Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, 55905, USA.
Department of Cancer Biology, Mayo Clinic, Jacksonville, FL, 32224, USA.
Breast Cancer Res Treat. 2017 Nov;166(2):641-650. doi: 10.1007/s10549-017-4441-z. Epub 2017 Aug 10.
Sclerosing adenosis (SA), found in ¼ of benign breast disease (BBD) biopsies, is a histological feature characterized by lobulocentric proliferation of acini and stromal fibrosis and confers a two-fold increase in breast cancer risk compared to women in the general population. We evaluated a NanoString-based gene expression assay to model breast cancer risk using RNA derived from formalin-fixed, paraffin-embedded (FFPE) biopsies with SA.
The study group consisted of 151 women diagnosed with SA between 1967 and 2001 within the Mayo BBD cohort, of which 37 subsequently developed cancer within 10 years (cases) and 114 did not (controls). RNA was isolated from benign breast biopsies, and NanoString-based methods were used to assess expression levels of 61 genes, including 35 identified by previous array-based profiling experiments and 26 from biological insight. Diagonal linear discriminant analysis of these data was used to predict cancer within 10 years. Predictive performance was assessed with receiver operating characteristic area under the curve (ROC-AUC) values estimated from 5-fold cross-validation.
Gene expression prediction models achieved cross-validated ROC-AUC estimates ranging from 0.66 to 0.70. Performing univariate associations within each of the five folds consistently identified genes DLK2, EXOC6, KIT, RGS12, and SORBS2 as significant; a model with only these five genes showed cross-validated ROC-AUC of 0.75, which compared favorably to risk prediction using established clinical models (Gail/BCRAT: 0.57; BBD-BC: 0.67).
Our results demonstrate that biomarkers of breast cancer risk can be detected in benign breast tissue years prior to cancer development in women with SA. These markers can be assessed using assay methods optimized for RNA derived from FFPE biopsy tissues which are commonly available.
硬化性腺病（SA）在四分之一的良性乳腺疾病（BBD）活检中发现，其组织学特征是小叶中心性的腺泡增生和间质纤维化，并使乳腺癌风险比普通人群增加两倍。我们评估了一种基于 NanoString 的基因表达检测方法，使用来自具有 SA 的福尔马林固定、石蜡包埋（FFPE）活检的 RNA 来模拟乳腺癌风险。
研究组包括在梅奥 BBD 队列中 1967 年至 2001 年间诊断为 SA 的 151 名女性，其中 37 名随后在 10 年内发生癌症（病例），114 名未发生（对照）。从良性乳腺活检中分离 RNA，并使用基于 NanoString 的方法评估 61 个基因的表达水平，包括之前基于阵列的分析实验鉴定的 35 个基因和来自生物学洞察力的 26 个基因。使用这些数据的对角线性判别分析来预测 10 年内的癌症。通过 5 倍交叉验证估计的接收者操作特征曲线（ROC-AUC）值来评估预测性能。
基因表达预测模型的交叉验证 ROC-AUC 估计值在 0.66 到 0.70 之间。在每个 5 倍交叉验证内进行单变量关联，始终鉴定出基因 DLK2、EXOC6、KIT、RGS12 和 SORBS2 为显著；仅使用这五个基因的模型的交叉验证 ROC-AUC 为 0.75，与使用既定临床模型的风险预测（Gail/BCRAT：0.57；BBD-BC：0.67）相比表现良好。
我们的结果表明，在具有 SA 的女性发生癌症之前数年，可以在良性乳腺组织中检测到乳腺癌风险的生物标志物。这些标志物可以使用针对源自 FFPE 活检组织的 RNA 进行优化的检测方法进行评估，这些组织通常可用。
