Moanna: Multi-Omics Autoencoder-Based Neural Network Algorithm for Predicting Breast Cancer Subtypes

Abstract

Cancer subtyping delivers valuable insights into the study of cancer heterogeneity and fulfills an essential step toward personalized medicine. For example, studies in breast cancer have shown that cancer subtypes based on molecular differences are associated with different patient survival and treatment responses. However, recent studies have suggested inconsistent breast cancer subtype classifications using alternative approaches, suggesting that current methods are yet to be optimized. Existing computation-based methods have also been limited by their dependency on incomplete prior knowledge and ineffectiveness in handling high-dimensional data beyond gene expression. Here, we propose a novel deep-learning-based algorithm, Moanna, that is trained to integrate multi-omics data for predicting breast cancer subtypes. Moanna’s architecture consists of a semi-supervised Autoencoder attached to a multi-task learning network for generalizing the combination of gene expression, copy number and somatic mutation data. We trained Moanna on a subset of the METABRIC breast cancer dataset and evaluated the performance on the remaining hold-out METABRIC samples and a fully independent cohort of TCGA samples. We evaluated our use of Autoencoder against other dimensionality reduction techniques and demonstrated its superiority in learning patterns associated with breast cancer subtypes. The overall Moanna model also achieved high accuracy in predicting samples’ ER status (96%), differentiating basal-like samples (98%), and classifying samples into PAM50 subtypes (85%). Moreover, Moanna’s predicted subtypes show a stronger correlation with patient survival when compared to the original PAM50 subtypes.