Comparative analysis of DNA and amino acid sequences is routinely employed in tracing the origins, patterns, and evolutionary relationships of homologous sequences from strains, populations, and species. Now, molecular evolutionary analysis is becoming a key framework in oncology, as sequencing of tumors becomes increasingly feasible and widespread. Tumor nucleotide sequence data reveal the evolutionary history of clones that comprise tumors, patterns of clonal diversity, and the tempo and mode of clonal evolution that underlie the origin and adaptive proliferation of cancerous cells. We propose to develop (a) new methods for accurately inferring clones from tumor profiles, which will employ principles of molecular phylogenetics and ancestral sequence inference for the first time and produce accurate estimates of the clones present in each tumor sample and the frequency of each clone in each sample; (b) new methods for evolutionary analysis of clone sequences, which will overcome biases introduced by the fact that tumor profile samples and, thus, clone sequences contain only a subset of all sites in the regions sequenced; and (c) a suite of software products to enable the use of our methodological advances in cancer research, which will thereby enable high-throughput and in-depth analysis of tumor genomic data. The proposed software and research developments will lead to advances in cancer evolution, bioinformatics, functional genomics, and biomedicine. New software and its source code will be made available free of charge for all uses, including research, education, and training.
Computational functional analysis of DNA and protein sequences from genes and genomes, or evolutionary bioinformatics, provides a powerful means to address fundamental questions of biology and medicine. We will develop advanced methods and software tools for evolutionary bioinformatics analysis of tumor genomic variation. These tools and methods will greatly facilitate cancer research pursued by basic biologists and clinicians.
| Patel, Ravi; Scheinfeldt, Laura B; Sanderford, Maxwell D et al. (2018) Adaptive Landscape of Protein Variation in Human Exomes. Mol Biol Evol 35:2015-2025 |
| Kumar, Sudhir; Stecher, Glen; Li, Michael et al. (2018) MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms. Mol Biol Evol 35:1547-1549 |
| Kumar, Sudhir; Patel, Ravi (2018) Neutral Theory, Disease Mutations, and Personal Exomes. Mol Biol Evol 35:1297-1303 |
| Miura, Sayaka; Gomez, Karen; Murillo, Oscar et al. (2018) Predicting clone genotypes from tumor bulk sequencing of multiple samples. Bioinformatics 34:4017-4026 |
| Gomez, Karen; Miura, Sayaka; Huuki, Louise A et al. (2018) Somatic evolutionary timings of driver mutations. BMC Cancer 18:85 |
| Cannataro, Vincent L; Townsend, Jeffrey P (2018) Neutral Theory and the Somatic Evolution of Cancer. Mol Biol Evol 35:1308-1315 |
