Department of Breast and Endocrine Surgery, Osaka University Graduate School of Medicine, Osaka, Japan.
Cancer Sci. 2021 Apr;112(4):1369-1375. doi: 10.1111/cas.14838. Epub 2021 Feb 26.
In clinical decision-making, to decide the indication for adjuvant chemotherapy for estrogen receptor-positive (ER+), human epidermal growth factor receptor-2-negative (HER2-), and node-negative (n0) breast cancer patients, the accurate estimation of recurrence risk is essential. Unfortunately, conventional prognostic factors, such as tumor size, histological grade and ER, progesterone receptor (PR), and HER2 status as well as Ki67 index, are not sufficiently accurate for such estimation. Therefore, several multigene assays (MGAs) based on the mRNA expression analysis of multiple genes in tumor tissue have been developed to better predict patient prognosis. These assays include Oncotype DX, MammaPrint, PAM50, GGI, EndoPredict, and BCI. We developed Curebest™ 95-Gene Classifier Breast (95GC) classifier, which is unique in that mRNA expression data of all 20 000 human genes are secondarily obtainable, as the 95GC assay is performed using Affymetrix microarray. This can capture mRNA expression of not only 95 genes but also every gene at once, and such gene expression data can be utilized by the other MGAs that we have developed, such as the 155GC, which is used for the prognostic prediction of ER+/HER2- breast cancer patients treated with neoadjuvant chemotherapy. We also developed the 42GC for predicting late recurrence in ER+/HER2- breast cancer patients. In this mini-review, our recent attempt at the development of various MGAs, which is expected to facilitate the implementation of precision medicine in ER+/HER2- breast cancer patients, is presented with a special emphasis on 95GC.
在临床决策中，为了决定雌激素受体阳性（ER+）、人表皮生长因子受体 2 阴性（HER2-）和淋巴结阴性（n0）乳腺癌患者辅助化疗的适应证，准确估计复发风险至关重要。不幸的是，传统的预后因素，如肿瘤大小、组织学分级和 ER、孕激素受体（PR）和 HER2 状态以及 Ki67 指数，对于这种估计并不足够准确。因此，已经开发了几种基于肿瘤组织中多个基因的 mRNA 表达分析的多基因检测（MGAs），以更好地预测患者的预后。这些检测包括 Oncotype DX、MammaPrint、PAM50、GGI、EndoPredict 和 BCI。我们开发了 Curebest™ 95-Gene Classifier Breast（95GC）分类器，该分类器的独特之处在于可以获得所有 20000 个人类基因的 mRNA 表达数据，因为 95GC 检测是使用 Affymetrix 微阵列进行的。这不仅可以捕获 95 个基因的 mRNA 表达，还可以同时捕获每个基因的 mRNA 表达，并且可以利用我们开发的其他 MGAs，如用于预测接受新辅助化疗的 ER+/HER2-乳腺癌患者预后的 155GC。我们还开发了 42GC 用于预测 ER+/HER2-乳腺癌患者的晚期复发。在这篇迷你综述中，我们特别强调了 95GC，介绍了我们最近在开发各种 MGA 方面的尝试，这有望促进 ER+/HER2-乳腺癌患者精准医学的实施。
