Rhea Malhotra

Cancer development is characterized by chromosomal instability, manifesting in frequent occurrences of different genomic alteration mechanisms ranging in extent and impact. Existing mathematical frameworks can only capture certain aspects of chromosomal instability (CIN). We present CINner, a mathematical framework for modeling genomic diversity and selection during tumor evolution, used to find chromosome-arm selection parameters that drive tumorigenesis in the absence of WGD in chromosomally… Show more

Cancer development is characterized by chromosomal instability, manifesting in frequent occurrences of different genomic alteration mechanisms ranging in extent and impact. Existing mathematical frameworks can only capture certain aspects of chromosomal instability (CIN). We present CINner, a mathematical framework for modeling genomic diversity and selection during tumor evolution, used to find chromosome-arm selection parameters that drive tumorigenesis in the absence of WGD in chromosomally stable cancer types. We found that the selective advantage of WGD cells hinges on their tolerance for aneuploidy and escape from nullisomy. Direct application of CINner to model the WGD proportion and fraction of genome altered (FGA) further uncovers the increase in CNA probabilities associated with WGD in each cancer type. CINner can also be utilized to study chromosomally stable cancer types. CINner presents analysis of CNA probabilities, chromosome selection parameters, tumor growth dynamics and population size on cancer fitness and heterogeneity. Show less

Pathogenic mutations in mitochondrial DNA (mtDNA) compromise cellular metabolism, contributing to cellular heterogeneity and disease. Diverse mutations are associated with diverse clinical phenotypes, suggesting distinct organ- and cell-type-specific metabolic vulnerabilities. Here we establish a multi-omics approach to quantify deletions in mtDNA alongside cell state features in single cells derived from six patients across the phenotypic spectrum of single large-scale mtDNA deletions (SLSMDs).

Conducted long-term clinical study regarding radiographic indications of body packing, in collaboration with Massachusetts General Hospital - Department of Radiology.

Cancer remains the second leading cause of death worldwide, yet diagnostics continue to lack the accuracy to gauge the metastatic severity of disease progression to enable effective therapeutics. The goal of this study was to develop an automated cell sorter for circulating tumor cell (CTC) detection using standing surface acoustic waves (SSAW). Similar existing modalities have low throughput and fail to account for the variation in extracellular markers between patients & cancer types. To… Show more

Cancer remains the second leading cause of death worldwide, yet diagnostics continue to lack the accuracy to gauge the metastatic severity of disease progression to enable effective therapeutics. The goal of this study was to develop an automated cell sorter for circulating tumor cell (CTC) detection using standing surface acoustic waves (SSAW). Similar existing modalities have low throughput and fail to account for the variation in extracellular markers between patients & cancer types. To address this, a machine learning-based sorting algorithm was integrated with a SSAW microfluidic chip sorter and parameters were optimized to compensate for computational processing time. Show less

Pearson Syndrome is a rare, multisystemic genetic condition caused by large deletions of mitochondrial DNA (mtDNA), typically resulting in sideroblastic anemia and exocrine pancreatic dysfunction. By analyzing single-cellATAC-seq and RNA-seq data of various patient-derived cell lines, we identified a set of 10 genes associated with Pearson Syndrome pathogenesis across sample sets. Further, more comprehensive associations further defined molecular pathways potentially involved in Pearson… Show more

Pearson Syndrome is a rare, multisystemic genetic condition caused by large deletions of mitochondrial DNA (mtDNA), typically resulting in sideroblastic anemia and exocrine pancreatic dysfunction. By analyzing single-cellATAC-seq and RNA-seq data of various patient-derived cell lines, we identified a set of 10 genes associated with Pearson Syndrome pathogenesis across sample sets. Further, more comprehensive associations further defined molecular pathways potentially involved in Pearson Syndrome development, including angiogenesis, extracellular adhesion, and protein binding. Overall, this work demonstrates the utility of single-cell genomics approaches to understand rare, Mendelian disorders in uncovering a molecular basis of disease. Currently under review for further publication. Show less