ABSTRACT
Most cancer deaths are due to metastatic dissemination to distant organs. Bone is the most frequently affected organ in metastatic prostate cancer and a major cause of prostate cancer deaths. Yet, our partial understanding of the molecular factors that drive bone metastasis has been a limiting factor for developing preventative and therapeutic strategies to improve patient survival and well-being. Although recent studies have uncovered molecular alterations that occur in prostate cancer metastasis, their functional relevance for bone metastasis is not well understood. Using genome-wide CRISPR activation and inhibition screens we have identified multiple drivers and suppressors of prostate cancer metastasis. Through functional validation, including an innovative organ-on-a-chip invasion platform for studying bone tropism, our study identifies the transcriptional modulator CITED2 as a novel driver of prostate cancer bone metastasis and uncovers multiple new potential molecular targets for bone metastatic disease.
ABSTRACT
Prioritizing treatments for individual patients with cancer remains challenging, and performing coclinical studies using patient-derived models in real time is often unfeasible. To circumvent these challenges, we introduce OncoLoop, a precision medicine framework that predicts drug sensitivity in human tumors and their preexisting high-fidelity (cognate) model(s) by leveraging drug perturbation profiles. As a proof of concept, we applied OncoLoop to prostate cancer using genetically engineered mouse models (GEMM) that recapitulate a broad spectrum of disease states, including castration-resistant, metastatic, and neuroendocrine prostate cancer. Interrogation of human prostate cancer cohorts by Master Regulator (MR) conservation analysis revealed that most patients with advanced prostate cancer were represented by at least one cognate GEMM-derived tumor (GEMM-DT). Drugs predicted to invert MR activity in patients and their cognate GEMM-DTs were successfully validated in allograft, syngeneic, and patient-derived xenograft (PDX) models of tumors and metastasis. Furthermore, OncoLoop-predicted drugs enhanced the efficacy of clinically relevant drugs, namely, the PD-1 inhibitor nivolumab and the AR inhibitor enzalutamide. SIGNIFICANCE: OncoLoop is a transcriptomic-based experimental and computational framework that can support rapid-turnaround coclinical studies to identify and validate drugs for individual patients, which can then be readily adapted to clinical practice. This framework should be applicable in many cancer contexts for which appropriate models and drug perturbation data are available. This article is highlighted in the In This Issue feature, p. 247.
