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

广泛的分子数据越来越多地用于临床肿瘤学，以确定对治疗方案的反应预测或获得预后信息。虽然生殖系基因检测已经输入了在这种情况下使用的数据（通过BRCA1/2和PARP抑制），但其他潜在的关系和靶点尚未得到很好的表征。该项目使用来自已知种系BRCA1/2致病变异患者的原发性乳腺癌的去鉴定RNA-seq。我们正在应用SELECT全转录组算法来确定除奥拉帕尼外，这些癌症中可能应用于临床的治疗方法的优先级（如三阴性乳腺癌（TNBC）的免疫治疗或激素受体阳性乳腺癌（HR+）的CDK4/6抑制剂）。该算法还可以识别BRCA1/2中额外的合成致死伴侣，并相应地预测对额外治疗的反应，为该患者群体增加潜在的治疗策略提供初步数据。

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.