Cancer Genomics and Systems Biology Laboratory

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Research

The ultimate goal of our research to identify fundamental principles of mutability and evolvability of cancer genomes using computational and emerging genomic technologies, and use that knowledge for better diagnosis, stratification, and treatment of cancer patients. We develop and apply computational and genomic methods, and collaborate with our colleagues from basic and clinical cancer research to address questions in related areas.

Mutagenesis and genomic instability in cancer

Genomic instability and somatic mutations are ubiquitous in all cancers. We use genomics, computational, and molecular biology techniques to study genomic instability and DNA repair defects that arise due to endogenous processes and environmental exposure in normal tissues, precancerous lesions, and malignant tumors. We have shown that nuclear localization, DNA replication timing and genomic context play critical roles in shaping the landscape of amplifications, deletions, point mutations, and loss of heterozygosity in cancer genomes. Our findings provide insights into different mutagenic processes during aging and tumorigenesis. 


Hu & De. Nature Cancer, in press

Singh et al. Comm Biol. 2020

Clements et al. Nature Comm. 2020

Smith et al. Nature Str Mol Biol. 2017

De et al. Nature Biotech.. 2011

De et al. Nature Str. Mol. Biol. 2011

Evolutionary dynamics of cancer

Despite growing from a single, renegade somatic cell, by the time of detection, a tumor typically comprises of billions of cells that show considerable genetic and non-genetic differences among them, a phenomenon known as intra-tumor heterogeneity (ITH). Genetic and non-genetic ITH appear to be hallmarks of nearly all types of malignancies, providing substrates for evolvability and emergence of drug resistance and leading to unpredictable prognosis. We use a combination of computational, genomics, and modeling approaches to study intra-tumor heterogeneity and clonal evolution in cancer, including order of mutation events and emergence of resistance. We suggest that non-genetic intra-tumor heterogeneity might be a key contributor to phenotypic heterogeneity and ongoing evolutionary dynamics in tumors. 

 

Biswas et al. Comm Biol. in press

Biswas et al. Am J. Pathol. 2021.

Sharma et al. Cell Rep. 2019 

Balaparya et al. Nature Genetics. 2018

Martins et al. Cancer Discovery. 2012.(cover story)

Tumor microenvironment interactions 

Tumor microenvironment comprises of tumor, immune, and stromal cells, and also tumor-associated microbiome - which promote or constrain tumor growth as well as modulate tumor hallmarks in the primary or metastatic sites. We use genomics and data driven approaches to develop new methods to examine the patterns of tumor-microenvironment interactions, and their impacts on cancer progression, cancer detection, and treatment.


Ghaddar et al. Nature Computational Science. 2023 

Ghaddar et al. Bioinformatics. 2023

Ghaddar et al. Cancer Cell. 2022

Ghaddar et al. Nucleic Acids Res. 2022

Sharma et al. Cell Rep. 2019

Non-invasive cancer detection and diagnosis

Genomic profiling of cell-free DNA from liquid biopsy using massively parallel sequencing is emerging as an attractive, non-invasive screening platform for sensitive detection of multiple types of cancer in a single assay. Genomic data from cell-free DNA can not only identify oncogenic mutation status, but also additional molecular signatures related to potential tissue of origin, the extent of clonal growth, and malignant disease states. Utilization of molecular signatures from cfDNA sequencing data can guide clinical management strategies. We develop genomic and computational methods non-invasive cancer detection and diagnosis. 

De, Front Genet, 2021

Sattar et al. in review