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.
