Organoid-evaluable clinical biomarkers predict drug responses and guide new breast cancer therapies
Poor therapeutic response in subsets of breast cancer (BC) patients poses an ongoing challenge. Here, we present a biomarker-guided characterization of 44 patient-derived BC organoids, with the aim of modeling resistant disease with greater fidelity and developing an in-vitro system grounded in clinical data for testing alternative treatment strategies. We utilized patient transcriptomic and outcome data from the I-SPY2 clinical trial to develop predictive models of response to a range of therapies, using only organoid-detectable biomarkers as input. A model predicting response to veliparib-platinum chemotherapy (VP) in triple-negative BC (TNBC) was validated in organoids, showing that in vitro drug responses matched predictions from the patient data-derived model. A drug screen in VP-resistant TNBC organoids identified combination treatments that overcame resistance to cisplatin, including pro-apoptotic therapies. This demonstrates that gene expression-based resistance models derived from patient data can be successfully modeled in organoids that can then be used for therapeutic evaluation.