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  • American Association for Cancer Research (AACR)  (10)
  • Doroshow, James H.  (10)
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  • American Association for Cancer Research (AACR)  (10)
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  • 1
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    Online Resource
    Abstract 5407: A pan-cancer PDX histology image repository with genomic and pathological annotations for deep learning analysis
    American Association for Cancer Research (AACR) ; 2023
    In:  Cancer Research Vol. 83, No. 7_Supplement ( 2023-04-04), p. 5407-5407
    Details
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 83, No. 7_Supplement ( 2023-04-04), p. 5407-5407
    Abstract: Patient-derived xenografts (PDXs) model human intra-tumoral heterogeneity in the context of the intact tissue of immunocompromised mice. Histological imaging via hematoxylin and eosin (H & E) staining is performed on PDX samples for routine assessment and, in principle, captures the complex interplay between tumor and stromal cells. Deep learning (DL)-based analysis of large human H & E image repositories has extracted inter-cellular and morphological signals correlated with disease phenotype and therapeutic response. Here, we present an extensive, pan-cancer repository of nearly 1,000 PDX and paired human progenitor H & E images. These images, curated from the PDXNet consortium, are associated with genomic and transcriptomic data, clinical metadata, pathological assessment of cell composition, and, in several cases, detailed pathological annotation of tumor, stroma, and necrotic regions. We demonstrate that DL can be applied to these images to classify tumor regions with an accuracy of 0.87. Further, we show that DL can predict xenograft-transplant lymphoproliferative disorder, the unintended outgrowth of human lymphocytes at the transplantation site, with an accuracy of 0.97. This repository enables PDX-specific investigations of cancer biology through histopathological analysis and contributes important model system data that expand on existing human histology repositories. We expect the PDXNet Image Repository to be valuable for controlled digital pathology analysis, both for the evaluation of technical issues such as stain normalization and for development of novel computational methods based on spatial behaviors within cancer tissues. Citation Format: Brian S. White, Xing Yi Woo, Soner Koc, Todd Sheridan, Steven B. Neuhauser, Shidan Wang, Yvonne A. Evrard, John David Landua, R Jay Mashl, Sherri R. Davies, Bingliang Fang, Maria Gabriela Raso, Kurt W. Evans, Matthew H. Bailey, Yeqing Chen, Min Xiao, Jill Rubinstein, Ali Foroughi pour, Lacey Elizabeth Dobrolecki, Maihi Fujita, Junya Fujimoto, Guanghua Xiao, Ryan C. Fields, Jacqueline L. Mudd, Xiaowei Xu, Melinda G. Hollingshead, Shahanawaz Jiwani, PDXNet consortium, Tiffany A. Wallace, Jeffrey A. Moscow, James H. Doroshow, Nicholas Mitsiades, Salma Kaochar, Chong-xian Pan, Moon S. Chen, Luis G. Carvajal-Carmona, Alana L. Welm, Bryan E. Welm, Ramaswamy Govindan, Shunqiang Li, Michael A. Davies, Jack A. Roth, Funda Meric-Bernstam, Yang Xie, Meenhard Herlyn, Li Ding, Michael T. Lewis, Carol J. Bolt, Dennis A. Dean, Jeffrey H. Chuang. A pan-cancer PDX histology image repository with genomic and pathological annotations for deep learning analysis. [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 5407.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    URL: Article
    DOI: 10.1158/1538-7445.AM2023-5407
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2023
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  • 2
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    Abstract 1202: A repository of PDX histology images for exploring spatial heterogeneity and cancer dynamics
    American Association for Cancer Research (AACR) ; 2022
    In:  Cancer Research Vol. 82, No. 12_Supplement ( 2022-06-15), p. 1202-1202
    Details
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 82, No. 12_Supplement ( 2022-06-15), p. 1202-1202
    Abstract: Patient-derived xenografts (PDXs) recapitulate intratumoral spatial heterogeneity and simulate a tumor microenvironment in which human immune and stromal cells in the PDX are replaced over passages by murine cells partially lacking immune function. Histological imaging enables exploring the spatial heterogeneity and dynamics of cancer, stromal, and immune cell interactions as correlates of tumor stage and therapeutic response over passages. We created a repository of curated, haematoxylin and eosin (H & E) images as a community resource for addressing these questions. Images were generated at five sites within the NCI’s PDX Development and Trial Centers Research Network (PDXNet) and the NCI Patient-Derived Models Repository. Over 900 images, including 739 from PDXs and 190 from paired patients, are hosted on the Seven Bridges Genomics Cancer Genomics Cloud. They represent 42 cancer subtypes, including breast cancer (n=134), colon adenocarcinoma (COAD; n=94), pancreatic cancer (n=87), lung adenocarcinoma (LUAD; n=80), melanoma (n=71), and squamous cell lung cancer (LUSC; n=65). Paired human/PDX images are available for each of these cancers. Human and/or PDX images generated following patient treatment are available for 37 of the subtypes. Most images are from early passages (P0: 158; P1: 292; P2: 152; P3: 69; & gt;P3: 55). Annotations include sex, age, race, ethnicity, and, for most images, pathological assessment of tissue-level percent cancer, stromal, and necrotic cell content (n=639) and tumor stage (n=650). RNA and exome sequencing data are available for 99 and 228 images, respectively, matched at the patient or sample level. Quality control was performed using HistoQC. Cells were segmented and labeled as neoplastic, necrotic, immune, stromal, or other using Hover-Net and predictions of total neoplastic cell area correlated with whole-slide pathological assessment of cancer cell percentage (COAD: r=0.51; LUSC: r=0.59). HD-Staining, another classification approach, was applied to a subset of images and our clinical annotations will facilitate validation of this and related methods. Features of 512 x 512 pixel tiles were computed using the Inception V3 convolutional neural network pre-trained on ImageNet. Unsupervised clustering of these features demonstrate inter-patient heterogeneity within pathologist-annotated tumor regions. A classifier developed using pathologist-annotated cancer, stromal, and necrotic regions and trained on the features in LUSC images (n=10 images) achieved a cross-validation accuracy of 96% for cancer tiles across (n=5) LUAD images. Accuracy was lower for stromal classification (90%), likely reflecting current limitations of our small, but growing, labeled training set. Our repository of clinically-annotated PDX H & E images should aid the community in studying spatial heterogeneity and in training deep learning-based image analysis methods. Citation Format: Brian S. White, Xingyi Woo, Soner Koc, Todd Sheridan, Steven B. Neuhauser, Akshat M. Savaliya, Lacey E. Dobrolecki, John D. Landua, Matthew H. Bailey, Maihi Fujita, Kurt W. Evans, Bingliang Fang, Junya Fujimoto, Maria Gabriela Raso, Shidan Wang, Guanghua Xiao, Yang Xie, Sherri R. Davies, Ryan C. Fields, R Jay Mashl, Jacqueline L. Mudd, Yeqing Chen, Min Xiao, Xiaowei Xu, Melinda G. Hollingshead, Shahanawaz Jiwani, PDXNet Consortium, Yvonne A. Evrard, Tiffany A. Wallace, Jeffrey A. Moscow, James H. Doroshow, Nicholas Mitsiades, Salma Kaochar, Chong-xian Pan, Moon S. Chen, Luis G. Carvajal-Carmona, Alana L. Welm, Bryan E. Welm, Michael T. Lewis, Ramaswamy Govindan, Li Ding, Shunqiang Li, Meenhard Herlyn, Michael A. Davies, Jack A. Roth, Funda Meric-Bernstam, Carol J. Bult, Brandi Davis-Dusenbery, Dennis A. Dean, Jeffrey H. Chuang. A repository of PDX histology images for exploring spatial heterogeneity and cancer dynamics [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 1202.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    URL: Article
    DOI: 10.1158/1538-7445.AM2022-1202
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2022
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  • 3
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    Abstract 6072: Chromosomal aneuploidy, whole-genome doubling and mutational signatures in NCI PDMR models
    American Association for Cancer Research (AACR) ; 2023
    In:  Cancer Research Vol. 83, No. 7_Supplement ( 2023-04-04), p. 6072-6072
    Details
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 83, No. 7_Supplement ( 2023-04-04), p. 6072-6072
    Abstract: 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 & gt;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 & lt;0.01, Fisher’s exact test). Among 30 PDX models where the patients had known platinum-based chemotherapy history, 40% of them had an identifiable platinum chemotherapy treatment signature (SBS31 or DBS5). Chromothripsis associated amplification signature (CN8) was enriched in models with WGD (p & lt;0.05). 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.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    URL: Article
    DOI: 10.1158/1538-7445.AM2023-6072
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2023
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  • 4
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    Abstract 2050: Molecular subclassification of NCI PDMR breast cancer models using PAM50 gene expression signature
    American Association for Cancer Research (AACR) ; 2023
    In:  Cancer Research Vol. 83, No. 7_Supplement ( 2023-04-04), p. 2050-2050
    Details
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 83, No. 7_Supplement ( 2023-04-04), p. 2050-2050
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    URL: Article
    DOI: 10.1158/1538-7445.AM2023-2050
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2023
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  • 5
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    Abstract 3012: Patient-derived models of rare cancers in the National Cancer Institute's patient-derived models repository
    American Association for Cancer Research (AACR) ; 2021
    In:  Cancer Research Vol. 81, No. 13_Supplement ( 2021-07-01), p. 3012-3012
    Details
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 81, No. 13_Supplement ( 2021-07-01), p. 3012-3012
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    URL: Article
    DOI: 10.1158/1538-7445.AM2021-3012
    RVK:
    XA 36000
    RVK:
    XA 10000
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2021
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  • 6
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    Abstract 36: Comparing twenty-two matched patient-derived cell lines developed from either patient, PDX, or organoid tumor cell material
    American Association for Cancer Research (AACR) ; 2023
    In:  Cancer Research Vol. 83, No. 7_Supplement ( 2023-04-04), p. 36-36
    Details
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 83, No. 7_Supplement ( 2023-04-04), p. 36-36
    Abstract: The National Cancer Institute has developed a Patient-Derived Models Repository (NCI PDMR; https://pdmr.cancer.gov) comprised of quality-controlled, early passage, and clinically-annotated patient-derived tumor xenografts (PDXs), organoids (PDOrgs), cell cultures (PDCs), and cancer associated fibroblasts (CAFs) available with genomic data to the extramural community for research use. Models are developed by the NCI PDMR in decreasing order of complexity, specifically 1) patient material can be used to develop PDXs, PDOrgs, PDCs, and CAFs, 2) PDX material for PDOrgs and PDCs, and 3) PDOrg material for PDCs, but a PDC is never used to develop a PDOrg or PDX. Eleven pairs of 22 matched PDCs have been developed in parallel from either patient, PDX, patient-derived organoid, or PDX-derived organoid tumor material and sequenced by WES and RNASeq. Genetic stability was assessed using multiple approaches including microsatellite instability (MSI) generated from MSISensor2, percentage of genomic loss of heterozygosity (LOH) using a set of ~800k heterozygous SNPs from a population level genomic database (gnomAD), pairwise Spearman correlation based on BIN level copy number (CN)/RNA expression profiles, and OncoKB annotated oncogenic/likely oncogenic variants. No systematic differences were observed within PDC pairs derived from different origins or compared to their patient and/or PDX material in MSI, LOH% and RNA expression profile but pairwise Spearman correlation (0.66-0.88) in CNV profiles were somewhat variable, likely due to low sequencing depth. In one PDC pair (299254), 3 out of 12 OncoKB annotated Indels and CNV showed opposite level of variant allele frequencies/CN when comparing a model derived from patient material to one developed from a PDX-derived organoid, possibly driven by a lineage-specific subclonal outgrowth when compared to patient and PDX data. Phenotypic characteristics of matched PDCs also overall show no major differences, though variability in growth rates and the ability to form spheroids in serum-free medium were noted. In one pair (919269), the PDC derived from patient material was able to form a cell line xenograft (CLX) in NSG mice but not the PDC developed from a patient-derived organoid. Overall, these models demonstrate a high degree of concordance at the genetic and phenotypic level when compared to the originating patient and/or PDX tumor. Though further characterization (e.g., preclinical drug testing) may be needed to define differences between matched PDC pairs, lack of access to patient tissue or failure to generate tumor cell cultures from one source of material should not hamper development of preclinical in vitro models from other patient-derived model types as long as the source-of-origin is clearly defined. Funded by NCI Contract No. HHSN261200800001E Citation Format: Cindy R. Timme, Ting-Chia Chang, Sergio Y. Alcoser, Gareth Bliss, Carrie Bonomi, Suzanne Borgel, John Carter, Alice Chen, Li Chen, Kevin Cooper, Biswajit Das, Kelly Dougherty, Lindsay Dutko, Marion Gibson, Michelle M. Gottholm-Ahalt, Tara Grinnage-Pulley, Shahanawaz Jiwani, Keegan Kalmbach, Chris Karlovich, Kimberly Klarmann, Tiffanie Chase, Michael Mullendore, Matthew Murphy, Kevin Plater, Gloryvee Rivera, Jessica Steed, Luke Stockwin, Yvonne A. Evrard, Mickey Williams, Dianne L. Newton, Melinda G. Hollingshead, James H. Doroshow. Comparing twenty-two matched patient-derived cell lines developed from either patient, PDX, or organoid tumor cell material [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 36.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    URL: Article
    DOI: 10.1158/1538-7445.AM2023-36
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2023
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    Abstract 40: NCI patient derived models repository: PDX, organoid and cell lines from the same patient - bridging the translational pipeline
    American Association for Cancer Research (AACR) ; 2023
    In:  Cancer Research Vol. 83, No. 7_Supplement ( 2023-04-04), p. 40-40
    Details
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 83, No. 7_Supplement ( 2023-04-04), p. 40-40
    Abstract: The National Cancer Institute’s Patient-Derived Models Repository (NCI PDMR; https://pdmr.cancer.gov) has developed a national repository of Patient-Derived Models (PDMs) comprised of patient-derived xenografts (PDXs), in vitro patient-derived tumor cell cultures (PDCs) and cancer associated fibroblasts (CAFs) as well as patient-derived organoids (PDOrg). These PDMs are clinically annotated with molecular information available in an easily accessible database for the extramural community. A key effort in developing these models is to develop matched models sets allowing for larger scale screening efforts using 2D or 3D models to prioritize selection of PDX models for preclinical translational research. To date, over 220 model sets with a PDX, PDOrg, and PDC from a single patient have been developed; 40 of these have matched CAF models allowing for exploration of research questions focused on tumor microenvironment. The largest model sets are in colorectal cancer (COADREAD, n=76), gynecologic cancers (n=33), pancreatic adenocarcinoma (PAAD, n=29), melanoma (MEL, n=19), and head and neck squamous cell carcinomas (HNSCC, n=17). Every model undergoes several quality control assessments that serve as go/no-go criteria including pathology assessment, STR validation, NGS concordance assessment and for PDXs, human:mouse DNA content assessment. It should be noted that not every model is successful in the development or QC phase so additional model sets with only one or two of the model types are also available for researcher requests, for example there are over 125 PDX/PDOrg matched model sets. The NCI is currently performing parallel preclinical screening of PDXs and PDCs or PDOrgs to determine the ability of the in vitro lines to predict in vivo activity. Genetic and histopathologic assessment of these matched model sets have demonstrated a high degree of stability by somatic mutation, copy number alteration (CNA) and gene expression data. Gene expression correlation analysis shows that mean of Spearman r between PDXs 0.89, between matched PDC/PDXs 0.79, and between matched PDOrg/PDXs 0.82. As expected, some variation at the gene expression level when comparing PDX to in vitro cultures by t-SNE can be observed, likely due to the differences in culture conditions. Funded by NCI Contract No. HHSN261200800001E Citation Format: Yvonne A. Evrard, Li Chen, Sergio Alcoser, Gareth Bliss, Carrie Bonomi, Suzanne Borgel, John Carter, Ting-Chia Chang, Alice Chen, Kevin Cooper, Biswajit Das, Kelly Dougherty, Lindsay Dutko, Marion Gibson, Michelle M. Ahalt-Gottholm, Tara Grinnage-Pulley, Keegan Kalmbach, Chris Karlovich, Kimberly Klarmann, Shahanawaz Jiwani, Tiffanie Miner, Michael Mullendore, Matthew Murphy, Kevin Plater, Gloryvee Rivera, Jessica Steed, Luke Stockwin, Cindy R. Timme, Dianne L. Newton, Paul Mickey Williams, Melinda G. Hollingshead, James H. Doroshow. NCI patient derived models repository: PDX, organoid and cell lines from the same patient - bridging the translational pipeline [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 40.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    URL: Article
    DOI: 10.1158/1538-7445.AM2023-40
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2023
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    Abstract 3916: 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
    American Association for Cancer Research (AACR) ; 2020
    In:  Cancer Research Vol. 80, No. 16_Supplement ( 2020-08-15), p. 3916-3916
    Details
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 80, No. 16_Supplement ( 2020-08-15), p. 3916-3916
    Abstract: 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) & gt;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 & gt;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.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    URL: Article
    DOI: 10.1158/1538-7445.AM2020-3916
    RVK:
    XA 36000
    RVK:
    XA 10000
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2020
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    Abstract 1913: Quality control workflows developed for the NCI Patient-Derived Models Repository using low pass whole genome sequencing and whole exome sequencing
    American Association for Cancer Research (AACR) ; 2022
    In:  Cancer Research Vol. 82, No. 12_Supplement ( 2022-06-15), p. 1913-1913
    Details
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 82, No. 12_Supplement ( 2022-06-15), p. 1913-1913
    Abstract: 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 & gt;= 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.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    URL: Article
    DOI: 10.1158/1538-7445.AM2022-1913
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2022
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    Abstract 3010: Single agent response comparisons in a large-scale, preclinical trial of rare cancer PDXs by the National Cancer Institute's patient-derived models repository
    American Association for Cancer Research (AACR) ; 2021
    In:  Cancer Research Vol. 81, No. 13_Supplement ( 2021-07-01), p. 3010-3010
    Details
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 81, No. 13_Supplement ( 2021-07-01), p. 3010-3010
    Abstract: The National Cancer Institute's Patient-Derived Models Repository (NCI PDMR; https://pdmr.cancer.gov) is performing a large-scale preclinical study with 39 patient-derived xenograft (PDX) models of rare cancers (including mesothelioma, MPNST, osteosarcoma, Merkel cell carcinoma) treated with 56 novel therapeutic combinations (targeted and cytotoxic agents) in an exploratory, n-of-4 arm, study design. Drug combinations with additive activity may undergo clinical evaluation in patients with rare cancers. PDX tumors are treated with a set of 8 combinations plus relevant vehicle controls while in parallel enough PDXs are serially passaged for the next passage and drug set. Every serial passage undergoes several quality control assessments that serve as go/no-go criteria. Combinations that show promising responses (e.g., regression or durable tumor growth inhibition) are repeated along with the single agent arms to determine if the response is driven by the combination or only one of the agents. We are currently at the half-way point in the overall study and here report interim results for the early combination agents that have single agent data for comparison. In a combination of a VEGFi and EGFRi, 6/37 models achieved a partial regression (30% shrinkage for more than one consecutive time point) and 17/37 had tumor growth inhibition while drug was on board. Single agent studies have been completed for 17/37 models with this combination and 7/9 responses were due to at least an additive effect of the combination. In contrast, while an HDACi + nucleoside analog combination had 16/36 responsive models, response in most of the single agent studies was due to only one of the agents. As part of this study, 3 models have been identified that have responded to at least 50% of the combinations tested possibly indicating a hypersensitive phenotype: two Merkel cell carcinomas (n=28 and 32) and one Neuroendocrine carcinoma (n=27). There is no immediate link between mechanism of action of the agents in the combinations, and the two Merkel cell carcinoma responses only had a moderate overlap. Finally, two Rhabdomyosarcoma models in the study have been the least responsive models to date. Funded by NCI Contract No. HHSN261200800001E Citation Format: Yvonne A. Evrard, Sergio Y. Alcoser, Suzanne Borgel, Devynn Breen, John Carter, Tiffanie Chase, Alice Chen, Li Chen, Kristen Cooley, Biswajit Das, Emily Delaney, Lyndsay Dutko, Shannon Ecker, Thomas Forbes, Kyle Georgius, Michelle M. Gottholm-Ahalt, Tara Grinnage-Pulley, Sierra Hoffman, Chris Karlovich, Kimberly Klarmann, Shahanawaz Jiwani, Justine Mills, Malorie Morris, Michael Mullendore, Dianne Newton, Gloryvee Rivera, Howard Stotler, Jesse Stottlemyer, Savanna Styers, Cindy R. Timme, Debbie Trail, Shannon Uzelac, Tomas Vilimas, Thomas Walsh, Nikki Walters, P. Mickey Williams, Melinda G. Hollingshead, James H. Doroshow. Single agent response comparisons in a large-scale, preclinical trial of rare cancer PDXs by 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 3010.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    URL: Article
    DOI: 10.1158/1538-7445.AM2021-3010
    RVK:
    XA 36000
    RVK:
    XA 10000
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2021
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