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Introduction: Structural variants (SVs) are a unique class of mutations which have certain therapeutic implications for the tumor. Certain SVs, such as chromosomal aneuploidy, whole-genome doubling (WGD), have specific therapeutic implications. The underlying cellular processes present in the tumor are reflected in mutational signatures. Here, we describe the landscape of chromosomal aneuploidy, WGD and mutational signatures in the National Cancer Institute’s Patient-Derived Models Repository (NCI PDMR) to facilitate the investigation of their roles in therapeutic responses of the preclinical models. Method: Chromosome arm-level aneuploidy was called by scoring at the individual arm level if >90% of the arm copy number (CN) was gained/lost based on whole-exome sequencing (WES) data. Aneuploidy score was defined as number of arms with aneuploidy. WGD was determined by derived allelic specific CN, purity and ploidy from tumor/normal matched samples and permutation test. Mutational signatures (COSMIC v3) including single base substitutions (SBS), doublet base substitutions (DBS), small insertions and deletions (ID) and CN signatures were derived using SigProfiler for specimens with somatic mutations and CNs. Results: A large fraction (85%) of patient-derived xenograft (PDX) models (N=755) have at least one arm -level aneuploidy. Certain chromosomes and arms (7, 8, 17p and 18) are more frequently aneuploid, which might be biased due to the overrepresentation of gastrointestinal cancer in the cohort. Histology specific differences were observed in the frequency of arm level aneuploidies. For example, synovial sarcoma (SYNS) and endometrioid carcinoma (UEC) have much lower level of aneuploidy than non-small cell lung cancer (NSCLC) or clear cell renal carcinoma (ccRCC) models. 61% of PDX models (N=277) have WGD, in which certain histologies have more WGD [NSCLC: 81%, head and neck squamous cell carcinomas (HNSCC): 71%] than others. Samples having WGD have a higher degree of aneuploidy and chromosomal instability. WGD and aneuploidy remain stable along the passages in 78% PDX models. Intra-model heterogeneity of WGD was observed due to lineage difference. Mutational signatures (SBS6,15,20) indicating concurrent DNA polymerase epsilon (POLE) mutation and defective DNA mismatch repair were highly enriched in microsatellite instability-high models (p Conclusion: We have characterized chromosomal aneuploidy, WGD and mutational signatures in NCI PDMR models. The models with SVs can be utilized in preclinical drug studies to understand their role in therapeutic response in patients. Citation Format: Li Chen, Biswajit Das, Ting-Chia Chang, Yvonne A. Evrard, Chris A. Karlovich, Alyssa Chapman, Brandie Fullmer, Ashley Hayes, Ruth Thornton, Nikitha Nair, Shahanawaz Jiwani, Lindsay Dutko, Kelly Benauer, Gloryvee Rivera, Corinne Camalier, John Carter, Suzanne Borgel, Tiffanie Miner, Chelsea McGlynn, Justine Mills, Shannon Uzelac, Tia Shearer, Lauren Hicks, Michelle Norris, Carley Border, Sergio Alcoser, Thomas Walsh, Michael Mullendore, Michelle Eugeni, Dianne Newton, Melinda G. Hollingshead, P. Mickey Williams, James H. Doroshow. Chromosomal aneuploidy, whole-genome doubling and mutational signatures in NCI PDMR models. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6072.

Background: Breast cancer is the second most common cancer in women. In 2022, it accounted for 15% of total new cancer cases and is the number four cause of death among all cancer types. To benefit from precision medicine, distinguishing molecular subtypes for prognosis and treatment in a clinical setting is essential. While intrinsic subtype classification from NGS results of patients is well established, the approach has not been comprehensively described for patient-derived xenograft (PDX) models, which have been shown to be powerful in translational research. The National Cancer Institute's Patient-Derived Models Repository (NCI PDMR; https://pdmr.cancer.gov) provides rich information in developing the method. Materials and Methods: Normalized gene expression data of breast cancer PDX and patient specimens (originators) were extracted using tximport and DESeq2 based on RNA-seq analysis. The immunohistochemistry (IHC) was used to determine the status of ER, PR and HER2 receptor expression in these tumor specimens. The PAM50 classification was performed by the R package Genefu. For further analysis, the PAM50 centroids for all 5 subtypes were also obtained from Genefu. Results: Using the RNA-seq data from 43 PDX models (180 PDX samples, 4~6 samples/model), we were able to predict subtypes at the model level based on the PAM50 method: There are 1 Luminal A subtypes, 5 Luminal B; 6 Her2; 30 Basal and 1 Normal, which encompasses the whole spectrum of PAM50. Thirty originators were also included and there are 8 Luminal A, 9 Luminal B, 2 Her2 and 11 Basal. With the matched 11 originators and the PDX models, 91% of their predicted subtypes are identical; 0.80 Cohen’s kappa was obtained, indicating high inter-rater agreement. We also described subsequent analysis with IHC data-based subtypes. For the 10 originators having IHC-based subtypes, 90% agreement was observed; for 24 PDX models with IHC data, 88% was observed. Of all the 180 PDX samples, 33 of the 43 PDX models (77%) have consistent predicted PAM50 molecular subtypes across different passages and lineages. Within the discordant samples, we observed cases such as a mixture of luminal B and Basal, which can be reasonably interpreted by AR positive signal from IHC. The discrepancy encourages further PDX subclassification from the Basal subtype. Conclusions: Using our high-throughput gene expression profiles from many patients and samples from patient derived models, we have demonstrated the feasibility of applying classic PAM50 classification algorithm, which was originally developed with microarray data, to be able to recognize the expression signals from our RNA-seq data. Overall, this study should set a primer for the identification of PDX-based subtypes, starting from breast cancer. Citation Format: Peter I. Wu, Lindsay Dutko, Shahanawaz Jiwani, Li Chen, Biswajit Das, Ting-Chia Chang, Yvonne A. Evrard, Chris A. Karlovich, Alyssa Chapman, Brandie Fullmer, Ashley Hayes, Ruth Thornton, Nikitha Nair, Kelly Benauer, Gloryvee Rivera, Thomas Forbes, John Carter, Suzanne Borgel, Tiffanie Miner, Chelsea McGlynn, Justine Mills, Shannon Uzelac, Tia Shearer, Lauren Hicks, Michelle Norris, Carley Border, Sergio Alcoser, Thomas Walsh, Michael Mullendore, Michelle Eugeni, Dianne Newton, Melinda G. Hollingshead, P. M. Williams, James H. Doroshow. Molecular subclassification of NCI PDMR breast cancer models using PAM50 gene expression signature [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2050.

NCI’s Patient-Derived Models Repository (NCI PDMR; pdmr.cancer.gov) has developed a variety of patient-derived models across most solid tumor histologies. These models are early passage, genetically characterized and associated with limited patient treatment history. As part of this effort, the NCI PDMR worked with the University of Nebraska Medical Center Rapid Autopsy Program and Johns Hopkins University Legacy Gift Rapid Autopsy Program to develop and optimize methods for collection, processing, and shipping of autopsy tumor material to maintain viability during overnight transit for use in patient-derived model development. These methods have been successfully transferred to two other participating rapid autopsy programs. To date, 412 autopsy tumor samples from 76 consented patients have been received for model development; 348 shipped overnight in media for next day implantation into NSG host mice and 64 cryopreserved prior to shipping for a comparative assessment of take-rate versus fresh tumor samples. On average 3-8 tumor samples, primary and metastatic, were collected post-mortem from the truncal region of each patient. Histologies include Pancreatic adenocarcinoma (n=43), Cholangiocarcinoma (n=6), Prostate adenocarcinoma (n=6), and 21 others with 1-2 patients/histology. The overall age range of enrolled patients was 5-88yo. The post-mortem cold ischemic time for collections ranged from 1.5 to 20 hours with a median of 3h (avg. 3.75h; outlier >11h removed). Collection methods were optimized to reduce contamination and increase viability of tumor tissues for successful PDX model generation. Of 348 fresh tumor samples collected to date, 69 PDX models from 33 patients have been generated (range 1-6 models/patient) and an additional 55 samples are being monitored for growth in passage 0. The largest public single-patient PDX model sets are for melanoma (899932-113-R, n=6) and two pancreatic adenocarcinomas (521955-158-R, n=6, 217524-143-R, n=4). Important methods for reducing contaminants in autopsy tumor material include sterilization of the surface of the body prior to opening, use of sterile fields, using separate sterile instruments for each collection site, rinsing the surface of the resected tumor tissue, and use of antibiotics in the collection media. The now established SOPs are publicly available on the NCI PDMR website (pdmr.cancer.gov/sops). We recommend incorporating as many of these methods as possible within the limitations of your individual site. Of the 69 models developed to date, 48 are publicly available from the NCI PDMR while the rest are undergoing quality control process prior to public release. Models developed from autopsy material provide a research tool to investigate tumor evolution, differences between primary and metastatic lesions, and assessment of differences in therapeutic response based on differences in the tumor biology. Citation Format: Yvonne A. Evrard, Michelle Eugeni, Michelle Ahalt-Gottholm, Carrie Bonomi, Suzanne Borgel, Thomas C. Caffrey, John Carter, Ting-Chia Chang, Li Chen, Kevin Cooper, Biswajit Das, Emily Delaney, Kelly Dougherty, Eleonora Duregon, Stephanie Ecker, Joe Geraghty, Marion Gibson, Lauren Hicks, Jenna Hull, Sharon Int Veldt, Shahanawaz Jiwani, Chris A. Karlovich, Jade Loewenstein, Candace Mallow, Chelsea McGlynn, Justine Mills, Tiffanie Miner, Jowaly Schneider, Tia Shearer, Savanna Styers, Shannon Uzelac, Paul Grandgenett, Michael Hollingsworth, Jody E. Hooper, P. Mickey Williams, Melinda Hollingshead, James H. Doroshow. Method development for generation of PDX models from rapid autopsy samples for 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 3120.

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.

There is an unmet need for preclinical models of rare cancers and rare disease sub-types. The National Cancer Institute's Patient-Derived Models Repository (NCI PDMR; https://pdmr.cancer.gov) is developing quality-controlled, early-passage, clinically-annotated patient-derived tumor xenografts (PDXs), in vitro tumor cell cultures (PDCs), cancer associated fibroblasts (CAFs), and patient-derived organoids (PDOrg) and has focused on addressing unmet needs in the preclinical model space including developing models from adult and pediatric patients with rare cancers. To date, NCI has created and molecularly characterized over 150 preclinical models of rare cancer including indications such as Hurthle cell carcinoma, osteosarcomas, Merkel cell carcinomas, salivary gland cancers, synovial sarcomas, and carcinosarcomas. Rare cancer models developed to date will be reviewed and their histopathologic and molecular characteristics compared to that reported in the clinical setting. A pipeline to identify fusion proteins in these rare cancers such as the Ewing sarcoma EWSR1-FLI1 fusion and NAB2-STAT6 fusions in solitary fibrous tumors (SFT) has been implemented. Four malignant peripheral nerve sheath tumors (MPNST) PDX models are available for researches; these models were developed from patients diagnosed between the ages of 37-68. At the time of model development, two patients were treatment naïve and two had prior radiotherapy. Two of the MPNST PDX models have NF1 oncogenic mutations, three have deep deletions in CDKN2A/B, and three have a mutation in either EED or SUZ12 consistent with the reported molecular characteristics of patients with MPNST. Also of clinical relevance, of two mesothelioma models available, one carries an NF2 driver mutation and the other BAP1 and LATS2 and a PDX model for Hurthle cell carcinoma has wide-spread loss of heterozygosity (LOH 80%). Models for other rare cancers are in development, including four cholangiocarcinoma PDXs with histopathologic confirmation that are currently being expanded for molecular characterization and distribution. Funded by NCI Contract No. HHSN261200800001E Citation Format: Cindy R. Timme, Sergio Y. Alcoser, Devynn Breen, John Carter, Ting-Chia Chang, Alice Chen, Li Chen, Kristen Cooley, Biswajit Das, Emily Delaney, Michelle A. Eugeni, Michelle M. Gottholm-Ahalt, Tara Grinnage-Polley, Jenna Hull, Chris Karlovich, Kimberly Klarmann, Shahanawaz Jiwani, Candace Mallow, Chelsea McGlynn, Justine Mills, Malorie Morris, Michael Mullendore, Dianne Newton, Tia Shearer, Jesse Stottlemyer, Shannon Uzelac, Thomas Walsh, P. Mickey Williams, Yvonne A. Evrard, Melinda G. Hollingshead, James H. Doroshow. Patient-derived models of rare cancers in the National Cancer Institute's patient-derived models repository [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3012.

Background: Well characterized patient derived xenograft models (PDX) are becoming the preferred pre-clinical tool in translational cancer research for biologic understanding of the disease, development of new treatments, and identifying potential therapy predictive and resistant biomarkers. Characterization of PDX models using a multi-omic approach is most desirable, however such efforts can be expensive and technically demanding. Immunohistochemistry (IHC) has become an indispensable ancillary tool in the accurate classification of tumor types, determination of cell of origin, identification of biologic properties like growth and metastatic potential, and evaluation for the presence/absence of therapeutic or prognostic biomarkers. Methods: 43 IHC assays were validated on the Leica Bond RX automated staining platform to identify common inconsistencies in PDX development including markers for classifying carcinomas, lymphomas, sarcomas, murine tumors, and theragnostic biomarkers. Rabbit antibodies are used rather than mouse antibodies to prevent non-specific staining of murine tissue. Results: 1. IHC evaluation of models within NCI's Patient Derived Models Repository (pdmr.cancer.gov) led to re-classification or sub-classification of 12 tumor models in accordance with WHO guidelines. 2. IHC evaluation of theragnostic markers in 8 breast cancer PDX models showed concordant results throughout passaging, suggesting stability of these biomarkers in our models. 3. We observe malignant transformation of murine or transplanted benign human tissue at a rate of 2.5%. On IHC analysis, 52% were human lymphomas, 20% were murine lymphomas, and 28% were other murine tumors. Conclusions: IHC is a rapid, cost-effective tool that can be used for accurate tumor classification, identifying subclonal outgrowth and tumor evolution, assessing stability of biomarkers and identifying malignant transformation of benign tissue. Funded by NCI Contract No. HHSN261200800001E ANTIBODYCLONEVENDORANTIBODYCLONEVENDORAndrogen Receptor[EPR1535(2)]abcamGATA3[EPR16651]abcamB-Catenin[E247]abcamGCDFP-15[EPR1582Y]abcamCD19polyclonalabcamGFAPpolyclonalDAKO/AgilentCD3polyclonalabcamHER2 ErbB2[SP3]abcamCD20[SP32]abcamKi-67[D2H10]Cell SignalingCD34[EP373Y]abcamKu80[EPR3468]abcamCD45polyclonalabcamMGMTMT3.1MilliporeCD56 (NCAM1)[EPR2566]abcamMitochondria Marker (Biotin)MTC02abcamCD68[EPR20545]abcamMyogenin[EPR4789]abcamCDX2[EPR2764Y]abcamNAPSIN A[EPR6252]abcamChromogranin A[SP12]abcamp63polyclonalGeneTexCK7 (purified)[EPR1619Y]abcamPD-1[EPR4877(2)]abcamCK19[EPR1580Y]abcamPD-L1 (CD274)RBT-PDL1LifeSpan BiosciencesCK20[EPR1622Y]abcamProgesterone Receptor[SP2]abcamCytokeratin wide spectrumpolyclonalabcamProstate Specific Antigen (PSA)[EP1588Y]abcamDesmin[Y66]abcamS100[EPR19013]abcamEBV LMP1[D24-G]abcamSmooth Muscle Actin (SMA)polyclonalabcamERG[EPR3864]abcamSynaptophysin[SP11]abcamEstrogen Receptor[SP1]abcamTTF1[SP141]abcamFOXP1monoclonalLifeSpan BiosciencesVimentin[EPR3776]abcamFOXP3(5H10L18)Invitrogen Citation Format: Lindsay Dutko, Gloryvee Rivera, Erin Cantu, Vishnuprabha Rahulkannan, Kelly Benauer, Tiffanie Chase, Emily Delaney, Jesse Stottlemyer, Chelsea McGlynn, Howard Stotler, John Carter, Suzanne Borgel, Michelle M. Gottholm Ahalt, Michelle Eugeni, Melinda Hollingshead, Yvonne Evrard, Chris Karlovich, Biswajit Das, Mickey Williams, James H. Doroshow, Shahanawaz Jiwani. Applications of immunohistochemistry in characterization of patient derived xenograft models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3015.

Background: Acquired Uniparental Disomy (aUPD) is relatively common in cancer. Occurrence of aUPD is more frequent in some tumor histologies (e.g., serous ovarian, colorectal) and may be relevant for choice of therapy. The Patient-Derived Models Repository (PDMR; https://pdmr.cancer.gov) developed by The National Cancer Institute (NCI) includes patient-derived xenograft (PDX) models from multiple tumor histologies with different passages and lineages. The associated clinical annotation and genomic data make it possible to assess the prevalence of aUPD in the PDMR cohort and the stability of aUPD in different passages and lineages within a PDX model. Methods: High tumor purity in the PDX specimens (after removal of mouse reads representing the stroma) enabled highly accurate assessment of loss of heterozygosity (LOH). Variants called by GATK Haplotype caller from whole exome sequencing (WES) data were used to identify segments of homozygosity using BCFtools/RoH (runs of homozygosity). The RoH segments were then intersected with the bed file for chromosome arms to get %LOH at the arm level. If %LOH on a chromosome arm was >90%, we considered the sample to have aUPD at the arm level. WES was also used to look for associations between DNA damage repair (DDR) pathway alterations and aUPD. Results: We made the following observations: a) aUPD was observed most frequently in chr18q (75/427, 17.6%) and chr3p (69/427, 16%) of PDX models; b) aUPD was observed more frequently in certain tumor histologies, e.g., clear cell renal cell carcinoma (6/8), small cell lung cancer (3/4) and non-small cell lung cancer (25/38); c) extensive aUPD was observed in 4 PDMR models (>50% of evaluated chromosome arms in these models have aUPD); d) aUPD was not observed in some tumor histologies, i.e., synovial sarcoma, uterine endometrioid carcinoma; e) in the vast majority of PDMR models (>90%), aUPD is maintained faithfully across lineages and through multiple passaging; f) subclonal aUPD events were observed in some models across different lineages; g) significant enrichment of double strand DNA break repair (DSBR) pathway alterations was observed in PDMR models without aUPD (p=0.0007, Fisher's exact test) suggesting defects in DSBR are not associated with aUPD; and h) aUPD was rarely observed in MSI-high models (1/30) suggesting mutual exclusivity of mismatch repair (MMR) pathway defects and aUPD. Conclusion: We observed a relatively high frequency of UPD in the PDMR models (at least 1 arm of a chromosome). UPD was more frequently observed in specific chromosomal arms. The frequency of aUPD was higher in some tumor histologies and absent in others. aUPD was stably maintained across passages and lineages, although some heterogeneity was observed. Our data suggest aUPD is not associated with defects in DSBR and MMR pathways. Preclinical drug studies using NCI PDMR models may suggest appropriate therapeutic options for cancers with aUPD. Citation Format: Rajesh Patidar, Li Chen, Chris A. Karlovich, Biswajit Das, Yvonne A. Evrard, 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, Suzanne D. Borgel, John P. Carter, Jesse M. Stottlemyer, Tiffanie L. Miner, Devynn R. Breen, Emily T. Delaney, Chelsea A. McGlynn, Candace N. Mallow, Marianne Radzyminski, Shannon N. Uzelac, Sergio Y. Alcoser, Tara L. Grinnage-Pulley, Michelle A. Eugeni, Dianne L. Newton, Melinda G. Hollingshead, Paul M. Williams, James H. Doroshow. Genomic landscape of acquired uniparental disomy in NCI PDMR patient derived xenograft models [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 3554.

Background: The National Cancer Institute (NCI) has developed a Patient-Derived Models Repository (PDMR; www.pdmr.cancer.gov) of patient-derived xenografts (PDXs) with clinical annotation and comprehensive genomic characterization using whole exome sequencing (WES) and RNASeq. An in-house data analysis pipeline has been developed and validated to call germline and somatic variants and to perform transcriptional profiling in these models. There is a need to incorporate additional biomarkers into standard data analysis pipeline, including loss of heterozygosity (LOH), microsatellite instability (MSI), structure variants (SVs)/fusions and copy number variation (CNV) for identifying appropriate PDX models for preclinical drug studies. Validation of the methods used for the assessment of these and other genomic biomarkers is a crucial aspect in the development of the PDMR data analysis pipeline. Methods: WGS, WES and RNASeq were conducted on 58 PDX samples and genomic biomarkers were derived from different assays. For LOH calling, a set of ~800,000 heterozygous SNPs was first constructed from a population level genomic database (gnomAD) and a specific list of ~3000 highly heterozygous SNPs from a previous study. LOH regions were detected using Runs of Homozygosity (BCFtools/RoH) based on the genotypes of ~800,000 SNPs. Finally, percent of genomic LOH was calculated as the percent of eligible LOH regions in the whole genome. For MSI calling. mSINGS was used to assign a microsatellite instability score based on the fraction of unstable microsatellite loci. Gene fusions were detected using Tophat-fusion and Fusion-catcher from RNASeq data and Manta from WGS. CNVs were derived from WGS and WES using CNVkit. Results: Genomic biomarkers derived from WES and RNASeq were highly concordant with the ones derived from WGS. Specifically, we found 1) the percent of genomic LOH was highly correlated between WGS and WES across 52 samples with R2=0.99, where LOH% ranged from Conclusions: We observed excellent consistency between WGS, WES and RNASeq data in the assessment of percent of LOH, MSI score, SVs/fusions and CNVs. Our data analysis pipeline can accurately call genomic biomarkers from WES and RNASeq data, which facilitates the molecular characterization and prioritization of PDMR models for preclinical drug treatment. Citation Format: Li Chen, Rajesh Patidar, Biswajit Das, Chris Karlovich, Tomas Vilimas, Corinne Camalier, Vivekananda Datta, Shahanawaz Jiwani, William Walsh, Palmer Fliss, Sean McDermott, Justine N. McCutcheon, Amanda Peach, Michelle Ahalt-Gottholm, Carrie Bonomi, Kelly Dougherty, John Carter, Sergio Y. Alcoser, Tiffanie Chase, Raymond Divelbiss1, Marion Gibson, Kelly Hedger, Candace Mallow, Chelsea McGlynn, Malorie Morris, Marianne Radzyminski, Howard Stotler, Jesse Stottlemyer, Debbie Trail, Yvonne Evrard, Melinda G. Hollingshead, Mickey Williams, James H. Doroshow. Comparison of genomic biomarkers identified by the whole exome, RNASeq and whole genome sequencing pipelines developed for the PDMR [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3385.

The National Cancer Institute (NCI) has developed a Patient-Derived Models Repository (PDMR) comprised of quality-controlled, early-passage, clinically-annotated patient-derived tumor xenografts (PDXs), in vitro tumor cell cultures (PDCs), cancer associated fibroblasts (CAFs), and patient-derived organoids (PDOrg). NCI has focused on generating models to complement existing PDX collections and address unmet needs in the preclinical model space. These models are offered to the extramural community for research use (https://pdmr.cancer.gov), along with clinical annotation and molecular information (whole exome sequence, gene expression using RNASeq), via a publicly accessible database. Currently, over 200 PDX models, 50 PDC models, and 100 CAF models are available for distribution to the US research community. Approximately 50 PDOrg models will be released in early 2019. As part of its rare cancer initiative, the NCI is also targeting the collection of infrequently-observed tumor histologies to advance both biological investigations and drug development efforts for under-studied malignancies. Comparison of matched models, models where more than one model type are available (e.g., PDX and PDC), demonstrate a high degree of concordance across the model types. Genetic stability across the models is assessed using multiple criteria including genetic assessment of CNVs and presence of driver mutations. Optimal CNV assessment uses whole exome sequence data corrected for cellularity in the patient specimen using germline reads and corrected for cellularity in the PDX specimens by subtraction of the mouse reads. Histomorphologic comparison of PDXs and cell line xenografts (CLX) generated from in vitro PDCs and PDOrgs also overall show a high degree of concordance, though loss of features and dedifferentiation can be observed in some models. Overall these models demonstrate a high degree of conservation at the genetic and pathologic level when compared to the patient tumor. These models can provide researchers the ability to perform high- or mid-throughput screening in 2D or 3D culture followed by targeted selection of PDX models for in vivo studies. Funded by NCI Contract No. HHSN261200800001E Citation Format: Yvonne A. Evrard, Dianne Newton, Biswajit Das, Sergio Y. Alcoser, Kaitlyn Arthur, Mariah Baldwin, Carrie Bonomi, Suzanne Borgel, John Carter, Tiffany Chase, Alice Chen, Lily Chen, Nikki E. Craig, Vivekananda Datta, Emily Delaney, Raymond Divelbiss, Kelly Dougherty, Thomas Forbes, Kyle Georgius, Joe Geraghty, Marion Gibson, Michelle M. Gottholm-Ahalt, Tara Grinnage-Pulley, Kelly Hedger, Sierra Hoffman, Chris Karlovich, Wiem Lassoued, Shahanawaz Jiwani, Candace Mallow, Chelsea McGlynn, Mallorie Morris, Jenna Moyer, Mike Mullendore, Matt Murphy, Rajesh Patidar, Kevin Plater, Marianne Radzyminski, Nicki Scott, Luke H. Stockwin, Howard Stotler, Jesse Stottlemyer, Savanna Styers, Debbie Trail, Tomas Vilimas, Anna Wade, Abigail Walke, Thomas Walsh, P. Mickey Williams, Melinda G. Hollingshead, James H. Doroshow. Comparison of PDX, PDC, and PDOrg models from the National Cancer Institute’s Patient-Derived Models Repository (PDMR) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4524.

The National Cancer Institute (NCI) has developed a Patient-Derived Models Repository (PDMR) comprised of quality-controlled, early-passage, clinically-annotated patient-derived xenografts (PDXs) to serve as a resource for public-private partnerships and academic drug discovery efforts. These models are offered to the extramural community for research use (https://pdmr.cancer.gov/), along with clinical annotation and molecular information (whole exome sequence, RNASeq), which is available in a publicly accessible database. The PDMR was established by NCI at the Frederick National Laboratory for Cancer Research (FNLCR) in direct response to discussions with academia and industry; the oncology community's highest priority need was preclinical models that more faithfully reflect the patient's tumor and are associated with the patient's treatment history. NCI has focused on generating models to complement existing PDX collections and address unmet needs in the preclinical model space. The PDMR generates the majority of its PDXs by subcutaneous implantation except for those histologies having better success rates in either orthotopic or alternate implant sites. All SOPs and quality-control standards developed by the PDMR as well as those shared by collaborators are posted to a public web site that houses the PDMR database. In May 2017, the public website (https://pdmr.cancer.gov/) went live with its first 100 models from histologies including pancreatic, colorectal, renal, head and neck, and lung squamous cell cancers as well as melanoma and adult soft tissue sarcomas. In early 2018, the PDMR will begin releasing models from gynecological cancers, small cell lung cancer, chondro/osteo sarcomas, lung adenocarcinoma, and squamous cell skin and Merkel cell carcinomas. In addition, wherever available germline sequence and somatic variant calls will be added to the existing molecular characterization data for each model. NCI has also increased its focus on creating PDXs from racial and ethnic minorities through several funding opportunities. The overall goal of NCI is to create a long-term home for at least 1000 models such that sufficient biological and clinical diversity is represented to allow researchers to ask questions regarding the impact of tumor heterogeneity on target qualification or clinical response, whether PDXs more faithfully represent the human tumor for pharmacodynamic assay and predictive marker development, or if adequately powered preclinical PDX clinical trials can lead to better evaluation of therapies for future clinical use. Moving forward the PDMR plans to distribute in vitro, early-passage tumor cell cultures and cancer-associated fibroblasts as well as releasing PDX drug response data for a panel of FNA-approved therapeutic agents. Funded by NCI Contract No. HHSN261200800001E Citation Format: Yvonne A. Evrard, Michelle M. Gottholm Ahalt, Sergio . Y. Alcoser, Kaitlyn Arthur, Mariah Baldwin, Linda L. Blumenauer, Carrie Bonomi, Suzanne Borgel, Elizabeth Bradtke, Corinne Camalier, John Carter, Tiffanie Chase, Alice Chen, Lily Chen, Donna W. Coakley, Nicole E. Craig, Biswajit Das, Vivekananda Datta, Jordyn Davidson, Margaret R. DeFreytas, Emily Delaney, Michelle A. Eugeni, Raymond Divelbiss, Palmer Fliss, Thomas Forbes, Marion Gibson, Tara Grinnage-Pulley, Sierra Hoffman, Lilia Ileva, Paula Jacobs, Franklyn Jimenez, Joseph Kalen, Catherine Karangwa, Chris Karlovich, Candace Mallow, Chelsea McGlynn, Jenna E. Moyer, Michael Mullendore, Dianne L. Newton, Nimit Patel, Rajesh Patidar, Kevin Plater, Marianne Radzyminski, Lisa Riffle, Larry Rubinstein, Luke H. Stockwin, Mickey Williams, Melinda G. Hollingshead, James H. Doroshow. The National Cancer Institute's patient-derived models repository (PDMR) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 986.

The National Cancer Institute (NCI) has developed a Patient-Derived Models Repository (PDMR) comprised of quality-controlled, early-passage, clinically-annotated patient-derived xenografts (PDXs) to serve as a resource for public-private partnerships and academic drug discovery efforts. These models are offered to the extramural community for research use (https://pdmr.cancer.gov/), along with clinical annotation and molecular information (whole exome sequence, RNASeq), which is available in a publicly accessible database. The PDMR was established by NCI at the Frederick National Laboratory for Cancer Research (FNLCR) in direct response to discussions with academia and industry; the oncology community9s highest priority need was preclinical models that more faithfully reflect the patient9s tumor and are associated with the patient9s treatment history. NCI has focused on generating models to complement existing PDX collections and address unmet needs in the preclinical model space. The PDMR generates the majority of its PDXs by subcutaneous implantation except for those histologies having better success rates in either orthotopic or alternate implant sites. All SOPs and quality-control standards developed by the PDMR as well as those shared by collaborators are posted to a public web site that houses the PDMR database. In May 2017, the public website (https://pdmr.cancer.gov/) went live with its first 100 models from histologies including pancreatic, colorectal, renal, head and neck, and lung squamous cell cancers as well as melanoma and adult soft tissue sarcomas. In early 2018, the PDMR will begin releasing models from gynecological cancers, small cell lung cancer, chondro/osteo sarcomas, lung adenocarcinoma, and squamous cell skin and Merkel cell carcinomas. In addition, wherever available germline sequence and somatic variant calls will be added to the existing molecular characterization data for each model. NCI has also increased its focus on creating PDXs from racial and ethnic minorities through several funding opportunities. The overall goal of NCI is to create a long-term home for at least 1000 models such that sufficient biological and clinical diversity is represented to allow researchers to ask questions regarding the impact of tumor heterogeneity on target qualification or clinical response, whether PDXs more faithfully represent the human tumor for pharmacodynamic assay and predictive marker development, or if adequately powered preclinical PDX clinical trials can lead to better evaluation of therapies for future clinical use. Moving forward the PDMR plans to distribute in vitro, early-passage tumor cell cultures and cancer-associated fibroblasts as well as releasing PDX drug response data for a panel of FNA-approved therapeutic agents. Funded by NCI Contract No. HHSN261200800001E Citation Format: Yvonne A. Evrard, Michelle M. Gottholm Ahalt, Sergio . Y. Alcoser, Kaitlyn Arthur, Mariah Baldwin, Linda L. Blumenauer, Carrie Bonomi, Suzanne Borgel, Elizabeth Bradtke, Corinne Camalier, John Carter, Tiffanie Chase, Alice Chen, Lily Chen, Donna W. Coakley, Nicole E. Craig, Biswajit Das, Vivekananda Datta, Jordyn Davidson, Margaret R. DeFreytas, Emily Delaney, Michelle A. Eugeni, Raymond Divelbiss, Palmer Fliss, Thomas Forbes, Marion Gibson, Tara Grinnage-Pulley, Sierra Hoffman, Lilia Ileva, Paula Jacobs, Franklyn Jimenez, Joseph Kalen, Catherine Karangwa, Chris Karlovich, Candace Mallow, Chelsea McGlynn, Jenna E. Moyer, Michael Mullendore, Dianne L. Newton, Nimit Patel, Rajesh Patidar, Kevin Plater, Marianne Radzyminski, Lisa Riffle, Larry Rubinstein, Luke H. Stockwin, Mickey Williams, Melinda G. Hollingshead, James H. Doroshow. The National Cancer Institute9s patient-derived models repository (PDMR) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 986.