Abstract Kimura’s neutral theory argued that positive selection was not responsible for an appreciable fraction of molecular substitutions. Correspondingly, quantitative analysis reveals that the vast majority of substitutions in cancer genomes are not detectably under selection. Insights from the somatic evolution of cancer reveal that beneficial substitutions in cancer constitute a small but important fraction of the molecular variants. The molecular evolution of cancer community will benefit by incorporating the neutral theory of molecular evolution into their understanding and analysis of cancer evolution—and accepting the use of tractable, predictive models, even when there is some evidence that they are not perfect. molecular evolution, neutral theory, cancer, tumor genetics, distribution of fitness effects, somatic mutation Introduction Molecular evolutionary biology is usually considered a historical science. Recent increases in sequencing technology have been lauded within the evolutionary biology community for their resolution of the deep history of life on earth (Hedges et al. 2006; Tamura et al. 2007; Ebersberger et al. 2012; Grant and Katz 2014; Blaimer et al. 2015; Petitjean et al. 2015; Prum et al. 2015; Bourguignon et al. 2017; Chen et al. 2017; Dornburg, Townsend, Brooks, et al. 2017; Dornburg, Townsend, and Wang 2017; Mello et al. 2017; Shin et al. 2018; Singhal et al. 2017; Wanke et al. 2017). However, the increase in sequence data has been revolutionary not only for deep evolutionary questions where genomic divergence is very large and signal is obscured by homoplasy (Tekle et al. 2010; Townsend et al. 2012), but also for recent evolution in cases where genomic divergence is very low and signal has been challenging to find (Townsend 2007), such as in recent studies of the evolutionary divergence of epidemic disease (Scarpino et al. 2015; Bayliss et al. 2017; Gardy and Loman 2018) and multi-site sequencing studies of cancer (Gerlinger et al. 2012,, 2014; Hong et al. 2015; Zhao et al. 2016; Schwartz and Schäffer 2017; Somarelli et al. 2017). In the field of cancer biology, excitement regarding new technologies has led to rapid deployment, gathering enormous sequence data sets to accumulate enough signal to obtain an understanding of the key genetic changes underlying the somatic evolution of cancer. With burgeoning data have come the potential to evaluate tractable models of cancer evolution. Whereas considerable time and effort have been dedicated to the de novo modeling of cancer evolution (Altrock et al. 2015), less effort has gone to mapping that which we already know from over a century of population genetics research and over a half century of molecular evolutionary research to the somatic evolution of cancer. In particular, there is good reason to believe that much of Dr Motoo Kimura’s Neutral Theory of Molecular Evolution (Kimura 1983) is applicable to the evolution of cancer. Kimura's theory rendered evolutionary predictions a level of analytical tractability that is needed in the field of cancer research today, and such tractable models have always been essential to the advancement of science. Using the Kolmogorov forward and backward equations, Kimura refined analytical calculations of the probability of fixation by Haldane, Fisher, Wright, and Malécot to include any level of dominance, the initial frequency of a mutant, fluctuating selection coefficients, and effective population size, for example, for a fully or partially dominant gene, in the process emphasizing the stochastic nature of the fixation process and the large role that neutral or effectively neutral variants play in population polymorphism and divergence (Crow 1987). This stochastic nature of the evolutionary process is one that is confronted by all of us when we contemplate the potential onset of cancer: Cancer is a game of chance that we are dying not to lose. History Repeats Itself The history of the application of molecular evolution to the field of cancer biology has mirrored the history of the field of molecular evolution as a whole. In both cases, technological advances made it possible to accumulate large amounts of data illustrating intrapopulation variation and phylogenetic divergence, inspiring genetic “clocks” to time evolutionary divergences (Zuckerkandl and Pauling 1965; Zuckerkandl 1987; Morgan 1998; Alexandrov et al. 2015; Zhao et al. 2016; Mello et al. 2017), and spurring analyses of the selective and stochastic processes operating on variant sites that underlie genetic divergences. An Early Focus on Adaptation Scientific fields progress with a pace proportional to the tools available to analyze the natural world. The first half century of the study of evolution, on the heels of the proposal of the origin of species by means of natural selection (Darwin 1859), was focused on observable large phenotypic differences between species, and thus traits that were adaptive in nature that must have been naturally selected to accumulate in the natural world. This focus carried over to the early study of population genetics. One of the founders of the field, Ronald Fisher, stated in 1936 that “Theories of evolution are of two kinds … for these two theories evolution is progressive adaptation and consists of nothing else. The production of differences recognizable by systematists is a secondary by-product, produced incidentally in the process of becoming better adapted” (Watson et al. 1936). Similarly, early study into the origins of cancer (from the somatic tissues from which they arise) by means of natural selection necessarily focused on large phenotypic differences between healthy tissues and cancers. The first research credited with popularizing the hypothesis of tumor evolution used the uniformity of immunoglobulins produced by plasma cell tumors, the uniformity of which X chromosome is functional in all the cells of a single tumor, and the homogeneity of karyotypes as evidence of the clonal evolution of tumor cell populations (Nowell 1976). The proposed model of cancer evolution was one of proliferating clonal lineages, where most lineages were not viable but some rare lineages expanded through selective advantage. These selective advantages corresponded to large phenotypic changes/adaptations viewed in the clinic, including drug resistance and metastases. The molecular biology revolution followed immediately after the proposal of cancer by means of natural selection. The same year that Nowell proposed the hypothesis of cancer evolution, Stehelin et al. (1976) demonstrated that a gene responsible for tumorigenesis in chickens also existed in healthy somatic cells. “Proto-oncogenes,” as they came to be called, were soon discovered in healthy human cells (Shih and Weinberg 1982), which was followed by the advancement of the first evolutionary model of cancer incorporating specific genetic variants in the 1990s (Fearon and Vogelstein 1990; Kinzler and Vogelstein 1996). These initial models were informed by the increasing prevalences of cancer-associated variants in successively more advanced stages of carcinogenesis, and thus were depicted as necessary, sequential, discrete steps in the formation of adenomas (Fearon and Vogelstein 1990), corresponding to the successive waves of clonal expansion described by Nowell (1976). This depiction of tumorigenesis shaped our understanding for over two decades, with influential mathematical models simulating the full history of tumor growth and molecular evolution as the product of sequential clonal sweeps of progressively more fit variants, and the full composite of genetic heterogeneity as the product of drivers, clonal sweeps, and passenger mutations (Beerenwinkel et al. 2007; Bozic et al. 2010). The clonal sweep model has largely been overturned by a branching, phylogenetic conception sometimes yielding high intratumor and metastatic divergence (Gerlinger et al. 2012,, 2014; Zhao et al. 2016; Schwartz and Schäffer 2017). Moreover, just as with the accumulation of molecular data in the second half of the 20th century, the deluge of tumor sequencing data in the 2010s painted a more informed picture of the extent of polymorphism and of fixation of substitutions in cancer. Drifting toward the Neutral Theory Following the development of techniques to readily obtain and analyze amino acid substitution data and the consequent revelation that substitutions in diverse mammals occur at roughly the same rate (Zuckerkandl and Pauling 1965), Kimura first proposed that “most mutations produced by nucleotide replacement are almost neutral in natural selection,” a statement that came to be popularized as the neutral theory of molecular evolution—although Kimura remarked that the “mutation-random drift theory” would be a better descriptor (Kimura 1983). Kimura extrapolated these data to the whole genome, proposing that substitution rates must be immense compared with previous estimates (Kimura 1968), and for such a large amount of substitutions to be tenable, most substitutions must be nearly neutral with respect to selection. Importantly, Kimura emphasized in later work that this theory is not antagonistic to adaptation by means of natural selection, that the evolution of form and function is guided by Darwinian selection, and that his neutral theory posits that the majority of the molecular variation found within and between species is the consequence of mutation and drift of selectively neutral or nearly neutral variants. Similarly, in cancer, it can be argued that the “form and function” of a tumor, that is, those adaptive phenotypic variations characteristic of and driving cancer growth (Hanahan and Weinberg 2011) can arise as a consequence of selection on specific molecular variants; and also that mutation and random drift are nevertheless the predominant sources of the variation observed at high frequencies in the genomes of tumors. Desire for knowledge about the genetic origin and the evolutionary progression of cancer has spurred efforts to sequence whole exomes and genomes of tumors and somatic tissues of patients in exhaustive efforts to identify genetic drivers and targets for therapy. Two extreme approaches of this method are conducting shallow sequencing with wide scope, or conducting deep sequencing with focused scope. The former method has resulted in thousands of whole exome sequences of moderate read depth. Analyzing the ratio of nonsynonymous substitutions to synonymous substitutions, Woo and Li (2012), Ostrow et al. (2014), and Martincorena et al. (2017) revealed that purifying selection is much weaker in somatic tissue compared with that observed in species evolution, and that on average four coding substitutions per tumor were under positive selection, suggesting that the vast majority of nonsynonymous substitutions is neutral regarding selection. The Predominant Utility of Neutrality Kimura's neutral theory of molecular evolution rests on an assertion that “the main cause of evolutionary change at the molecular level—changes in the genetic material itself—is random fixation of selectively neutral or nearly neutral mutations rather than positive Darwinian selection. (Kimura 1983).” This assertion has received considerable support when evaluated in the light of data on organismal molecular evolution, yet may well be as applicable—or even more so—to data on the somatic evolution of cancer. Reconciling Weak Purifying Selection, Strong Directional Selection, and Neutrality of Molecular Change Cancers are hypothesized to be caricatures of the renewal process of the tissues from which they arise (Pierce and Speers 1988). That is, many types of cancers may be fueled by small subpopulations capable of maintaining continuous growth (Kreso and Dick 2014). These “metapopulation” dynamics have been demonstrated in cancers where lineage tracing experiments are possible, including cancers of the brain (Chen et al. 2012), skin (Driessens et al. 2012), and intestines (Schepers et al. 2012). In the intestines, where adenomas grow via the fission of stem-cell maintained glands, the population size of the cell populations maintaining individual glands were measured to be on the order of normal tissue—that is, <20 cells (Baker et al. 2014). At these low population sizes, purifying selection is extremely weak—mathematical modeling predicts that, among the rare variants with a fitness effect, deleterious substitutions are expected to outnumber beneficial substitutions in intestinal crypts (Cannataro, McKinley, et al. 2017). Even mutations that confer extreme fitness benefits, such as the 3.5× higher division rate measured for the KRAS G12D variant in colon tumors in mice, go extinct within ∼30% of the populations polymorphic for the variant (Vermeulen et al. 2013; Snippert et al. 2014). A key aspect of Kimura’s neutral theory was his argument that mutations are effectively neutral if the selection coefficient s < 1/(2Ne), where Ne is the effective population size. For a population that is subdivided into independent evolutionary units, the effective population size can be much less than the apparent total population size (Kimura 1983). If Ne for relevant fixation events is small (as has been measured in colorectal adenomas), we can expect effective neutrality for mutations that would otherwise be influenced by the forces of selection in populations with experimental population sizes (i.e. those accessible in the laboratory evolution experiments). Kimura’s neutral theory is compatible with these considerations regarding the neutral drift dynamics of small founder populations, and the observations of weakened purifying selection in cancer (Woo and Li 2012; Ostrow et al. 2014; Martincorena et al. 2017), considerations which nevertheless contrast with the strong selection believed to be imparted by classical cancer driver mutations: Kimura’s neutral theory is in essence a claim that a high relative proportion of polymorphic and divergent sites are neutral versus selected, and was never intended to decry occasional strong positive selection (Kimura 1983). The task for understanding the somatic evolution of cancer is to characterize these proportions and describe the patterns and timing of selected and neutral variants that spread through tumors. One reconciliation of the extensive number of neutral variants with a few strongly selected variants is that the selection occurs early—and subsequently, neutral variation dominates. Indeed, an analysis of variant allele frequencies across many tumors among different tumor types found that the variant allele frequency was not inconsistent with predictions of neutral mutations in an exponentially growing tumor (Williams et al. 2016). Deep and focused sequencing of several samples from colorectal tumors (Sottoriva et al. 2015) and a hepatocellular carcinoma (Ling et al. 2015) revealed no evidence of adaptive evolution in late tumorigenesis. Nevertheless, known driver mutations have been mapped (even convergently) to late evolutionary branches (Zhao et al. 2016; Gomez et al. 2018; Zhang et al. 2018), illustrating that complexities of differential timings in different tissue types (Zhang et al. 2018) and other tissue sampling issues (Hong et al. 2015) deserve continued consideration. Furthermore, mutation rates as well as effective population size likely change markedly across tumorigenesis and cancer development, and even the sign of indirect selection for mutation rate modifier alleles may change from negative to positive as population size increases (Raynes et al. 2018). One can speculate that this indirect selection is why TP53, the “guardian of the genome” and the most frequently mutated gene in cancer, is estimated to preferentially fix subsequent to the fixation of other oncogenes (Kinzler and Vogelstein 1996)—substitution of a mutator allele in small population sizes may lead to mutational meltdown and population extinction. A Neutral View of Neutrality One of the early misconceptions about neutral theory was that all amino acid substitutions should occur at equal frequency relative to their inherent prevalence in the genome (Kimura 1983). This notion came from a misunderstanding of the neutral theory as a completely random process in the absence of selection. However, Kimura argued that not only are selection and the neutral theory compatible, but also that selection is the filtering force that demonstrates the viability of the neutral theory. As the physico-chemical distances between amino acids increase, the probability that a variant is selectively neutral decreases. Consequently, the probability of that amino acid substitution fixing also decreases, because it will be subjected to the forces of purifying selection. Hence, Kimura’s neutral theory predicts a negative correlation between relative substitution rates and amino acid physico-chemical distance, as low-distance variants are more likely to be neutral and reach fixation. In contrast, if mutations were caused exclusively by positive Darwinian selection, and selective effect was proportional to physico-chemical distance and smaller effects had a higher probability of being beneficial (Fisher 1930), we would expect the evolutionary rate versus distance function to be bell-shaped. Using data from 17 homologous families of proteins obtained from McLachlan (1972) and physico-chemical distance estimates from Miyata et al. (1979), Kimura demonstrated a negative correlation between relative substitution rates and physico-chemical distance (“Between species” panel, fig. 1). This result is consistent with a model of evolution where the majority of substitutions is neutral with respect to selection (Kimura 1983). Similar plots of the relative amino acid substitution frequencies against the same physico-chemical distance yield a similar negative relationship in 23 prevalent tumor types (fig. 1). Fig. 1. View largeDownload slide Frequency of a codon change versus the molecular distance of that change, in 23 cancers and in an organismal data set (lower right) analyzed by McLachlan (1972) and plotted by Kimura (1983). For the 23 cancer types, the frequency is normalized based on the relative frequency we would expect each amino acid substitution given the trinucleotide mutational signature (Rosenthal et al. 2016) of nonrecurrent variants in each tumor type, and the relative frequency of each amino acid in the exome. BLCA: bladder urothelial carcinoma; BRCA: breast invasive carcinoma; CESC: cervical squamous cell carcinoma and endocervical adenocarcinoma; COAD: colon adenocarcinoma; ESCA: esophageal carcinoma; GBM: glioblastoma multiforme; HNSC: head and neck squamous cell carcinoma, broken into HPV+ and HPV− tumor samples using the criteria described within Cao et al. (2016); KIRC: kidney renal clear cell carcinoma; LAML: Acute Myeloid Leukemia; LGG: Brain Lower Grade Glioma; LIHC: liver hepatocellular carcinoma; LUAD: lung adenocarcinoma; LUSC: lung squamous cell carcinoma; OV: ovarian serous cystadenocarcinoma; PAAD: pancreatic adenocarcinoma; PRAD: prostate adenocarcinoma; READ: rectum adenocarcinoma; SKCM: skin cutaneous melanoma, broken into primary skin tumors and metastatic skin tumors; STAD: stomach adenocarcinoma; THCA: thyroid carcinoma; UCEC: uterine corpus endometrial carcinoma. Fig. 1. View largeDownload slide Frequency of a codon change versus the molecular distance of that change, in 23 cancers and in an organismal data set (lower right) analyzed by McLachlan (1972) and plotted by Kimura (1983). For the 23 cancer types, the frequency is normalized based on the relative frequency we would expect each amino acid substitution given the trinucleotide mutational signature (Rosenthal et al. 2016) of nonrecurrent variants in each tumor type, and the relative frequency of each amino acid in the exome. BLCA: bladder urothelial carcinoma; BRCA: breast invasive carcinoma; CESC: cervical squamous cell carcinoma and endocervical adenocarcinoma; COAD: colon adenocarcinoma; ESCA: esophageal carcinoma; GBM: glioblastoma multiforme; HNSC: head and neck squamous cell carcinoma, broken into HPV+ and HPV− tumor samples using the criteria described within Cao et al. (2016); KIRC: kidney renal clear cell carcinoma; LAML: Acute Myeloid Leukemia; LGG: Brain Lower Grade Glioma; LIHC: liver hepatocellular carcinoma; LUAD: lung adenocarcinoma; LUSC: lung squamous cell carcinoma; OV: ovarian serous cystadenocarcinoma; PAAD: pancreatic adenocarcinoma; PRAD: prostate adenocarcinoma; READ: rectum adenocarcinoma; SKCM: skin cutaneous melanoma, broken into primary skin tumors and metastatic skin tumors; STAD: stomach adenocarcinoma; THCA: thyroid carcinoma; UCEC: uterine corpus endometrial carcinoma. The decline in relative frequency with molecular distance is less pronounced within cancer data (fig. 1), which is to be expected as a consequence of the weaker purifying selection found in cancer, especially in the context of functional variants (Ostrow et al. 2014). Furthermore, the deviations from this relationship arise as a consequence of the strong positive selection for specific variants found mutated in many tumors (Cannataro, Gaffney, et al. 2017). For instance, in thyroid carcinoma (THCA), the largest relative frequency of bidirectional amino acid change among THCA protein sequences was a V ↔ E substitution, predominantly driven by the fact that 55% of THCA tumors have a specific BRAF V600E substitution. This single recurrent change at a single site in the genome leads this bidirectional amino acid change across the entire genome to occur at a much higher frequency than expected in an evolutionary model without the potential for adaptive evolution. Thus, whereas the vast majority of amino acid substitutions occurring in the somatic evolution of cancer is changing at frequencies consistent with a neutral mode of evolution, there is ample evidence that recurrently selected substitutions confer a significant reproductive and survival benefit to cancer lineages—cancer biologists must recognize the role that positive selection plays in tumorigenesis. Are these positively selected substitutions in contradiction to the application of Kimura’s neutral theory of molecular evolution to the somatic evolution of cancer? It depends on whether they constitute an “appreciable fraction” of total molecular change. Kimura’s neutral theory “does deny that any appreciable fraction of molecular change is due to positive selection or that molecular polymorphisms are determined by balanced selective forces,” (Kimura 1983). Addressing this question requires use of a tremendous strength of the somatic evolution of cancer as an object of study, which is that data obtained (whole exome or whole-genome sequences of cancerous tissue) represent numerous independent evolutionary replicates, albeit in the context of many divergent germline genetic, carcinogenetic, immuno-oncological, and tissue developmental factors. These replicates are “replays of the tape of life” that permit alternate and powerful approaches to the analysis of mutation, drift, selection, and substitution. Consequently, the selection intensity on substitutions during the somatic evolution of cancer can be quantified by the ratio of site-specific substitution rate to site-specific mutation rate (Cannataro, Gaffney, et al. 2017; Cannataro et al. 2018). Such estimates are uniformly biased upward for nonrecurrent mutations because of the low rate of mutation compared with the genome size (such that every observed nonrecurrent mutation is present at a higher frequency than expected). However, a straightforward application of the classical normalization to “silent sites” indicates that across 23 cancer types, the vast majority of nonsynonymous substitutions has a selection intensity that is indistinguishable from that measured for synonymous substitutions (fig. 2; Cannataro, Gaffney, et al. 2017). Copy-number aberrations in tumors also follow a power law distribution—that is, their frequency can be predicted given their size (Zack et al. 2013)—suggesting that most are likely neutral with higher-prevalence deviates from this curve being selected. This vast majority of nonsynonymous mutations that are evolving neutrally does nothing to diminish the evolutionary importance of the small number of molecular variants with large selection intensities that drive cancer growth. Fig. 2. View largeDownload slide Density plots of the selection intensities of synonymous (dashed line) and amino acid replacement (solid line) somatic substitutions across 23 cancers (Cannataro, Gaffney, et al. 2017). The upward bias for nonrecurrent mutations is worse for tumors with lower rates of mutation and cancer types for which fewer tumors have been sequenced, because for those tumor types counts of zero substitutions among all tumors sequenced are not tallied and counts of one substitution among all tumors sequenced tend to be especially higher than the expected number of substitutions. The observed distributions are consistent with this bias (for instance, the UCEC distribution is based on the greatest number of substitutions and exhibits the least apparent bias; the LAML distribution is based on the least number of substitutions and exhibits the greatest apparent bias). This bias is presumably greater for nonsynonymous changes than synonymous changes, because synonymous mutations often can be produced by a higher number of potential nucleotide replacements in a codon, giving them a higher intrinsic mutation rate per codon. Fig. 2. View largeDownload slide Density plots of the selection intensities of synonymous (dashed line) and amino acid replacement (solid line) somatic substitutions across 23 cancers (Cannataro, Gaffney, et al. 2017). The upward bias for nonrecurrent mutations is worse for tumors with lower rates of mutation and cancer types for which fewer tumors have been sequenced, because for those tumor types counts of zero substitutions among all tumors sequenced are not tallied and counts of one substitution among all tumors sequenced tend to be especially higher than the expected number of substitutions. The observed distributions are consistent with this bias (for instance, the UCEC distribution is based on the greatest number of substitutions and exhibits the least apparent bias; the LAML distribution is based on the least number of substitutions and exhibits the greatest apparent bias). This bias is presumably greater for nonsynonymous changes than synonymous changes, because synonymous mutations often can be produced by a higher number of potential nucleotide replacements in a codon, giving them a higher intrinsic mutation rate per codon. Kimura closed his 1983 book by recalling the well-quoted ending of Darwin’s On the Origin of Species, reflecting on the “grandeur in this view of life.” He expanded upon this notion, describing how an enormous amount of evolutionary change has occurred and is still occurring at the molecular level, and how this change is inherently random in nature. He wrote that “although such random processes are slow and insignificant for our ephemeral existence, in the span of geological times, they become colossal. In this way, the footprints of time are evident in all the genomes on the earth. This adds still more to the grandeur of our view of biological evolution.” To further expand upon this grandeur, we would point out that these random processes are certainly not “insignificant for our ephemeral existence,”: They are the phenomenon behind the vast majority of the molecular variation that accumulates within our tissues and tumors during our lifetimes. This accumulation of predominantly neutral variation is a double-edged sword. With only very few of the substitutions within cancer genomes exerting an appreciable beneficial effect on the fitness of cancer lineages, and thus being causative of cancer, we can expect that molecular targets directly inhibiting the function of highly selected, oncogenic, gain-of-function substitutions should, in turn, markedly inhibit cancer growth and survival. Indeed, the molecular revolution has ushered in many very effective targeted therapies (Huang et al. 2014). The other edge of the sword is just as sharp; however, because most polymorphisms and substitutions are neutral with respect to selection, they can drift to high frequency or persist in tumors at low frequencies. While the neoantigens created in cells by erstwhile neutral nonsynonymous variants often become the target of our immune system’s surveillance against neoplasms as well as the reason for the remarkable success of immune checkpoint inhibitors (Diaz and Le 2015; Rizvi et al. 2015; Schumacher and Schreiber 2015; Van Allen et al. 2015; McGranahan et al. 2016), tumors can also randomly accumulate molecular variants that are the seeds for resistance to targeted therapies, the rapid evolution of which has hamstrung some of our most effective treatments (Marusyk et al. 2012; Misale et al. 2014; Wood 2015). Kimura refers to selectively neutral or nearly neutral alleles that have a latent potential for selection given a change in genetic background or environment as the “Dykhuizen–Hartl effect,” (Dykhuizen and Hartl 1980; Kimura 1983) and this effect appears to be the cause not only of the renewed immune response of tumors treated with immune checkpoint inhibitors (Diaz and Le 2015; Rizvi et al. 2015; Schumacher and Schreiber 2015; Van Allen et al. 2015; McGranahan et al. 2016), but also the cause of failures of targeted treatment (Sasaki et al. 2011; Sequist et al. 2011; Oberholzer et al. 2012; Su et al. 2012; Van Allen et al. 2014). Fortunately, the utility of the neutrality of polymorphism and divergence within Kimura’s neutral theory, both for its explanation of extant molecular polymorphism and divergence and for its coherent null hypothesis against which selection may be detected in organismal evolution (McDonald and Kreitman 1991; Sawyer and Hartl 1992; Zhao et al. 2017), can be brought to bear on the somatic evolution of cancer as well. Together with deep sequencing and estimation of the rates of mutation and selection intensities of variants, Kimura’s neutral theory provides a key framework for predictions regarding pathways to adaptations that confer resistance to targeted therapy (Cannataro et al. 2018), allowing clinicians to harness random and nonrandom evolution to fight cancer. Acknowledgments We thank Gilead Sciences Inc., the Notsew Orm Sands Foundation, and NIH R01 LM012487 for funding to support this research. 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Molecular Biology and Evolution – Oxford University Press
Published: Apr 19, 2018
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