TCGA Breast Cancer data

Heterogeneity of breast cancer poses several challenges for detection and treatment. With next-generation sequencing, we can now map the transcriptional profile of each patient’s breast tissue, which has the potential for identifying and characterizing cancer subtypes. However, the large dimensionality of this transcriptomic data and the heterogeneity between the molecular profiles of breast cancers poses a barrier to identifying minimal markers and mechanistic consequences. In this study, we develop an autoencoder to identify a reduced set of gene markers that characterize the four major breast cancer subtypes with high accuracy. The reduced feature space created by our model captures the functional characteristics of each breast cancer subtype highlighting mechanisms that are unique to each subtype as well as those that are shared. Our high prediction accuracy shows that our markers can be valuable for breast cancer subtype detection. Additionally, they have the potential to provide insights into mechanisms associated with each subtype.

乳腺癌的异质性为其检测与治疗带来了多重挑战。借助下一代测序（next-generation sequencing）技术，当前我们已可绘制每位患者乳腺组织的转录组谱（transcriptional profile），这为识别并表征癌症亚型提供了可行路径。然而，此类转录组数据的高维度特性，以及乳腺癌分子谱之间的异质性，却成为筛选核心标志物及解析其机制性后果的阻碍。本研究中，我们构建了自编码器（autoencoder），以筛选出一组精简的基因标志物，可高精度地表征四种主要乳腺癌亚型。本模型生成的精简特征空间，能够捕捉各乳腺癌亚型的功能特征，凸显各亚型独有的以及共有的分子机制。本模型的高预测准确率证实，我们所获标志物可有效应用于乳腺癌亚型的检测。此外，这些标志物还有助于深入解析与各亚型相关的分子机制。