Personalizing Breast Cancer Treatment with Patient-derived Xenografts (PDX): Analyzing PDXs Fidelity, Stability, and Intra-Tumoral Heterogeneity

Personalizing Breast Cancer Treatment with Patient-derived Xenografts (PDX): Analyzing PDXs Fidelity, Stability, and Intra-Tumoral 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 Personalizing Breast Cancer Treatment with Patient-derived Xenografts (PDX): Analyzing PDXs Fidelity, Stability, and Intra-Tumoral Heterogeneity Ariel Sobarzo, Maha Msamra, Rena Elkin, Ravit Agasi, Natalie Aizenberg, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6730867/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Breast cancer (BC) is a complex disease with varied histology and molecular behaviors. Patient-derived xenografts (PDXs) have been developed to study tumor progression and personalized treatment strategies. Here, we assessed the stability and fidelity of BC PDXs in maintaining key characteristics of their source biopsies across generations and various engraftment sites in mice. Using NSG-SGM3 mice, we established PDX models from primary tumors of 22 patients, creating three luminal A subtype models. Our results showed high concordance in histopathological and molecular profiles between the PDXs and their corresponding biopsies. PDX engraftment success was linked to the aggressiveness of patients' tumors and specific gene expressions. We found strong correlations with genes associated with intrinsic tumor pathways and variability related to immune responses. Our findings highlight the effectiveness of BC PDXs in replicating patient tumors and provide insights into factors affecting engraftment, growth, and stability, enhancing their role in personalized BC research. Biological sciences/Cancer/Breast cancer Health sciences/Oncology/Cancer/Cancer models Patient-derived xenograft Engraftment tumor stability tumor heterogenicity Breast cancer patients OMICS Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Breast cancer (BC) is a heterogeneous disease characterized by diversity in histology and molecular and metastatic behavior. ( 1 – 3 ). It is the leading cause of cancer-related death in women worldwide ( 4 ). Classification of BC subtypes based on biological markers is divided into three subtypes: those expressing estrogen receptor alpha (ER+) (and typically also the progesterone receptor (PR +)), those that overexpress ERBB2 (HER2+), and those that express none of these markers (termed “triple negative” breast cancer (TNBC)) ( 1 , 3 , 5 ). Each type correlated with a unique clinical course, prognostics, and treatment response. ( 5 – 7 ). The heterogeneity and complexity of BC have presented significant obstacles in identifying effective therapies for individual patients ( 8 – 11 ). BC contains considerable inter and intra-tumoral heterogeneity ( 12 – 14 ). This heterogeneity is critical for making effective treatment decisions and presents significant challenges to generating and using relevant pre-clinical models ( 2 , 5 , 11 , 15 ). Determining a patient's response to treatment and the treatment's effects is complex and challenging. It involves intricate factors, including tumor characteristics, host variables, and environmental influences, as well as their synergistic interactions ( 16 , 17 ). Thus, there is an increasing need for personalized, predictive preclinical models that can accurately represent the tumor.( 18 , 19 ). Patient-derived xenograft (PDX) models have been developed and utilized to study solid tumors, including BC ( 20 – 22 ). These models have been suggested to preserve the patient's tumor biological and molecular characteristics and heterogeneity ( 1 , 23 , 24 ), offering valuable tools for testing novel therapies in preclinical models ( 25 – 27 ). However, their potential to serve as a "true" preclinical tool is still questioned ( 28 – 31 ). While PDX models for BC have been introduced, their development and establishment remain challenging ( 20 , 22 ). BC PDXs were shown to be affected by the quality of the tumor sample, the choice of engraftment site, the use of host mice, and the timing and method of engraftment ( 22 , 31 – 33 ). This resulted in a relatively low engraftment rate, hampering their widespread use and emphasizing their need ( 1 , 20 , 22 ). One of the main challenges of the PDX model is its ability to accurately reflect the tumor's properties over multiple passages in mice, which allows for prolonged study durations ( 31 , 34 , 35 ). Developing such models is critical, particularly in biopsies collected from patients with advanced progressive disease, likely showing treatment resistance ( 23 , 31 , 36 – 38 ). This study established PDX models of primary luminal A subtype BC using innovative NSG-SGM3 mice. Our study aimed to evaluate various clinical and preclinical factors that may impact the success of PDX engraftment, growth rates, fidelity, and stability across multiple passages in the mice. We focused on differences with the original patient biopsy, highlighting similarities and discrepancies. Material and Methods Patient population Patients enrolled in this study had histologically confirmed invasive breast cancer with tumors larger than one cm that were detected through physical examination or imaging. Those who achieved complete radiologic remission or a significant reduction in tumor mass following neoadjuvant chemotherapy (NAC) were excluded. Twenty-two surgically resected tumor samples were collected from March 2022 to 2024. Ethics statement The institutional review board (IRB) (SOR-0217-22) approved this research. All patients signed informed consent forms before participation. Data acquisition Demographic and clinical features were gathered from the patient’s medical records, including medical history and previous surgical and pathological reports. The following data were collected for each patient: age, number of prior pregnancies, body mass index (BMI), smoking status, tumor stage at diagnosis, NAC treatment, and treatment response. A qualified pathologist retrospectively reviewed previous histopathological findings for all surgical specimens. Animals Nonobese diabetic/severe combined immune deficiency (NOD/SCID) (4–5-week-old) triple transgenic NSG-SGM3 mice were obtained from the Jackson Laboratory (Sacramento, CA, USA). Animals were housed in individually ventilated cages at an appropriate temperature (21–25°C) with a 12 h light/dark cycle and free access to food and water. All animal care and experiments were performed under an animal protocol approved by the Institutional Animal Care and Use Committee at Ben-Gurion University and the Israel Ministry of Health (MOH) (authorization number IL-13-04-2021D). Establishment of Breast cancer PDXs Following surgery and after obtaining a pathological conformation for the specific sample, tumor samples (approximately 2–3 mm 3 ) from each patient with BC were engrafted into female NSG-SGM3 mice. Tumor samples were subcutaneously (SC) implanted into the flanks for the heterotrophic model. They were implanted into the fourth mammary fat pad for the orthotropic (OR) model. Tumor size was measured by a caliper twice a week. The modified ellipsoidal formula calculated the tumor volume: V = ½ (Length × Width 2 ). When the tumor size reached 500 mm 3 for the SC model and 200 mm 3 for the OR, mice were euthanized, and the tumors were harvested for subsequent analysis, including sequencing and preparation of formalin-fixed paraffin-embedded blocks. Engraftment development was monitored for 60 weeks. Molecular Analysis DNA Extraction, Library Construction and Sequencing DNA analysis was done on a panel of 175 selected genes involved in cancer development. The list of genes is shown in the supplemental material (Table S1). DNA extraction was done using the GRS Genomic DNA Kit (Grisp, cat. no. GK03). DNA concentrations were measured using NanoDrop One (Thermo Scientific). Genomic DNA (> 3 µg) was used to construct DNA libraries. To generate DNA sequencing libraries, target enrichment was performed using the xGen cfDNA and FFPE DNA library prep kit (Integrated DNA Technologies (IDT), Coralville, Iowa, USA) according to the manufacturer's instructions (IDT, cat. no. 10010206). DNA capture and hybridization were done using the xGen™ Custom Hybridization Capture Panels (IDT, Coralville, Iowa, USA) according to the manufacturer's instructions. DNAs were sequenced with Illumina HiSeq 2000 (Illumina Inc., San Diego, CA, USA). Sequence reads were aligned to the California Santa Cruz hg19 human genome release. Somatic mutation analysis was done with the SEQ platform (Genomize, Istanbul, Turkey) and verified using Sanger sequencing. We focused on exonic regions exhibiting single nucleotide variation (SNV) frequencies above 10% of the total readouts. RNA Extraction, sequencing, and analysis Total RNA was extracted from patient BC biopsies and their PDXs using the Triple Xtractor direct RNA Kit (Grisp, cat. no. GK23.0100). RNA (0.1–1.0 µg) was used to generate cDNA using the Poly(A) mRNA Universal Plus RNA-Seq with NuQuant (Tecan Genomics, cat. no. 0361). RNAs were sequenced on an Illumina NovaSeq 6000. TopHat2 was used to align RNA-seq reads to the reference. DESeq2 was used to identify differentially expressed genes. Only genes with an mRNA mean count above two were analyzed. A differential gene expression (DGE) was scored when there was > 2 fold change (FC) in RNA levels. Gene Network Analysis Gene network analysis (GNA) was performed as was previously described ( 39 ), using the Human Protein Reference Database ( 40 ). To define the network, genes associated with the engraftment outcome and differences between PDXs and matched patient biopsies were evaluated for their cooperation capacity by GNA and quantified using Ollivier-Ricci network curvature ( 41 ). For further details, see Supplementary Material File 1. Histopathology Patient and PDX tumor tissue sections were freshly cut to 4 µm. According to manufacturer instructions, Hematoxylin and Eosin (H&E) staining was automated (Ventana HE 600; Ventana Medical Systems, Tucson, AZ, USA). ER and PR status were determined through immunohistochemical staining of formalin-fixed, paraffin-embedded tumor tissue sections. Positive staining was defined as nuclear staining in at least 1% of the tumor cells, and the hormone receptor-positive (HR+) type was defined as ER and PR IHC-positive. HER2 positivity was defined as IHC 2+<. The percentage of tumor cells showing any degree of nuclear staining for Ki-67 was recorded for each tumor. A certified medical pathologist analyzed the H&E and IHC images and reviewed the pathological type and architecture between the original and xenograft tumor to confirm the diagnosis. Statistical analysis Logistic regression analysis, chi-squared, or Fisher's exact tests were used to compare PDX engraftment status, patient characteristics, and pathological parameters. The significance of differences between normally distributed molecular data from PDXs and parental patient biopsies was assessed by T-tests and one-way ANOVA. The statistics package of Prism software v9.1 (GraphPad Software, Boston, Massachusetts, USA) was used for all statistical analyses. A p- value (two-sided) of < 0.05 was considered to indicate a significant difference. SRplot was used for data visualization and graphing ( 42 ). Results Establishment of breast cancer PDX mouse model platform in NSG-SGM3 mice After pathological confirmation, twenty-two biopsies were engrafted in mice within two hours of collection. Molecular analyses classified biopsies as luminal A (n = 17), luminal B (n = 3), and basal (n = 2). Three biopsies successfully formed tumors in mice, creating PDX models, all exhibiting a luminal A phenotype. These successful PDXs were then passed serially to create a "hot" sample bank for future histopathological and molecular analyses. Figure S1 outlines the experimental procedures. Patient demographics, clinical and pre-clinical features We investigated the correlation between PDX engraftment success and patient demographics, collecting data summarized in Table 1. The study included twenty-two female donors with an average age of 66 (31–87). Tumor types comprised infiltrated lobular carcinoma (n = 4) and infiltrated ductal carcinoma (n = 18), all sourced from primary BC lesions. Pathological and molecular features were analyzed, showing typical ductal carcinoma characteristics through H&E staining, including pleomorphic cells and high proliferation rates. IHC staining of Ki-67, ER, PR, and HER2 revealed varied expression levels (Figure S2). Targeted DNA analysis of 175 cancer driver genes across samples identified mutations in 104 genes, with six genes—CDC7, DKC1, ERCC6, mTOR, MUC16, and VCAM—mutated in all cases. Patient samples had an average of 275 SNVs, mostly synonymous and missense (Figure S3A and S3B). High mutation rates were noted in genes such as MUC16, LAMC3, FANCD2, ASPM, and FN1 (Figure S3C). RNA sequencing of PAM50 genes illustrated the common BC subtypes (Figure S3D). Factors affecting the success rate of initial PDX engraftment. Patient demographic and clinical data were analyzed for their correlation with the success of tumor biopsy engraftment in PDX models (Table 1). A significant correlation was found between engraftment success and tumor molecular subtype (p = 0.0152), radiotherapy treatment history (p = 2.94x10^ − 13), and hormonal therapy (p = 0.0077). In contrast, histopathological analysis showed no notable difference between successfully and failed engrafted biopsies (Figure S4). We identified 98 altered genes common to both groups, with six genes (DLGAP5, FOXO3, KIF2C, RECQL, TERT, TTK) exclusive to the failed engraftment group (Fig. 1 A). Most alterations in both groups were synonymous substitutions, followed by missense mutations (Fig. 1 B). Focusing on the missense mutation subtype, we observed that both groups had similar genes with the uppermost mutation rates (Fig. 1 C), with the highest mutation rate in genes RAD51D, DNMT1, ESCO2, FOXM1, LAMB2, PALB2, APEX1, and MUTYH (Fig. 1 D). However, no statistically significant differences in mutation types or frequencies between the two groups were found. Analysis of RNA from 778 genes associated with BC identified 534 differential DGEs. Among these, 98 were found to be upregulated and 129 downregulated in successfully engrafted biopsies (Fig. 1 E). Variations in the expression of the most prominent DGEs were noted between the two groups (Fig. 1 F). Statistically significant differences (p-value < 0.05) were detected in the expression levels of six genes: COL1A1, COL4A5, COL6A6, HSPB1, IFNA8, and MAP2K4 (Fig. 1 G). Gene Ontology analysis of failed engraftment specimens showed a significant association with biological processes related to immune responses (Fig. 1 H). GNA revealed notable differences in communication properties, emphasizing distinct gene cooperation between the two groups (Fig. 1 I). Unsupervised PCA successfully differentiated the two biopsy groups based on the highest DGEs (Fig. 1 J). Factors affecting PDX growth kinetics Successfully engrafted BC PDXs were passed through three rounds in NSG-SGM3 mice, and tumor sizes were measured twice weekly to evaluate PDX growth kinetics. The growth patterns for all models are presented in Fig. 2 . Notable variations in growth were observed between the initial engraftment and the subsequent passages in successful models (Figs. 2 A and 2 B). The time until the initial successful PDX engraftment was 43 weeks for patient 11, 51 weeks for patient 18, and 33 weeks for patient 26 (Figs. 2 A and 2 C). The average duration for the first passage was 46 weeks, followed by 39 weeks for the second passage and 23 weeks for the third passage (Fig. 2 D). A statistically significant difference was found between the duration of the first and third passages, with a P-value of 0.0417 (Fig. 2 D). Breast cancer PDXs and biopsies comparison We investigated whether the source tumor's key molecular and pathological characteristics are preserved after engraftment. The histopathological features of PDXs closely resembled those of the original patient biopsies (Figure S5). Among the 175 analyzed genes, 96 were mutated in the biopsies and their corresponding PDXs. Thirteen showed mutations exclusively in the biopsies; no new mutations were noted in the PDX samples (Fig. 3 A). Key genes like BRCA1/2, ASPM, and TP53 maintained similar mutational profiles in both samples (Fig. 3 B). An analysis of mutation subtypes showed minor variations between the biopsies and PDXs (Fig. 3 C), with a high correlation in DNA mutations (Pearson correlation coefficient of 0.95, P = 8.72 × 10^-9). RNA analysis revealed 627 DEGs, with 401 shared between the biopsies and PDXs and 90 or 136 specific to each group, respectively (Fig. 3 D). Of these, 114 were upregulated and 336 downregulated in the biopsies (Fig. 3 E). Statistically significant downregulation was found in three genes—GNG4, EYA2, and ENPP2 (Fig. 3 F). The profiles of highly expressed genes varied between the biopsies and the PDXs (Fig. 3 G). Gene ontology enrichment analysis did not reveal any significant associations. GNA demonstrated differences in the network interactions of specific genes (Figure S6), although a high degree of similarity in communication properties was noted (Fig. 3 H). PCA indicated a strong correlation in the expression profiles between the two groups (Fig. 3 I). Breast cancer PDXs stability during passages in mice We evaluated whether our PDXs maintained key characteristics across multiple mouse passages, comparing them to the original biopsy. Our analysis focused on PDXs from three passages (P1-P3). H&E staining and IHC analyses demonstrated that the histological type and differentiation grade were preserved across passages (Figure S7 A). IHC results for the ER, PR, HER2, and Ki67 markers showed minimal changes, indicating consistent marker expression between PDX passages and biopsy samples (Figure S7 B). Genomic and gene expression analyses revealed high consistency across passages. Of the 175 genes analyzed, 88 were mutated in both biopsy and PDXs, with 80 mutations common across all samples (Fig. 4 A). The number of SNV and mutation subtypes remained similar between biopsy and PDXs (Fig. 4 B), particularly in critical genes associated with BC (Fig. 4 C). RNA analysis identified 579 DEGs across samples. Among these, 35 were common, while others were specific to either the biopsy or specific PDX passages (Fig. 4 D). Cluster analysis revealed distinct expression trends, particularly in genes related to DNA damage, epithelial-mesenchymal transformation (EMT), cell proliferation, and tumor microenvironment (TME), with notable similarities in NOTCH and TGF-beta pathways (Figs. 4 E and F). GO analysis of high DEGs in biopsies compared to PDXs showed strong enrichment in functions like "peptide receptor activity" and biological processes such as "chemokine-mediated signaling" and "leukocyte migration." Additionally, significant enrichment was observed for "extracellular space" in cellular components (Fig. 4 G). GNA demonstrated high similarity in communication capacity between biopsies and different passages of PDXs (Fig. 4 H), although the gene communication varied among the PDXs passaging (Figure S8). Breast cancer PDXs intra-tumoral heterogeneity To improve our understanding of tumor stability in mice, we further studied the intra-tumoral heterogeneity of our PDXs by analyzing specimens from five distinct tumor sample regions: right, left, middle, upper, and lower. H&E and IHC staining revealed no significant histological type or differentiation changes (Figures S9 A and B), although variations in specific biomarkers, HER2 and Ki67 levels were observed. Genomic mutation analysis across the different regions showed strong correlations, with only minor gene variations (Fig. 5 A and B). RNA analysis identified 524 DEGs, with 358 shared among all groups (Fig. 5 C). DEG profiles were consistent across regions (Figs. 5 D), and correlation analysis showed a strong relationship among tumor regions, with Pearson coefficients between 0.79 and 0.98. The PDX right specimen had the lowest correlation, with values between 0.60 and 0.82. GNA analysis supported the DEGs results, demonstrating high concordance between the different PDX regions (Fig. 5 E). Engraftment site's effect on PDX histopathology and molecular features Finally, we examined the impact of PDX engraftment sites, i.e., heterotrophic subcutaneous (SC) and orthotopic (OR) transplant sites, on tumor features of our PDX models. H&E and IHC staining indicated a strong correlation between specimens from both sites (Figure S10A and B). DNA analysis identified 80 shared mutations across both PDX models (Fig. 6 A), with synonymous mutations being the most frequent (n = 58), followed by missense (n = 50) (Fig. 6 B). The DNA mutation profiles correlated strongly, yielding a Pearson coefficient 1 (P < 2.2 × 10^-16). RNA analysis revealed 395 DGEs, with 370 shared between the models (Fig. 6 C). In the OR model, 30 genes were upregulated, and six were downregulated (Fig. 6 D). The top expressed genes varied between the two engraftment sites (Fig. 6 E). Notable associations with signaling pathways of highly expressed genes (n = 28) were found in the OR model, particularly in adhesion, angiogenesis, and TGF-beta, which showed statistically significant differences (Fig. 6 F). The correlation coefficient between the models was 0.8 (P < 2.2 × 10^-16). GNA analysis supported the DEG results, demonstrating a high similarity between network communication at both engraftment sites (Fig. 6 G). Discussion Despite improvements in treatment, BC complexity poses significant challenges ( 2 , 25 ), which might be productively tackled by the development of better preclinical models. To address this need, we developed PDX models from BC patients. To evaluate our model feasibility, we studied factors affecting engraftment success and tumor stability across continuous passaging and different tumor regions and engraftment sites. Our model has reached an overall 15% engraftment success rate, consistent with previously reported studies ( 1 , 23 , 43 ). Clinical analysis revealed that tumor progression, indicated by disease recurrence, was linked to higher rates of successful PDX engraftment and faster growth rates. Transcriptomic analysis revealed distinct gene expression patterns, identifying unfavorable prognostic biomarkers as predictors for successful PDXs( 44 – 48 ) and more favorable outcomes for failed models ( 49 – 55 ). We also observed differences in gene network communication between successful and failed PDXs. Our findings indicate that specific genes associated with tumor aggressiveness and the tumor microenvironment (TME) impact the success rate of PDXs and could serve as potential biomarkers. These biomarkers consist of highly expressed genes, and those exhibit strong communication functionality. Patients' selection based on these biomarkers will likely improve biopsy engraftment and lead to more successful preclinical models. PDXs and parental biopsies analysis revealed strong alignment in histopathologic and genomic features. However, biopsies showed higher mutation rates and genomic drift at the individual patient level, likely due to clonal evolution and variations in the TME ( 23 , 56 ). PDXs retained key mutations in genes such as TP53, BRCA1, and BRCA2, alongside several homologous recombination repair (HRR) genes like ATM and PALB2. This genetic stability is critical for treatment studies, as HRR deficiencies were suggested to enhance sensitivity to PARP inhibitors and platinum-based therapies ( 57 ). RNA analysis indicated high gene expression and network communication similarity between biopsies and PDXs, although DGEs associated with disease aggressiveness differed. Evaluating successful BC PDXs across passages (P1-P3) in mice showed minimal histopathological changes. Genomic analysis revealed that mutations in patient biopsies were mainly conserved. DGEs were primarily observed in DNA damage repair, EMT, ER signaling, and proliferation genes. Notable differences were seen in tumor microenvironment interactions and immune responses, aligning with previous data on PDX molecular drift ( 1 , 58 ). Signaling pathway analysis indicated increased ER, Notch, and TGF-beta pathways activity in biopsies and early PDX passages, while Hedgehog and Wnt signaling showed varying expressions. This heightened pathway activity may suggest greater tumor aggressiveness ( 59 ) and reflect differences between biopsies and PDXs growth environment ( 60 ). Studies indicate that the surrounding stroma and immune interactions influence ITH, which is essential for drug screening ( 56 ). Our analysis of tumor samples taken from different regions of PDXs revealed significant consistency in histopathological and molecular characteristics, with only minor variations in the genomic landscape and gene expression profiles. While we observed variations in gene network communication, the effects of these variations on PDX growth and heterogeneity remain unclear. We also explored how transplantation sites impact PDX characteristics. Histopathological analyses revealed only slight differences between SC and OR PDX models. While genomic analysis showed identical mutation counts and types, gene expression data indicated that the OR model had more upregulated genes linked to tumor proliferation and migration. In line with previously published studies ( 1 , 23 ), these findings suggest that OR PDXs may better represent the complexity of BC. However, notable differences were observed only in genes related to the TGF-beta signaling pathway, whose role in BC progression remains unclear ( 61 ). Overall, a strong correlation between DGE levels and communication capacity was found between the two models. Our findings provide valuable insights into the stability and fidelity of BC PDXs. We have identified potential biomarkers and predictors linked to gene expression levels and communication capacity that may influence these models' success and growth kinetics. However, our study has some limitations. Due to the complexity and low engraftment rates, we analyzed a relatively small number of PDXs. Nevertheless, our comprehensive analysis of various aspects of these models offers essential information for their enhancement. Additionally, the patient cohort restricted our outcome measures. To address this limitation, we used time intervals for disease recurrence to evaluate tumor aggressiveness. Finally, our study utilized pre-selected panels of DNA and RNA instead of performing exhaustive analyses. This approach allowed us to focus on results related to known cancer-related panels. Our data demonstrates that our PDX models effectively replicate key biological characteristics of their original tumors, highlighting their potential for personalized treatment studies. Further research is needed to evaluate our models' clinical relevance and impact on patient care. Declarations Declaration of Conflict of Interest The authors declare no conflict of interest. Ethics statement This study was approved by Soroka Medical Center's institutional review board (IRB) (SOR-0217-22). All the patients participating in this study provided signed informed consent. Author Contribution MM, AS, and RK designed the experiments. MM performed all the animal work. MM and SN acquired and processed biopsy samples. MM and SN acquired and processed demographic and clinical patient data. NA executed the pathologic procedures, and BD performed the HE and IHC pathologic analysis. AS, MM, RE, JHO, MiM, LN, and RK performed data analysis. The manuscript was written by AS and MM and edited by BD, LN, and RK. Acknowledgment We acknowledge the Breast Cancer Research Foundation (Grant Number MATH-23-002) for funding this research study. 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Available from: https://biosignaling.biomedcentral.com/articles/ 10.1186/s12964-024-01812-6 Buck MB, Knabbe C. TGF-beta signaling in breast cancer. Ann N Y Acad Sci [Internet]. Ann N Y Acad Sci; 2006 [cited 2025 Feb 4];1089:119–26. Available from: https://pubmed.ncbi.nlm.nih.gov/17261761/ Tables Table. 1 | Patient demographics, clinical and pre-clinical features Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-6730867","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":469327022,"identity":"3851fa36-c3e2-47e4-a0fa-a0c7a95d3004","order_by":0,"name":"Ariel Sobarzo","email":"","orcid":"","institution":"Ben-Gurion University of the Negev","correspondingAuthor":false,"prefix":"","firstName":"Ariel","middleName":"","lastName":"Sobarzo","suffix":""},{"id":469327025,"identity":"b1679723-872b-43e1-bb76-d2c20e2ed8f9","order_by":1,"name":"Maha Msamra","email":"","orcid":"","institution":"Ben-Gurion University of the Negev","correspondingAuthor":false,"prefix":"","firstName":"Maha","middleName":"","lastName":"Msamra","suffix":""},{"id":469327029,"identity":"6705079e-aa68-4437-857e-954d632429f6","order_by":2,"name":"Rena Elkin","email":"","orcid":"","institution":"Memorial Sloan Kettering Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Rena","middleName":"","lastName":"Elkin","suffix":""},{"id":469327030,"identity":"b45ac116-63c1-4089-9f04-68c1c6261a58","order_by":3,"name":"Ravit Agasi","email":"","orcid":"","institution":"Soroka University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Ravit","middleName":"","lastName":"Agasi","suffix":""},{"id":469327031,"identity":"03ec93e7-4dd1-4f5a-8a90-adc3e0bc5d71","order_by":4,"name":"Natalie Aizenberg","email":"","orcid":"","institution":"Soroka University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Natalie","middleName":"","lastName":"Aizenberg","suffix":""},{"id":469327032,"identity":"d6b6743e-e32a-4dc8-80eb-deef150810d1","order_by":5,"name":"Jung Hun Oh","email":"","orcid":"","institution":"Memorial Sloan Kettering Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Jung","middleName":"Hun","lastName":"Oh","suffix":""},{"id":469327033,"identity":"8e75aa03-9a67-4ab3-afc6-106ba188567e","order_by":6,"name":"Bertha Delgado","email":"","orcid":"","institution":"Soroka University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Bertha","middleName":"","lastName":"Delgado","suffix":""},{"id":469327034,"identity":"0db1069b-c106-4f24-9a3e-26d2f4239103","order_by":7,"name":"Larry Norton","email":"","orcid":"","institution":"Memorial Sloan Kettering Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Larry","middleName":"","lastName":"Norton","suffix":""},{"id":469327036,"identity":"5e0b2b1e-1a93-445a-a8b7-ee1f353cfd0b","order_by":8,"name":"Roy Kessous","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYBACCRiDH8ZiYydWi+QMmBZmYrUY3ICxCGmRbG9//OFjW12+8e32axIMNXYMfIS0SPOcMZOc2XbYctudM2USDMeSCTtMTiKHjZm37YCB2Y2cNAkGtgNEaJF//vgzb1udgfEMkJZ/RGiRlmAwkOZtYzYwkEg/JsHYRoQWyZ4cM8kZ5w4bSNw5w2yR2JfMQziQjx9//OFDWZ0B/+z2hzc+fLOTk29vIKAHAXgMGBKAJNHqgYD9ASmqR8EoGAWjYAQBABSMN7+hWY9bAAAAAElFTkSuQmCC","orcid":"","institution":"Ben-Gurion University of the Negev","correspondingAuthor":true,"prefix":"","firstName":"Roy","middleName":"","lastName":"Kessous","suffix":""}],"badges":[],"createdAt":"2025-05-23 08:23:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6730867/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6730867/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84530151,"identity":"e9cf1f31-629f-45e3-ad78-bfc6f94f34d8","added_by":"auto","created_at":"2025-06-13 05:58:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":305435,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBreast cancer biopsies with successful and failed PDX engraftment\u003c/strong\u003e. Twenty-two tumor biopsies from breast cancer patients were collected during surgery and implanted into NSG-SGM3 mice, resulting in 3 successful patient-derived xenograft (PDX) models. (\u003cstrong\u003eA\u003c/strong\u003e) A Venn diagram shows each group's mutated genes identified in successful vs. failed PDX models and exclusive mutations. (\u003cstrong\u003eB\u003c/strong\u003e) Types of single-nucleotide variants (SNVs) in successful (light blue) and failed (pink) PDX models, including synonymous, missense, frameshift, splice, and stop mutations. (\u003cstrong\u003eC\u003c/strong\u003e) Genes with the most missense mutations in both successful and failed PDX models. (\u003cstrong\u003eD\u003c/strong\u003e) Genes with the highest fold-change ratios of missense mutations between model types. (\u003cstrong\u003eE\u003c/strong\u003e) A scatter plot illustrates gene expression, identifying up-regulated (pink), down-regulated (light blue), and unchanged (gray) genes. (\u003cstrong\u003eF\u003c/strong\u003e) The top twenty differentially expressed genes (DEGs) are shown by their log2 fold changes (FC). (\u003cstrong\u003eG\u003c/strong\u003e) Statistically significant DEGs are presented with mean mRNA counts ± standard error, with significance levels indicated by asterisks: *P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001, ****P \u0026lt; 0.0001 (ANOVA). (\u003cstrong\u003eH\u003c/strong\u003e) Gene ontology enrichment analysis of DEGs found only in failed PDXs covers biological processes, cellular components, and molecular functions. (\u003cstrong\u003eI\u003c/strong\u003e) Gene network analysis (GNA) curvature of top differential interactions between successful and failed engraftment biopsies. (For additional details, refer to supplemental file 1.) The edges and nodes are color-coded based on the average difference in edge and scalar curvature between successful and failed engraftment biopsies: blue indicates greater cooperation in successful biopsies. In comparison, red suggests less cooperation in failed cases. Genes with the most significant (positive and negative) differences in scalar curvature are enlarged for emphasis. (\u003cstrong\u003eJ\u003c/strong\u003e) Unsupervised PCA cluster plots of expression changes for genes chosen (n = 40, see Fig. 3F) are displayed, with individuals colored by cluster membership and 95% confidence intervals represented by ellipses.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6730867/v1/92f94fa06e937e7bbeea9f9e.png"},{"id":84530153,"identity":"e9ce689d-45fe-4537-8cce-16072725d409","added_by":"auto","created_at":"2025-06-13 05:58:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":233752,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGrowth patterns of breast cancer PDX grafts in NSG-SGM3 mice. \u003c/strong\u003eFollowing the engraftment of tumors in mice, the size of PDXs was measured regularly. Mice were euthanized once the xenograft reached a size of 500 mm³ or if the mice exhibited severe clinical symptoms. The PDX was then serially transferred to the next generation of mice. A growing PDX was considered a successful engraftment if its volume exceeded 200 mm³. Three PDX models have been successfully established based on these criteria: patients 11, 18, and 26. (\u003cstrong\u003eA\u003c/strong\u003e) Growth kinetics of breast cancer (BC) PDXs during initial engraftment. (\u003cstrong\u003eB\u003c/strong\u003e) Growth kinetics of successful PDX model passaging in NSG-SGM3 mice. (\u003cstrong\u003eC\u003c/strong\u003e) A panel illustrating the passage patterns of the three successful PDXs engrafted in NSG-SGM3 mice over 130 weeks. Each colored bar represents the time from the engraftment of the PDX to the subsequent passage, along with the passage number. Mouse icons indicate the transfer of the PDX to a new generation of NSG-SGM3 mice. (\u003cstrong\u003eD\u003c/strong\u003e) The panel shows the time intervals between the engraftment of PDXs and their subsequent passages: from the first passage to the second passage (P1–P2) and the second passage to the third passage (P2–P3). The lines represent the median results, and asterisks signify significance levels, with *P \u0026lt; 0.05 as determined by ANOVA.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6730867/v1/f139142d7204e7896170eaf4.png"},{"id":84530155,"identity":"23e7dc47-0d90-4c99-b3f2-4e19d22ff465","added_by":"auto","created_at":"2025-06-13 05:58:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":675252,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConcordance of Molecular Features Between PDXs and Source Breast Cancer Biopsies. \u003c/strong\u003eBiopsies and their corresponding PDXs from patients 11, 18, and 26 were analyzed for DNA mutation profiles, focusing on single-nucleotide variations (SNVs) and gene expression. (\u003cstrong\u003eA\u003c/strong\u003e) A Venn diagram shows the number of mutated genes in biopsies compared to matched PDXs, highlighting exclusive mutations in each group. (\u003cstrong\u003eB\u003c/strong\u003e) An oncoplot displays the DNA mutation landscape for selected genes, with mutations color-coded as synonymous (gray), missense (light blue), and splice (light green). (\u003cstrong\u003eC\u003c/strong\u003e) The types of SNVs and mutations in biopsies (light blue) and PDXs (pink), including synonymous (syn), missense (mis), frameshift (fra), and splice (spl). (\u003cstrong\u003eD\u003c/strong\u003e) A Venn diagram illustrates differentially expressed genes (DEGs) in biopsies and matched PDXs. (\u003cstrong\u003eE\u003c/strong\u003e) A scatter plot shows gene expression, identifying up-regulated (pink), down-regulated (light blue), and unchanged genes (gray). (\u003cstrong\u003eF\u003c/strong\u003e) A volcano plot highlights statistical differences in gene expression, plotting log2 fold change (X-axis) against -log10 p-value (Y-axis), with significant genes (P \u0026lt; 0.05) marked. (\u003cstrong\u003eG\u003c/strong\u003e) Top DEGs between biopsies and matched PDXs. (\u003cstrong\u003eH\u003c/strong\u003e) Ridge plot histogram of gene network analysis (GNA), showing differences in gene curvatures between PDXs and source biopsies (for additional details, refer to Supplemental Material file 1). (\u003cstrong\u003eI\u003c/strong\u003e) Unsupervised PCA cluster plots show fold gene expression changes, with clusters indicated by color and confidence intervals represented by ellipses. A green circle indicates a correlation.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6730867/v1/ee8f3c93fe286fde5be59cf4.png"},{"id":84530561,"identity":"3036a3a6-e888-4970-b8ee-bacb25dbd49b","added_by":"auto","created_at":"2025-06-13 06:06:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":542148,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular consistency of breast cancer biopsy during PDX passages.\u003c/strong\u003e To determine if our PDXs maintain the molecular characteristics of the original tumors across multiple mouse generations, we analyzed DNA mutations and RNA gene expression in PDXs over three passages (P1-P3). The figure shows the results from breast cancer patient 11. (\u003cstrong\u003eA\u003c/strong\u003e) A Venn diagram shows the number of mutated genes found in the biopsy versus PDX samples, highlighting exclusive mutations in each. (\u003cstrong\u003eB\u003c/strong\u003e) The types of single-nucleotide variants (SNVs) detected in both the biopsy and PDX samples include synonymous (syn), missense (mis), frameshift (fra), and splice (spl). (\u003cstrong\u003eC\u003c/strong\u003e) An oncoplot illustrates the DNA mutation landscape in selected genes for both samples, color-coded by mutation type. (\u003cstrong\u003eD\u003c/strong\u003e) A Venn diagram presents the differentially expressed genes (DEGs) in the biopsy compared to PDX passages. (\u003cstrong\u003eE and F\u003c/strong\u003e) Cluster analysis identifies shared (\u003cstrong\u003eE\u003c/strong\u003e) and unshared (\u003cstrong\u003eF\u003c/strong\u003e) DEG trends between biopsy and PDXs, supported by a heatmap of selected DEGs (middle graph) and signaling pathways (lower graph). (\u003cstrong\u003eG\u003c/strong\u003e) Gene ontology enrichment analysis of distinctive DEGs in the biopsy compared to PDX passages, representing biological processes, cellular components, and molecular functions, with significant results highlighted by p-value and circle size indicating gene count. (\u003cstrong\u003eH\u003c/strong\u003e) Ridge plot histogram of gene network analysis (GNA), showing differences in node curvatures between biopsies and PDXs (for additional details, refer to Supplemental Material file 1).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6730867/v1/963d93ac5a7d47c5f0cbd961.png"},{"id":84531578,"identity":"693c8015-6b79-4709-a8b2-da9236d9c17c","added_by":"auto","created_at":"2025-06-13 06:14:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":314214,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBreast Cancer PDX Model: Molecular Intra-Tumoral Heterogeneity. \u003c/strong\u003eTo investigate intra-tumoral heterogeneity, we analyzed PDX specimens obtained from five tumor regions: right (R), left (L), middle (M), upper (U), and lower (L). The figure shows the results for breast cancer patient 18 (PDX passage 2). (\u003cstrong\u003eA\u003c/strong\u003e) A Venn diagram shows the mutated genes in different PDX regions, highlighting exclusive mutations. (\u003cstrong\u003eB\u003c/strong\u003e) The mutated genes with varying SNV counts across PDX regions are color-coded as synonymous (syn), missense (mis), and frameshift (fra). (\u003cstrong\u003eC\u003c/strong\u003e) A Venn diagram illustrates differentially expressed genes (DEGs) identified in the regions. (\u003cstrong\u003eD\u003c/strong\u003e) A ridgeline plot shows overlapping density patterns of DEGs, with log2 transcript values on the x-axis. (\u003cstrong\u003eE\u003c/strong\u003e) The ridge plot histogram of gene network analysis (GNA) shows differences in node curvatures between PDX regions (for additional details, refer to Supplemental Material file 1).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6730867/v1/6b1d985a62657413e0a52b08.png"},{"id":84531577,"identity":"ee6555df-1d2f-48c7-9452-513bdfd92877","added_by":"auto","created_at":"2025-06-13 06:14:05","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":417137,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison between PDX engraftment site in NSG-SGM3 mice. \u003c/strong\u003e\u003cbr\u003e\nTo evaluate the impact of the transplant site, we analyzed the molecular characteristics of PDX models engrafted subcutaneously (SC) in the lower back and orthotopically (OR) in the fourth mammary gland of mice, comparing these to the original biopsy. Figure 6 summarizes the results from breast cancer patient 18 (PDX passage 2): (\u003cstrong\u003eA\u003c/strong\u003e) A Venn diagram shows mutated genes in the biopsy and PDX models. (\u003cstrong\u003eB\u003c/strong\u003e) Single-nucleotide variants (SNVs) are found in both, indicating exclusive mutations. (\u003cstrong\u003eC\u003c/strong\u003e) A Venn diagram illustrates differentially expressed genes (DEGs) in the biopsy and PDX models. (\u003cstrong\u003eD\u003c/strong\u003e) A scatter plot presents gene expression changes between OR and SC PDX models, categorizing genes as up-regulated (pink), down-regulated (light blue), and unchanged (gray). (\u003cstrong\u003eE\u003c/strong\u003e) The top DEGs are listed by fold-change ratios (log2) between the OR and SC models. (\u003cstrong\u003eF\u003c/strong\u003e) A combined Sankey dot plot chart shows upregulated DEGs in the OR model, with the left side representing pathways and the right-side showing gene counts and p/FDR values. (\u003cstrong\u003eG\u003c/strong\u003e) Ridge plot histogram of gene network analysis (GNA), showing differences in gene curvatures between PDX engraftment sites (for additional details, refer to Supplemental Material file 1).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6730867/v1/906be43be46d57cddc2851b1.png"},{"id":86613099,"identity":"4fa3e68d-8753-4b0e-97d1-ef7677206197","added_by":"auto","created_at":"2025-07-13 21:16:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3609900,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6730867/v1/a7a06b37-82c6-4bc9-892a-aee118aae293.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Personalizing Breast Cancer Treatment with Patient-derived Xenografts (PDX): Analyzing PDXs Fidelity, Stability, and Intra-Tumoral Heterogeneity","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer (BC) is a heterogeneous disease characterized by diversity in histology and molecular and metastatic behavior. (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). It is the leading cause of cancer-related death in women worldwide (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Classification of BC subtypes based on biological markers is divided into three subtypes: those expressing estrogen receptor alpha (ER+) (and typically also the progesterone receptor (PR +)), those that overexpress ERBB2 (HER2+), and those that express none of these markers (termed \u0026ldquo;triple negative\u0026rdquo; breast cancer (TNBC)) (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Each type correlated with a unique clinical course, prognostics, and treatment response. (\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe heterogeneity and complexity of BC have presented significant obstacles in identifying effective therapies for individual patients (\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). BC contains considerable inter and intra-tumoral heterogeneity (\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). This heterogeneity is critical for making effective treatment decisions and presents significant challenges to generating and using relevant pre-clinical models (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Determining a patient's response to treatment and the treatment's effects is complex and challenging. It involves intricate factors, including tumor characteristics, host variables, and environmental influences, as well as their synergistic interactions (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Thus, there is an increasing need for personalized, predictive preclinical models that can accurately represent the tumor.(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePatient-derived xenograft (PDX) models have been developed and utilized to study solid tumors, including BC (\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). These models have been suggested to preserve the patient's tumor biological and molecular characteristics and heterogeneity (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), offering valuable tools for testing novel therapies in preclinical models (\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). However, their potential to serve as a \"true\" preclinical tool is still questioned (\u003cspan additionalcitationids=\"CR29 CR30\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). While PDX models for BC have been introduced, their development and establishment remain challenging (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). BC PDXs were shown to be affected by the quality of the tumor sample, the choice of engraftment site, the use of host mice, and the timing and method of engraftment (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). This resulted in a relatively low engraftment rate, hampering their widespread use and emphasizing their need (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). One of the main challenges of the PDX model is its ability to accurately reflect the tumor's properties over multiple passages in mice, which allows for prolonged study durations (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Developing such models is critical, particularly in biopsies collected from patients with advanced progressive disease, likely showing treatment resistance (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study established PDX models of primary luminal A subtype BC using innovative NSG-SGM3 mice. Our study aimed to evaluate various clinical and preclinical factors that may impact the success of PDX engraftment, growth rates, fidelity, and stability across multiple passages in the mice. We focused on differences with the original patient biopsy, highlighting similarities and discrepancies.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient population\u003c/h2\u003e \u003cp\u003ePatients enrolled in this study had histologically confirmed invasive breast cancer with tumors larger than one cm that were detected through physical examination or imaging. Those who achieved complete radiologic remission or a significant reduction in tumor mass following neoadjuvant chemotherapy (NAC) were excluded. Twenty-two surgically resected tumor samples were collected from March 2022 to 2024.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthics statement\u003c/h3\u003e\n\u003cp\u003eThe institutional review board (IRB) (SOR-0217-22) approved this research. All patients signed informed consent forms before participation.\u003c/p\u003e\n\u003ch3\u003eData acquisition\u003c/h3\u003e\n\u003cp\u003eDemographic and clinical features were gathered from the patient\u0026rsquo;s medical records, including medical history and previous surgical and pathological reports. The following data were collected for each patient: age, number of prior pregnancies, body mass index (BMI), smoking status, tumor stage at diagnosis, NAC treatment, and treatment response. A qualified pathologist retrospectively reviewed previous histopathological findings for all surgical specimens.\u003c/p\u003e\n\u003ch3\u003eAnimals\u003c/h3\u003e\n\u003cp\u003eNonobese diabetic/severe combined immune deficiency (NOD/SCID) (4\u0026ndash;5-week-old) triple transgenic NSG-SGM3 mice were obtained from the Jackson Laboratory (Sacramento, CA, USA). Animals were housed in individually ventilated cages at an appropriate temperature (21\u0026ndash;25\u0026deg;C) with a 12 h light/dark cycle and free access to food and water. All animal care and experiments were performed under an animal protocol approved by the Institutional Animal Care and Use Committee at Ben-Gurion University and the Israel Ministry of Health (MOH) (authorization number IL-13-04-2021D).\u003c/p\u003e\n\u003ch3\u003eEstablishment of Breast cancer PDXs\u003c/h3\u003e\n\u003cp\u003eFollowing surgery and after obtaining a pathological conformation for the specific sample, tumor samples (approximately 2\u0026ndash;3 mm\u003csup\u003e3\u003c/sup\u003e) from each patient with BC were engrafted into female NSG-SGM3 mice. Tumor samples were subcutaneously (SC) implanted into the flanks for the heterotrophic model. They were implanted into the fourth mammary fat pad for the orthotropic (OR) model. Tumor size was measured by a caliper twice a week. The modified ellipsoidal formula calculated the tumor volume: V = \u0026frac12; (Length \u0026times; Width\u003csup\u003e2\u003c/sup\u003e). When the tumor size reached 500 mm\u003csup\u003e3\u003c/sup\u003e for the SC model and 200 mm\u003csup\u003e3\u003c/sup\u003e for the OR, mice were euthanized, and the tumors were harvested for subsequent analysis, including sequencing and preparation of formalin-fixed paraffin-embedded blocks. Engraftment development was monitored for 60 weeks.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMolecular Analysis\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eDNA Extraction, Library Construction and Sequencing\u003c/h2\u003e \u003cp\u003eDNA analysis was done on a panel of 175 selected genes involved in cancer development. The list of genes is shown in the supplemental material (Table S1). DNA extraction was done using the GRS Genomic DNA Kit (Grisp, cat. no. GK03). DNA concentrations were measured using NanoDrop One (Thermo Scientific). Genomic DNA (\u0026gt;\u0026thinsp;3 \u0026micro;g) was used to construct DNA libraries. To generate DNA sequencing libraries, target enrichment was performed using the xGen cfDNA and FFPE DNA library prep kit (Integrated DNA Technologies (IDT), Coralville, Iowa, USA) according to the manufacturer's instructions (IDT, cat. no. 10010206). DNA capture and hybridization were done using the xGen\u0026trade; Custom Hybridization Capture Panels (IDT, Coralville, Iowa, USA) according to the manufacturer's instructions. DNAs were sequenced with Illumina HiSeq 2000 (Illumina Inc., San Diego, CA, USA). Sequence reads were aligned to the California Santa Cruz hg19 human genome release. Somatic mutation analysis was done with the SEQ platform (Genomize, Istanbul, Turkey) and verified using Sanger sequencing. We focused on exonic regions exhibiting single nucleotide variation (SNV) frequencies above 10% of the total readouts.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eRNA Extraction, sequencing, and analysis\u003c/h3\u003e\n\u003cp\u003eTotal RNA was extracted from patient BC biopsies and their PDXs using the Triple Xtractor direct RNA Kit (Grisp, cat. no. GK23.0100). RNA (0.1\u0026ndash;1.0 \u0026micro;g) was used to generate cDNA using the Poly(A) mRNA Universal Plus RNA-Seq with NuQuant (Tecan Genomics, cat. no. 0361). RNAs were sequenced on an Illumina NovaSeq 6000. TopHat2 was used to align RNA-seq reads to the reference. DESeq2 was used to identify differentially expressed genes. Only genes with an mRNA mean count above two were analyzed. A differential gene expression (DGE) was scored when there was \u0026gt;\u0026thinsp;2 fold change (FC) in RNA levels.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGene Network Analysis\u003c/h2\u003e \u003cp\u003eGene network analysis (GNA) was performed as was previously described (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e), using the Human Protein Reference Database (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). To define the network, genes associated with the engraftment outcome and differences between PDXs and matched patient biopsies were evaluated for their cooperation capacity by GNA and quantified using Ollivier-Ricci network curvature (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). For further details, see Supplementary Material File 1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eHistopathology\u003c/h2\u003e \u003cp\u003ePatient and PDX tumor tissue sections were freshly cut to 4 \u0026micro;m. According to manufacturer instructions, Hematoxylin and Eosin (H\u0026amp;E) staining was automated (Ventana HE 600; Ventana Medical Systems, Tucson, AZ, USA). ER and PR status were determined through immunohistochemical staining of formalin-fixed, paraffin-embedded tumor tissue sections. Positive staining was defined as nuclear staining in at least 1% of the tumor cells, and the hormone receptor-positive (HR+) type was defined as ER and PR IHC-positive. HER2 positivity was defined as IHC 2+\u0026lt;. The percentage of tumor cells showing any degree of nuclear staining for Ki-67 was recorded for each tumor. A certified medical pathologist analyzed the H\u0026amp;E and IHC images and reviewed the pathological type and architecture between the original and xenograft tumor to confirm the diagnosis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eLogistic regression analysis, chi-squared, or Fisher's exact tests were used to compare PDX engraftment status, patient characteristics, and pathological parameters. The significance of differences between normally distributed molecular data from PDXs and parental patient biopsies was assessed by T-tests and one-way ANOVA. The statistics package of Prism software v9.1 (GraphPad Software, Boston, Massachusetts, USA) was used for all statistical analyses. A \u003cem\u003ep-\u003c/em\u003evalue (two-sided) of \u0026lt;\u0026thinsp;0.05 was considered to indicate a significant difference. SRplot was used for data visualization and graphing (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eEstablishment of breast cancer PDX mouse model platform in NSG-SGM3 mice\u003c/h2\u003e \u003cp\u003eAfter pathological confirmation, twenty-two biopsies were engrafted in mice within two hours of collection. Molecular analyses classified biopsies as luminal A (n\u0026thinsp;=\u0026thinsp;17), luminal B (n\u0026thinsp;=\u0026thinsp;3), and basal (n\u0026thinsp;=\u0026thinsp;2). Three biopsies successfully formed tumors in mice, creating PDX models, all exhibiting a luminal A phenotype. These successful PDXs were then passed serially to create a \"hot\" sample bank for future histopathological and molecular analyses. Figure S1 outlines the experimental procedures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePatient demographics, clinical and pre-clinical features\u003c/h2\u003e \u003cp\u003eWe investigated the correlation between PDX engraftment success and patient demographics, collecting data summarized in Table\u0026nbsp;1. The study included twenty-two female donors with an average age of 66 (31\u0026ndash;87). Tumor types comprised infiltrated lobular carcinoma (n\u0026thinsp;=\u0026thinsp;4) and infiltrated ductal carcinoma (n\u0026thinsp;=\u0026thinsp;18), all sourced from primary BC lesions.\u003c/p\u003e \u003cp\u003ePathological and molecular features were analyzed, showing typical ductal carcinoma characteristics through H\u0026amp;E staining, including pleomorphic cells and high proliferation rates. IHC staining of Ki-67, ER, PR, and HER2 revealed varied expression levels (Figure S2). Targeted DNA analysis of 175 cancer driver genes across samples identified mutations in 104 genes, with six genes\u0026mdash;CDC7, DKC1, ERCC6, mTOR, MUC16, and VCAM\u0026mdash;mutated in all cases. Patient samples had an average of 275 SNVs, mostly synonymous and missense (Figure S3A and S3B). High mutation rates were noted in genes such as MUC16, LAMC3, FANCD2, ASPM, and FN1 (Figure S3C). RNA sequencing of PAM50 genes illustrated the common BC subtypes (Figure S3D).\u003c/p\u003e \u003cp\u003e \u003cb\u003eFactors affecting the success rate of initial PDX engraftment.\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePatient demographic and clinical data were analyzed for their correlation with the success of tumor biopsy engraftment in PDX models (Table\u0026nbsp;1). A significant correlation was found between engraftment success and tumor molecular subtype (p\u0026thinsp;=\u0026thinsp;0.0152), radiotherapy treatment history (p\u0026thinsp;=\u0026thinsp;2.94x10^\u003csup\u003e\u0026minus;\u003c/sup\u003e13), and hormonal therapy (p\u0026thinsp;=\u0026thinsp;0.0077). In contrast, histopathological analysis showed no notable difference between successfully and failed engrafted biopsies (Figure S4).\u003c/p\u003e \u003cp\u003eWe identified 98 altered genes common to both groups, with six genes (DLGAP5, FOXO3, KIF2C, RECQL, TERT, TTK) exclusive to the failed engraftment group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Most alterations in both groups were synonymous substitutions, followed by missense mutations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Focusing on the missense mutation subtype, we observed that both groups had similar genes with the uppermost mutation rates (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), with the highest mutation rate in genes RAD51D, DNMT1, ESCO2, FOXM1, LAMB2, PALB2, APEX1, and MUTYH (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). However, no statistically significant differences in mutation types or frequencies between the two groups were found.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAnalysis of RNA from 778 genes associated with BC identified 534 differential DGEs. Among these, 98 were found to be upregulated and 129 downregulated in successfully engrafted biopsies (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Variations in the expression of the most prominent DGEs were noted between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). Statistically significant differences (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were detected in the expression levels of six genes: COL1A1, COL4A5, COL6A6, HSPB1, IFNA8, and MAP2K4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003eGene Ontology analysis of failed engraftment specimens showed a significant association with biological processes related to immune responses (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH). GNA revealed notable differences in communication properties, emphasizing distinct gene cooperation between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eI). Unsupervised PCA successfully differentiated the two biopsy groups based on the highest DGEs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eJ).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eFactors affecting PDX growth kinetics\u003c/h2\u003e \u003cp\u003eSuccessfully engrafted BC PDXs were passed through three rounds in NSG-SGM3 mice, and tumor sizes were measured twice weekly to evaluate PDX growth kinetics. The growth patterns for all models are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Notable variations in growth were observed between the initial engraftment and the subsequent passages in successful models (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The time until the initial successful PDX engraftment was 43 weeks for patient 11, 51 weeks for patient 18, and 33 weeks for patient 26 (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The average duration for the first passage was 46 weeks, followed by 39 weeks for the second passage and 23 weeks for the third passage (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). A statistically significant difference was found between the duration of the first and third passages, with a P-value of 0.0417 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eBreast cancer PDXs and biopsies comparison\u003c/h2\u003e \u003cp\u003eWe investigated whether the source tumor's key molecular and pathological characteristics are preserved after engraftment. The histopathological features of PDXs closely resembled those of the original patient biopsies (Figure S5).\u003c/p\u003e \u003cp\u003eAmong the 175 analyzed genes, 96 were mutated in the biopsies and their corresponding PDXs. Thirteen showed mutations exclusively in the biopsies; no new mutations were noted in the PDX samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Key genes like BRCA1/2, ASPM, and TP53 maintained similar mutational profiles in both samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). An analysis of mutation subtypes showed minor variations between the biopsies and PDXs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), with a high correlation in DNA mutations (Pearson correlation coefficient of 0.95, P\u0026thinsp;=\u0026thinsp;8.72 \u0026times; 10^-9).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRNA analysis revealed 627 DEGs, with 401 shared between the biopsies and PDXs and 90 or 136 specific to each group, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Of these, 114 were upregulated and 336 downregulated in the biopsies (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Statistically significant downregulation was found in three genes\u0026mdash;GNG4, EYA2, and ENPP2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). The profiles of highly expressed genes varied between the biopsies and the PDXs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). Gene ontology enrichment analysis did not reveal any significant associations. GNA demonstrated differences in the network interactions of specific genes (Figure S6), although a high degree of similarity in communication properties was noted (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH). PCA indicated a strong correlation in the expression profiles between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eI).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eBreast cancer PDXs stability during passages in mice\u003c/h2\u003e \u003cp\u003eWe evaluated whether our PDXs maintained key characteristics across multiple mouse passages, comparing them to the original biopsy. Our analysis focused on PDXs from three passages (P1-P3). H\u0026amp;E staining and IHC analyses demonstrated that the histological type and differentiation grade were preserved across passages (Figure S7 A). IHC results for the ER, PR, HER2, and Ki67 markers showed minimal changes, indicating consistent marker expression between PDX passages and biopsy samples (Figure S7 B).\u003c/p\u003e \u003cp\u003eGenomic and gene expression analyses revealed high consistency across passages. Of the 175 genes analyzed, 88 were mutated in both biopsy and PDXs, with 80 mutations common across all samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The number of SNV and mutation subtypes remained similar between biopsy and PDXs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), particularly in critical genes associated with BC (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRNA analysis identified 579 DEGs across samples. Among these, 35 were common, while others were specific to either the biopsy or specific PDX passages (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Cluster analysis revealed distinct expression trends, particularly in genes related to DNA damage, epithelial-mesenchymal transformation (EMT), cell proliferation, and tumor microenvironment (TME), with notable similarities in NOTCH and TGF-beta pathways (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE and F). GO analysis of high DEGs in biopsies compared to PDXs showed strong enrichment in functions like \"peptide receptor activity\" and biological processes such as \"chemokine-mediated signaling\" and \"leukocyte migration.\" Additionally, significant enrichment was observed for \"extracellular space\" in cellular components (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG). GNA demonstrated high similarity in communication capacity between biopsies and different passages of PDXs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH), although the gene communication varied among the PDXs passaging (Figure S8).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eBreast cancer PDXs intra-tumoral heterogeneity\u003c/h2\u003e \u003cp\u003eTo improve our understanding of tumor stability in mice, we further studied the intra-tumoral heterogeneity of our PDXs by analyzing specimens from five distinct tumor sample regions: right, left, middle, upper, and lower.\u003c/p\u003e \u003cp\u003eH\u0026amp;E and IHC staining revealed no significant histological type or differentiation changes (Figures S9 A and B), although variations in specific biomarkers, HER2 and Ki67 levels were observed. Genomic mutation analysis across the different regions showed strong correlations, with only minor gene variations (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and B).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRNA analysis identified 524 DEGs, with 358 shared among all groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). DEG profiles were consistent across regions (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD), and correlation analysis showed a strong relationship among tumor regions, with Pearson coefficients between 0.79 and 0.98. The PDX right specimen had the lowest correlation, with values between 0.60 and 0.82. GNA analysis supported the DEGs results, demonstrating high concordance between the different PDX regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eEngraftment site's effect on PDX histopathology and molecular features\u003c/h2\u003e \u003cp\u003eFinally, we examined the impact of PDX engraftment sites, i.e., heterotrophic subcutaneous (SC) and orthotopic (OR) transplant sites, on tumor features of our PDX models. H\u0026amp;E and IHC staining indicated a strong correlation between specimens from both sites (Figure S10A and B). DNA analysis identified 80 shared mutations across both PDX models (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), with synonymous mutations being the most frequent (n\u0026thinsp;=\u0026thinsp;58), followed by missense (n\u0026thinsp;=\u0026thinsp;50) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). The DNA mutation profiles correlated strongly, yielding a Pearson coefficient 1 (P\u0026thinsp;\u0026lt;\u0026thinsp;2.2 \u0026times; 10^-16).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRNA analysis revealed 395 DGEs, with 370 shared between the models (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). In the OR model, 30 genes were upregulated, and six were downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). The top expressed genes varied between the two engraftment sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Notable associations with signaling pathways of highly expressed genes (n\u0026thinsp;=\u0026thinsp;28) were found in the OR model, particularly in adhesion, angiogenesis, and TGF-beta, which showed statistically significant differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). The correlation coefficient between the models was 0.8 (P\u0026thinsp;\u0026lt;\u0026thinsp;2.2 \u0026times; 10^-16). GNA analysis supported the DEG results, demonstrating a high similarity between network communication at both engraftment sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eDespite improvements in treatment, BC complexity poses significant challenges (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), which might be productively tackled by the development of better preclinical models. To address this need, we developed PDX models from BC patients. To evaluate our model feasibility, we studied factors affecting engraftment success and tumor stability across continuous passaging and different tumor regions and engraftment sites.\u003c/p\u003e \u003cp\u003eOur model has reached an overall 15% engraftment success rate, consistent with previously reported studies (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Clinical analysis revealed that tumor progression, indicated by disease recurrence, was linked to higher rates of successful PDX engraftment and faster growth rates. Transcriptomic analysis revealed distinct gene expression patterns, identifying unfavorable prognostic biomarkers as predictors for successful PDXs(\u003cspan additionalcitationids=\"CR45 CR46 CR47\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e) and more favorable outcomes for failed models (\u003cspan additionalcitationids=\"CR50 CR51 CR52 CR53 CR54\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). We also observed differences in gene network communication between successful and failed PDXs. Our findings indicate that specific genes associated with tumor aggressiveness and the tumor microenvironment (TME) impact the success rate of PDXs and could serve as potential biomarkers. These biomarkers consist of highly expressed genes, and those exhibit strong communication functionality. Patients' selection based on these biomarkers will likely improve biopsy engraftment and lead to more successful preclinical models.\u003c/p\u003e \u003cp\u003ePDXs and parental biopsies analysis revealed strong alignment in histopathologic and genomic features. However, biopsies showed higher mutation rates and genomic drift at the individual patient level, likely due to clonal evolution and variations in the TME (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). PDXs retained key mutations in genes such as TP53, BRCA1, and BRCA2, alongside several homologous recombination repair (HRR) genes like ATM and PALB2. This genetic stability is critical for treatment studies, as HRR deficiencies were suggested to enhance sensitivity to PARP inhibitors and platinum-based therapies (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). RNA analysis indicated high gene expression and network communication similarity between biopsies and PDXs, although DGEs associated with disease aggressiveness differed.\u003c/p\u003e \u003cp\u003eEvaluating successful BC PDXs across passages (P1-P3) in mice showed minimal histopathological changes. Genomic analysis revealed that mutations in patient biopsies were mainly conserved. DGEs were primarily observed in DNA damage repair, EMT, ER signaling, and proliferation genes. Notable differences were seen in tumor microenvironment interactions and immune responses, aligning with previous data on PDX molecular drift (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). Signaling pathway analysis indicated increased ER, Notch, and TGF-beta pathways activity in biopsies and early PDX passages, while Hedgehog and Wnt signaling showed varying expressions. This heightened pathway activity may suggest greater tumor aggressiveness (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e) and reflect differences between biopsies and PDXs growth environment (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStudies indicate that the surrounding stroma and immune interactions influence ITH, which is essential for drug screening (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). Our analysis of tumor samples taken from different regions of PDXs revealed significant consistency in histopathological and molecular characteristics, with only minor variations in the genomic landscape and gene expression profiles. While we observed variations in gene network communication, the effects of these variations on PDX growth and heterogeneity remain unclear.\u003c/p\u003e \u003cp\u003eWe also explored how transplantation sites impact PDX characteristics. Histopathological analyses revealed only slight differences between SC and OR PDX models. While genomic analysis showed identical mutation counts and types, gene expression data indicated that the OR model had more upregulated genes linked to tumor proliferation and migration. In line with previously published studies (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), these findings suggest that OR PDXs may better represent the complexity of BC. However, notable differences were observed only in genes related to the TGF-beta signaling pathway, whose role in BC progression remains unclear (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). Overall, a strong correlation between DGE levels and communication capacity was found between the two models.\u003c/p\u003e \u003cp\u003eOur findings provide valuable insights into the stability and fidelity of BC PDXs. We have identified potential biomarkers and predictors linked to gene expression levels and communication capacity that may influence these models' success and growth kinetics. However, our study has some limitations. Due to the complexity and low engraftment rates, we analyzed a relatively small number of PDXs. Nevertheless, our comprehensive analysis of various aspects of these models offers essential information for their enhancement. Additionally, the patient cohort restricted our outcome measures. To address this limitation, we used time intervals for disease recurrence to evaluate tumor aggressiveness. Finally, our study utilized pre-selected panels of DNA and RNA instead of performing exhaustive analyses. This approach allowed us to focus on results related to known cancer-related panels. Our data demonstrates that our PDX models effectively replicate key biological characteristics of their original tumors, highlighting their potential for personalized treatment studies. Further research is needed to evaluate our models' clinical relevance and impact on patient care.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eDeclaration of Conflict of Interest\u003c/h2\u003e \u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics statement\u003c/h2\u003e \u003cp\u003eThis study was approved by Soroka Medical Center's institutional review board (IRB) (SOR-0217-22). All the patients participating in this study provided signed informed consent.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMM, AS, and RK designed the experiments. MM performed all the animal work. MM and SN acquired and processed biopsy samples. MM and SN acquired and processed demographic and clinical patient data. NA executed the pathologic procedures, and BD performed the HE and IHC pathologic analysis. AS, MM, RE, JHO, MiM, LN, and RK performed data analysis. The manuscript was written by AS and MM and edited by BD, LN, and RK.\u003c/p\u003e\u003ch2\u003eAcknowledgment\u003c/h2\u003e \u003cp\u003eWe acknowledge the Breast Cancer Research Foundation (Grant Number MATH-23-002) for funding this research study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDobrolecki LE, Airhart SD, Alferez DG, Aparicio S, Behbod F, Bentires-Alj M, et al. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/17261761/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/17261761/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable. 1 | Patient demographics, clinical and pre-clinical features\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research 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Patient-derived xenografts (PDXs) have been developed to study tumor progression and personalized treatment strategies. Here, we assessed the stability and fidelity of BC PDXs in maintaining key characteristics of their source biopsies across generations and various engraftment sites in mice. Using NSG-SGM3 mice, we established PDX models from primary tumors of 22 patients, creating three luminal A subtype models. Our results showed high concordance in histopathological and molecular profiles between the PDXs and their corresponding biopsies. PDX engraftment success was linked to the aggressiveness of patients' tumors and specific gene expressions. We found strong correlations with genes associated with intrinsic tumor pathways and variability related to immune responses. Our findings highlight the effectiveness of BC PDXs in replicating patient tumors and provide insights into factors affecting engraftment, growth, and stability, enhancing their role in personalized BC research.\u003c/p\u003e","manuscriptTitle":"Personalizing Breast Cancer Treatment with Patient-derived Xenografts (PDX): Analyzing PDXs Fidelity, Stability, and Intra-Tumoral Heterogeneity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-13 05:58:01","doi":"10.21203/rs.3.rs-6730867/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b513518e-a4df-4298-bf02-eaee13eae845","owner":[],"postedDate":"June 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49829818,"name":"Biological sciences/Cancer/Breast cancer"},{"id":49829819,"name":"Health sciences/Oncology/Cancer/Cancer models"}],"tags":[],"updatedAt":"2025-07-13T21:08:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-13 05:58:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6730867","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6730867","identity":"rs-6730867","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}