title: Evolution-informed modeling improves outcome prediction for cancers.
abstract: Despite wide applications of high-throughput biotechnologies in cancer research, many biomarkers discovered by exploring large-scale omics data do not provide satisfactory performance when used to predict cancer treatment outcomes. This problem is partly due to the overlooking of functional implications of molecular markers. Here, we present a novel computational method that uses evolutionary conservation as prior knowledge to discover bona fide biomarkers. Evolutionary selection at the molecular level is nature's test on functional consequences of genetic elements. By prioritizing genes that show significant statistical association and high functional impact, our new method reduces the chances of including spurious markers in the predictive model. When applied to predicting therapeutic responses for patients with acute myeloid leukemia and to predicting metastasis for patients with prostate cancers, the new method gave rise to evolution-informed models that enjoyed low complexity and high accuracy. The identified genetic markers also have significant implications in tumor progression and embrace potential drug targets. Because evolutionary conservation can be estimated as a gene-specific, position-specific, or allele-specific parameter on the nucleotide level and on the protein level, this new method can be extended to apply to miscellaneous "omics" data to accelerate biomarker discoveries.
authors.affiliation: Department of Biomedical Informatics Arizona State University Tempe AZ USA.
authors.forename: Li
authors.initials: L
authors.lastname: Liu
authors.affiliation: School of Life Science Arizona State University Tempe AZ USA.
authors.forename: Yung
authors.initials: Y
authors.lastname: Chang
authors.affiliation: Department of Computer Science and Engineering Arizona State University Tempe AZ USA.
authors.forename: Tao
authors.initials: T
authors.lastname: Yang
authors.affiliation: Department of Bioengineering Rice University Houston TX USA.
authors.forename: David P
authors.initials: DP
authors.lastname: Noren
authors.affiliation: Department of Bioengineering Rice University Houston TX USA.
authors.forename: Byron
authors.initials: B
authors.lastname: Long
authors.affiliation: The University of Texas MD Anderson Cancer Center Houston TX USA.
authors.forename: Steven
authors.initials: S
authors.lastname: Kornblau
authors.affiliation: Department of Bioengineering Rice University Houston TX USA.
authors.forename: Amina
authors.initials: A
authors.lastname: Qutub
authors.affiliation: Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA.
authors.forename: Jieping
authors.initials: J
authors.lastname: Ye
keywords: evolutionary medicine; genomics/proteomics; molecular evolution; transcriptomics