Therapeutic failure in cancer is often associated with tumor heterogeneity and the natural selection of resistant clones, a mechanism shared by blood and solid malignancies. Recent studies strongly suggest that limiting the knowledge of tumor genetics to the dominant clone may be uninformative for an accurate prediction of outcome and optimal therapeutic decision; therefore, it is imperative to capture the distribution of mutants and the extent of genomic diversity in tumor sub-population structure. The goal of our lab, at Center for Systems and Computational Biology, is to identify markers of selection, study the impact of treatment strategies on tumor molecular history and time the rise of resistance in cancer.
High-throughput genomics techniques facilitate rapid sequencing of specimens from cancer patients and provide the means for studying the mutational landscapes and patterns of clonal evolution in human malignancies. To this end, we develop computational approaches to study tumor evolution in the contexts of disease development and transformation as well as therapeutic resistance and relapse. We design statistical approaches that address the challenges in interpreting clinical sequencing data and help resolve sub-clonal tumor alterations from those originating from the non-tumor component in the microenvironment. We routinely refine these algorithms by interpreting individual patient data followed by cyclical hypothesis generation and evidence evaluation in the clinic. We are now focused on developing methods that reveal tumor mutational landscapes that correspond to transcriptional heterogeneity and precisely quantify clonal remodeling during disease development and under treatment.
To learn more about the lab, and employment and collaborative opportunities, please visit khiabanian-lab.org.