Comparative Genomics of Protein Structure-Function in Networks and Disease Clinical exome sequencing is increasingly frequent but still of limited impact on patient care and personalized medicine. Key difficulties keeping these data from being translated into therapeutic plans are that in each patient harmful mutations are few and seemlessly blended into the vast background of harmless ones. As a result, disease-causing genes are difficult to spot. Also, even when imputable genes are found their functions are typically unknown. We now tackle these problems by merging two lines of investigation that both model the evolution and propagation of biological information as a smooth process. In protein structures, smoothing has improved predictions of evolutionary important sites, functions and substrates. In networks, it has led to function predictions without need of structural knowledge. And, with respect to the genotype-phenotype relationship, it has led to an equation for the harmful action of coding mutations on fitness.
Aim 1 now proposes to refine this equation to score the harm of coding mutations in order to uncover disease- causing genes and pathways. The focus will be on cancer given the ready availability of exome data from The Cancer Genome Atlas. In parallel, Aim 2 will develop new and general network-based techniques to decipher the role of proteins of interest.
Aim 3 will experimentally validate select predictions of cancer-causing genes by testing in head and neck and lung cancer cell lines whether they behave as expected of tumor suppressors and oncogenes. This work should yield innovative, formal analyses of the genotype to phenotype relationship with direct applications to cancer genomics; it will unite molecular evolution and population genetics, whilst enabling the clinical interpretation of genome variations and the discovery of cancer genes that determine morbidity and mortality and eventually lead to novel therapeutics.

Public Health Relevance

In order to guide patient therapy, this work will address two key problems: to understand the harm that comes from the many genome mutations we each carry, and to identify the genetic basis of diseases. While personal sequencing of genomes is on the brink of becoming routine, so far we have not been able to spot which of the many mutations it uncovers are in fact health threats. Innovatively, our solutions draw on fundamental concepts from mathematics, computer science and biology. Encouragingly, early results show these new methods have direct applications to understand cancer risk and treatment options, such as in head and neck cancers. We now aim to refine these ideas and systematically test them experimentally towards effective and personalized therapy.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM079656-11
Application #
9403255
Study Section
Macromolecular Structure and Function D Study Section (MSFD)
Program Officer
Wehrle, Janna P
Project Start
2007-04-01
Project End
2019-12-31
Budget Start
2018-01-01
Budget End
2018-12-31
Support Year
11
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Baylor College of Medicine
Department
Genetics
Type
Schools of Medicine
DUNS #
051113330
City
Houston
State
TX
Country
United States
Zip Code
77030
Chun, Yun Shin; Passot, Guillaume; Yamashita, Suguru et al. (2017) Deleterious Effect of RAS and Evolutionary High-risk TP53 Double Mutation in Colorectal Liver Metastases. Ann Surg :
Gallion, Jonathan; Koire, Amanda; Katsonis, Panagiotis et al. (2017) Predicting phenotype from genotype: Improving accuracy through more robust experimental and computational modeling. Hum Mutat 38:569-580
Wilson, Stephen J; Wilkins, Angela D; Lin, Chih-Hsu et al. (2017) DISCOVERY OF FUNCTIONAL AND DISEASE PATHWAYS BY COMMUNITY DETECTION IN PROTEIN-PROTEIN INTERACTION NETWORKS. Pac Symp Biocomput 22:336-347
Koire, Amanda; Kim, Young Won; Wang, Jarey et al. (2017) Codon-level co-occurrences of germline variants and somatic mutations in cancer are rare but often lead to incorrect variant annotation and underestimated impact prediction. PLoS One 12:e0174766
Katsonis, Panagiotis; Lichtarge, Olivier (2017) Objective assessment of the evolutionary action equation for the fitness effect of missense mutations across CAGI-blinded contests. Hum Mutat 38:1072-1084
Xu, Qifang; Tang, Qingling; Katsonis, Panagiotis et al. (2017) Benchmarking predictions of allostery in liver pyruvate kinase in CAGI4. Hum Mutat 38:1123-1131
Schönegge, Anne-Marie; Gallion, Jonathan; Picard, Louis-Philippe et al. (2017) Evolutionary action and structural basis of the allosteric switch controlling ?2AR functional selectivity. Nat Commun 8:2169
Gallion, Jonathan; Wilkins, Angela D; Lichtarge, Olivier (2017) HUMAN KINASES DISPLAY MUTATIONAL HOTSPOTS AT COGNATE POSITIONS WITHIN CANCER. Pac Symp Biocomput 22:414-425
Cancer Genome Atlas Research Network. Electronic address: wheeler@bcm.edu; Cancer Genome Atlas Research Network (2017) Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma. Cell 169:1327-1341.e23
Lua, Rhonald C; Wilson, Stephen J; Konecki, Daniel M et al. (2016) UET: a database of evolutionarily-predicted functional determinants of protein sequences that cluster as functional sites in protein structures. Nucleic Acids Res 44:D308-12

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