In:
Cancer Research, American Association for Cancer Research (AACR), Vol. 80, No. 16_Supplement ( 2020-08-15), p. LB-003-LB-003
Abstract:
Quantitative metrics to objectively assess the fidelity of cancer models, such as cell lines, organoids, or patient-derived xenografts (PDXs), remain elusive, with histological criteria or the presence of specific mutations often used as driving principles. However, focusing on individual mutations inevitably ignores the effect of a large complement of additional, model-specific genetic and epigenetic events. As a result, effective model fidelity assessment is best performed a posteriori, for instance, by determining whether dependencies identified in a specific tumor model (e.g., a cell line or organoid) are recapitulated in vivo in PDXs or in patients, via pre-clinical or clinical trials. Unfortunately, such an approach is inefficient and time-consuming, creating an urgent need for methodologies capable of effectively and quantitatively assessing model fidelity a priori. This unmet need motivated us to develop and test a quantitative, molecular-level framework (OncoMatch), to assess the fidelity of a given tumor model in the context of a specific biological question, and in particular for addressing issues of drug sensitivity. We addressed this challenge by integrating two independent computational metrics to assess: (a) conservation of regulatory networks inferred from patient-derived samples in a model of interest, and; (b) overlap of master regulator (MR) proteins–i.e., proteins representing the mechanistic determinants of the transcriptional state associated with the phenotype of interest–as inferred from patient-derived and model-derived samples. We show that these molecular-level criteria can effectively identify cell lines that recapitulate patient-specific drug mechanism of action and drug sensitivity, independent of histological consideration. By leveraging gene expression profiles of drug-perturbations in primary cells and explants derived from gastroenteropancreatic-neuroendocrine tumor (GEP-NET) patients, we show that H-STS, an EBV-immortalized lymphoblastoid cell line, represents a high-fidelity model for the assessment of drug mechanism of action and drug sensitivity in these tumors. In particular, our analysis shows highly significant conservation of drug mechanism of action for 60 of 95 profiled drugs (63%, p & lt; 10-10, Bonferroni's corrected), and higher conservation among drugs exhibiting greater bioactivity in this context. This rate is comparable and, in fact higher than what is achieved using tumor-type-matched cell line pairs representative of glioma, pancreatic and prostate carcinoma, and dramatically higher than the conservation observed between unrelated models, which we used as negative controls. Based on this systematic analysis, OncoMatch represents a valuable addition to our repertoire of tools for prioritizing cell lines, organoids and patient-derived xenograft models as high-fidelity human cancer models. We provide comprehensive prioritization of 921 cell lines as potential high-fidelity models for 10,024 human tumor samples in TCGA. This represents an actionable resource to guide selection of cell line models for specific drug mechanism of action and drug sensitivity studies. Citation Format: Mariano J. Alvarez, Yan Pengrong, Mary L. Alpaugh, Michaela Bowden, Ewa T. Sicinska, Chensheng W. Zhou, Charles Karan, Ronald B. Realubit, Prabhjot S. Mundi, Adina Grunn, Jager Dirk, John A. Chabot, Antonio T. Fojo, Paul E. Oberstein, Hanina Hibshoosh, Jeffrey W. Milsom, Matthew H. Kulke, Massimo Loda, Gabriela Chiosis, Diane L. Reidy-Lagunes, Andrea Califano. OncoMatch: Unbiased, quantitative assessment of cancer model fidelity for drug sensitivity and mechanism of action elucidation [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 LB-003.
Type of Medium:
Online Resource
ISSN:
0008-5472
,
1538-7445
DOI:
10.1158/1538-7445.AM2020-LB-003
Language:
English
Publisher:
American Association for Cancer Research (AACR)
Publication Date:
2020
detail.hit.zdb_id:
2036785-5
detail.hit.zdb_id:
1432-1
detail.hit.zdb_id:
410466-3
Permalink
