Aswin Jose supervised by Dr. Vinod P K  received his Dual Degree in Computational and Natural Science  (CND). Here’s a summary of his research work on Network-Based Approaches for Cancer Subtype Identification and Prognosis:

Cancer remains a leading cause of morbidity and mortality worldwide. Despite advances in genomics, identifying clinically relevant subtypes of cancer remains challenging due to its complex and heterogeneous nature. This thesis explores the application of network-based stratification (NBS) approaches to stratify cancer types into clinically relevant subtypes, which can aid in precision medicine and targeted therapy efforts. First, we investigate the effectiveness of a standard NBS pipeline using somatic mutation data to stratify Renal Cell Carcinoma (RCC). We explore the impact of network composition on RCC stratification performance and extend the NBS approach to copy number variation (CNV) data for RCC subtyping. Next, we introduce DeepGraphMut (DGM), a novel graph-based deep-learning pipeline that integrates somatic mutation data with protein-protein interaction (PPI) networks. By employing a graph autoencoder with a graph attention layer and a node-level attention decoder, DGM generates patient-specific clinically relevant encodings for unsupervised and supervised tasks. We demonstrate the effectiveness of DGM across 16 cancer types comprising of 7352 samples from The Cancer Genome Atlas (TCGA). Unsupervised clustering reveals distinct subtypes with significant survival differences in 11 cancer types. In supervised analysis using a Cox regression model, DGM demonstrates excellent performance in predicting survival outcomes, achieving a high concordance index (c-index) value in the range of 0.7 across most cancers, underscoring its robust predictive performance using only somatic mutation data. Furthermore, DGM outperforms traditional methods and its lightweight variant in both unsupervised and supervised analyses. In summary, this thesis presents a promising approach for cancer subtype identification and prognosis, especially in resource-limited settings where multi-omics data may not be readily available. By leveraging the strengths of graph learning and network biology, DGM offers a valuable tool for advancing personalized medicine.

January 2025