Progression inference for somatic mutations in cancer

Leif E Peterson; Tatiana Kovyrshina

doi:10.1016/j.heliyon.2017.e00277

Progression inference for somatic mutations in cancer

Heliyon. 2017 Apr 11;3(4):e00277. doi: 10.1016/j.heliyon.2017.e00277. eCollection 2017 Apr.

Authors

Leif E Peterson^{1

2

3

4

5}, Tatiana Kovyrshina^{1

6}

Affiliations

¹ Center for Biostatistics, Houston Methodist Research Institute, Houston, TX 77030, USA.
² Dept. of Healthcare Policy and Research, Weill Cornell Medical College, Cornell University, New York, NY 10065, USA.
³ Dept. of Biostatistics, School of Public Health, University of Texas - Health Science Center, Houston, TX 77030, USA.
⁴ Dept. of Medicine, Baylor College of Medicine, Houston, TX 77030, USA.
⁵ Dept. of Neuroscience and Experimental Therapeutics, Texas A&M University Health Science Center, College Station, TX 77843, USA.
⁶ Dept. of Mathematics and Statistics, University of Houston - Downtown, Houston, TX 77002, USA.

Abstract

Computational methods were employed to determine progression inference of genomic alterations in commonly occurring cancers. Using cross-sectional TCGA data, we computed evolutionary trajectories involving selectivity relationships among pairs of gene-specific genomic alterations such as somatic mutations, deletions, amplifications, downregulation, and upregulation among the top 20 driver genes associated with each cancer. Results indicate that the majority of hierarchies involved TP53, PIK3CA, ERBB2, APC, KRAS, EGFR, IDH1, VHL, etc. Research into the order and accumulation of genomic alterations among cancer driver genes will ever-increase as the costs of nextgen sequencing subside, and personalized/precision medicine incorporates whole-genome scans into the diagnosis and treatment of cancer.

Keywords: Cancer research; Computational biology; Genetics; Oncology.