Yoon Seokhyun, Won Hye Sung, Kang Keunsoo, Qiu Kexin, Park Woong June, Ko Yoon Ho
Department of Electronics Eng., College of Engineering, Dankook University, Yongin-si 16890, Korea.
Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea.
Cancers (Basel). 2020 May 5;12(5):1165. doi: 10.3390/cancers12051165.
The cost of next-generation sequencing technologies is rapidly declining, making RNA-seq-based gene expression profiling (GEP) an affordable technique for predicting receptor expression status and intrinsic subtypes in breast cancer patients. Based on the expression levels of co-expressed genes, GEP-based receptor-status prediction can classify clinical subtypes more accurately than can immunohistochemistry (IHC). Using data from The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA BRCA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) datasets, we identified common predictor genes found in both datasets and performed receptor-status prediction based on these genes. By assessing the survival outcomes of patients classified using GEP- or IHC-based receptor status, we compared the prognostic value of the two methods. We found that GEP-based HR prediction provided higher concordance with the intrinsic subtypes and a stronger association with treatment outcomes than did IHC-based hormone receptor (HR) status. GEP-based prediction improved the identification of patients who could benefit from hormone therapy, even in patients with non-luminal breast cancer. We also confirmed that non-matching subgroup classification affected the survival of breast cancer patients and that this could be largely overcome by GEP-based receptor-status prediction. In conclusion, GEP-based prediction provides more reliable classification of HR status, improving therapeutic decision making for breast cancer patients.
新一代测序技术的成本正在迅速下降，使得基于RNA测序的基因表达谱分析（GEP）成为预测乳腺癌患者受体表达状态和内在亚型的一种经济实惠的技术。基于共表达基因的表达水平，基于GEP的受体状态预测比免疫组织化学（IHC）能更准确地对临床亚型进行分类。利用来自癌症基因组图谱乳腺癌浸润性癌（TCGA BRCA）和国际乳腺癌分子分类联盟（METABRIC）数据集的数据，我们确定了两个数据集中共同的预测基因，并基于这些基因进行受体状态预测。通过评估使用基于GEP或IHC的受体状态分类的患者的生存结果，我们比较了这两种方法的预后价值。我们发现，与基于IHC的激素受体（HR）状态相比，基于GEP的HR预测与内在亚型的一致性更高，与治疗结果的关联更强。基于GEP的预测改善了对可能从激素治疗中获益的患者的识别，即使在非腔面型乳腺癌患者中也是如此。我们还证实，不匹配的亚组分类会影响乳腺癌患者的生存，而基于GEP的受体状态预测在很大程度上可以克服这一问题。总之，基于GEP的预测为HR状态提供了更可靠的分类，改善了乳腺癌患者的治疗决策。
