Laboratory of Pathology, West China Hospital, Sichuan University, Chengdu, China; Key Laboratory of Transplant Engineering and Immunology, Ministry of Health, West China Hospital, Sichuan University, Chengdu, China; Department of Pathology, West China Hospital, Sichuan University, Chengdu, China.
Big Data Research Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China.
Comput Methods Programs Biomed. 2020 Aug;192:105458. doi: 10.1016/j.cmpb.2020.105458. Epub 2020 Mar 19.
Chemotherapy is useful to many breast cancer patients, however, it is not therapeutic for some patients. Pathologic complete response (pCR) is an indicator to good response in Neoadjuvant chemotherapy (NAC). In this study, we aimed to develop a way to predict pCR before NAC.
We retrospectively collected 287 stage II-III breast cancer cases either to a training set (N = 197) or to a test set (N = 90). Fourteen candidate genes were selected from four public microarray data sets. A prediction model was built, by using these fourteen candidate genes and three reference genes expression which were tested by TaqMan probe-based quantitative polymerase chain reaction, after selecting a better algorithm.
The Naive Bayes algorithm had a relatively higher predictive value, compared with random forest, support vector machine (SVM), and k-nearest neighbor (knn) algorithms (P < 0.05). This 17-gene prediction model showed a high positive correlation with pCR (odds ratio, 8.914, 95% confidence interval, 4.430-17.934, P < 0.001). By using this model, the enrolled patients were classified into sensitive (SE) and insensitive (INS) groups. The pCR rates between the SE and INS groups were highly different (42.3% vs.7.6%, P < 0.001). The sensitivity and specificity of this prediction model were 84.5% and 62.0%.
Instead of whole transcriptome-based technologies, panel gene expression with tens of essential genes implemented in a machine learning model has predictive potential for chemosensitivity in breast cancers.
化疗对许多乳腺癌患者有效，但对某些患者无效。病理完全缓解（pCR）是新辅助化疗（NAC）中良好反应的指标。在这项研究中，我们旨在开发一种在 NAC 之前预测 pCR 的方法。
我们回顾性地收集了 287 例 II-III 期乳腺癌患者，将其分为训练集（N=197）和测试集（N=90）。从四个公共微阵列数据集选择了 14 个候选基因。使用 TaqMan 探针定量聚合酶链反应检测了这 14 个候选基因和 3 个参考基因的表达，选择了一种更好的算法后，构建了预测模型。
与随机森林、支持向量机（SVM）和 K-最近邻（knn）算法相比，朴素贝叶斯算法具有相对较高的预测值（P<0.05）。该 17 基因预测模型与 pCR 呈高度正相关（优势比 8.914，95%置信区间 4.430-17.934，P<0.001）。使用该模型，将入组患者分为敏感（SE）和不敏感（INS）组。SE 组和 INS 组的 pCR 率差异很大（42.3% vs.7.6%，P<0.001）。该预测模型的敏感性和特异性分别为 84.5%和 62.0%。
与基于全转录组的技术不同，机器学习模型中数十个关键基因的面板基因表达具有预测乳腺癌化疗敏感性的潜力。
