Computational Clinical Oncology

Objectives

Our group is dedicated to computational cancer research and precision oncology. Drawing on cancer genomics, multimodal integration of patient data, and statistical learning, we study the molecular basis of tumor evolution, genotype-phenotype relationships, and the determinants of treatment response. We develop clinical decision-support tools for diagnostic classification and risk stratification in patients.

Research Areas

Molecular Classification and Predictive Modeling of Cancer — We conduct large-scale molecular profiling studies to improve disease classification, risk stratification, and the prediction of treatment response. By integrating multimodal patient data, we develop statistical models of clinical outcomes. Our current projects focus on (i) molecular subtypes of myeloid malignancies, (ii) multimodal prognostic modeling, and (iii) monitoring molecular responses to treatment using cell-free DNA assays.

Clonal Hematopoiesis in Cancer — We study the acquisition of somatic mutations in blood cells, a phenomenon known as clonal hematopoiesis, as well as its role in cancer development and patient survival. Our research explores the relationships between cancer therapies, clonal evolution, and treatment-related malignancies. Using long-term patient cohorts and single-cell analysis technologies, we characterize the evolutionary dynamics and systemic effects of clonal hematopoiesis, including its influence on the progression of solid tumors.

Early Detection and Management of Secondary Malignant Tumors — We conduct clinico-genomic studies on treatment-related malignant tumors to improve diagnostic accuracy and management strategies. By conducting longitudinal follow-up of patients at risk for leukemia following cancer treatments, we aim to identify biomarkers predictive of transformation and to develop prevention strategies.

Key Findings and Future Directions

Our work has contributed to improving molecular classification and risk stratification for many types of cancer, to a better understanding of clonal hematopoiesis and treatment-related malignant tumors, and to the development of predictive models and computational tools for precision oncology. Our future work will incorporate single-cell and circulating biomarkers to track clonal evolution and develop precision interception strategies. Through interdisciplinary and translational research, we aim to advance precision oncology and improve patient care.