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Our goal is to identify markers of selection and progression in cancer, especially through studying the dynamics of tumor heterogeneity, as a model of clonal evolution.
Clonal expansion is a process in which a single organism reproduces asexually to give rise to a diversifying population. It is pervasive in nature, from emerging infectious disease outbreaks, to intra-host pathogen evolution, to unregulated cell growth in cancer. The study of clonal expansions began in the 1940s, when Salvador Luria and Max Delbrück designed a simple system of single-cell organisms to investigate patterns of mutation accumulation. Their rigorous quantitative methodology led to discovering that mutations arise randomly and their numbers follow a distinct probability distribution. We now know that a clonal population diversifies as it expands, enabling it to explore the fitness landscape. Studying the dynamics of genomic heterogeneity can yield insight into when an expansion started, how fast a population evolved, and if specific genomic alterations are selected for in a particular host or treatment regime.
Cancer follows clonal Darwinian evolution. As genetic alterations accumulate, fitter clones dominate, ultimately leading to macroscopic disease. The evolutionary behavior of cancer progression can be described through linear or branched models of growth. In linear evolution, genetic lesions of the dominant clone from an earlier phase are present in later phases. In contrast, dominant clones in branched evolution share only partial genetic alterations in different phases. Selective pressures can spur tumor evolution and change the mode of its progression from linear to branched; this may lead to more aggressive and treatment-refractory disease. It is, therefore, imperative to capture the extent of genomic diversity in the sub-population structure.
Our previous work elucidated the role of mutated subclones as strong predictors of survival and therapeutic response in pediatric and adults leukemia. More recently, we reported on pervasive hypermutation of regulatory regions as a new layer of genetic alterations that dysregulate gene expression in B-cell lymphoma. We are now focused on developing methods that reveal tumor mutational landscapes that correspond to transcriptional heterogeneity and quantify clonal remodeling during disease development and under treatment. We develop new information-theoretic methods for capturing global structural properties of high dimensional datasets and work on novel biclustering algorithms with underlying statistical metrics that allow inference and classification of phenotype-genotype relationships.
We also design bioinformatics approaches that address the challenges in interpreting clinical sequencing data and help resolve subclonal tumor alterations from those originating from the non-tumor component in the microenvironment. Our recent analysis of deep clinical sequencing data from patients with solid tumors has showed that some detected mutations arise from infiltrating hematopoietic cells. These mutations are due to an age-related condition known as clonal hematopoiesis of indeterminate potential or CHIP. Our results have raised the hypothesis that CHIP exhibits a distinct genomic landscape when enriched in tumor microenvironment, evolves under solid tumor treatment, and is correlated with the development of therapy-related adverse sequelae. Through developing and integrating novel computational and experimental methods, we are aiming to demonstrate the significance of molecularly defined clonal analysis of hematopoietic populations as a fundamental predictor of disease transformation and therapy-induced complications.
As a National Cancer Institute-designated Comprehensive Cancer Center, Rutgers Cancer Institute of New Jersey provides an excellent environment to transform basic science discoveries by experimental and quantitative scientists into clinical practice.
The history of science is marked by the efforts of those who strove
for precise observations and aimed to decipher the world with the
quantitative language of mathematics and geometry. In describing the
Solar System, it took more than fifteen centuries of staring at the
sky, from Ptolemy to Al-Biruni to Brahe, and eventually to Kepler, to
find the best fit to the data. Half a century passed and new tools
—the telescope and calculus— were devised before
Newton could finally describe a graceful approximation to the
fundamental governing laws of celestial bodies. More accurate laws
of gravity, and physics in general, wouldn't be discovered for
another two hundred and fifty years.