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Dahut, W. L.

Publications and source records attributed to Dahut, W. L..

2 recordsLinked to original sources

A cancer stem cell population underlies a multi-lineage phenotype and drug resistance in prostate cancer

PurposeTo resist lineage-dependent therapies such as androgen receptor inhibition in prostate cancer, cancer cells often adopt a stem-like state resulting in lineage-plasticity and phenotypic heterogeneity. We assessed the dynamics of lineage determination and cellular subpopulation expansion in treatment-resistant adenocarcinoma, amphicrine, and small cell neuroendocrine castrate resistant prostate cancers (CRPCs). Experimental DesignWe developed CRPC patient-derived organoid models that preserve heterogeneity of the originating tumor, including an amphicrine model harboring epigenetic driver mutations, ARID1A and ARID1B, and displaying a range of luminal and neuroendocrine phenotypes. We used single-cell RNA-seq, barcode lineage-tracing, single-cell ATAC-seq, and RNA-FISH to delineate the subpopulation structure of the heterogeneous organoids and define the lineage hierarchy, determine potential transcriptional regulators of amphicrine lineage-plasticity, and identify subpopulation-specific molecular targets for therapeutic intervention. ResultsTranscriptionally similar stem/progenitor cells were identified for all lineage populations. Lineage tracing in amphicrine CRPC showed that heterogeneity originated from distinct subclones of infrequent stem/progenitor cells that produced mainly quiescent differentiated amphicrine progeny. Amphicrine cells were enriched for secretory luminal, mesenchymal, and enzalutamide treatment persistent signatures. By contrast, adenocarcinoma CRPC had a less defined hierarchy, as progeny originated from stem/progenitor cells and self-renewing differentiated luminal cells. NEPC was composed almost exclusively of self-renewing stem/progenitor cells. Amphicrine stem cells demonstrated concurrent transcription factor activities associated with stem/progenitor, luminal epithelial and mesenchymal lineages. Finally, the amphicrine stem/progenitor subpopulation was specifically depleted with an AURKA inhibitor, which blocked tumor growth. ConclusionsThese data illuminate distinct origins and dynamics of subtype-specific CRPC plasticity in addition to demonstrating a strategy for targeting differentiation-competent stem cells. Translational RelevanceFor advanced prostate cancer, therapeutic resistance to androgen signaling suppression increasingly involves the development of lineage plasticity. The cellular states of transition and subpopulation heterogeneity that underlie lineage-plasticity are not well understood, which is an ongoing challenge to the design of effective treatments. Using patient-derived organoid models of various CRPC lineage subtypes, we observed distinct patterns with respect to stem/progenitor activity and associated growth phenotypes. The simultaneous expression of AR-driven and neuroendocrine identities, so-called amphicrine tumors, are thought to be an early dedifferentiation stage in plasticity-mediated resistance. We observed in an epigenetically-driven, amphicrine model of CRPC that a rare but necessary bipotent stem/progenitor population is suppressed by AURKA inhibitors, leading to tumor regression, while ARPC demonstrates both self-renewing differentiated luminal cells and stem/progenitors. These data suggest that AURKA inhibition may block the amplification of a resistance dedifferentiation pathway and should be considered in combination with AR signaling inhibitors for ARPC with characteristics of lineage plasticity.

cancer biology↗

Clinically oriented prediction of patient response to targeted and immunotherapies from the tumor transcriptome

BackgroundPrecision oncology is gradually advancing into mainstream clinical practice, demonstrating significant survival benefits. However, eligibility and response rates remain limited in many cases, calling for better predictive biomarkers. MethodsWe present ENLIGHT, a transcriptomics-based computational approach that identifies clinically relevant genetic interactions and uses them to predict a patients response to a variety of therapies in multiple cancer types, without training on previous treatment response data. We study ENLIGHT in two translationally oriented scenarios: Personalized Oncology (PO), aimed at prioritizing treatments for a single patient, and Clinical Trial Design (CTD), selecting the most likely responders in a patient cohort. FindingsEvaluating ENLIGHTs performance on 21 blinded clinical trial datasets in the PO setting, we show that it can effectively predict a patients treatment response across multiple therapies and cancer types. Its prediction accuracy is better than previously published transcriptomics-based signatures and is comparable to that of supervised predictors developed for specific indications and drugs. In combination with the IFN-{gamma}signature, ENLIGHT achieves an odds ratio larger than 4 in predicting response to immune checkpoint therapy. In the CTD scenario, ENLIGHT can potentially enhance clinical trial success for immunotherapies and other monoclonal antibodies by excluding non-responders, while overall achieving more than 90% of the response rate attainable under an optimal exclusion strategy. ConclusionENLIGHT demonstrably enhances the ability to predict therapeutic response across multiple cancer types from the bulk tumor transcriptome. FundingThis research was supported in part by the Intramural Research Program, NIH and by the Israeli Innovation Authority.

bioinformatics↗