David L. Masica, PhD
Assistant Research Professor
Office: Hackerman 220
B.A., Physics, Oakland University, 2003
Ph.D., Molecular Biophysics, Johns Hopkins University, 2009
Recent focus is on developing novel computational methods to predict the impact of (epi)genetic alterations on human disease and drug response.
The Multivariate Organization of Combinatorial Alterations (MOCA) algorithm
The MOCA algorithm finds networks of coordinated, disease-related (epi)genetic alteration from large-scale genomics data (for instance, copy number, sequence, expression, and methylation across 10’s to 1000’s of samples/patients). Additionally, MOCA can correlate these cooperatively altered gene networks with phenotypic data, such as drug response or progression-free survival. MOCA has been applied to several cancer genomics datasets, and correctly captured known cancer-driver interactions, synthetic-lethal and survival-gene interactions, and biomarkers/targets of drug response.
The Phenotype-Optimized Sequence Ensembles (POSE) algorithm
Given a target protein and training set of amino acid substitutions of known phenotypic impact, the POSE algorithm finds an optimal set of evolutionarily related protein sequences for predicting the impact of other amino acid substitutions. The algorithm has been successfully applied to predicting cystic fibrosis disease from CFTR missense mutation and in determining breast cancer-causing variants in BRCA2.
Masica, David L., and Rachel Karchin. “Collections of simultaneously altered genes as biomarkers of cancer cell drug response.” Cancer research 73, no. 6 (2013): 1699–1708.
Masica, David L., Patrick R. Sosnay, Garry R. Cutting, and Rachel Karchin. “Phenotype-optimized sequence ensembles substantially improve prediction of disease-causing mutation in cystic fibrosis.” Human Mutation (2012).
Masica, David L., and Rachel Karchin. “Correlation of somatic mutation and expression identifies genes important in human glioblastoma progression and survival.” Cancer research 71, no. 13 (2011): 4550–4561.
Masica, David L., Jeffrey J. Gray, and Wendy J. Shaw. “Partial high-resolution structure of phosphorylated and non-phosphorylated leucine-rich amelogenin protein adsorbed to hydroxyapatite.” The Journal of Physical Chemistry C 115, no. 28 (2011): 13775–13785.
Masica, David L., Sarah B. Schrier, Elizabeth A. Specht, and Jeffrey J. Gray. “De Novo Design of Peptide-Calcite Biomineralization Systems.” Journal of the American Chemical Society 132, no. 35 (2010): 12252.
Masica, David L., Jason T. Ash, Moise Ndao, Gary P. Drobny, and Jeffrey J. Gray. “Toward a structure determination method for biomineral-associated protein using combined solid-state NMR and computational structure prediction.” Structure 18, no. 12 (2010): 1678–1687.