Tackling evolved drug resistance in advanced tumors
Our computational oncology lab spans several research directions in evolution, precision medicine, and oncology. These directions are directed toward improving patient care and providing a better understanding of the fundamental processes that govern the evolution of drug resistance in advanced malignancies.
Transcriptional Archetypes
Characterization of the entire “landscape” of drug-resistance states by inferring the central drug-resistance “Archetype” as convergent evolutionary states, focusing on Metastatic Breast Cancer (MBC) and distilling commonalities across epithelial tumors.
Single-cell resolution of clinically interpretable transcription programs
Joint analysis of single-cell and bulk RNA-seq toward the study of clinically informative gene expression (leveraging clinically annotated bulk-profiles biopsies) at the single-cell resolution (leveraging single-cell RNA-seq)
Evolutionary Models
Developing novel computational methods to study cancer evolution while encompassing clonal dynamics (DNA-level) and epigenetic adaptation.
Medical Informatics
Leveraging Machine Learning (ML) strategies to improve patient stratification in precision oncology and precision medicine.