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 预测对治疗选择的反应或获得预后信息。而 生殖系基因检测已经输入了在此背景下使用的数据（通过BRCA 1/2和PARP 抑制），其他潜在的关系和目标尚未得到很好的表征， 约会该项目使用来自原发性乳腺癌患者的去识别RNA-seq， 已知生殖系BRCA 1/2致病性变体。我们使用SELECT 基于全转录组的算法，以确定这些癌症的优先顺序， 除了奥拉帕尼之外，还可以在临床上应用的疗法（例如免疫疗法， 三阴性乳腺癌（TNBC）或激素受体阳性（HR+）的CDK 4/6抑制剂 乳腺癌）。该算法还可以识别其他合成的致命伙伴， BRCA 1/2，并相应预测对其他治疗的反应，提供初步数据 为这一患者群体增加潜在的治疗策略。

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.