003eRB1\u003c/em\u003e at relapse/progression following autologous stem cell transplant (MC1286PE1). In contrast, a biallelic focal \u003cem\u003eRB1\u003c/em\u003e deletion at the 3’ end of the gene (approximately 12 kb, covering exons 24 - 27) was identified in subsequent cell lines (MC1286PE2, MC1286PE3, and MC1286PE5, \u003cstrong\u003eSupplemental Figure 2A\u003c/strong\u003e). MC1286PE7 had a larger biallelic deletion that removed all but the last exon (exon 27) (\u003cstrong\u003eSupplemental Figure 2A)\u003c/strong\u003e. RB1 protein was detected only in MC1286PE1 by unbiased global proteomics analysis and was not detected in MC1286PE3 and MC1286PE7 cell lines, consistent with genetic findings. We also identified partial biallelic deletions of the terminal end of \u003cem\u003eTRAF3 \u003c/em\u003e(approximately 150 kb, covering exons 11 and 12) and monoallelic deletions of \u003cem\u003eTENT5C\u003c/em\u003e (approximately 60 kb, covering exon 2) in all cell lines (\u003cstrong\u003eSupplemental Figure 2B\u003c/strong\u003e). TRAF3 is a NFkB negative regulator whose common deletion/mutation in MM patients induces constitutive NFkB signaling and 3’ deletion of TRAF3 induces protein degradation \u003csup\u003e15\u003c/sup\u003e \u003csup\u003e16\u003c/sup\u003e. \u003cem\u003eTENT5C\u003c/em\u003e (which encodes the protein FAM46C), is a poly(A) RNA polymerase that enhances mRNA stability and gene expression, and variants are common in smoldering myeloma (SMM) and MGUS \u003csup\u003e5\u003c/sup\u003e \u003csup\u003e7\u003c/sup\u003e \u003csup\u003e17\u003c/sup\u003e. While TRAF3 protein was not identified, FAM46C protein abundance remained unchanged in proteomic analysis of MC1286 cell lines.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIdentification of a duplication telomeric of MYC \u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMC1286 cell lines established from the pleural effusions collected after subsequent, sequential combination therapies (MC1286PE2, MC1286PE3, MC1286PE5, see \u003cstrong\u003eFigure 1A\u003c/strong\u003e for details regarding therapies) were found to have a 420 kb bp duplication located about half a megabase telomeric of \u003cem\u003eMYC. \u003c/em\u003eBoth mate pair and linked-read sequencing detected the duplication, but unexpectedly not custom capture (\u003cstrong\u003eSupplemental Figure 3A\u003c/strong\u003e). To delineate the boundaries of the duplication, oligonucleotides flanking the approximate breakpoint were used to amplify the region by PCR. Sanger sequencing identified the precise breakpoint which indicated that the duplicated region spanned position 129,207,090 to 129,628,867 (\u003cstrong\u003eSupplemental Figure 4A\u003c/strong\u003e). Breakpoint PCR analysis identified the duplication in cell lines MC1286PE2, MC1286PE3, and MC1286PE5, but not in MC1286PE1 or MC1286PE7 (\u003cstrong\u003eSupplemental Figure 4B\u003c/strong\u003e). \u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDuplications telomeric of MYC are associated with increased MYC expression \u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo distinguish expression of the two \u003cem\u003eMYC\u003c/em\u003e alleles, a heterozygous \u003cem\u003eMYC\u003c/em\u003e single nucleotide variant (SNV) in the promoter region (c.1350G\u0026gt;A) was used. Linked-read sequencing established that the \u003cem\u003eMYC\u003c/em\u003e c.1350A allele was linked to the telomeric duplication (\u003cstrong\u003eFigure 2A, B and D\u003c/strong\u003e). Sequencing of bulk cDNA showed that the \u003cem\u003eMYC\u003c/em\u003e c.1350A allele was preferentially transcribed only in MC1286PE2, MC1286PE3, and MC1286PE5 cell lines, while both were equally expressed in MC1286PE1 and MC1286PE7 (\u003cstrong\u003eFigure 2C\u003c/strong\u003e). These results directly demonstrate the \u003cem\u003ecis\u003c/em\u003e-enhancing activity of the telomeric duplication on the adjacent \u003cem\u003eMYC\u003c/em\u003e gene. In line with these findings, MYC protein abundance was mildly higher in MC1286PE3 (0.52 log\u003csub\u003e2\u003c/sub\u003e fold-change) and also in MC1286PE7 compared to MC1286PE1 (0.7 log\u003csub\u003e2\u003c/sub\u003e fold-change). \u003c/p\u003e\n\u003cp\u003e\u003cem\u003eResistance to IMiD therapy correlated with patient disease refractory to IMiD therapy\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eImmunomodulatory (IMiD) agents (Lenalidomide and Pomalidomide) have been used for MM treatment for years. One important aspect of their anti-tumor activity is that they direct degradation of the enhancer-binding transcription factors Ikaros and Ailos which are involved in the dysregulation of \u003cem\u003eMYC\u003c/em\u003e. MC1286 cell lines were treated with Pomalidomide for three days to determine the effects on cell proliferation. We found that IMiD resistant cell lines correlated with the patient’s disease progression while on IMiD therapy (MC1286PE1 and MC1286PE3) (\u003cstrong\u003eSupplemental\u003c/strong\u003e \u003cstrong\u003eTable 1\u003c/strong\u003e). IMiD sensitive cell lines (MC1286PE2 and MC1286PE7) were collected when the patient was not on IMiD therapy. In cell lines with increased \u003cem\u003eMYC\u003c/em\u003e expression (MC1286PE2 and MC1286PE3), only MC1286PE2 showed sensitivity to IMiD therapy (\u003cstrong\u003eSupplemental\u003c/strong\u003e \u003cstrong\u003eTable 1\u003c/strong\u003e). \u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIdentification of significant single nucleotide variants (SNVs) \u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSignificant SNVs were found in \u003cem\u003eNRAS\u003c/em\u003e, \u003cem\u003eTENT5A\u003c/em\u003e, \u003cem\u003eACTG1\u003c/em\u003e, \u003cem\u003eTLR4\u003c/em\u003e, \u003cem\u003eIFNGR2\u003c/em\u003e, \u003cem\u003eIDH3A\u003c/em\u003e, and \u003cem\u003eDUSP2 \u003c/em\u003ein all cell lines(\u003cstrong\u003eFigure 3A\u003c/strong\u003e). While all other significant SNVs were relatively stable with disease progression, \u003cem\u003eDUSP2 \u003c/em\u003ec.388+5G\u0026gt;A variant allele frequency increased as the patient’s disease progressed. Despite this increase, the variant was predicted to have minimal impact on splicing by SpliceAI and was not associated with alternative or abnormal splice isoforms. A summary of SVs and SNVs are summarized in \u003cstrong\u003eFigure 3B\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eVariability between MC1286 cell lines and other HMCLs\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePrincipal component analysis (PCA) of RNA-seq data from MC1286 cell lines, approximately 70 other HMCLs, and 825 samples from the publicly available CoMMpass cohort (NCT01454297) revealed that the MC1286 cell lines clustered closest to samples without IgH translocations and without trisomy of chromosome 11, that have frequent monosomy 13 and 14 that we have called nHRD2 (\u003cstrong\u003eFigure 4A and Supplemental Table 2\u003c/strong\u003e) \u003csup\u003e8\u003c/sup\u003e \u003csup\u003e18\u003c/sup\u003e. Despite distinct clustering of MC1286 cell lines compared to other HMCLs, we found divergent expression of genes commonly dysregulated in MM between MC1286 cell lines (\u003cstrong\u003eFigure 4B\u003c/strong\u003e). MC1286 cell lines progressively decreased expression of \u003cem\u003eRB1\u003c/em\u003e from MC1286PE1 to MC1286PE7, consistent with the telomeric biallelic and complete biallelic \u003cem\u003eRB1\u003c/em\u003e deletion in MC1286PE2, MC1286PE3, MC1286PE5, and MC1286PE7, respectively. In MC1286PE7, the very low expression of \u003cem\u003eRB1\u003c/em\u003e was associated with decreased \u003cem\u003eCCND2\u003c/em\u003e expression. \u003c/p\u003e\n\u003cp\u003eWe found relative increased expression of \u003cem\u003eMYC\u003c/em\u003e in cell lines with the duplication telomeric to \u003cem\u003eMYC\u003c/em\u003e (MC1286PE2, MC1286PE3, and MC1286PE5, \u003cstrong\u003eFigure 4B\u003c/strong\u003e). The NFkB index (NFkBi) was higher in cell lines without \u003cem\u003eMYC\u003c/em\u003e SV (MC1286PE1 and MC1286PE7), consistent with previously reported results, which showed an inverse relationship between high \u003cem\u003eMYC\u003c/em\u003e expression and NFkBi \u003csup\u003e5\u003c/sup\u003e. High expression of \u003cem\u003eIL6\u003c/em\u003e was found in MC1286PE2, MC1286PE3, and MC1286PE5 but plummeted in MC1286PE7. Interestingly, decreased \u003cem\u003eIL6\u003c/em\u003e expression was associated with increased pSTAT3 suggesting that this cell line’s activation of STAT3 was independent of IL6 signaling (\u003cstrong\u003eSupplemental Figure 5\u003c/strong\u003e). Osteopontin (\u003cem\u003eSPP1\u003c/em\u003e), a bone matrix glycoprotein involved in angiogenesis, cell survival and tumor progression, was highly expressed in MC1286PE7 \u003csup\u003e19\u003c/sup\u003e. Overall, \u003cem\u003eCRBN\u003c/em\u003e in all cell lines was low relative to other HMCLs other than MC1286PE7. Proteomic analysis showed mildly decreased CRBN protein in MC1286PE3 and MC1286PE7 lines (-0.2 log\u003csub\u003e2\u003c/sub\u003e fold change and -0.2 log\u003csub\u003e2\u003c/sub\u003e fold change, respectively). This correlated with pomalidomide sensitivity \u003cem\u003ein vitro\u003c/em\u003e (\u003cstrong\u003eSupplemental Table 1\u003c/strong\u003e). There was decreased expression of \u003cem\u003eTP53\u003c/em\u003e in MC1286PE3, MC1286PE5, and MC1286PE7. \u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMass spectrometry-based proteomics reveals novel proteins in MC1286 cell lines\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA total of 7,590 proteins were quantified in at least two replicates in proteomic analysis. Differential expression analysis identified more significantly altered proteins in MC1286PE3 and MC1286PE7 compared to MC1286PE1. We identified \u0026lt; 50 significantly differentially expressed proteins between MC1286PE3 and MC1286PE7 and a sample correlation heat map showed higher Pearson correlation coefficients among these two cell lines (\u003cstrong\u003eSupplemental Figure 6A and 6B\u003c/strong\u003e). Differential expression analysis of the two cell lines compared to MC1286PE1 indeed showed similar altered protein expression signatures (\u003cstrong\u003eFigure 5\u003c/strong\u003e). This makes sense as these cell lines were obtained at closer time points compared to MC1286PE1, suggesting that the major proteomic changes occurred earlier in disease progression.\u003c/p\u003e\n\u003cp\u003eInterestingly, differential abundance identified decreased CDK6 in MC1286PE3 and MC1286PE7 compared to MC1286PE1, which is consistent with loss of RB1 expression (\u003cstrong\u003eFigure 5\u003c/strong\u003e) \u003csup\u003e20\u003c/sup\u003e. We also found expression of CDKN2A and CDKN1A in MC1286PE3 and MC1286PE7, but not MC1286PE1, both of which may be an attempt by the cell lines to compensate for the loss of RB1 \u003csup\u003e20\u003c/sup\u003e \u003csup\u003e21\u003c/sup\u003e. We found increased MYC expression in MC1286PE3 and MC1286PE7, despite the loss of the duplication telomeric to MYC in MC1286PE7. We also found increased expression of MTOR, CD40, and CD320, and SMYD3 in MC1286PE3 and MC1286PE7 compared to MC1286PE1, all of which have been described in MM and may promote proliferation and/or MM cell survival \u003csup\u003e22\u003c/sup\u003e \u003csup\u003e23\u003c/sup\u003e \u003csup\u003e24\u003c/sup\u003e \u003csup\u003e25\u003c/sup\u003e \u003csup\u003e26\u003c/sup\u003e \u003csup\u003e27\u003c/sup\u003e \u003csup\u003e28\u003c/sup\u003e. We found high expression of two cancer testes antigens, PAGE2 and GAGE12F \u003csup\u003e29\u003c/sup\u003e. Cancer testes antigens are expressed in late stage MM and may be linked to poorer outcomes \u003csup\u003e30\u003c/sup\u003e \u003csup\u003e31\u003c/sup\u003e \u003csup\u003e32\u003c/sup\u003e \u003csup\u003e33\u003c/sup\u003e. Interestingly, GSEA analysis showed enrichment of cholesterol synthesis pathways (\u003cstrong\u003eSupplemental Figure 6\u003c/strong\u003e). FADS2 and LRPAP1, proteins linked to the cholesterol/lipid pathway, were both overexpressed in MC1286PE3 and MC1286PE7 compared to MC1286PE1. FADS2 has been shown to be important for tumor cell lipid metabolism and is increased in most cancers, associated with poor survival outcomes \u003csup\u003e34\u003c/sup\u003e. LRPAP1 has been identified as a B cell receptor (BCR) antigen in mantle cell lymphoma (in both patient samples and cell lines), associated with proliferation by BCR pathway activation \u003csup\u003e35\u003c/sup\u003e. A recent study showed the prognostic value of cholesterol metabolism-related genes in MM \u003csup\u003e36\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe genetic diversity of a large number of cell lines derived from patients with MM have been an extremely valuable tool to understand the mechanisms and pathogenesis of the disease. Most existing models represent a single snapshot of the disease and do not capture the temporal dynamics of tumor evolution. In this study, we describe a series of five novel HMCLs derived from serial pleural effusion samples collected from a single patient with relapsed/refractory hyperdiploid MM, allowing us to comprehensively examine clonal evolution and molecular adaptation over the course of disease progression. By integrating genomic, transcriptomic, and proteomic profiling, we show key oncogenic events, including progressive RB1 loss and MYC dysregulation, emerging and evolving through disease progression, highlighting the dynamic complexity of MM biology. \u003c/p\u003e\n\u003cp\u003eWe found a progressive evolution of \u003cem\u003eRB1\u003c/em\u003e loss, progressing from monoallelic deletion to focal biallelic loss, and ultimately a large biallelic deletion of the entire gene. This coincided with loss of RB1 protein expression, consistent with previous observations linking biallelic \u003cem\u003eRB1\u003c/em\u003e loss to poor prognosis. This also provided evidence that biallelic RB1 loss may act as a late-stage driver event. In parallel, we identified a duplication telomeric to \u003cem\u003eMYC\u003c/em\u003e that arose during intermediate stages of disease progression, associated with allele specific increased relative \u003cem\u003eMYC\u003c/em\u003e expression. This duplication was subsequently lost in the terminal cell line, despite persistent MYC overexpression, suggesting that MYC activation may be maintained through other mechanisms during clonal evolution. Proteomic analysis revealed consistent upregulation of proteins involved in mTOR signaling, lipid and cholesterol metabolism, and cancer-testis antigen expression in later-stage cell lines, changes that may be associated with advanced and treatment refractory MM. \u003c/p\u003e\n\u003cp\u003eInterestingly, despite originating from a hyperdiploid state, the serial MC1286 HMCLs exhibited dynamic changes in chromosomal copy number during disease progression, including loss and/or acquisition of partial or whole chromosomes. These findings suggest that hyperdiploidy in MM may not be a static genomic state, but rather dynamic and subject to positive and negative selection over time. Certain chromosomal gains may confer a selective advantage under specific biological contexts or therapeutic pressures, while others may be neutral or detrimental for the myeloma cell through disease progression. \u003c/p\u003e\n\u003cp\u003eThis study has several limitations. First, although the serial MC1286 HMCLs provide a unique longitudinal model of disease evolution, we lacked bone marrow biopsy samples at identical time points, limiting our ability to directly assess how the molecular profile of the pleural-derived HMCLs compared to the bone marrow. As a result, some observed molecular features may reflect site-specific evolution or adaptation. In addition, while these cell lines preserve many clinically relevant features for the patient’s disease, we cannot exclude the possibility that additional molecular changes arose during the process of immortalization and \u003cem\u003ein vitro\u003c/em\u003e adaptation. Nonetheless, the molecular and evolutionary trends observed through this multi-omic approach, with the concordant clinical disease course, supports our model for studying late-stage MM through progression and therapy resistance. In addition, the molecular profiling results presented here are consistent with previous studies in MM showing that not all mutations in a given tumor are conserved over time \u003csup\u003e10\u003c/sup\u003e \u003csup\u003e37\u003c/sup\u003e \u003csup\u003e38\u003c/sup\u003e \u003csup\u003e39\u003c/sup\u003e. \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eWe would like to acknowledge the patient who consented for this study. This work was supported in part by a grant from the NCI CPTAC program (U01CA271410) to AP, RF, and PLB.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJEWN was responsible for summarizing patient data, extracting and analyzing data, interpreting results, writing the manuscript, and creating final figures. DLR was responsible for extracting and analyzing data, interpreting results, and contributed to writing the manuscript. CKS was responsible for extracting and analyzing data and interpreting results. YWA was responsible for extracting and analyzing data. MC generated the cell lines and obtained their genomic profiling. EB was responsible for overseeing the project and PLB was responsible for study design and overseeing the project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData available on request to corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are no competing financial interests in relation to the work described.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKuehl, W. M. \u0026amp; Bergsagel, P. L. Early genetic events provide the basis for a clinical classification of multiple myeloma. \u003cem\u003eHematology Am Soc Hematol Educ Program\u003c/em\u003e, 346-352 (2005). https://doi.org/10.1182/asheducation-2005.1.346\u003c/li\u003e\n\u003cli\u003eCorre, J.\u003cem\u003e et al.\u003c/em\u003e Multiple myeloma clonal evolution in homogeneously treated patients. \u003cem\u003eLeukemia\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 2636-2647 (2018). https://doi.org/10.1038/s41375-018-0153-6\u003c/li\u003e\n\u003cli\u003eKuehl, W. M. \u0026amp; Bergsagel, P. L. Molecular pathogenesis of multiple myeloma and its premalignant precursor. \u003cem\u003eJ Clin Invest\u003c/em\u003e \u003cstrong\u003e122\u003c/strong\u003e, 3456-3463 (2012). https://doi.org/10.1172/JCI61188\u003c/li\u003e\n\u003cli\u003eJovanović, K. K.\u003cem\u003e et al.\u003c/em\u003e Targeting MYC in multiple myeloma. \u003cem\u003eLeukemia\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 1295-1306 (2018). https://doi.org/10.1038/s41375-018-0036-x\u003c/li\u003e\n\u003cli\u003eMisund, K.\u003cem\u003e et al.\u003c/em\u003e MYC dysregulation in the progression of multiple myeloma. \u003cem\u003eLeukemia\u003c/em\u003e \u003cstrong\u003e34\u003c/strong\u003e, 322-326 (2020). https://doi.org/10.1038/s41375-019-0543-4\u003c/li\u003e\n\u003cli\u003eAffer, M.\u003cem\u003e et al.\u003c/em\u003e Promiscuous MYC locus rearrangements hijack enhancers but mostly super-enhancers to dysregulate MYC expression in multiple myeloma. \u003cem\u003eLeukemia\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 1725-1735 (2014). https://doi.org/10.1038/leu.2014.70\u003c/li\u003e\n\u003cli\u003eMaura, F.\u003cem\u003e et al.\u003c/em\u003e Genomic landscape and chronological reconstruction of driver events in multiple myeloma. \u003cem\u003eNat Commun\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 3835 (2019). https://doi.org/10.1038/s41467-019-11680-1\u003c/li\u003e\n\u003cli\u003eChesi, M.\u003cem\u003e et al.\u003c/em\u003e Monosomic Loss of MIR15A/MIR16-1 Is a Driver of Multiple Myeloma Proliferation and Disease Progression. \u003cem\u003eBlood Cancer Discovery\u003c/em\u003e \u003cstrong\u003e1\u003c/strong\u003e, 68-81 (2020). https://doi.org/10.1158/0008-5472.Bcd-19-0068\u003c/li\u003e\n\u003cli\u003eBinder, M.\u003cem\u003e et al.\u003c/em\u003e Prognostic implications of abnormalities of chromosome 13 and the presence of multiple cytogenetic high-risk abnormalities in newly diagnosed multiple myeloma. \u003cem\u003eBlood Cancer Journal\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, e600-e600 (2017). https://doi.org/10.1038/bcj.2017.83\u003c/li\u003e\n\u003cli\u003eKeats, J. J.\u003cem\u003e et al.\u003c/em\u003e Clonal competition with alternating dominance in multiple myeloma. \u003cem\u003eBlood\u003c/em\u003e \u003cstrong\u003e120\u003c/strong\u003e, 1067-1076 (2012). https://doi.org/10.1182/blood-2012-01-405985\u003c/li\u003e\n\u003cli\u003eKort\u0026uuml;m, K. M.\u003cem\u003e et al.\u003c/em\u003e Targeted sequencing of refractory myeloma reveals a high incidence of mutations in CRBN and Ras pathway genes. \u003cem\u003eBlood\u003c/em\u003e \u003cstrong\u003e128\u003c/strong\u003e, 1226-1233 (2016). https://doi.org/10.1182/blood-2016-02-698092\u003c/li\u003e\n\u003cli\u003eKortuem, K. M.\u003cem\u003e et al.\u003c/em\u003e Panel sequencing for clinically oriented variant screening and copy number detection in 142 untreated multiple myeloma patients. \u003cem\u003eBlood Cancer J\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, e397 (2016). https://doi.org/10.1038/bcj.2016.1\u003c/li\u003e\n\u003cli\u003eHansen, K. D., Irizarry, R. A. \u0026amp; Wu, Z. Removing technical variability in RNA-seq data using conditional quantile normalization. \u003cem\u003eBiostatistics\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 204-216 (2012). https://doi.org/10.1093/biostatistics/kxr054\u003c/li\u003e\n\u003cli\u003eMangalaparthi, K. K.\u003cem\u003e et al.\u003c/em\u003e Molecular Characterization of Esophageal Squamous Cell Carcinoma Using Quantitative Proteomics. \u003cem\u003eCancers (Basel)\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e (2023). https://doi.org/10.3390/cancers15133302\u003c/li\u003e\n\u003cli\u003eH\u0026auml;cker, H., Tseng, P.-H. \u0026amp; Karin, M. Expanding TRAF function: TRAF3 as a tri-faced immune regulator. \u003cem\u003eNature Reviews Immunology\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 457-468 (2011). https://doi.org/10.1038/nri2998\u003c/li\u003e\n\u003cli\u003eKeats, J. J.\u003cem\u003e et al.\u003c/em\u003e Promiscuous mutations activate the noncanonical NF-kappaB pathway in multiple myeloma. \u003cem\u003eCancer Cell\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 131-144 (2007). https://doi.org/10.1016/j.ccr.2007.07.003\u003c/li\u003e\n\u003cli\u003eKortuem, K. M.\u003cem\u003e et al.\u003c/em\u003e Panel sequencing for clinically oriented variant screening and copy number detection in 142 untreated multiple myeloma patients. \u003cem\u003eBlood Cancer Journal\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, e397-e397 (2016). https://doi.org/10.1038/bcj.2016.1\u003c/li\u003e\n\u003cli\u003ede Leval, L.\u003cem\u003e et al.\u003c/em\u003e Genomic profiling for clinical decision making in lymphoid neoplasms. \u003cem\u003eBlood\u003c/em\u003e \u003cstrong\u003e140\u003c/strong\u003e, 2193-2227 (2022). https://doi.org/10.1182/blood.2022015854\u003c/li\u003e\n\u003cli\u003eStandal, T.\u003cem\u003e et al.\u003c/em\u003e Osteopontin is an adhesive factor for myeloma cells and is found in increased levels in plasma from patients with multiple myeloma. \u003cem\u003eHaematologica\u003c/em\u003e \u003cstrong\u003e89\u003c/strong\u003e, 174-182 (2004). \u003c/li\u003e\n\u003cli\u003eNevins, J. R. The Rb/E2F pathway and cancer. \u003cem\u003eHuman Molecular Genetics\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 699-703 (2001). https://doi.org/10.1093/hmg/10.7.699\u003c/li\u003e\n\u003cli\u003eEngeland, K. Cell cycle regulation: p53-p21-RB signaling. \u003cem\u003eCell Death \u0026amp; Differentiation\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e, 946-960 (2022). https://doi.org/10.1038/s41418-022-00988-z\u003c/li\u003e\n\u003cli\u003eWang, Y.\u003cem\u003e et al.\u003c/em\u003e Targeting mTOR signaling pathways in multiple myeloma: biology and implication for therapy. \u003cem\u003eCell Communication and Signaling\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, 320 (2024). https://doi.org/10.1186/s12964-024-01699-3\u003c/li\u003e\n\u003cli\u003eTai, Y.-T.\u003cem\u003e et al.\u003c/em\u003e CD40 induces human multiple myeloma cell migration via phosphatidylinositol 3\u0026ndash;kinase/AKT/NF-\u0026kappa;B signaling. \u003cem\u003eBlood\u003c/em\u003e \u003cstrong\u003e101\u003c/strong\u003e, 2762-2769 (2003). https://doi.org/10.1182/blood-2002-09-2813\u003c/li\u003e\n\u003cli\u003eHan, T.-S., Kim, D.-S., Son, M.-Y. \u0026amp; Cho, H.-S. SMYD family in cancer: epigenetic regulation and molecular mechanisms of cancer proliferation, metastasis, and drug resistance. \u003cem\u003eExperimental \u0026amp; Molecular Medicine\u003c/em\u003e \u003cstrong\u003e56\u003c/strong\u003e, 2325-2336 (2024). https://doi.org/10.1038/s12276-024-01326-8\u003c/li\u003e\n\u003cli\u003eFasano, C.\u003cem\u003e et al.\u003c/em\u003e Identifying novel SMYD3 interactors on the trail of cancer hallmarks. \u003cem\u003eComputational and Structural Biotechnology Journal\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 1860-1875 (2022). https://doi.org/https://doi.org/10.1016/j.csbj.2022.03.037\u003c/li\u003e\n\u003cli\u003eGarofano, F.\u003cem\u003e et al.\u003c/em\u003e Identification of CD320, SLC44A1 and TNFRSF13B as potential novel therapeutic targets for CAR T-cell therapy in multiple myeloma. \u003cem\u003eFrontiers in Medicine\u003c/em\u003e \u003cstrong\u003eVolume 12 - 2025\u003c/strong\u003e (2026). https://doi.org/10.3389/fmed.2025.1737919\u003c/li\u003e\n\u003cli\u003eShu, L.\u003cem\u003e et al.\u003c/em\u003e CD320 Regulates Bone Marrow Angiogenesis in Multiple Myeloma Via HGF-ERK Axis. \u003cem\u003eBlood\u003c/em\u003e \u003cstrong\u003e140\u003c/strong\u003e, 12466-12467 (2022). https://doi.org/10.1182/blood-2022-169581\u003c/li\u003e\n\u003cli\u003eGarofano, F.\u003cem\u003e et al.\u003c/em\u003e MM-055: Identification of CD320, CD92, and CD267 as Potential Novel Therapeutic Targets for CAR-T Therapy in Multiple Myeloma. \u003cem\u003eClinical Lymphoma Myeloma and Leukemia\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, S891 (2025). https://doi.org/https://doi.org/10.1016/S2152-2650(25)02548-0\u003c/li\u003e\n\u003cli\u003eYilmaz-Ozcan, S.\u003cem\u003e et al.\u003c/em\u003e Epigenetic Mechanisms Underlying the Dynamic Expression of Cancer-Testis Genes, PAGE2, -2B and SPANX-B, during Mesenchymal-to-Epithelial Transition. \u003cem\u003ePLOS ONE\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, e107905 (2014). https://doi.org/10.1371/journal.pone.0107905\u003c/li\u003e\n\u003cli\u003eOhlstrom, D.\u003cem\u003e et al.\u003c/em\u003e Longitudinal multi-omic profiling uncovers immune escape and predictors of response in multiple myeloma. \u003cem\u003ebioRxiv\u003c/em\u003e, 2025.2005.2027.656392 (2025). https://doi.org/10.1101/2025.05.27.656392\u003c/li\u003e\n\u003cli\u003eAtanackovic, D.\u003cem\u003e et al.\u003c/em\u003e Cancer-testis antigens are commonly expressed in multiple myeloma and induce systemic immunity following allogeneic stem cell transplantation. \u003cem\u003eBlood\u003c/em\u003e \u003cstrong\u003e109\u003c/strong\u003e, 1103-1112 (2006). https://doi.org/10.1182/blood-2006-04-014480\u003c/li\u003e\n\u003cli\u003eJungbluth, A. A.\u003cem\u003e et al.\u003c/em\u003e The cancer-testis antigens CT7 (MAGE-C1) and MAGE-A3/6 are commonly expressed in multiple myeloma and correlate with plasma-cell proliferation. \u003cem\u003eBlood\u003c/em\u003e \u003cstrong\u003e106\u003c/strong\u003e, 167-174 (2005). https://doi.org/10.1182/blood-2004-12-4931\u003c/li\u003e\n\u003cli\u003evan Duin, M.\u003cem\u003e et al.\u003c/em\u003e Cancer testis antigens in newly diagnosed and relapse multiple myeloma: prognostic markers and potential targets for immunotherapy. \u003cem\u003eHaematologica\u003c/em\u003e \u003cstrong\u003e96\u003c/strong\u003e, 1662-1669 (2011). https://doi.org/10.3324/haematol.2010.037978\u003c/li\u003e\n\u003cli\u003eChen, E., Wang, C., Lv, H. \u0026amp; Yu, J. The role of fatty acid desaturase 2 in multiple tumor types revealed by bulk and single-cell transcriptomes. \u003cem\u003eLipids in Health and Disease\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, 25 (2023). https://doi.org/10.1186/s12944-023-01789-0\u003c/li\u003e\n\u003cli\u003eThurner, L.\u003cem\u003e et al.\u003c/em\u003e LRPAP1 is a frequent proliferation-inducing antigen of BCRs of mantle cell lymphomas and can be used for specific therapeutic targeting. \u003cem\u003eLeukemia\u003c/em\u003e \u003cstrong\u003e33\u003c/strong\u003e, 148-158 (2019). https://doi.org/10.1038/s41375-018-0182-1\u003c/li\u003e\n\u003cli\u003eZhao, N.\u003cem\u003e et al.\u003c/em\u003e Identification of a cholesterol metabolism-related prognostic signature for multiple myeloma. \u003cem\u003eSci Rep\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 19395 (2023). https://doi.org/10.1038/s41598-023-46426-z\u003c/li\u003e\n\u003cli\u003eLandau, H. J.\u003cem\u003e et al.\u003c/em\u003e Accelerated single cell seeding in relapsed multiple myeloma. \u003cem\u003eNature Communications\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 3617 (2020). https://doi.org/10.1038/s41467-020-17459-z\u003c/li\u003e\n\u003cli\u003eRasche, L.\u003cem\u003e et al.\u003c/em\u003e The spatio-temporal evolution of multiple myeloma from baseline to relapse-refractory states. \u003cem\u003eNature Communications\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 4517 (2022). https://doi.org/10.1038/s41467-022-32145-y\u003c/li\u003e\n\u003cli\u003eMaura, F.\u003cem\u003e et al.\u003c/em\u003e Temporal genomic dynamics shape clinical trajectory in multiple myeloma. \u003cem\u003eNature Genetics\u003c/em\u003e \u003cstrong\u003e57\u003c/strong\u003e, 2203-2214 (2025). https://doi.org/10.1038/s41588-025-02292-1\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"blood-cancer-journal","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"bcj","sideBox":"Learn more about [Blood Cancer Journal](http://www.nature.com/bcj/)","snPcode":"41408","submissionUrl":"https://mts-bcj.nature.com/cgi-bin/main.plex","title":"Blood Cancer Journal","twitterHandle":"@bloodcancerjnl","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9003051/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9003051/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Multiple myeloma (MM) is an incurable plasma cell neoplasm characterized by diverse and complex genetic abnormalities, including translocations of immunoglobulin (Ig) genes and a hyperdiploid (HRD) karyotype. Much of our current understanding of MM genetics, epigenetics, pathogenesis and response/resistance to myeloma directed therapies originated from studies on human MM-derived cell lines (HMCL). In this study, we conducted an integrated multi-omics analysis in five hyperdiploid cell lines established from serial samples from a single relapsed/refractory hyperdiploid MM patient, enabling detailed characterization of the genetic landscape and molecular alterations through disease progression.","manuscriptTitle":"Establishment and characterization of multiple myeloma cell lines from a single patient’s disease course immortalizes clonal heterogeneity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-12 07:22:09","doi":"10.21203/rs.3.rs-9003051/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"This content is not available.","date":"2026-04-16T15:33:22+00:00","index":3,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-04-14T08:27:30+00:00","index":3,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-03-13T15:02:38+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-03-05T17:38:56+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2026-03-05T16:27:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-05T16:14:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-05T16:14:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"Blood Cancer Journal","date":"2026-03-03T19:31:05+00:00","index":"","fulltext":""},{"type":"checksFailed","content":"","date":"2026-03-02T13:56:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"blood-cancer-journal","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"bcj","sideBox":"Learn more about [Blood Cancer Journal](http://www.nature.com/bcj/)","snPcode":"41408","submissionUrl":"https://mts-bcj.nature.com/cgi-bin/main.plex","title":"Blood Cancer Journal","twitterHandle":"@bloodcancerjnl","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"84684f89-bbff-480e-a67f-b88517ca84f0","owner":[],"postedDate":"March 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":64001737,"name":"Biological sciences/Cancer/Cancer genomics"},{"id":64001738,"name":"Biological sciences/Genetics/Cancer genomics"}],"tags":[],"updatedAt":"2026-03-12T07:22:09+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-12 07:22:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9003051","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9003051","identity":"rs-9003051","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}