Department of Cell, Development, and Regenerative Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Internal Medicine, UCLA David Geffen School of Medicine, CA 90095, USA.
Dis Model Mech. 2024 Jul 1;17(7). doi: 10.1242/dmm.050191. Epub 2024 Jul 3.
Accounting for 10-20% of breast cancer cases, triple-negative breast cancer (TNBC) is associated with a disproportionate number of breast cancer deaths. One challenge in studying TNBC is its genomic profile: with the exception of TP53 loss, most breast cancer tumors are characterized by a high number of copy number alterations (CNAs), making modeling the disease in whole animals challenging. We computationally analyzed 186 CNA regions previously identified in breast cancer tumors to rank genes within each region by likelihood of acting as a tumor driver. We then used a Drosophila p53-Myc TNBC model to identify 48 genes as functional drivers. To demonstrate the utility of this functional database, we established six 3-hit models; altering candidate genes led to increased aspects of transformation as well as resistance to the chemotherapeutic drug fluorouracil. Our work provides a functional database of CNA-associated TNBC drivers, and a template for an integrated computational/whole-animal approach to identify functional drivers of transformation and drug resistance within CNAs in other tumor types.
三阴性乳腺癌（TNBC）占乳腺癌病例的 10-20%，与不成比例的乳腺癌死亡人数有关。研究 TNBC 的一个挑战是其基因组特征：除了 TP53 缺失外，大多数乳腺癌肿瘤的特征是大量拷贝数改变（CNAs），使得在全动物中对疾病进行建模具有挑战性。我们对先前在乳腺癌肿瘤中鉴定的 186 个 CNA 区域进行了计算分析，根据每个区域中作为肿瘤驱动因素的可能性对基因进行排名。然后，我们使用果蝇 p53-Myc TNBC 模型来鉴定 48 个作为功能驱动基因的基因。为了证明这个功能数据库的实用性，我们建立了六个三击模型；改变候选基因导致转化的各个方面增加，以及对氟尿嘧啶化疗药物的耐药性增加。我们的工作提供了一个与 CNA 相关的 TNBC 驱动基因的功能数据库，以及一个用于在其他肿瘤类型的 CNA 中识别转化和耐药性的功能驱动基因的集成计算/全动物方法的模板。
