Classifications within Molecular Subtypes Enables Identification of BRCA1/BRCA2 Mutation Carriers by RNA Tumor Profiling. Homo sapiens

Pathogenic germline mutations in BRCA1 or BRCA2 are detected in less than one third of families with a strong history of breast cancer. It is therefore expected that mutations still remain undetected by currently used screening methods. In addition, a growing number of BRCA1/2 sequence variants of unclear pathogen significance are found in the families, constituting an increasing clinical challenge. New methods are therefore needed to improve the detection rate and aid the interpretation of the clinically uncertain variants. In this study we analyzed a series of 33 BRCA1, 22 BRCA2, and 128 sporadic tumors by RNA profiling to investigate the classification potential of RNA profiles to predict BRCA1/2 mutation status. We found that breast tumors from BRCA1 and BRCA2 mutation carriers display characteristic RNA expression patterns, allowing them to be distinguished from sporadic tumors. The majority of BRCA1 tumors were basal-like while BRCA2 tumors were mainly luminal B. Using RNA profiles, we were able to distinguish BRCA1 tumors from sporadic tumors among basal-like tumors with 83% accuracy and BRCA2 from sporadic tumors among luminal B tumors with 89% accuracy. Furthermore, subtype-specific BRCA1/2 gene signatures were successfully validated in two independent data sets with high accuracies. Although additional validation studies are required, indication of BRCA1/2 involvement (“BRCAness”) by RNA profiling could potentially be valuable as a tool for distinguishing pathogenic mutations from benign variants, for identification of undetected mutation carriers, and for selecting patients sensitive to new therapeutics such as PARP inhibitors. Overall design: Gene expression profiling of 183 breast tumor samples. Breast tumors from hereditary breast cancer patients carrying a pathogenic BRCA1 (n=33) or BRCA2 (n=22) germ-line mutation were included in the study. Serving as a representative control group, primary breast tumor samples (n=128) were randomly selected. The study was conducted using Agilent-029949 Custom SurePrint G3 Human GE 8x60K Microarray platform. For cross-platform validation, a subset of the tumor samples (92 of the 183 samples) were analyzed by our in-house spotted microarray platform.

在具有明确乳腺癌家族史的家庭中，仅不足三分之一的家庭能检测出BRCA1或BRCA2的致病性生殖系突变（germline mutation）。因此，当前常用的筛查方法仍有大量突变未被检出。此外，在这类家族中陆续发现了越来越多意义未明的BRCA1/2序列变异体，这一情况正日益成为临床诊疗的挑战。因此亟需开发新方法以提升突变检出率，并辅助解读临床意义未明的变异体。
本研究通过RNA表达谱分析（RNA profiling），对33例BRCA1突变型、22例BRCA2突变型及128例散发性乳腺肿瘤进行了检测，以探究RNA表达谱在预测BRCA1/2突变状态方面的分类潜力。研究发现，携带BRCA1或BRCA2突变的乳腺肿瘤具有特征性的RNA表达模式，可与散发性肿瘤相区分。多数BRCA1突变型肿瘤为基底样型，而BRCA2突变型肿瘤则以管腔B型为主。借助RNA表达谱，我们可在基底样型肿瘤中以83%的准确率区分BRCA1突变型肿瘤与散发性肿瘤，在管腔B型肿瘤中以89%的准确率区分BRCA2突变型肿瘤与散发性肿瘤。此外，亚型特异性BRCA1/2基因特征经两个独立数据集验证，均取得了较高的准确率。尽管仍需开展更多验证研究，但通过RNA表达谱分析识别BRCA1/2相关功能缺陷（"BRCAness"）的方法，有望成为区分致病性突变与良性变异、识别未被检出的突变携带者，以及筛选对PARP抑制剂等新型治疗手段敏感的患者的有效工具。
研究设计：本研究对183例乳腺肿瘤样本进行了基因表达谱分析。纳入研究的样本包括携带致病性BRCA1（n=33）或BRCA2（n=22）生殖系突变的遗传性乳腺癌患者的肿瘤样本；同时随机选取128例原发性乳腺肿瘤样本作为代表性对照组。本研究采用Agilent-029949 Custom SurePrint G3 Human GE 8x60K 微阵列平台进行检测。为开展跨平台验证，研究人员使用自研的点样微阵列平台对183例样本中的92例亚组样本进行了分析。