Leveraging RNA and synthetic lethality in germline BRCA-mut breast cancer

Division Of Basic Sciences - Nci

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Abstract

Extensive molecular data is increasingly used in clinical oncology to identify predictors of response to therapeutic options or to gain prognostic information. While germline genetic testing has entered the data used in this context (via BRCA1/2 and PARP inhibition), other potential relationships and targets have not been well characterized to date. This project uses de-identified RNA-seq from primary breast cancers in patients with known germline BRCA1/2 pathogenic variants. We are applying the SELECT whole-transcriptome-based algorithm to determine across these cancers the prioritization of therapies that may be applied clinically besides olaparib (such as immunotherapy in triple-negative breast cancer (TNBC) or CDK4/6 inhibitors in hormone receptor positive (HR+) breast cancer). This algorithm can also identify additional synthetic lethal partners in BRCA1/2 and predict response to additional therapies accordingly, providing preliminary data to add potential treatment strategies to this patient population.