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Updated: Feb 4, 2026

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors
Published on: August 18, 2023
Pharmacogenomic landscape of patient-derived tumor cells informs precision oncology therapy
Jin-Ku Lee1,2,3, Zhaoqi Liu4,5, Jason K Sa1,3
1Institute for Refractory Cancer Research, Samsung Medical Center, Seoul, Republic of Korea.
Abstract:
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.
Insights
Patient-derived tumor cells (PDCs) offer precise models for cancer research. This study maps drug responses across many PDCs, revealing insights into targeted therapy resistance and repurposing opportunities.
Area of Science:
- Oncology
- Genomics
- Pharmacology
Background:
- Anticancer therapy outcomes vary due to tumor heterogeneity.
- Precision oncology relies on molecular tumor characterization for tailored treatments.
Purpose of the Study:
- To create a pharmacological landscape of patient-derived tumor cells (PDCs) with genomic and transcriptomic data.
- To identify pharmacogenomic associations and therapeutic resistance mechanisms.
Main Methods:
- Established pharmacological profiling of 462 patient-derived tumor cells (PDCs) across 14 cancer types.
- Performed genomic and transcriptomic profiling on 385 PDCs.
- Analyzed drug sensitivities and clinical response data.
Main Results:
- PDCs more accurately recapitulate tumor molecular properties than traditional cell lines.
- Identified molecular drivers of resistance to EGFR inhibitors.
- Discovered potential repurposing of ibrutinib for EGFR-specific glioma therapy.
Conclusions:
- PDC models provide valuable insights into cancer drug response and resistance.
- Pharmacogenomic data from PDCs can predict clinical response to targeted therapies.
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