Table_10_Machine learning models based on immunological genes to predict the response to neoadjuvant therapy in breast cancer patients.docx

Breast cancer (BC) is the most common malignancy worldwide and neoadjuvant therapy (NAT) plays an important role in the treatment of patients with early BC. However, only a subset of BC patients can achieve pathological complete response (pCR) and benefit from NAT. It is therefore necessary to predict the responses to NAT. Although many models to predict the response to NAT based on gene expression determined by the microarray platform have been proposed, their applications in clinical practice are limited due to the data normalization methods during model building and the disadvantages of the microarray platform compared with the RNA-seq platform. In this study, we first reconfirmed the correlation between immune profiles and pCR in an RNA-seq dataset. Then, we employed multiple machine learning algorithms and a model stacking strategy to build an immunological gene based model (Ipredictor model) and an immunological gene and receptor status based model (ICpredictor model) in the RNA-seq dataset. The areas under the receiver operator characteristic curves for the Ipredictor model and ICpredictor models were 0.745 and 0.769 in an independent external test set based on the RNA-seq platform, and were 0.716 and 0.752 in another independent external test set based on the microarray platform. Furthermore, we found that the predictive score of the Ipredictor model was correlated with immune microenvironment and genomic aberration markers. These results demonstrated that the models can accurately predict the response to NAT for BC patients and will contribute to individualized therapy.

乳腺癌（Breast cancer，BC）是全球范围内最常见的恶性肿瘤，新辅助治疗（neoadjuvant therapy，NAT）在早期BC患者的治疗中发挥着重要作用。然而，仅有部分BC患者能够达到病理完全缓解（pathological complete response，pCR）并从NAT中获益，因此预测患者对NAT的治疗应答实属必要。此前已有诸多基于微阵列平台检测的基因表达数据构建的NAT应答预测模型被提出，但由于模型构建过程中采用的数据标准化方法，以及微阵列平台相较于RNA测序（RNA-seq）平台的固有缺陷，这类模型在临床实践中的应用受到了极大限制。本研究首先在RNA-seq数据集中再次验证了免疫特征与pCR之间的相关性。随后，我们基于该RNA-seq数据集，采用多种机器学习算法与模型堆叠策略，分别构建了基于免疫基因的预测模型（Ipredictor模型）以及基于免疫基因与受体状态的预测模型（ICpredictor模型）。在基于RNA-seq平台的独立外部测试集中，Ipredictor模型与ICpredictor模型的受试者工作特征曲线（Receiver Operating Characteristic curves，ROC曲线）下面积分别为0.745与0.769；在另一项基于微阵列平台的独立外部测试集中，二者的曲线下面积分别为0.716与0.752。此外，我们发现Ipredictor模型的预测评分与免疫微环境及基因组畸变标志物存在显著相关性。上述结果表明，本研究构建的模型能够精准预测BC患者对NAT的治疗应答，将有助于实现乳腺癌患者的个体化治疗。