用于预测三阴性乳腺癌同源重组缺陷的基因表达特征。
Gene expression signature for predicting homologous recombination deficiency in triple-negative breast cancer.
发表日期：2024 Jul 19
作者：
Jia-Wern Pan, Zi-Ching Tan, Pei-Sze Ng, Muhammad Mamduh Ahmad Zabidi, Putri Nur Fatin, Jie-Ying Teo, Siti Norhidayu Hasan, Tania Islam, Li-Ying Teoh, Suniza Jamaris, Mee-Hoong See, Cheng-Har Yip, Pathmanathan Rajadurai, Lai-Meng Looi, Nur Aishah Mohd Taib, Oscar M Rueda, Carlos Caldas, Suet-Feung Chin, Joanna Lim, Soo-Hwang Teo
来源：
npj Breast Cancer
摘要：
三阴性乳腺癌（TNBC）是乳腺癌的一个子集，目前仍难以治疗。部分由 BRCA 致病性变异非携带者产生的 TNBC 具有与 BRCA 携带者相似的基因组特征，也可能受益于 PARP 抑制剂治疗。利用来自马来西亚乳腺癌 (MyBrCa) 队列的 129 个 TNBC 样本的基因组数据，我们开发了一种基于基因表达的机器学习分类器，用于 TNBC 中的同源重组缺陷 (HRD)。分类器在 MyBrCa 验证数据集中的 AUROC 为 0.93，在 TCGA TNBC 中的 AUROC 为 0.84，识别出具有 HRD 突变特征的样本。此外，分类器将 TNBC 中与 HRD 相关的基因组特征与 TCGA、METABRIC 和 ICGC 强烈分离。因此，我们的基因表达分类器可以识别具有同源重组缺陷的三阴性乳腺癌患者，这提出了一种替代方法来识别可能受益于 PARP 抑制剂或铂类化疗治疗的个体。© 2024。作者。
Triple-negative breast cancers (TNBCs) are a subset of breast cancers that have remained difficult to treat. A proportion of TNBCs arising in non-carriers of BRCA pathogenic variants have genomic features that are similar to BRCA carriers and may also benefit from PARP inhibitor treatment. Using genomic data from 129 TNBC samples from the Malaysian Breast Cancer (MyBrCa) cohort, we developed a gene expression-based machine learning classifier for homologous recombination deficiency (HRD) in TNBCs. The classifier identified samples with HRD mutational signature at an AUROC of 0.93 in MyBrCa validation datasets and 0.84 in TCGA TNBCs. Additionally, the classifier strongly segregated HRD-associated genomic features in TNBCs from TCGA, METABRIC, and ICGC. Thus, our gene expression classifier may identify triple-negative breast cancer patients with homologous recombination deficiency, suggesting an alternative method to identify individuals who may benefit from treatment with PARP inhibitors or platinum chemotherapy.© 2024. The Author(s).