DeepCC: a novel deep learning-based framework for cancer molecular subtype classification
DeepCC: a novel deep learning-based framework for cancer molecular subtype classification
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DOI：
10.1038/s41389-019-0157-8
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发表时间：
2019-08-16
期刊：
影响因子：
6.2
通讯作者：
Wang, Xin
中科院分区：
文献类型：
--
作者：
Gao, Feng;Wang, Wei;Wang, Xin
癌症的分子分型是迈向更个体化治疗的关键一步，并为癌症异质性提供了重要的生物学见解。尽管基于基因表达特征的分类在过去十年中已被广泛证明是一种有效的方法，但其广泛实施长期以来受到平台差异、批次效应和难以对个体患者样本进行分类的限制。在这里，我们描述了一种新的监督癌症分类框架，深度癌症亚型分类（DeepCC），基于对功能谱的深度学习，量化生物途径的活性。在关于结直肠癌和乳腺癌分类的两个案例研究中，与其他广泛使用的分类方法（如随机森林（RF），支持向量机（SVM），梯度提升机（GBM）和多项式逻辑回归算法）相比，DeepCC分类器和DeepCC单样本预测器都实现了更高的灵敏度，特异性和准确性。基于基因随机子采样的模拟分析证明了DeepCC对缺失数据的鲁棒性。此外，DeepCC学习的深度特征捕获了与不同分子亚型相关的生物学特征，使患者样本的亚型内分布和亚型间分离更加紧凑，从而大大减少了以前无法分类的样本数量。总之，DeepCC提供了一种新的癌症分类框架，该框架独立于平台，对缺失数据具有鲁棒性，并且可用于单样本预测，从而促进癌症分子亚型的临床实施。
Molecular subtyping of cancer is a critical step towards more individualized therapy and provides important biological insights into cancer heterogeneity. Although gene expression signature-based classification has been widely demonstrated to be an effective approach in the last decade, the widespread implementation has long been limited by platform differences, batch effects, and the difficulty to classify individual patient samples. Here, we describe a novel supervised cancer classification framework, deep cancer subtype classification (DeepCC), based on deep learning of functional spectra quantifying activities of biological pathways. In two case studies about colorectal and breast cancer classification, DeepCC classifiers and DeepCC single sample predictors both achieved overall higher sensitivity, specificity, and accuracy compared with other widely used classification methods such as random forests (RF), support vector machine (SVM), gradient boosting machine (GBM), and multinomial logistic regression algorithms. Simulation analysis based on random subsampling of genes demonstrated the robustness of DeepCC to missing data. Moreover, deep features learned by DeepCC captured biological characteristics associated with distinct molecular subtypes, enabling more compact within-subtype distribution and between-subtype separation of patient samples, and therefore greatly reduce the number of unclassifiable samples previously. In summary, DeepCC provides a novel cancer classification framework that is platform independent, robust to missing data, and can be used for single sample prediction facilitating clinical implementation of cancer molecular subtyping.