Attribution of Cancer Origins to Endogenous, Exogenous, and Preventable Mutational Processes.

Mutational processes in tumors create distinctive patterns of mutations, composed of neutral "passenger" mutations and oncogenic drivers that have quantifiable effects on the proliferation and survival of cancer cell lineages. Increases in proliferation and survival are mediated by natural selection, which can be quantified by comparing the frequency at which we detect substitutions to the frequency at which we expect to detect substitutions assuming neutrality. Most of the variants detectable with whole-exome sequencing in tumors are neutral or nearly neutral in effect, and thus the processes generating the majority of mutations may not be the primary sources of the tumorigenic mutations. Across 24 cancer types, we identify the contributions of mutational processes to each oncogenic variant and quantify the degree to which each process contributes to tumorigenesis. We demonstrate that the origination of variants driving melanomas and lung cancers is predominantly attributable to the preventable, exogenous mutational processes associated with ultraviolet light and tobacco exposure, respectively, whereas the origination of selected variants in gliomas and prostate adenocarcinomas is largely attributable to endogenous processes associated with aging. Preventable mutations associated with pathogen exposure and apolipoprotein B mRNA-editing enzyme activity account for a large proportion of the cancer effect within head-and-neck, bladder, cervical, and breast cancers. These attributions complement epidemiological approaches-revealing the burden of cancer driven by single-nucleotide variants caused by either endogenous or exogenous, nonpreventable, or preventable processes, and crucially inform public health strategies.

Identifying modules of cooperating cancer drivers.

Identifying cooperating modules of driver alterations can provide insights into cancer etiology and advance the development of effective personalized treatments. We present Cancer Rule Set Optimization (CRSO) for inferring the combinations of alterations that cooperate to drive tumor formation in individual patients. Application to 19 TCGA cancer types revealed a mean of 11 core driver combinations per cancer, comprising 2-6 alterations per combination and accounting for a mean of 70% of samples per cancer type. CRSO is distinct from methods based on statistical co-occurrence, which we demonstrate is a suboptimal criterion for investigating driver cooperation. CRSO identified well-studied driver combinations that were not detected by other approaches and nominated novel combinations that correlate with clinical outcomes in multiple cancer types. Novel synergies were identified in NRAS-mutant melanomas that may be therapeutically relevant. Core driver combinations involving NFE2L2 mutations were identified in four cancer types, supporting the therapeutic potential of NRF2 pathway inhibition. CRSO is available at https://github.com/mikekleinsgit/CRSO/.

Molecular Biology and Evolution of Cancer: From Discovery to Action.

Cancer progression is an evolutionary process. During this process, evolving cancer cell populations encounter restrictive ecological niches within the body, such as the primary tumor, circulatory system, and diverse metastatic sites. Efforts to prevent or delay cancer evolution-and progression-require a deep understanding of the underlying molecular evolutionary processes. Herein we discuss a suite of concepts and tools from evolutionary and ecological theory that can inform cancer biology in new and meaningful ways. We also highlight current challenges to applying these concepts, and propose ways in which incorporating these concepts could identify new therapeutic modes and vulnerabilities in cancer.

Neutral Theory and the Somatic Evolution of Cancer.

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.

Vector-Borne Pathogen and Host Evolution in a Structured Immuno-Epidemiological System.

Vector-borne disease transmission is a common dissemination mode used by many pathogens to spread in a host population. Similar to directly transmitted diseases, the within-host interaction of a vector-borne pathogen and a host's immune system influences the pathogen's transmission potential between hosts via vectors. Yet there are few theoretical studies on virulence-transmission trade-offs and evolution in vector-borne pathogen-host systems. Here, we consider an immuno-epidemiological model that links the within-host dynamics to between-host circulation of a vector-borne disease. On the immunological scale, the model mimics antibody-pathogen dynamics for arbovirus diseases, such as Rift Valley fever and West Nile virus. The within-host dynamics govern transmission and host mortality and recovery in an age-since-infection structured host-vector-borne pathogen epidemic model. By considering multiple pathogen strains and multiple competing host populations differing in their within-host replication rate and immune response parameters, respectively, we derive evolutionary optimization principles for both pathogen and host. Invasion analysis shows that the [Formula: see text] maximization principle holds for the vector-borne pathogen. For the host, we prove that evolution favors minimizing case fatality ratio (CFR). These results are utilized to compute host and pathogen evolutionary trajectories and to determine how model parameters affect evolution outcomes. We find that increasing the vector inoculum size increases the pathogen [Formula: see text], but can either increase or decrease the pathogen virulence (the host CFR), suggesting that vector inoculum size can contribute to virulence of vector-borne diseases in distinct ways.

Structural and Practical Identifiability Issues of Immuno-Epidemiological Vector-Host Models with Application to Rift Valley Fever.

In this article, we discuss the structural and practical identifiability of a nested immuno-epidemiological model of arbovirus diseases, where host-vector transmission rate, host recovery, and disease-induced death rates are governed by the within-host immune system. We incorporate the newest ideas and the most up-to-date features of numerical methods to fit multi-scale models to multi-scale data. For an immunological model, we use Rift Valley Fever Virus (RVFV) time-series data obtained from livestock under laboratory experiments, and for an epidemiological model we incorporate a human compartment to the nested model and use the number of human RVFV cases reported by the CDC during the 2006-2007 Kenya outbreak. We show that the immunological model is not structurally identifiable for the measurements of time-series viremia concentrations in the host. Thus, we study the non-dimensionalized and scaled versions of the immunological model and prove that both are structurally globally identifiable. After fixing estimated parameter values for the immunological model derived from the scaled model, we develop a numerical method to fit observable RVFV epidemiological data to the nested model for the remaining parameter values of the multi-scale system. For the given (CDC) data set, Monte Carlo simulations indicate that only three parameters of the epidemiological model are practically identifiable when the immune model parameters are fixed. Alternatively, we fit the multi-scale data to the multi-scale model simultaneously. Monte Carlo simulations for the simultaneous fitting suggest that the parameters of the immunological model and the parameters of the immuno-epidemiological model are practically identifiable. We suggest that analytic approaches for studying the structural identifiability of nested models are a necessity, so that identifiable parameter combinations can be derived to reparameterize the nested model to obtain an identifiable one. This is a crucial step in developing multi-scale models which explain multi-scale data.

Optimal sampling strategies for detecting zoonotic disease epidemics.

The early detection of disease epidemics reduces the chance of successful introductions into new locales, minimizes the number of infections, and reduces the financial impact. We develop a framework to determine the optimal sampling strategy for disease detection in zoonotic host-vector epidemiological systems when a disease goes from below detectable levels to an epidemic. We find that if the time of disease introduction is known then the optimal sampling strategy can switch abruptly between sampling only from the vector population to sampling only from the host population. We also construct time-independent optimal sampling strategies when conducting periodic sampling that can involve sampling both the host and the vector populations simultaneously. Both time-dependent and -independent solutions can be useful for sampling design, depending on whether the time of introduction of the disease is known or not. We illustrate the approach with West Nile virus, a globally-spreading zoonotic arbovirus. Though our analytical results are based on a linearization of the dynamical systems, the sampling rules appear robust over a wide range of parameter space when compared to nonlinear simulation models. Our results suggest some simple rules that can be used by practitioners when developing surveillance programs. These rules require knowledge of transition rates between epidemiological compartments, which population was initially infected, and of the cost per sample for serological tests.

Mutations, substitutions, and selection: Linking mutagenic processes to cancer using evolutionary theory.

Cancers are the product of evolutionary events, where molecular variation occurs and accumulates in tissues and tumors. Sequencing of this molecular variation informs not only which variants are driving tumorigenesis, but also the mechanisms behind what is fueling mutagenesis. Both of these details are crucial for preventing premature deaths due to cancer, whether it is by targeting the variants driving the cancer phenotype or by measures to prevent exogenous mutations from contributing to somatic evolution. Here, we review tools to determine both molecular signatures and cancer drivers, and avenues by which these metrics may be linked.

Estimation of Neutral Mutation Rates and Quantification of Somatic Variant Selection Using cancereffectsizeR.

Somatic nucleotide mutations can contribute to cancer cell survival, proliferation, and pathogenesis. Although research has focused on identifying which mutations are "drivers" versus "passengers," quantifying the proliferative effects of specific variants within clinically relevant contexts could reveal novel aspects of cancer biology. To enable researchers to estimate these cancer effects, we developed cancereffectsizeR, an R package that organizes somatic variant data, facilitates mutational signature analysis, calculates site-specific mutation rates, and tests models of selection. Built-in models support effect estimation from single nucleotides to genes. Users can also estimate epistatic effects between paired sets of variants, or design and test custom models. The utility of cancer effect was validated by showing in a pan-cancer dataset that somatic variants classified as likely pathogenic or pathogenic in ClinVar exhibit substantially higher effects than most other variants. Indeed, cancer effect was a better predictor of pathogenic status than variant prevalence or functional impact scores. In addition, the application of this approach toward pairwise epistasis in lung adenocarcinoma showed that driver mutations in BRAF, EGFR, or KRAS typically reduce selection for alterations in the other two genes. Companion reference data packages support analyses using the hg19 or hg38 human genome builds, and a reference data builder enables use with any species or custom genome build with available genomic and transcriptomic data. A reference manual, tutorial, and public source code repository are available at https://townsend-lab-yale.github.io/cancereffectsizeR. Comprehensive estimation of cancer effects of somatic mutations can provide insights into oncogenic trajectories, with implications for cancer prognosis and treatment. SIGNIFICANCE: An R package provides streamlined, customizable estimation of underlying nucleotide mutation rates and of the oncogenic and epistatic effects of mutations in cancer cohorts.

Environmental and sex-specific molecular signatures of glioma causation.

BACKGROUND: The relative importance of genetic and environmental risk factors in gliomagenesis remains uncertain. METHODS: Using whole-exome sequencing data from 1105 adult gliomas, we evaluate the relative contribution to cancer cell lineage proliferation and survival of single-nucleotide mutations in tumors by IDH mutation subtype and sex. We also quantify the contributions of COSMIC cancer mutational signatures to these tumors, identifying possible risk exposures. RESULTS: IDH-mutant tumors exhibited few unique recurrent substitutions-all in coding regions, while IDH wild-type tumors exhibited many substitutions in non-coding regions. The importance of previously reported mutations in IDH1/2, TP53, EGFR, PTEN, PIK3CA, and PIK3R1 was confirmed; however, the largest cancer effect in IDH wild-type tumors was associated with mutations in the low-prevalence BRAF V600E. Males and females exhibited mutations in a similar set of significantly overburdened genes, with some differences in variant sites-notably in the phosphoinositide 3-kinase (PI3K) pathway. In IDH-mutant tumors, PIK3CA mutations were located in the helical domain for females and the kinase domain for males; variants of import also differed by sex for PIK3R1. Endogenous age-related mutagenesis was the primary molecular signature identified; a signature associated with exogenous exposure to haloalkanes was identified and noted more frequently in males. CONCLUSIONS: Cancer-causing mutations in glioma primarily originated as a consequence of endogenous rather than exogenous factors. Mutations in helical vs kinase domains of genes in the phosphoinositide 3-kinase (PI3K) pathway are differentially selected in males and females. Additionally, a rare environmental risk factor is suggested for some cases of glioma-particularly in males.

APOBEC mutagenesis and selection for NFE2L2 contribute to the origin of lung squamous-cell carcinoma.

Lung squamous-cell carcinoma originates as a consequence of oncogenic molecular variants arising from diverse mutagenic processes such as tobacco, defective homologous recombination, aging, and cytidine deamination by APOBEC proteins. Only some of the many variants generated by these processes actually contribute to tumorigenesis. Therefore, molecular investigation of mutagenic processes such as cytidine deamination by APOBEC should also determine whether the mutations produced by these processes contribute substantially to the growth and survival of cancer. Here, we determine the processes that gave rise to mutations of 681 lung squamous-cell carcinomas, and quantify the probability that each mutation was the product of each process. We then calculate the contribution of each mutation to increases in cellular proliferation and survival. We performed in vitro experiments to determine cytidine deamination activity of APOBEC3B against oligonucleotides corresponding with genomic sequences that give rise to variants of high cancer effect size. The largest APOBEC-related cancer effects are attributable to mutations in PIK3CA and NFE2L2. We demonstrate that APOBEC effectively deaminates NFE2L2 at the locations that confer high cancer effect. Overall, we demonstrate that APOBEC activity can lead to mutations in NFE2L2 that have large contributions to cancer cell growth and survival, and that NFE2L2 is an attractive potential target for therapeutic intervention.

Cancer Relevance of Human Genes.

BACKGROUND: We hypothesize that genes that directly or indirectly interact with core cancer genes (CCGs) in a comprehensive gene-gene interaction network may have functional importance in cancer. METHODS: We categorized 12 767 human genes into CCGs (n = 468), 1 (n = 5467), 2 (n = 5573), 3 (n = 915), and more than 3 steps (n = 416) removed from the nearest CCG in the Search Tool for the Retrieval of Interacting Genes/Proteins network. We estimated cancer-relevant functional importance in these neighborhood categories using 1) gene dependency score, which reflects the effect of a gene on cell viability after knockdown; 2) somatic mutation frequency in The Cancer Genome Atlas; 3) effect size that estimates to what extent a mutation in a gene enhances cell survival; and 4) negative selection pressure of germline protein-truncating variants in healthy populations. RESULTS: Cancer biology-related functional importance of genes decreases as their distance from the CCGs increases. Genes closer to cancer genes show greater connectedness in the network, have greater importance in maintaining cancer cell viability, are under greater negative germline selection pressure, and have higher somatic mutation frequency in cancer. Based on these 4 metrics, we provide cancer relevance annotation to known human genes. CONCLUSIONS: A large number of human genes are connected to CCGs and could influence cancer biology to various extent when dysregulated; any given mutation may be functionally important in one but not in another individual depending on genomic context.

Transfer RNA methyltransferase gene NSUN2 mRNA expression modifies the effect of T cell activation score on patient survival in head and neck squamous carcinoma.

OBJECTIVES: To investigate how T-cell activation interacts with NSUN2 to influence HNSCC patient survival. MATERIALS AND METHODS: The relationships between T-cell activation status (Activation, Intermediate, and Exhaustion), NSUN2 expression, and patient survival were evaluated using Kaplan-Meier survival curves and multivariate Cox regression models in a public dataset with 520 HNSCC patients. HPV status was determined based on a VirusScan analysis of RNA-seq data. RESULTS: Among the patients with high NSUN2 expression, the Activation group exhibited longer survival than the Exhaustion group (trend P = 0.056). Adjusted hazards ratios (HRs) were 0.77 (95% CI: 0.49-1.19) for the Intermediate vs Exhaustion, and 0.61 (0.36-1.03) for Activation vs. Exhaustion. In contrast, there is a positive association between T-cell activation score and mortality in the patients with low NSUN2 expression (trend P = 0.016). The adjusted HRs were 1.97 (1.12-3.47) for the Intermediate vs Exhaustion, and 2.06 (1.16-3.68) for the Activation vs Exhaustion. In multivariate cox models with or without HPV status, the interaction between T-cell activation status and NSUN2 expression was statistically significant (P = 0.004 for with HPV status, and P = 0.002 for without, respectively). When not controlling for NSUN2 expression, there was no significant association between T-cell activation score and patient mortality (P = 0.84). CONCLUSIONS: An interaction between NSUN2 expression and T-cell activation status affects patient survival in HNSCC regardless of HPV status, suggesting that NSUN2 is a potential precision marker for immune-checkpoint blockade, and a potential therapeutic target.

APOBEC-induced mutations and their cancer effect size in head and neck squamous cell carcinoma.

Recent studies have revealed the mutational signatures underlying the somatic evolution of cancer, and the prevalences of associated somatic genetic variants. Here we estimate the intensity of positive selection that drives mutations to high frequency in tumors, yielding higher prevalences than expected on the basis of mutation and neutral drift alone. We apply this approach to a sample of 525 head and neck squamous cell carcinoma exomes, producing a rank-ordered list of gene variants by selection intensity. Our results illustrate the complementarity of calculating the intensity of selection on mutations along with tallying the prevalence of individual substitutions in cancer: while many of the most prevalently-altered genes were heavily selected, their relative importance to the cancer phenotype differs from their prevalence and from their P value, with some infrequent variants exhibiting evidence of strong positive selection. Furthermore, we extend our analysis of effect size by quantifying the degree to which mutational processes (such as APOBEC mutagenesis) contributes mutations that are highly selected, driving head and neck squamous cell carcinoma. We calculate the substitutions caused by APOBEC mutagenesis that make the greatest contribution to cancer phenotype among patients. Lastly, we demonstrate via in vitro biochemical experiments that the APOBEC3B protein can deaminate the cytosine bases at two sites whose mutant states are subject to high net realized selection intensities-PIK3CA E545K and E542K. By quantifying the effects of mutations, we deepen the molecular understanding of carcinogenesis in head and neck squamous cell carcinoma.

Effect Sizes of Somatic Mutations in Cancer.

A major goal of cancer biology is determination of the relative importance of the genetic alterations that confer selective advantage to cancer cells. Tumor sequence surveys have frequently ranked the importance of substitutions to cancer growth by P value or a false-discovery conversion thereof. However, P values are thresholds for belief, not metrics of effect. Their frequent misuse as metrics of effect has often been vociferously decried, even in cases when the only attributable mistake was omission of effect sizes. Here, we propose an appropriate ranking-the cancer effect size, which is the selection intensity for somatic variants in cancer cell lineages. The selection intensity is a metric of the survival and reproductive advantage conferred by mutations in somatic tissue. Thus, they are of fundamental importance to oncology, and have immediate relevance to ongoing decision making in precision medicine tumor boards, to the selection and design of clinical trials, to the targeted development of pharmaceuticals, and to basic research prioritization. Within this commentary, we first discuss the scope of current methods that rank confidence in the overrepresentation of specific mutated genes in cancer genomes. Then we bring to bear recent advances that draw upon an understanding of the development of cancer as an evolutionary process to estimate the effect size of somatic variants leading to cancer. We demonstrate the estimation of the effect sizes of all recurrent single nucleotide variants in 22 cancer types, quantifying relative importance within and between driver genes.

Analysis of mutation, selection, and epistasis: an informed approach to cancer clinical trials.

Currently, drug development efforts and clinical trials to test them are often prioritized by targeting genes with high frequencies of somatic variants among tumors. However, differences in oncogenic mutation rate-not necessarily the effect the variant has on tumor growth-contribute enormously to somatic variant frequency. We argue that decoupling the contributions of mutation and cancer lineage selection to the frequency of somatic variants among tumors is critical to understanding-and predicting-the therapeutic potential of different interventions. To provide an indicator of that strength of selection and therapeutic potential, the frequency at which we observe a given variant across patients must be modulated by our expectation given the mutation rate and target size to provide an indicator of that strength of selection and therapeutic potential. Additionally, antagonistic and synergistic epistasis among mutations also impacts the potential therapeutic benefit of targeted drug development. Quantitative approaches should be fostered that use the known genetic architectures of cancer types, decouple mutation rate, and provide rigorous guidance regarding investment in targeted drug development. By integrating evolutionary principles and detailed mechanistic knowledge into those approaches, we can maximize our ability to identify those targeted therapies most likely to yield substantial clinical benefit.

Heterogeneity and mutation in KRAS and associated oncogenes: evaluating the potential for the evolution of resistance to targeting of KRAS G12C.

Activating mutations in RAS genes are associated with approximately 20% of all human cancers. New targeted therapies show preclinical promise in inhibiting the KRAS G12C variant. However, concerns exist regarding the effectiveness of such therapies in vivo given the possibilities of existing intratumor heterogeneity or de novo mutation leading to treatment resistance. We performed deep sequencing of 27 KRAS G12-positive lung tumors to determine the prevalence of other oncogenic mutations within KRAS or within commonly mutated downstream genes that could confer resistance at the time of treatment. We also passaged patient-derived xenografts to assess the potential for novel KRAS mutation to arise during subsequent tumor evolution. Furthermore, we estimate the de novo mutation rate in KRAS position 12 and in genes downstream of KRAS. Finally, we present an approach for estimation of the selection intensity for these point mutations that explains their high prevalence in tumors. We find no evidence of heterogeneity that may compromise KRAS G12C targeted therapy within sequenced lung tumors or passaged xenografts. We find that mutations that confer resistance are even less likely to occur downstream of KRAS than to occur within KRAS. Our approach predicts that BRAF V600E would provide the highest fitness advantage for de novo-resistant subclones. Overall, our findings suggest that resistance to targeted therapy of KRAS G12C-positive tumors is unlikely to be present at the time of treatment and, among the de novo mutations likely to confer resistance, mutations in BRAF, a gene with targeted inhibitors presently available, result in subclones with the highest fitness advantage.

The evolutionary trade-off between stem cell niche size, aging, and tumorigenesis.

Many epithelial tissues within multicellular organisms are continually replenished by small independent populations of stem cells largely responsible for maintaining tissue homeostasis. These continually dividing populations are subject to mutations that can lead to tumorigenesis but also contribute to aging. Mutations accumulate in stem cell niches and change the rate of cell division and differentiation; the pace of this process and the fate of specific mutations depend strongly on niche population size. Here, we create a mathematical model of the intestinal stem cell niche, crypt system, and epithelium. We calculate the expected effect of fixed mutations in stem cell niches and their effect on tissue homeostasis throughout the intestinal epithelium over organismal lifetime. We find that, due to the small population size of stem cell niches, mutations predominantly fix via genetic drift and decrease stem cell fitness, leading to niche and tissue attrition, and contributing to organismal aging. We also explore mutation accumulation at various stem cell niche sizes and demonstrate that an evolutionary trade-off exists between niche size, tissue aging, and the risk of tumorigenesis. Further, mouse and human niches exist at a size that minimizes the probability of tumorigenesis, at the expense of accumulating deleterious mutations due to genetic drift. Finally, we show that the trade-off between the probability of tumorigenesis and the extent of aging depends on whether or not mutational effects confer a selective advantage in the stem cell niche.