Patient-Derived Organoids from Multiple Colorectal Cancer Liver Metastases Reveal Moderate Intra-patient Pharmacotranscriptomic Heterogeneity

Clin Cancer Res. 2020 Aug 1;26(15):4107-4119. doi: 10.1158/1078-0432.CCR-19-3637. Epub 2020 Apr 16.

Abstract

Purpose: Molecular tumor heterogeneity may have important implications for the efficacy of targeted therapies in metastatic cancers. Inter-metastatic heterogeneity of sensitivity to anticancer agents has not been well explored in colorectal cancer.

Experimental design: We established a platform for ex vivo pharmacogenomic profiling of patient-derived organoids (PDO) from resected colorectal cancer liver metastases. Drug sensitivity testing (n = 40 clinically relevant agents) and gene expression profiling were performed on 39 metastases from 22 patients.

Results: Three drug-response clusters were identified among the colorectal cancer metastases, based primarily on sensitivities to EGFR and/or MDM2 inhibition, and corresponding with RAS mutations and TP53 activity. Potentially effective therapies, including off-label use of drugs approved for other cancer types, could be nominated for eighteen patients (82%). Antimetabolites and targeted agents lacking a decisive genomic marker had stronger differential activity than most approved chemotherapies. We found limited intra-patient drug sensitivity heterogeneity between PDOs from multiple (2-5) liver metastases from each of ten patients. This was recapitulated at the gene expression level, with a highly proportional degree of transcriptomic and pharmacological variation. One PDO with a multi-drug resistance profile, including resistance to EGFR inhibition in a RAS-mutant background, showed sensitivity to MEK plus mTOR/AKT inhibition, corresponding with low-level PTEN expression.

Conclusions: Intra-patient inter-metastatic pharmacological heterogeneity was not pronounced and ex vivo drug screening may identify novel treatment options for metastatic colorectal cancer. Variation in drug sensitivities was reflected at the transcriptomic level, suggesting potential to develop gene expression-based predictive signatures to guide experimental therapies.

Publication types

  • Research Support, Non-U.S. Gov't