Louisiana Biomedical Research Network

Kun (Karen) Zhang

Link to Pubmed Publications

Xavier University of Louisiana

Project Title

Detecting Race-Relevant Molecular Biomarkers with Clinical Utilities Using Multi- Omics Data Across Tumor Types


Erik Flemington, Tulane University

Funding Periods

Full Project (May 1, 2018 - April 30, 2021)

Pilot Project (May 1, 2010 – April 30, 2012)


Significant progress has been achieved to date in our understanding of the role of socioeconomic factors in cancer racial disparities. Ever-increasing evidence is now suggesting that a number of intrinsic molecular factors specific to malignant cells must also partly account for the observed health inequalities. Although research has begun to explore the biological bases of cancer disparities, most existing work is limited to several common cancer types and does not methodically explore whether the observed genetic and molecular differences represent the clinically-meaningful racial disparities in other fatal human cancers. Moreover, massive amounts of multi-faceted omics data generated by high-throughput technologies have not been fully utilized and well integrated with clinical data to search for race-specific molecular characteristics, biomarkers or even drug targets. The goal of this LBRN full project is therefore to address these significant limitations by establishing a pan-cancer, race-relevant assemblage of mutation spectra, DNA methylation patterns and gene regulation network modules, some of which will hold significance and promise for clinical utility, via novel algorithm, pipeline and database development. To this end, we plan to accomplish the following specific aims: 1). To elucidate race-specific genetic variations by correlating somatic mutations in tumor genomes with patient race and/or patient survival via a novel cancer-driver-gene mutation based clustering algorithm; 2). To reveal race-specific epigenetic alterations by linking tumor DNA methylation to patient race and/or patient survival via a hybrid Gaussian Mixture Model; 3) To divulge race-specific differences in gene (and microRNA) expression by associating gene expression regulation in tumor cells with patient race and/or patient survival by a unique pipeline; and 4) To validate the identified biomarkers for prostate cancer using human specimens. Moreover, we will implement a database for the pinpointed biomarkers, facilitating cancer researchers to interrogate how multiple-level molecular variations may alter gene functions in different cancers and races. A set of efficient and powerful analytical tools for predicting patient outcomes and analyzing health disparities in cancer will be also made publicly available as open source software.