Computational biology and cancer bioinformatics. Research in the lab is focused on applying new genomic technologies, computational analysis and AI methods on data from patients’ tumors to understand the biology behind tumor development, treatment evasion, and progression to metastasis. We are developing and applying tools for simultaneous analysis of multiple samples from the same patient, clonal structure (PhylogicNDT), integration of single cell genomics and transcriptomics, reconstruction of cell subpopulations, their growth kinetics and expression, tumor micro-environment effects, estimation of order of events (“timing”) during tumor development and progression. We work with pre- and post- treatment samples, autopsies and longitudinal blood biopsies in solid and blood malignancies.
Multiomic Technology Development. Integration of multi-modal data (genomic, transcriptomic, proteomic, imaging and single cell) obtained from patients opens up a new era of computational analysis. We are developing tools and technologies to analyze and improve single cell/nuclei RNA, DNA and ATAC sequencing, interpret spatial imaging and spatial transcriptomic datasets, patients’ medical records and radiographic data.
Predicting Treatment Response. Distant tumor sites often respond to treatment and grow at varying rates, which suggests multiple resistance mechanisms evolving at the same time in one patient. Understanding subclonal dynamics and relationships is key for identifying and studying independently resistant cell subpopulations and their deferential response to chemo-, immuno- or targeted therapy. We develop methodologies to discover and monitor evolution of multiple simultaneous resistance mechanisms in a patient, study short- and long-term dynamics and phenotypes of subclonal cell populations before and during treatment, early response detection and understanding the differential treatment response of subclones, including dynamics of response and recurrence under drug pressure and transcriptional changes upon drug exposure.