We propose an unsupervised multi-omics integration pipeline, using deep-learning autoencoder algorithm, to predict the survival subtypes in bladder cancer (BC). We used TCGA dataset comprising mRNA, miRNA and methylation to infer two survival subtypes. We then constructed a supervised classification model to predict the survival subgroups of any new individual sample. Our training data gave two subgroups with significant survival differences (p-value=8e-4), where high-risk survival subgroup was enriched with KRT6/14 overexpression and PI3K-Akt pathways. We tested the robustness of model by randomly splitting the main dataset into multiple training and test folds, which gave overall significant p-values. Then, we successfully inferred the subtypes for a subset of samples kept as test dataset (p-value=0.03). We further applied our pipeline to predict the survival subgroups from another validation dataset with miRNA data (p-value=0.02). Conclusively, present pipeline is an effective approach to infer the survival subtype of a new sample, exemplified by BC.

Learning Objective 1: Integrate multi-omics data using deep learning and implementing for survival based stratification


Olivier Poirion (Presenter)
University of Hawaii Cancer Center

Kumardeep Chaudhary, University of Hawaii Cancer Center
Lana Garmire, University of Hawaii Cancer Center

Presentation Materials: