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Outcomes of anticancer therapy vary dramatically among patients due to diverse genetic and molecular backgrounds, highlighting extensive intertumoral heterogeneity. The fundamental tenet of precision oncology defines molecular characterization of tumors to guide optimal patient-tailored therapy. Towards this goal, we have established a compilation of pharmacological landscapes of 462 patient-derived tumor cells (PDCs) across 14 cancer types, together with genomic and transcriptomic profiling in 385 of these tumors. Compared with the traditional long-term cultured cancer cell line models, PDCs recapitulate the molecular properties and biology of the diseases more precisely. Here, we provide insights into dynamic pharmacogenomic associations, including molecular determinants that elicit therapeutic resistance to EGFR inhibitors, and the potential repurposing of ibrutinib (currently used in hematological malignancies) for EGFR-specific therapy in gliomas. Lastly, we present a potential implementation of PDC-derived drug sensitivities for the prediction of clinical response to targeted therapeutics using retrospective clinical studies.
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Genomic and molecular tumor profiling enables the identification of effective drugs tailored to cancer patients1-9. However, predicting successful anticancer therapy remains extremely challenging10-12, largely due to extensive inter- and intratumoral heterogeneity13-15. Recent efforts have established a framework for genetic predictions of anticancer drug responses using standard in vitro cancer cell line models16-23. In particular, large-scale drug-screening systems, using conventional cancer cell lines have provided reference points for gene-drug associations, enabling the discovery of molecular markers that may predict therapeutic response16,17. However, there are several challenges that hamper broad clinical utility of the current gene-drug association map in the oncology clinic. First, as most solid cancers harbor multiple molecular aberrations, predicting therapeutic efficacy of a targeted agent based on genomic profiling alone can be a complicated process. Second, the prediction of treatment outcome extrapolated from conventional cancer cell lines may not recapitulate each cancer patient's tumor. To address these challenges, we present a comprehensive integrated approach using genomic analysis of the patient tumor, ex vivo assessment of drug effects on patient tumor derivatives, and in vivo validation of the selected compounds' therapeutic efficacies.
While patient-derived xenograft (PDX) systems respect both interpatient genomic diversity and the intratumor microenvironment24, the generation of PDX models exhibits a relatively lower tumor-formation rate, and requires a longer establishment period compared with patient-derived tumor cells (PDCs). In glioblastoma, it takes...