Cancer Evolution and the Limits of Predictability in Precision Cancer Medicine
Authors: Kamil A. Lipinski, Louise J. Barber, Matthew N. Davies, Matthew Ashenden, Andrea Sottoriva, Marco Gerlinger
Journal: Trends in cancer (2016)

Large-scale genomic cancer medicine initiatives are under way in several countries across the globe. However, it remains a major challenge to use genomic information to make accurate predictions for individual cancer patients. Multiple genomic studies substantiated the notion of cancer as an evolutionary process that can readily adapt within the lifetime of a patient. Evolutionary adaptation results from the interplay of mutation generation and genetic drift, which are both stochastic processes, and clonal selection, which is deterministic in nature. The influence of stochastic factors fundamentally limits the predictability of cancer evolution. Understanding the limits of predictability and the development of more accurate prediction algorithms using evolutionary models is key to improving outcomes through genomic cancer medicine. The ability to predict the future behavior of an individual cancer is crucial for precision cancer medicine. The discovery of extensive intratumor heterogeneity and ongoing clonal adaptation in human tumors substantiated the notion of cancer as an evolutionary process. Random events are inherent in evolution and tumor spatial structures hinder the efficacy of selection, which is the only deterministic evolutionary force. This review outlines how the interaction of these stochastic and deterministic processes, which have been extensively studied in evolutionary biology, limits cancer predictability and develops evolutionary strategies to improve predictions. Understanding and advancing the cancer predictability horizon is crucial to improve precision medicine outcomes. The ability to predict the future behavior of an individual cancer is crucial for precision cancer medicine. The discovery of extensive intratumor heterogeneity and ongoing clonal adaptation in human tumors substantiated the notion of cancer as an evolutionary process. Random events are inherent in evolution and tumor spatial structures hinder the efficacy of selection, which is the only deterministic evolutionary force. This review outlines how the interaction of these stochastic and deterministic processes, which have been extensively studied in evolutionary biology, limits cancer predictability and develops evolutionary strategies to improve predictions. Understanding and advancing the cancer predictability horizon is crucial to improve precision medicine outcomes. The ability to precisely predict the future clinical course of an individual patient's cancer would be highly beneficial for oncological care. For example, patients whose cancers will never progress to the point of affecting their health may not require any treatment and those who need systemic therapy should only be treated with drugs that have a realistic chance of being effective. Genomic aberrations differ between cancers of the same histological type, to the extent that no two tumors are thought to show an identical somatic genetic aberration profile [1Stratton M.R. et al.The cancer genome.Nature. 2009; 458: 719-724Crossref PubMed Scopus (1414) Google Scholar]. The specific combination of somatic genetic and epigenetic aberrations within a tumor, in the context of the germline variants present in the same patient, is thought to be a major determinant of the biology and hence of the clinical course of a cancer. Recognition of this intertumor heterogeneity led to the concept of personalized cancer medicine: deciphering individual cancer genomic profiles should provide precise insights into disease biology and allow the targeting of genetically encoded susceptibilities for therapeutic benefit. Next-generation sequencing technologies enable the routine interrogation of these (epi)genomic landscapes [2Meyerson M. et al.Advances in understanding cancer genomes through second-generation sequencing.Nat. Rev. Genet. 2010; 11: 685-696Crossref PubMed Scopus (674) Google Scholar, 3Reuter J.A. et al.High-throughput sequencing technologies.Mol. Cell. 2015; 58: 586-597Abstract Full Text Full Text PDF PubMed Google Scholar]. In parallel, an increasing number of cancer drugs expand the therapeutic options to target specific genetic alterations. Yet, despite noticeable advances of personalized therapy approaches in some tumor types, the ability to predict whether and for how long an individual cancer will respond to therapy and what genotype will eventually evolve to drive resistance remain suboptimal [4Dienstmann R. et al.Genomic medicine frontier in human solid tumors: prospects and challenges.J. Clin. Oncol. 2013; 31: 1874-1884Crossref PubMed Scopus (67) Google Scholar]. 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This hypothesis has since been substantiated by the discovery of intratumor subclonal heterogeneity and ongoing clonal selection in multiple cancer types [7Anderson K. et al.Genetic variegation of clonal architecture and propagating cells in leukaemia.Nature. 2011; 469: 356-361Crossref PubMed Scopus (486) Google Scholar, 8Diaz L.A. et al.The molecular evolution of acquired resistance to targeted EGFR blockade in colorectal cancers.Nature. 2012; 486: 537-540Crossref PubMed Scopus (799) Google Scholar, 9Gerlinger M. et al.Intratumor heterogeneity and branched evolution revealed by multiregion sequencing.N. Engl. J. Med. 2012; 366: 883-892Crossref PubMed Scopus (3265) Google Scholar, 10Awad M.M. et al.Acquired resistance to crizotinib from a mutation in CD74-ROS1.N. Engl. J. Med. 2013; 368: 2395-2401Crossref PubMed Scopus (149) Google Scholar, 11Gerlinger M. et al.Genomic architecture and evolution of clear cell renal cell carcinomas defined by multiregion sequencing.Nat. 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Heritable somatic variation encompasses genetic alterations such as point mutations, insertions, deletions, and chromosomal aberrations, as well as random epigenetic changes that are heritable over cell generations. For simplicity, the term 'mutation' is used for all heritable somatic alterations throughout this review. A baseline mutation rate can be detected in any mitotic tissue, but mutation rates are often elevated in cancer [18Roberts S.A. Gordenin D.A. Hypermutation in human cancer genomes: footprints and mechanisms.Nat. Rev. Cancer. 2014; 14: 786-800Crossref PubMed Scopus (115) Google Scholar]. Mutations can result from cell extrinsic (e.g., tobacco smoke exposure) or intrinsic processes (e.g., oxidative damage or defects in DNA repair). Many mutational processes preferentially strike in specific DNA sequence contexts, biasing mutations towards genomic regions in which these are overrepresented. Distinct mechanisms can hence leave specific footprints or mutational signatures in the genome, as shown by a pan-cancer analysis that revealed 20 different mutational signatures, nine of which could be linked to known molecular mutational mechanisms [19Alexandrov L.B. et al.Signatures of mutational processes in human cancer.Nature. 2013; 500: 415-421Crossref PubMed Scopus (1889) Google Scholar]. The preferential deamination of cytosine in 5′-TC-3′ dinucleotides and regional hypermutation clusters caused by the aberrant activity of the apolipoprotein B mRNA editing enzyme catalytic polypeptide-like (APOBEC) RNA-editing enzymes is one example [20Nik-Zainal S. et al.Mutational processes molding the genomes of 21 breast cancers.Cell. 2012; 149: 979-993Abstract Full Text Full Text PDF PubMed Scopus (685) Google Scholar]. 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Clear cell renal cell carcinomas (ccRCC) and non-small cell lung cancers (NSCLCs) both exhibited distinct mutational signatures during early carcinogenesis compared with cancer progression and between different tumor subclones [9Gerlinger M. et al.Intratumor heterogeneity and branched evolution revealed by multiregion sequencing.N. Engl. J. Med. 2012; 366: 883-892Crossref PubMed Scopus (3265) Google Scholar, 26de Bruin E.C. et al.Spatial and temporal diversity in genomic instability processes defines lung cancer evolution.Science. 2014; 346: 251-256Crossref PubMed Scopus (337) Google Scholar]. Ongoing tobacco exposure had a minor influence on mutation generation during NSCLC progression where mutations were predominantly induced by APOBEC enzymes [26de Bruin E.C. et al.Spatial and temporal diversity in genomic instability processes defines lung cancer evolution.Science. 2014; 346: 251-256Crossref PubMed Scopus (337) Google Scholar]. Single cell sequencing of two breast cancers showed that point mutations were generated continuously during cancer progression, whereas copy number aberrations had been acquired early [27Wang Y. et al.Clonal evolution in breast cancer revealed by single nucleus genome sequencing.Nature. 2014; 512: 155-160Crossref PubMed Scopus (319) Google Scholar]. Whole genome doubling events can lead to tetraploidy, which is permissive for further chromosome gains and losses [28Dewhurst S.M. et al.Tolerance of whole-genome doubling propagates chromosomal instability and accelerates cancer genome evolution.Cancer Discov. 2014; 4: 175-185Crossref PubMed Scopus (99) Google Scholar]. Genome doubling can occur early in carcinogenesis [26de Bruin E.C. et al.Spatial and temporal diversity in genomic instability processes defines lung cancer evolution.Science. 2014; 346: 251-256Crossref PubMed Scopus (337) Google Scholar] but also late during cancer progression [9Gerlinger M. et al.Intratumor heterogeneity and branched evolution revealed by multiregion sequencing.N. Engl. J. Med. 2012; 366: 883-892Crossref PubMed Scopus (3265) Google Scholar]. Extra gene copies acquired through genome doubling may buffer potentially deleterious effects of new mutations [9Gerlinger M. et al.Intratumor heterogeneity and branched evolution revealed by multiregion sequencing.N. Engl. J. Med. 2012; 366: 883-892Crossref PubMed Scopus (3265) Google Scholar]. Genome doubling might therefore not only catalyze mutation generation but also increase mutation tolerance. Cancer originates from a single cell with a diploid genome. This encodes the blueprint for embryological development and adult homoeostasis of a complex multicellular organism and is also structurally optimized to undergo meiosis and recombination during sexual reproduction [15Lynch M. The Origins of Genome Architecture. Sinauer Associates, 2007Google Scholar]. Such constraints on genome structure and many genes regulating tissue-specific functions are likely to be irrelevant for cancer cells, which permits their survival despite highly aberrant genomes. This mutational robustness allows cancers to probe a vast genomic space for novel phenotypes [29Garsed D.W. et al.The architecture and evolution of cancer neochromosomes.Cancer Cell. 2014; 26: 653-667Abstract Full Text Full Text PDF PubMed Google Scholar, 30Shlien A. et al.Combined hereditary and somatic mutations of replication error repair genes result in rapid onset of ultra-hypermutated cancers.Nat. Genet. 2015; 47: 257-262Crossref PubMed Scopus (95) Google Scholar]. Taken together, mutations are the prerequisite for cancer evolution. Mutation rates, the genomic regions that are prone to mutagenesis, and the timing when particular mutagenic processes operate during cancer progression can vary significantly between but also within individual cancers. This influences the accessibility of novel genotypes and phenotypes and hence the opportunities for evolution, as shown for APOBEC-driven mutagenesis, which generates activating phosphoinositide (PI)3-kinase mutations in many cancers where it is active [31Henderson S. et al.APOBEC-mediated cytosine deamination links PIK3CA helical domain mutations to human papillomavirus-driven tumor development.Cell Rep. 2014; 7: 1833-1841Abstract Full Text Full Text PDF PubMed Scopus (93) Google Scholar]. Yet, even if the mutational mechanisms operating in a cancer cell could be measured exactly, mutations still occur randomly with regard to their timing and exact genomic location. Genetic drift refers to changes of the frequency of an allele in a population due to random birth and death events: each cell in a newly generated cancer subclone has a certain probability of dying as a result of random factors and occasionally all cells of a small subclone die, even if this clone harbors a highly beneficial mutation. Drift has a bigger impact in smaller populations [15Lynch M. The Origins of Genome Architecture. Sinauer Associates, 2007Google Scholar] and is more likely to eradicate a single cell or a small clone that has not yet expanded significantly. Drift is more pronounced after population bottlenecks, for example, when a few or single cells colonize a new metastatic niche or after a massive reduction in population size through cytotoxic treatment. As a consequence of drift, the expansion of a clone with a beneficial mutation may not be predictable with certainty until this clone exceeds a certain abundance at which it escapes potential extinction through drift [32Levy S.F. et al.Quantitative evolutionary dynamics using high-resolution lineage tracking.Nature. 2015; 519: 181-186Crossref PubMed Scopus (65) Google Scholar]. Drift influences cancer initiation [33Vermeulen L. et al.Defining stem cell dynamics in models of intestinal tumor initiation.Science. 2013; 342: 995-998Crossref PubMed Scopus (131) Google Scholar, 34Kozar S. et al.Continuous clonal labeling reveals small numbers of functional stem cells in intestinal crypts and adenomas.Cell Stem Cell. 2013; 13: 626-633Abstract Full Text Full Text PDF PubMed Scopus (21) Google Scholar] but experimental data demonstrating the strength of this effect in cancer progression is lacking. New technologies assessing clonal composition at the single cell level [27Wang Y. et al.Clonal evolution in breast cancer revealed by single nucleus genome sequencing.Nature. 2014; 512: 155-160Crossref PubMed Scopus (319) Google Scholar] or clonal dynamics through lineage tracing in model systems [33Vermeulen L. et al.Defining stem cell dynamics in models of intestinal tumor initiation.Science. 2013; 342: 995-998Crossref PubMed Scopus (131) Google Scholar, 35Bhang H.E. et al.Studying clonal dynamics in response to cancer therapy using high-complexity barcoding.Nat. Med. 2015; 21: 440-448Crossref PubMed Scopus (170) Google Scholar] may provide such insights. A new mutation that increases the ability of the cell to survive and reproduce under particular environmental conditions and that has escaped drift will gradually increase in its abundance within the population. This clonal selection is arguably the only deterministic force in evolution [16Szendro I.G. et al.Predictability of evolution depends nonmonotonically on population size.Proc. Natl. Acad. Sci. U.S.A. 2013; 110: 571-576Crossref PubMed Scopus (0) Google Scholar, 36Jain K. Krug J. Deterministic and stochastic regimes of asexual evolution on rugged fitness landscapes.Genetics. 2007; 175: 1275-1288Crossref PubMed Scopus (0) Google Scholar]. Next-generation sequencing technologies revealed these clonal selection processes for the first time in detail and drafted the first chapters of cancer evolution rulebooks. 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These resistant clones were already present before rociletinib therapy initiation and probably harbored alternative resistance drivers to first generation inhibitors [48Piotrowska Z. et al.Heterogeneity underlies the emergence of EGFRT790 wild-type clones following treatment of T790M-positive cancers with a third-generation EGFR inhibitor.Cancer Discov. 2015; 5: 713-722Crossref PubMed Scopus (161) Google Scholar]. Thus, subclonal heterogeneity was a key driver of treatment failure. C797S EGFR mutations are an alternative resistance mechanism to third generation EGFR inhibitors [49Niederst M.J. et al.The allelic context of the C797S mutation acquired upon treatment with third generation EGFR inhibitors impacts sensitivity to subsequent treatment strategies.Clin. Cancer Res. 2015; 21: 3924-3933Crossref PubMed Scopus (106) Google Scholar]. Importantly, EGFR signaling could still be inhibited with a combination of first and third generation inhibitors if the C797S mutation was located in trans with T790M but this combination was ineffective if these were located in cis on the same EGFR allele. As C797S mutations occur randomly on one of the two EGFR alleles, the optimal further therapy cannot be predicted until the mutational event has occurred and has been detected. This compellingly demonstrates how stochastic events can limit predictability. Somatic mutation detection in circulating tumor DNA (ctDNA) is likely to provide a more comprehensive overview over the subclonal heterogeneity of solid tumors than single biopsies. ctDNA analysis indeed detected up to 12 distinct subclones, each harboring a different mutation in RAS-type family GTPases (RAS) or v-Raf murine sarcoma viral oncogene homolog B1 (BRAF) genes, in individual patients with colorectal cancer (CRC) after they had developed anti-EGFR therapy resistance [12Bettegowda C. et al.Detection of circulating tumor DNA in early- and late-stage human malignancies.Sci. Transl. Med. 2014; 6: 224ra24Crossref PubMed Scopus (904) Google Scholar]. 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Polyclonal resistance may thus be a common phenomenon in solid tumors, demonstrating the enormous evolutionary adaptability of cancer. Clonal dynamics analyses in the ctDNA from CRC patients further suggested that Kirsten rat sarcoma viral oncogene homolog (KRAS) resistance mutations had been present in small subclones before anti-EGFR therapy initiation [8Diaz L.A. et al.The molecular evolution of acquired resistance to targeted EGFR blockade in colorectal cancers.Nature. 2012; 486: 537-540Crossref PubMed Scopus (799) Google Scholar]. Thus, the standing genetic variation in cancers has been recurrently found to provide a reservoir of phenotypes permitting evolutionary rescue from extinction in changing environments. Overexpression of the BRAF V600E oncoprotein caused resistance but also dependency on BRAF inhibitor therapy in melanoma xenografts [54Das Thakur M. et al.