PhyloOncology: Understanding cancer through phylogenetic analysis.
Despite decades of research and an enormity of resultant data, cancer remains a significant public health problem. New tools and fresh perspectives are needed to obtain fundamental insights, to develop better prognostic and predictive tools, and to identify improved therapeutic interventions. With increasingly common genome-scale data, one suite of algorithms and concepts with potential to shed light on cancer biology is phylogenetics, a scientific discipline used in diverse fields. From grouping subsets of cancer samples to tracing subclonal evolution during cancer progression and metastasis, the use of phylogenetics is a powerful systems biology approach. Well-developed phylogenetic applications provide fast, robust approaches to analyze high-dimensional, heterogeneous cancer data sets. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.
Duke Cancer Institute and the Department of Medicine, Duke University Medical Center, Durham, NC 27710, United States. Electronic address: jason.somarelli@duke.edu.
Jason A
JA
Somarelli
Duke Cancer Institute and the Department of Medicine, Duke University Medical Center, Durham, NC 27710, United States.
Kathryn E
KE
Ware
Pediatric Oncology, School of Medicine, Johns Hopkins University, United States.
Rumen
R
Kostadinov
Anatomy Department, College of Medicine, Howard University, Washington, DC 20059, United States; Digestive Disorders Unit, National Institute of Nursing Research, NIH, Bethesda, MD 20892, United States.
Jeffrey M
JM
Robinson
Department of Biochemistry and Cellular and Molecular Biology, Georgetown University Medical Center, Washington, DC 20007, United States.
Hakima
H
Amri
Section of Ultrastructural Biology, National Eye Institute, NIH, Bethesda, MD 20892, United States.
Mones
M
Abu-Asab
Digestive Disorders Unit, National Institute of Nursing Research, NIH, Bethesda, MD 20892, United States.
Nicolaas
N
Fourie
Anatomy Department, College of Medicine, Howard University, Washington, DC 20059, United States.
Rui
R
Diogo
Department of Biology, Duke University Trinity College of Arts and Sciences, Durham, NC 27710, United States.
David
D
Swofford
Department of Biostatistics, Yale University, United States; Department of Ecology and Evolutionary Biology, Yale University, United States; Department of Program in Computational Biology and Bioinformatics, Yale University, United States. Electronic address: Jeffrey.Townsend@Yale.edu.
Jeffrey P
JP
Townsend
D000222:Adaptation, Physiological; D000465:Algorithms; D000818:Animals; D014408:Biomarkers, Tumor / Q000235:genetics* / Q000378:metabolism; D002471:Cell Transformation, Neoplastic / Q000235:genetics* / Q000378:metabolism / Q000473:pathology; D019143:Evolution, Molecular; D015972:Gene Expression Regulation, Neoplastic; D056084:Genetic Fitness; D020022:Genetic Predisposition to Disease; D023281:Genomics / Q000379:methods; D040941:Heredity; D006801:Humans; D008957:Models, Genetic; D009154:Mutation; D009369:Neoplasms / Q000188:drug therapy / Q000235:genetics* / Q000378:metabolism / Q000473:pathology; D010375:Pedigree; D010641:Phenotype; D010802:Phylogeny; D015398:Signal Transduction / Q000235:genetics; D049490:Systems Biology; D013997:Time Factors
D014408:Biomarkers, Tumor
Cancer stratification; Cancer types; Clonal evolution; Tumor heterogeneity; Tumor trees