Research

Deciphering the Immunogenomic Landscape of Lung Cancer

Our laboratory conducts cutting-edge research at the intersection of genomics and immunology, with a primary focus on lung cancer. We utilize high-throughput sequencing, bioinformatics, and advanced experimental models to unravel the molecular mechanisms driving tumorigenesis and immune evasion. Our goal is to translate these fundamental discoveries into novel diagnostic, preventive, and therapeutic strategies, aiming to significantly improve patient outcomes.

Specific Research Directions:

1. Early Oncogenesis and Immunoprevention: We investigate the genomic and immunological events that drive the initiation and progression of precancerous lesions. By characterizing the dynamic interplay between evolving tumor cells and the host immune response, we aim to identify critical vulnerabilities and develop effective immunoprevention strategies. This research has culminated in the design and implementation of two pioneering clinical trials: IMPRINT-lung, evaluating the efficacy of pembrolizumab in preventing lung cancer in high-risk individuals, and Can-Prevent-lung, the first clinical trial to assess the potential of canakinumab, an IL-1β inhibitor, for lung cancer prevention.
2. Intratumor Heterogeneity (ITH) and Therapeutic Resistance: We explore the complex landscape of ITH at the genomic, epigenetic, transcriptomic, and immunological levels. Our investigations have revealed the profound impact of ITH on tumor evolution, treatment response, and clinical outcomes. We are actively developing novel approaches to decipher the mechanisms by which ITH contributes to therapeutic resistance and identify predictive biomarkers to guide personalized treatment strategies.
3. Immune Evasion and Immunotherapy Response: We dissect the intricate mechanisms employed by tumor cells to evade immune surveillance and suppress anti-tumor immunity. Our research focuses on characterizing the dynamic interplay between tumor cells and immune cells within the tumor microenvironment, with a particular emphasis on identifying novel biomarkers that predict response to immunotherapy. We utilize advanced computational approaches, including machine learning and deep learning, to integrate multi-dimensional data and uncover hidden patterns that inform the development of more effective immunotherapeutic strategies.