Statistical and computational methods for the analysis of tumor heterogeneity

Cancer is a highly heterogeneous disease, with each tumor evolving as a multicellular, self-organized system. Tumors comprise diverse cell types of distinct origins, interacting dynamically to shape a complex ecosystem. This cellular heterogeneity is a major driver of cancer progression, yet remains challenging to observe, quantify, and interpret. Our limited ability to accurately estimate it continues to hamper a comprehensive understanding of oncogenesis.
At the intersection of bioinformatics, biostatistics, and oncology, our work develops computational approaches to analyze high-dimensional, multimodal molecular data.
In this talk, I will present two examples of our research. First, a novel statistical framework based on mixture models to infer cellular heterogeneity from DNA methylation rates. Second, a high-dimensional mediation analysis showing how DNA methylation and immune infiltration mediate the effect of tobacco exposure on pancreatic adenocarcinoma outcomes.
Beyond these contributions, I will also discuss our efforts to promote collaborative benchmarking and evaluation of computational algorithms through data challenge frameworks, with the goal of building robust and reproducible tools for the cancer research community.