Computational Biology Lab

The Challenge

The advances in sequencing technologies have ushered in the era of ‘big data’ and transformed precision oncology. Big data cancer research holds great promise in diagnosis and clinical management, but substantial hurdles remain in the efficient analysis of vast and diverse multiomics datasets to uncover actionable insights. Moreover, the inherent diversity within and between tumors poses challenges in the identification of consistent biomarkers and therapeutic targets. Our lab addresses these challenges by integrating bioinformatics, statistics and AI to advance our understanding of tumor biology and improve patient outcomes.

Main research areas

Cancer Bioinformatics

Cancer bioinformatics is an interdisciplinary field straddling cancer biology, statistics and data and computer sciences. It is indispensible in the omics era. We have developed automated omics data processing pipelines to enable robust and reproducible analysis of omics data. We contribute to the establishment of the state-of-the-art sequencing analysis pipelines for major cancer genome consortia  and have contributed to recommendations on the clinical interpretation of cancer variants.

Characterizing Human Cancers via Multiomics Profiling and Data Integration

We employ cutting-edge multiomics technologies to define the molecular features and heterogeneity of cancers, aiming to provide a comprehensive understanding of cancer biology and to identify potential therapeutic targets. Using state-of-the-art single-cell multiomics and spatial transcriptomics profiling, we delve into the cellular dynamics within tumors to understand the factors driving heterogeneity and therapy resistance.

Understanding Cancer Evolution and Genomic Instability

Akin to the evolution of biological populations over generations, cancer evolution is driven by the sequential acquisition and selection of somatic genetic alterations. Genomic instability is simultaneously a driving force behind cancer evolution and a therapeutic target. Our research focuses on deciphering the evolutionary trajectories and genomic instability of cancer, with the goal of addressing clinical challenges related to metastasis and therapy resistance through detailed genomic analyses.

Developing AI Prognostic and Predictive Models

AI is becoming an integral component to big data cancer research. AI helps identifying patterns in the data that traditional statistical analyses miss. We develop artificial intelligence models that integrate multiomics and clinicopathological data to enhance prognostication and inform personalized treatment strategies in oncology.