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Background: The National Cancer Institute’s Patient-Derived Models Repository (NCI PDMR; https://pdmr.cancer.gov) has developed a large number of patient-derived xenograft (PDX) models from a diverse set of rare cancers. These models have been genomically characterized using whole-exome sequencing (WES) and RNAseq. The resource provides a unique opportunity to explore the genomic features of rare tumor models in NCI PDMR and to understand the oncogenic processes in pre-clinical models to identify biomarkers associated with therapeutic responses. Methods: Genomic characterization was done in 4-6 PDX samples across multiple passages and lineages from each model. As the samples exhibited a high level of genomic stability within each model, consensus mutation and copy number variation (CNV), microsatellite instability (MSI), genomic loss of heterozygosity (LOH), homologous recombination deficiency score (scarHRD), and mutational signature data were generated from WES. Fusions were identified from RNASeq data using Star-Fusion and FusionInspector. Gene set enrichment analysis was conducted from the gene expression data obtained from RNAseq. Results: 1) 233 PDX models have been developed and characterized from more than 45 different rare malignancies. Most frequent cancer types are different sarcomas (n=63), head & neck squamous cell carcinoma (n=61), and malignant fibrous histiocytoma (MFH) (n=11); 2) TP53 was the most frequently altered gene, mutated in 51% of models, followed by NOTCH1 (16%) and PIK3CA (11%). In terms of CNVs, ovarian epithelial cancer (OVT) showed relatively high chromosomal instability, while uterine endometrioid carcinoma (UEC) and synovial sarcoma (SYNS) had low instability; 3) MSI-H was observed in only 7 models. Esophageal adenocarcinoma (ESCA), OVT, and cervical squamous cell carcinoma (CESC) had high scarHRD and genomic LOH scores, while both scores were low in UEC and anal squamous cell carcinoma (ANSC). COSMIC v2 mutational signature 3 is significantly associated with a high scarHRD score (p-value < 0.01, Wilcoxon rank-sum test); 4) Characteristic fusions were observed in certain sarcoma models: SS18-SSX1 and ASPSCR1-TFE3 fusions were observed in SYNS and alveolar soft part sarcoma (ASPS) models respectively. EWSR1-FLI1 fusion was present in 2 out of 3 Ewing sarcoma (ES) models. 5) Gene set enrichment analysis from RNASeq data showed that epithelial-mesenchymal transition score could accurately distinguish carcinoma from sarcoma models, confirming the divergent gene expression programs. Conclusion: Comprehensive genomic characterization of NCI PDMR models generated from rare cancers solves an unmet need in the community. It will serve as a valuable resource for translational researchers interested in pre-clinical drug development and discovery. Citation Format: Li Chen, Rini Pauly, Ting-Chia Chang, Biswajit Das, Yvonne A. Evrard, Chris A. Karlovich, Tomas Vilimas, Alyssa Chapman, Nikitha Nair, Luis Romero, Anna Lee Fong, Amanda Peach, Shahanawaz Jiwani, Nastaran Neishaboori, Lindsay Dutko, Kelly Benauer, Gloryvee Rivera, Erin Cantu, Corinne Camalier, Thomas Forbes, Michelle Gottholm-Ahalt, John Carter, Suzanne Borgel, Chelsea McGlynn, Candace Mallow, Emily Delaney, Tiffanie Miner, Michelle A. Eugeni, Dianne Newton, Melinda G. Hollingshead, P. Mickey Williams, James H. Doroshow. Genomic characterization of PDX models from rare cancer patients in the NCI Patient-Derived Models Repository [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 80.

Background: The National Cancer Institute (NCI) has developed a Patient-Derived Models Repository (PDMR; https://pdmr.cancer.gov) of preclinical models including patient-derived xenografts (PDX), organoids (PDOrg) and patient-derived cell cultures (PDC). Extensive clinical annotation and genomic datasets are available for these preclinical models. However, it is unclear if the molecular profiles of the corresponding patient tumors are stably propagated in these models. We have previously demonstrated that PDX models from the NCI PDMR faithfully represent the patient tumors both in terms of genomic stability and tumor heterogeneity. Here, we conduct an in-depth investigation of genomic representation of patient tumors in the PDOrgs and PDCs. Methods: PDOrgs (n=64) and PDCs (n=94) were established from tumor fragments (i.e., initiator specimens) obtained either from patient specimens or from PDX specimens of early passage. For some models (n=19), both PDOrgs and PDCs were generated from the same tumor tissue; in fewer cases (n=4), PDCs were established from organoids derived from patient specimens. Whole Exome Sequencing and RNA-Seq were performed on all PDCs and PDOrgs, and data were compared with patient specimens or early passage PDXs. Results: A majority of the PDOrgs and PDCs have stably inherited the genome of the corresponding patient specimens based on the following observations: (1) >87% of PDOrgs and PDCs maintained similar copy number alteration profiles compared with the initiator specimens of the preclinical model; (2) the variant allele frequency (VAF) of clinically relevant mutations remained consistent between the PDOrgs, PDCs, and the initiator specimens, with none of the PDCs or PDOrgs deviating by >15% VAF; and (3) clinically relevant biomarkers (e.g., MSI, LOH, mutational signatures etc.) are concordant amongst the PDOrgs, PDCs, and the initiator specimens. We observed that the majority of SNVs and indels present in the initiator specimens were also found in the PDOrgs and PDCs, suggesting almost all the tumor heterogeneity was preserved in these preclinical models. Conclusions: This large and histologically diverse set of PDOrgs and PDCs from the NCI PDMR exhibited genomic stability and faithfully represented the tumor heterogeneity observed in corresponding patient specimens. These preclinical models thus represent a valuable resource for researchers interested in pre-clinical drug or other studies. Citation Format: Biswajit Das, Yvonne A. Evrard, Li Chen, Rajesh Patidar, Tomas Vilimas, Justine N. McCutcheon, Amanda L. Peach, Nikitha V. Nair, Thomas D. Forbes, Brandie A. Fullmer, Anna J. Lee Fong, Luis E. Romero, Alyssa K. Chapman, Kelsey A. Conley, Robin D. Harrington, Shahanawaz S. Jiwani, Peng Wang, Michelle M. Gottholm-Ahalt, Erin N. Cantu, Gloryvee Rivera, Lindsay M. Dutko, Kelly M. Benauer, Vishnuprabha R. Kannan, Carrie A. Bonomi, Kelly M. Dougherty, Joseph P. Geraghty, Marion V. Gibson, Savanna S. Styers, Abigail J. Walke, Jenna E. Moyer, Anna Wade, Mariah L. Baldwin, Kaitlyn A. Arthur, Kevin J. Plater, Luke Stockwin, Matthew R. Murphy, Michael E. Mullendore, Dianne L. Newton, Melinda G. Hollingshead, Chris A. Karlovich, Paul M. Williams, James H. Doroshow. Patient-derived organoid and cell culture models from the NCI Patient-Derived Models Repository (NCI PDMR) preserve genomic stability and heterogeneity of patient tumor specimens [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3916.

Background: The National Cancer Institute's Patient-Derived Models Repository (NCI PDMR; pdmr.cancer.gov) is developing a variety of patient-derived xenograft (PDX) models for pre-clinical drug studies. All NCI PDMR models undergo quality control (QC) processes. Two unique QC challenges are: a) to assess genomic stability across PDX model passages; and b) to confirm the suitability of PDX-derived cancer associated fibroblasts (CAFs) as germline surrogates when blood is not available. Multiple bioinformatics QC assessments have been developed to measure the genomic fidelity in these PDX models using low-pass whole genome sequencing (LP-WGS) and in CAFs using whole exome sequencing (WES). Methods: LP-WGS was performed on 502 PDX samples from 38 models of rare cancer across passages 2 through 9 and WES was performed on 92 CAFs from 32 different histologies. In the QC workflow for estimating the genomic stability of passages within models, BBSplit was used for the assessment of human/mouse DNA content. CNVkit was utilized for copy number (CN) detection. The fraction of genome changed was calculated by comparing the copy numbers of each passage sample to the original patient sample. To evaluate purity of CAFs, three QC steps were constructed: a) plot of SNP variant allele frequency (ideogram); b) variant annotation using OncoKB (www.oncokb.org); c) percentage of genomic loss of heterozygosity (LOH), based on a set of ~800,000 heterozygous SNPs from a population-level genomic database (gnomAD) based on WES data. Results: PDX models showed genomic stability in CN profile when measured by LP-WGS. Human tumor DNA content remains stable ranging from 75-85% across different tiers of PDX passages from Donor +1 to Donor +6 and more. No models showed statistically significant evolution in CN profile, given the average 5 samples per model in each tier of passages. The QC workflow for CAFs generated five categories based on SNP ideograms, the presence/absence of oncogenic variants and LOH. Following observations were made: a) 72.5% CAFs were confirmed as matched diploid CAFs (category 1); b) 6.6% of CAFs were diploid and had >= 1 germline oncogenic variant - classified as category 2. CAFs in category 1&2 were suitable as germline surrogates; c) 12% of CAFs (category 3) showed putative polyploidy on SNP ideograms with no oncogenic variants and suitable for somatic variant calling; d) 8.8% of CAFs (category 4) had polyploidy and oncogenic variants present; e) LOH high CAF (category 5) - we identified a CAF with 42% LOH, later confirmed to be a tumor cell line by immunohistochemistry (IHC). Other CAFs (n=91) showed little variance, ranging from 0.6%-1.7% LOH. Conclusions: We developed standard QC workflows to evaluate genomic stability of PDX models during passaging and qualify CAFs as germline surrogates for pre-clinical study. Citation Format: Ting-Chia Chang, Li Chen, Biswajit Das, Yvonne A. Evrard, Chris A. Karlovich, Tomas Vilimas, Alyssa Chapman, Nikitha Nair, Luis Romero, Anna J. Lee Fong, Amanda Peach, Brandie Fullmer, Lindsay Dutko, Kelly Benauer, Gloryvee Rivera, Erin Cantu, Shahanawaz Jiwani, Nastaran Neishaboori, Tomas Forbes, Corinne Camalier, Luke Stockwin, Michael Mullendore, Michelle A. Eugeni, Dianne Newton, Melinda G. Hollingshead, Mickey P. Williams, James H. Doroshow. Quality control workflows developed for the NCI Patient-Derived Models Repository using low pass whole genome sequencing and whole exome sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1913.

Background: A PDX bladder cancer model, BL0293-F563, spontaneously metastasizes to the liver and bone, and sheds high numbers of circulating tumor cells (CTCs). This PDX model provides a unique opportunity to explore the relationships between primary tumors, CTCs, and metastases. Methods: BL0293-F563 tumors (available from the NCI Patient-Derived Models Repository [https://pdmr.cancer.gov/] and originally developed by Jackson Laboratories) were implanted into NSG mice, and primary tumors, metastatic nodules in the liver, and blood were collected at maximal allowable tumor burden. Tumor tissue was dissociated using Miltenyi Tumor Dissociation Kit with OctoDissociator, and Human CTCs were enriched from mouse blood through negative selection with anti-mouse CD45 and anti-mouse MHC-1 magnetic beads. Single cell sequencing was done using 10X Genomics 3’ gene expression assay v3.1. Data processing and analysis was done using 10X Genomics’ Cell Ranger pipeline, Seurat, and cNMF. Results: single cell RNAseq data from primary tumors, CTCs, and metastases from 9 mice were aggregated into a single dataset, and cells were classified into 17 clusters using Seurat FindNeighbors. All clusters contained cells from multiple sites (primary tumor, CTCs, metastases), but three clusters were enriched in CTCs and one cluster was composed of mostly primary tumor cells. All clusters exhibited epithelial-like gene expression signature scores, suggesting that CTC shedding was occurring without prominent epithelial-mesenchymal transition. CTC-enriched clusters showed elevated expression of RHO pathway genes, implicating ameboid-like migration in CTC shedding in this PDX model. Consistent with expected differences in oxygenation states, CTC-enriched clusters exhibited a lower hypoxia gene expression score than primary tumor and metastasis-enriched clusters. CTC-enriched clusters also showed higher expression of oxidative phosphorylation genes, suggesting metabolic differences between CTCs and cells from other sites. Additionally, two of three CTC-enriched clusters had elevated expression of mitosis-associated genes, indicating that at least some subpopulations of CTCs are actively cycling. A metastasis suppressor gene KISS1 was expressed in a subset of primary tumor cells but undetectable in CTCs, suggesting that KISS1 expression loss occurs before CTC shedding. Conclusions: Utilizing single cell gene expression profiling, we have linked the gene expression profile of CTCs to specific cell subpopulations in primary tumors and metastases. We show that CTC-enriched cell clusters appear to maintain an epithelial phenotype. Subpopulations of CTC cells exhibit enrichment of motility-associated transcripts and features of active cell cycling. Our results implicate a known metastasis suppressor gene KISS1 in CTC shedding and metastatic dissemination in this PDX model. Citation Format: Tomas Vilimas, Brandie Fullmer, Alyssa Chapman, Rini Pauly, Ting-Chia Chang, Li Chen, Biswajit Das, Chris Karlovich, Yvonne Evrard, Melinda Hollingshead, Howard Stotler, Michelle Ahalt-Gottholm, Tara Grinnage-Pulley, Mickey Williams, James H. Doroshow. Comparative single cell transcriptome profiling of primary tumors, CTCs and metastatic sites from a bladder cancer PDX model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 973.