A Sidow

A Sottoriva

C Swanton

CA Aktipis

CC Maley

D Shibata

David Posada

F Michor

I Bozic

J Cairns

J Lachance

J Zhao

LH Mengelbier

LM Merlo

M Gerlinger

M Greaves

M Jamal-Hanjani

N Beerenwinkel

N McGranahan

NE Navin

O Savolainen

PC Nowell

RA Burrell

RA Neher

Y Wang

English

Crossref

RANDOM WALKINGCancer Molecular EvolutionDavid Posada1Received: 10 August 2015 / Accepted: 11 August 2015 / Published online: 20 August 2015 The Author(s) 2015. This article is published with open access at Springerlink.comBy far, the majority of studies in molecular evolution havefocused on genetic change across one or more generations.Much less is known about the genetic changes that occurduring the life time of an individual, or somatic evolution,of which cancer is probably the best-known example.Cancer is an adaptive evolutionary process in which dis-tinct genetic clones compete for space and resources(Cairns 1975; Greaves and Maley 2012; Nowell 1976).Modern cancer biology and genomics have validated theevolutionary nature of cancer, which has attracted muchattention in recent years (Burrell and Swanton 2014; Ger-linger et al. 2014). Not surprisingly, cancer genomics hasunveiled a significant amount of intratumor heterogeneityin most tumor types (Burrell et al. 2013; Michor andPolyak 2010; Swanton 2012). However, but logically, moststudies in cancer genomics have been mostly concernedwith the identification of the genetic and epigenetic chan-ges that lead to cell transformation, tumor growth, metas-tasis and drug resistance, and much less with molecularevolutionary aspects. Despite a relatively rich literature oncancer evolutionary dynamics (Michor et al. 2004; Sot-toriva et al. 2011), little is known about the evolutionarymechanisms that drive tumor progression at the molecularand cellular level, and evolutionary insights in cancer arebased, for the most part, on mathematical models of car-cinogenesis (Beerenwinkel et al. 2015).Fortunately, the chance to obtain a more quantitativeunderstanding of cancer molecular evolution is here.Decreasing NGS costs and the already large amount ofgenomic data made available by the International CancerGenome Consortium (https://dcc.icgc.org) or The CancerGenome Atlas (http://cancergenome.nih.gov) should facil-itate detailed studies on the molecular evolution of cancer.Still, the paramount opportunity for molecular evolutionarystudies in cancer will be the one provided by single-cellgenomics techniques (Wang and Navin 2015), which openwide the door for the application of the conceptual andanalytical machinery of molecular evolution. In particular,population genomics and phylogenomics, which are mostlybased on individualized data (e.g., DNA or proteinsequences), will prove to be critical tools in this regard.There are multiple fundamental questions in cancer thatmight be at least partially answered under a soundmolecular evolution framework. For example, what are therelative roles of selection and genetic drift? While thepresumption is that selection is the solely driving force incancer, this might depend on the particular scenario (Sot-toriva et al. 2015). In fact, estimates derived from mathe-matical model fitting suggest that the selective advantageof driver mutations can be as low as 0.4–1 % (Beeren-winkel et al. 2007; Bozic et al. 2010). Basic parameters—such as tumor effective population size, cell generationtimes or cell turnover—for understanding the role ofgenetic drift in cancer progression have not yet been for-mally measured (Merlo et al. 2006). Indeed, the charac-terization of the adaptation process and the role ofneutrality have always been fundamental themes inmolecular evolution (Lachance and Tishkoff 2013; Savo-lainen et al. 2013), and existing methods should be used tointerrogate cancer genomic data.In addition, statistical models of evolution will be usefulin testing hypotheses about potentially different substitu-tion processes acting in a tumor, or to measure tumoral& David Posadadposada@uvigo.es1 Department of Biochemistry, Genetics and Immunology andInstitute of Biomedical Research of Vigo (IBIV), Universityof Vigo, 36310 Vigo, Spain123J Mol Evol (2015) 81:81–83DOI 10.1007/s00239-015-9695-7evolutionary rates from genomic data. Relaxed molecularclocks could be utilized to estimate whether a metastasiswas established early or late after transformation, or thetemporal dynamics of tumor growth (see for example Zhaoet al. 2014). Also, tumor genealogies could help inunderstanding tumor demographics in tempo and space, forexample as typically done in viral epidemiology. Likewise,models of tumor growth including the cancer stem cellmodel (see Navin and Hicks 2010) could be formallycompared under a phylogenetic framework using availablestatistical tests. Existing methods for the identification ofconvergent evolution might help to pinpoint new ‘‘drivergenes’’, particularly for non-coding regions. Moreover,ancestral character reconstruction techniques could beexploited to infer the genomic composition of the initialcell that originated a primary tumor or a metastasis, and theorder of the different genomic events that took place.Major evolutionary aspects like the role and extent oftumor population structure, admixture, or different formsof gene flow—not just metastases but also the movement ofcells within tumors, or into contiguous regions—currentlynot well understood, could also benefit from the use ofstandard tools in population genomics.Indeed, somatic cancer evolution differs from germlineevolution in fundamental ways (Sidow and Spies 2015).Most importantly, somatic evolution is asexual, proceedingthrough cell mitosis and therefore without chromosomalsegregation or crossing over. The fact that the whole cancergenome is non-recombining implies that genetic hitchhik-ing and clonal interference should be common (see forexample Neher 2013), but also that the variance of thedifferent estimates from a single locus will be huge, sosome care is need in this regard. Furthermore, all signifi-cant variations in cancer occur de novo in each individ-ual—with the exception of a few transmissible cancers—meaning that the time scale and the level of divergence willbe very narrow. Importantly, many parameter estimates ortests in molecular evolution assume neutrality, so therobustness of these methods to the presence of selectionshould be assessed before their application in cancer.Understanding cancer evolution can have significantapplications, such as more robust predictive biomarkers toimprove personalized treatment and prevent drug resis-tance (Jamal-Hanjani et al. 2015; McGranahan andSwanton 2015). Evolutionary signatures such as clonalexpansions or mutational diversity could help predictingcancer progression (Maley et al. 2006; Mengelbier et al.2015; see also Neher et al. 2014). Evolutionary biologyconcepts and tools remain underused in cancer biology(Aktipis et al. 2011; Shibata 2012). The availability ofgenomic data, in particular that from single cells, shouldhelp change this.Acknowledgments David Posada is supported by the EuropeanResearch Council (ERC-617457- PHYLOCANCER) and participatesin the BIOCAPS (Biomedical Capacities Support Program) initiativefunded by the EU Seventh Framework Program (REGPOT2012-2013-1).Open Access This article is distributed under the terms of theCreative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use,distribution, and reproduction in any medium, provided you giveappropriate credit to the original author(s) and the source, provide alink to the Creative Commons license, and indicate if changes weremade.ReferencesAktipis CA, Kwan VS, Johnson KA, Neuberg SL, Maley CC (2011)Overlooking evolution: a systematic analysis of cancer relapseand therapeutic resistance research. 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