Deep Learning Approach to Identifying Breast Cancer Subtypes Using High-Dimensional Genomic Data

Abstract

Motivation: Cancer subtype classification has the potential to significantly improve disease prognosis and develop individualized patient management. Existing methods are limited by their ability to handle extremely high-dimensional data and by the influence of misleading, irrelevant factors, resulting in ambiguous and overlapping subtypes. Results: To address the above issues, we proposed a novel approach to disentangling and eliminating irrelevant factors by leveraging the power of deep learning. Specifically, we designed a deep-learning framework, referred to as DeepType, that performs joint supervised classification, unsupervised clustering and dimensionality reduction to learn cancer-relevant data representation with cluster structure. We applied DeepType to the METABRIC breast cancer dataset and compared its performance to alternative state-of-the-art methods. Deep-Type significantly outperformed the existing methods, identifying more robust subtypes while using fewer genes. The new approach provides a framework for the derivation of more accurate and robust molecular cancer subtypes by using increasingly complex, multi-source data. Availability and implementation: An open-source software package for the proposed method is freely available at

DeePaN: deep patient graph convolutional network integrating clinico-genomic evidence to stratify lung cancers for immunotherapy

“…Recently, artificial intelligence (AI) and deep learning methods have demonstrated great potential for discovery of cancer subtypes [25][26][27][28] , stemming from effective high-dimensional data integration and capture of complex nonlinear relationships [29][30][31] . However, most AI studies use a grid-based model 28,32,33 for patient-data representation which overlook patient-patient relationships and are sub-optimal for inclusion of multiple data modalities.…”

Identifying Cross-Cancer Similar Patients via a Semi-Supervised Deep Clustering Approach

“…Secondly, we propose a semi-supervised learning algorithm that extends a previous deep learning clustering method designed for subtype discovery [24]. Unlike [24] approach, our model also uses survival information of the patients to guide the clustering along with their diagnosed cancer types. Although the ultimate aim is to reach good clusters of the patients, solving the auxiliary tasks of cancer type classification and the survival prediction serve to learn a good representation of the patients.…”

“…The first contribution is that we define the crosscancer patient identification problem and propose a novel method to identify cross-cancer patients based on transcriptomic data of tumors biopsied from patients and clinical information. Secondly, we propose a semi-supervised learning algorithm that extends a previous deep learning clustering method designed for subtype discovery [24]. Unlike [24] approach, our model also uses survival information of the patients to guide the clustering along with their diagnosed cancer types.…”