ABSTRACT Human cancers are complex diseases with pathobiology driven by heritable genetics, environmental exposures, somatic genomic and epigenomic alterations, and contributions for immunological and other responses in the host micro- and macroenvironment. Cancer therapeutics overlay another dimension with respect to particular vulnerabilities that underly concepts of precision medicine. Currently, integrating genome-scale data from mechanistic ‘cause-effect’ laboratory-based studies to the pathobiology of in vivo tumors in the context of clinical care remains challenging. This proposal is designed to provide support for a highly skilled and productive scientist with expertise across laboratory, bioinformatics, and clinical medicine. The objectives are (i) to provide biostatistical and bioinformatics skills for a range of program investigators that seek to bi-directional integration for the development and testing of hypotheses involving cancer genomics; (ii) to develop new approaches (modeling and computational tools) for the analyses and integration of genome-scale data; and (iii) apply rigor and reproducibility in data annotation for submission/sharing of program genomics data to the research community.