Effect Sizes of Somatic Mutations in Cancer

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.

Interpreting and integrating genomic tests results in clinical cancer care: Overview and practical guidance

The last decade has seen rapid progress in the use of genomic tests, including gene panels, whole-exome sequencing, and whole-genome sequencing, in research and clinical cancer care. These advances have created expansive opportunities to characterize the molecular attributes of cancer, revealing a subset of cancer-associated aberrations called driver mutations. The identification of these driver mutations can unearth vulnerabilities of cancer cells to targeted therapeutics, which has led to the development and approval of novel diagnostics and personalized interventions in various malignancies. The applications of this modern approach, often referred to as precision oncology or precision cancer medicine, are already becoming a staple in cancer care and will expand exponentially over the coming years. Although genomic tests can lead to better outcomes by informing cancer risk, prognosis, and therapeutic selection, they remain underutilized in routine cancer care. A contributing factor is a lack of understanding of their clinical utility and the difficulty of results interpretation by the broad oncology community. Practical guidelines on how to interpret and integrate genomic information in the clinical setting, addressed to clinicians without expertise in cancer genomics, are currently limited. Building upon the genomic foundations of cancer and the concept of precision oncology, the authors have developed practical guidance to aid the interpretation of genomic test results that help inform clinical decision making for patients with cancer. They also discuss the challenges that prevent the wider implementation of precision oncology.

Cancer Evolution: A Multifaceted Affair

Cancer cells adapt and survive through the acquisition and selection of molecular modifications. This process defines cancer evolution. Building on a theoretical framework based on heritable genetic changes has provided insights into the mechanisms supporting cancer evolution. However, cancer hallmarks also emerge via heritable nongenetic mechanisms, including epigenetic and chromatin topological changes, and interactions between tumor cells and the tumor microenvironment. Recent findings on tumor evolutionary mechanisms draw a multifaceted picture where heterogeneous forces interact and influence each other while shaping tumor progression. A comprehensive characterization of the cancer evolutionary toolkit is required to improve personalized medicine and biomarker discovery.

SIGNIFICANCE

Tumor evolution is fueled by multiple enabling mechanisms. Importantly, genetic instability, epigenetic reprogramming, and interactions with the tumor microenvironment are neither alternative nor independent evolutionary mechanisms. As demonstrated by findings highlighted in this perspective, experimental and theoretical approaches must account for multiple evolutionary mechanisms and their interactions to ultimately understand, predict, and steer tumor evolution.

Pairwise and higher-order epistatic effects among somatic cancer mutations across oncogenesis

Cancer occurs as a consequence of multiple somatic mutations that lead to uncontrolled cell growth. Mutual exclusivity and co-occurrence of mutations imply-but do not prove-that mutations exert synergistic or antagonistic epistatic effects on oncogenesis. Knowledge of these interactions, and the consequent trajectories of mutation and selection that lead to cancer has been a longstanding goal within the cancer research community. Recent research has revealed mutation rates and scaled selection coefficients for specific recurrent variants across many cancer types. However, there are no current methods to quantify the strength of selection incorporating pairwise and higher-order epistatic effects on selection within the trajectory of likely cancer genotoypes. Therefore, we have developed a continuous-time Markov chain model that enables the estimation of mutation origination and fixation (flux), dependent on somatic cancer genotype. Coupling this continuous-time Markov chain model with a deconvolution approach provides estimates of underlying mutation rates and selection across the trajectory of oncogenesis. We demonstrate computation of fluxes and selection coefficients in a somatic evolutionary model for the four most frequently variant driver genes (TP53, LRP1B, KRAS and STK11) from 565 cases of lung adenocarcinoma. Our analysis reveals multiple antagonistic epistatic effects that reduce the possible routes of oncogenesis, and inform cancer research regarding viable trajectories of somatic evolution whose progression could be forestalled by precision medicine. Synergistic epistatic effects are also identified, most notably in the somatic genotype TP53 LRP1B for mutations in the KRAS gene, and in somatic genotypes containing KRAS or TP53 mutations for mutations in the STK11 gene. Large positive fluxes of KRAS variants were driven by large selection coefficients, whereas the flux toward LRP1B mutations was substantially aided by a large mutation rate for this gene. The approach enables inference of the most likely routes of site-specific variant evolution and estimation of the strength of selection operating on each step along the route, a key component of what we need to know to develop and implement personalized cancer therapies.

Mutation and Selection Induce Correlations between Selection Coefficients and Mutation Rates

AbstractThe joint distribution of selection coefficients and mutation rates is a key determinant of the genetic architecture of molecular adaptation. Three different distributions are of immediate interest: (1) the "nominal" distribution of possible changes, prior to mutation or selection; (2) the "de novo" distribution of realized mutations; and (3) the "fixed" distribution of selectively established mutations. Here, we formally characterize the relationships between these joint distributions under the strong-selection/weak-mutation (SSWM) regime. The de novo distribution is enriched relative to the nominal distribution for the highest rate mutations, and the fixed distribution is further enriched for the most highly beneficial mutations. Whereas mutation rates and selection coefficients are often assumed to be uncorrelated, we show that even with no correlation in the nominal distribution, the resulting de novo and fixed distributions can have correlations with any combination of signs. Nonetheless, we suggest that natural systems with a finite number of beneficial mutations will frequently have the kind of nominal distribution that induces negative correlations in the fixed distribution. We apply our mathematical framework, along with population simulations, to explore joint distributions of selection coefficients and mutation rates from deep mutational scanning and cancer informatics. Finally, we consider the evolutionary implications of these joint distributions together with two additional joint distributions relevant to parallelism and the rate of adaptation.

Single-cell phylogenies reveal changes in the evolutionary rate within cancer and healthy tissues

Cell lineages accumulate somatic mutations during organismal development, potentially leading to pathological states. The rate of somatic evolution within a cell population can vary due to multiple factors, including selection, a change in the mutation rate, or differences in the microenvironment. Here, we developed a statistical test called the Poisson Tree (PT) test to detect varying evolutionary rates among cell lineages, leveraging the phylogenetic signal of single-cell DNA sequencing (scDNA-seq) data. We applied the PT test to 24 healthy and cancer samples, rejecting a constant evolutionary rate in 11 out of 15 cancer and five out of nine healthy scDNA-seq datasets. In six cancer datasets, we identified subclonal mutations in known driver genes that could explain the rate accelerations of particular cancer lineages. Our findings demonstrate the efficacy of scDNA-seq for studying somatic evolution and suggest that cell lineages often evolve at different rates within cancer and healthy tissues.

Unraveling the Drivers of Tumorigenesis in the Context of Evolution: Theoretical Models and Bioinformatics Tools

Cancer originates from somatic cells that have accumulated mutations. These mutations alter the phenotype of the cells, allowing them to escape homeostatic regulation that maintains normal cell numbers. The emergence of malignancies is an evolutionary process in which the random accumulation of somatic mutations and sequential selection of dominant clones cause cancer cells to proliferate. The development of technologies such as high-throughput sequencing has provided a powerful means to measure subclonal evolutionary dynamics across space and time. Here, we review the patterns that may be observed in cancer evolution and the methods available for quantifying the evolutionary dynamics of cancer. An improved understanding of the evolutionary trajectories of cancer will enable us to explore the molecular mechanism of tumorigenesis and to design tailored treatment strategies.

Mutation bias and the predictability of evolution

Predicting evolutionary outcomes is an important research goal in a diversity of contexts. The focus of evolutionary forecasting is usually on adaptive processes, and efforts to improve prediction typically focus on selection. However, adaptive processes often rely on new mutations, which can be strongly influenced by predictable biases in mutation. Here, we provide an overview of existing theory and evidence for such mutation-biased adaptation and consider the implications of these results for the problem of prediction, in regard to topics such as the evolution of infectious diseases, resistance to biochemical agents, as well as cancer and other kinds of somatic evolution. We argue that empirical knowledge of mutational biases is likely to improve in the near future, and that this knowledge is readily applicable to the challenges of short-term prediction. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.

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.

Shared Cancer Dataset Analysis Identifies and Predicts the Quantitative Effects of Pan-Cancer Somatic Driver Variants

Driver mutations endow tumors with selective advantages and produce an array of pathogenic effects. Determining the function of somatic variants is important for understanding cancer biology and identifying optimal therapies. Here, we compiled a shared dataset from several cancer genomic databases. Two measures were applied to 535 cancer genes based on observed and expected frequencies of driver variants as derived from cancer-specific rates of somatic mutagenesis. The first measure comprised a binary classifier based on a binomial test; the second was tumor variant amplitude (TVA), a continuous measure representing the selective advantage of individual variants. TVA outperformed all other computational tools in terms of its correlation with experimentally derived functional scores of cancer mutations. TVA also highly correlated with drug response, overall survival, and other clinical implications in relevant cancer genes. This study demonstrates how a selective advantage measure based on a large cancer dataset significantly impacts our understanding of the spectral effect of driver variants in cancer. The impact of this information will increase as cancer treatment becomes more precise and personalized to tumor-specific mutations.

SIGNIFICANCE

A new selective advantage estimation assists in oncogenic driver identification and relative effect measurements, enabling better prognostication, therapy selection, and prioritization.

Pan-cancer landscape of AID-related mutations, composite mutations, and their potential role in the ICI response

Activation-induced cytidine deaminase, AICDA or AID, is a driver of somatic hypermutation and class-switch recombination in immunoglobulins. In addition, this deaminase belonging to the APOBEC family may have off-target effects genome-wide, but its effects at pan-cancer level are not well elucidated. Here, we used different pan-cancer datasets, totaling more than 50,000 samples analyzed by whole-genome, whole-exome, or targeted sequencing. AID mutations are present at pan-cancer level with higher frequency in hematological cancers and higher presence at transcriptionally active TAD domains. AID synergizes initial hotspot mutations by a second composite mutation. AID mutational load was found to be independently associated with a favorable outcome in immune-checkpoint inhibitors (ICI) treated patients across cancers after analyzing 2000 samples. Finally, we found that AID-related neoepitopes, resulting from mutations at more frequent hotspots if compared to other mutational signatures, enhance CXCL13/CCR5 expression, immunogenicity, and T-cell exhaustion, which may increase ICI sensitivity.

Whole-exome Sequencing of Nigerian Prostate Tumors from the Prostate Cancer Transatlantic Consortium (CaPTC) Reveals DNA Repair Genes Associated with African Ancestry

In this study, we used whole-exome sequencing of a cohort of 45 advanced-stage, treatment-naïve Nigerian (NG) primary prostate cancer tumors and 11 unmatched nontumor tissues to compare genomic mutations with African American (AA) and European American (EA) The Cancer Genome Atlas (TCGA) prostate cancer. NG samples were collected from six sites in central and southwest Nigeria. After whole-exome sequencing, samples were processed using GATK best practices. BRCA1 (100%), BARD1 (45%), BRCA2 (27%), and PMS2(18%) had germline alterations in at least two NG nontumor samples. Across 111 germline variants, the AA cohort reflected a pattern [BRCA1 (68%), BARD1 (34%), BRCA2 (28%), and PMS2 (16%)] similar to NG samples. Of the most frequently mutated genes, BRCA1 showed a statistically (P ≤ 0.05) higher germline mutation frequency in men of African ancestry (MAA) and increasing variant frequency with increased African ancestry. Disaggregating gene-level mutation frequencies by variants revealed both ancestry-linked and NG-specific germline variant patterns. Driven by rs799917 (T>C), BRCA1 showed an increasing mutation frequency as African ancestry increased. BRCA2_rs11571831 was present only in MAA, and BRCA2_rs766173 was elevated in NG men. A total of 133 somatic variants were present in 26 prostate cancer-associated genes within the NG tumor cohort. BRCA2 (27%), APC (20%), ATM (20%), BRCA1 (13%), DNAJC6 (13%), EGFR (13%), MAD1L1 (13%), MLH1 (11%), and PMS2 (11%) showed mutation frequencies >10%. Compared with TCGA cohorts, NG tumors showed statistically significant elevated frequencies of BRCA2, APC, and BRCA1. The NG cohort variant pattern shared similarities (cosign similarities ≥0.734) with Catalogue of Somatic Mutations in Cancer signatures 5 and 6, and mutated genes showed significant (q < 0.001) gene ontology (GO) and functional enrichment in mismatch repair and non-homologous repair deficiency pathways. Here, we showed that mutations in DNA damage response genes were higher in NG prostate cancer samples and that a portion of those mutations correlate with African ancestry. Moreover, we identified variants of unknown significance that may contribute to population-specific routes of tumorigenesis and treatment. These results present the most comprehensive characterization of the NG prostate cancer exome to date and highlight the need to increase diversity of study populations.

Significance

MAA have higher rates of prostate cancer incidence and mortality, however, are severely underrepresented in genomic studies. This is the first study utilizing whole-exome sequencing in NG men to identify West African ancestry-linked variant patterns that impact DNA damage repair pathways.

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.

Pervasive conditional selection of driver mutations and modular epistasis networks in cancer

Cancer driver mutations often display mutual exclusion or co-occurrence, underscoring the key role of epistasis in carcinogenesis. However, estimating the magnitude of epistasis and quantifying its effect on tumor evolution remains a challenge. We develop a method (Coselens) to quantify conditional selection on the excess of nonsynonymous substitutions in cancer genes. Coselens infers the number of drivers per gene in different partitions of a cancer genomics dataset using covariance-based mutation models and determines whether coding mutations in a gene affect selection for drivers in any other gene. Using Coselens, we identify 296 conditionally selected gene pairs across 16 cancer types in the TCGA dataset. Conditional selection affects 25%-50% of driver substitutions in tumors with >2 drivers. Conditionally co-selected genes form modular networks, whose structures challenge the traditional interpretation of within-pathway mutual exclusivity and across-pathway synergy, suggesting a more complex scenario where gene-specific across-pathway epistasis shapes differentiated cancer subtypes.

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.

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.

Cross talk between RNA modification writers and tumor development as a basis for guiding personalized therapy of gastric cancer

Background

Gastric cancer (GC) shows high metastasis and low survival. RNA modification writers play critical roles in tumor development. This study examined the clinical significance of RNA modification writers in GC prognosis based on four types of adenosine modifications (m¹A, m⁶A, APA and A-to-I).

Results

Writers demonstrated high mutation and expression in GC patients. Different expressions of 26 RNA modification writers were differentially associated with GC prognosis. High-WM score group appeared worse overall survival, higher immune infiltration and activation of EMT pathways than low-WM score group. WM score was correlated with both miRNAs-targeted signaling pathways and patients’ sensitivity to chemotherapeutic drugs and efficacy of immunotherapy.

Conclusions

This study further revealed the close association between adenosine-related RNA modifications and progression of GC. A cross talk between EMT and RNA modification was identified to be one of the mechanisms underlying GC development. Our WM scoring system could serve as a clinical indicator for predicting GC prognosis. Importantly, the WM score could guide personalized treatments such as chemotherapy and immunotherapy for GC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40246-022-00386-z.

Premetastatic shifts of endogenous and exogenous mutational processes support consolidative therapy in EGFR-driven lung adenocarcinoma

The progression of cancer is an evolutionary process that is challenging to assess between sampling timepoints. However, investigation of cancer evolution over specific time periods is crucial to the elucidation of key events such as the acquisition of therapeutic resistance and subsequent fatal metastatic spread of therapy-resistant cell populations. Here we apply mutational signature analyses within clinically annotated cancer chronograms to detect and describe the shifting mutational processes caused by both endogenous (e.g. mutator gene mutation) and exogenous (e.g. mutagenic therapeutics) factors between tumor sampling timepoints. In one patient, we find that cisplatin therapy can introduce mutations that confer genetic resistance to subsequent targeted therapy with Erlotinib. In another patient, we trace detection of defective mismatch-repair associated mutational signature SBS3 to the emergence of known driver mutation CTNNB1 S37C. In both of these patients, metastatic lineages emerged from a single ancestral lineage that arose during therapy-a finding that argues for the consideration of local consolidative therapy over other therapeutic approaches in EGFR-positive non-small cell lung cancer. Broadly, these results demonstrate the utility of phylogenetic analysis that incorporates clinical time course and mutational signature deconvolution to inform therapeutic decision making and retrospective assessment of disease etiology.

Comparison of somatic mutation landscapes in Chinese versus European breast cancer patients

Recent genomic studies suggest that Asian breast cancer (BC) may have distinct somatic features; however, most comparisons of BC genomic features across populations did not account for differences in age, subtype, and sequencing methods. In this study, we analyzed whole-exome sequencing (WES) data to characterize somatic copy number alterations (SCNAs) and mutation profiles in 98 Hong Kong BC (HKBC) patients and compared with those from The Cancer Genome Atlas of European ancestry (TCGA-EA, N = 686), which had similar distributions of age at diagnosis and PAM50 subtypes as in HKBC. We developed a two-sample Poisson model to compare driver gene selection pressure, which reflects the effect sizes of cancer driver genes, while accounting for differences in sample size, sequencing platforms, depths, and mutation calling methods. We found that somatic mutation and SCNA profiles were overall very similar between HKBC and TCGA-EA. The selection pressure for small insertions and deletions (indels) in GATA3 (false discovery rate (FDR) corrected p < 0.01) and single-nucleotide variants (SNVs) in TP53 (nominal p = 0.02, FDR corrected p = 0.28) was lower in HKBC than in TCGA-EA. Among the 13 signatures of single-base substitutions (SBS) that are common in BC, we found a suggestively higher contribution of SBS18 and a lower contribution of SBS1 in HKBC than in TCGA-EA, while the two APOBEC-induced signatures showed similar prevalence. Our results suggest that the genomic landscape of BC was largely very similar between HKBC and TCGA-EA, despite suggestive differences in some driver genes and mutational signatures that warrant future investigations in large and diverse Asian populations.

The somatic molecular evolution of cancer: Mutation, selection, and epistasis

Cancer progression has been attributed to somatic changes in single-nucleotide variants, copy-number aberrations, loss of heterozygosity, chromosomal instability, epistatic interactions, and the tumor microenvironment. It is not entirely clear which of these changes are essential and which are ancillary to cancer. The dynamic nature of cancer evolution in a patient can be illuminated using several concepts and tools from classical evolutionary biology. Neutral mutation rates in cancer cells are calculable from genomic data such as synonymous mutations, and selective pressures are calculable from rates of fixation occurring beyond the expectation by neutral mutation and drift. However, these cancer effect sizes of mutations are complicated by epistatic interactions that can determine the likely sequence of gene mutations. In turn, longitudinal phylogenetic analyses of somatic cancer progression offer an opportunity to identify key moments in cancer evolution, relating the timing of driver mutations to corresponding landmarks in the clinical timeline. These analyses reveal temporal aspects of genetic and phenotypic change during tumorigenesis and across clinical timescales. Using a related framework, clonal deconvolution, physical locations of clones, and their phylogenetic relations can be used to infer tumor migration histories. Additionally, genetic interactions with the tumor microenvironment can be analyzed with longstanding approaches applied to organismal genotype-by-environment interactions. Fitness landscapes for cancer evolution relating to genotype, phenotype, and environment could enable more accurate, personalized therapeutic strategies. An understanding of the trajectories underlying the evolution of neoplasms, primary, and metastatic tumors promises fundamental advances toward accurate and personalized predictions of therapeutic response.

Mutational signatures impact the evolution of anti-EGFR antibody resistance in colorectal cancer

Anti-EGFR antibodies such as cetuximab are active against KRAS/NRAS wild-type colorectal cancers (CRC) but acquired resistance invariably evolves. Which mutational mechanisms enable resistance evolution and whether adaptive mutagenesis, a transient cetuximab-induced increase in mutation generation, contributes in patients is unknown. Here, we investigate this in exome sequencing data of 42 baseline and progression biopsies from cetuximab treated CRCs. Mutation loads did not increase from baseline to progression and evidence for a contribution of adaptive mutagenesis was limited. However, the chemotherapy-induced mutational signature SBS17b was the main contributor of specific KRAS/NRAS and EGFR driver mutations that are enriched at acquired resistance. Detectable SBS17b activity before treatment predicted for shorter progression free survival and for the evolution of these specific mutations during subsequent cetuximab treatment. This suggests that chemotherapy mutagenesis can accelerate resistance evolution. Mutational signatures may be a new class of cancer evolution predictor.

Cancer as a form of life: Musings of the cancer and evolution symposium

Advanced cancer is one of the major problems in oncology as currently, despite the recent technological and scientific advancements, the mortality of metastatic disease remains very high at 70-90%. The field of oncology is in urgent need of novel ideas in order to improve quality of life and prognostic of cancer patients. The Cancer and Evolution Symposium organized online October 14-16, 2020 brought together a group of specialists from different fields that presented innovative strategies for better understanding, preventing, diagnosing, and treating cancer. Today still, the main reasons behind the high incidence and mortality of advanced cancer are, on one hand, the paucity of funding and effort directed to cancer prevention and early detection, and, on the other hand, the lack of understanding of the cancer process itself. I argue that besides being a disease, cancer is also a form of life, and, this frame of reference may provide a fresh look on this complex process. Here, I provide a different angle to several contemporary cancer theories discussing them from the perspective of "cancer-forms of life" (i.e. bionts) point of view. The perspectives and the several "bionts" introduced here, by no means exclusive or comprehensive, are just a shorthand that will hopefully encourage the readers, to further explore the contemporary oncology theoretical landscape.

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.

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/.

Teneurins: Role in Cancer and Potential Role as Diagnostic Biomarkers and Targets for Therapy

Teneurins have been identified in vertebrates as four different genes (TENM1-4), coding for membrane proteins that are mainly involved in embryonic and neuronal development. Genetic studies have correlated them with various diseases, including developmental problems, neurological disorders and congenital general anosmia. There is some evidence to suggest their possible involvement in cancer initiation and progression, and drug resistance. Indeed, mutations, chromosomal alterations and the deregulation of teneurins expression have been associated with several tumor types and patient survival. However, the role of teneurins in cancer-related regulatory networks is not fully understood, as both a tumor-suppressor role and pro-tumoral functions have been proposed, depending on tumor histotype. Here, we summarize and discuss the literature data on teneurins expression and their potential role in different tumor types, while highlighting the possibility of using teneurins as novel molecular diagnostic and prognostic biomarkers and as targets for cancer treatments, such as immunotherapy, in some tumors.

Feature selection algorithm based on dual correlation filters for cancer-associated somatic variants

Background

Since the development of sequencing technology, an enormous amount of genetic information has been generated, and human cancer analysis using this information is drawing attention. As the effects of variants on human cancer become known, it is important to find cancer-associated variants among countless variants.

Results

We propose a new filter-based feature selection method applicable for extracting cancer-associated somatic variants considering correlations of data. Both variants associated with the activation and deactivation of cancer’s characteristics are analyzed using dual correlation filters. The multiobjective optimization is utilized to consider two types of variants simultaneously without redundancy. To overcome high computational complexity problem, we calculate the correlation-based weight to select significant variants instead of directly searching for the optimal subset of variants. The proposed algorithm is applied to the identification of melanoma metastasis or breast cancer stage, and the classification results of the proposed method are compared with those of conventional single correlation filter-based method.

Conclusions

We verified that the proposed dual correlation filter-based method can extract cancer-associated variants related to the characteristics of human cancer.

Evolutionary dynamics of neoantigens in growing tumors

Cancers accumulate mutations that lead to neoantigens, novel peptides that elicit an immune response, and consequently undergo evolutionary selection. Here we establish how negative selection shapes the clonality of neoantigens in a growing cancer by constructing a mathematical model of neoantigen evolution. The model predicts that, without immune escape, tumor neoantigens are either clonal or at low frequency; hypermutated tumors can only establish after the evolution of immune escape. Moreover, the site frequency spectrum of somatic variants under negative selection appears more neutral as the strength of negative selection increases, which is consistent with classical neutral theory. These predictions are corroborated by the analysis of neoantigen frequencies and immune escape in exome and RNA sequencing data from 879 colon, stomach and endometrial cancers.

Identification of new driver and passenger mutations within APOBEC-induced hotspot mutations in bladder cancer

BACKGROUND

APOBEC-driven mutagenesis and functional positive selection of mutated genes may synergistically drive the higher frequency of some hotspot driver mutations compared to other mutations within the same gene, as we reported for FGFR3 S249C. Only a few APOBEC-associated driver hotspot mutations have been identified in bladder cancer (BCa). Here, we systematically looked for and characterised APOBEC-associated hotspots in BCa.

METHODS

We analysed 602 published exome-sequenced BCas, for part of which gene expression data were also available. APOBEC-associated hotspots were identified by motif-mapping, mutation signature fitting and APOBEC-mediated mutagenesis comparison. Joint analysis of DNA hairpin stability and gene expression was performed to predict driver or passenger hotspots. Aryl hydrocarbon receptor (AhR) activity was calculated based on its target genes expression. Effects of AhR knockout/inhibition on BCa cell viability were analysed.

RESULTS

We established a panel of 44 APOBEC-associated hotspot mutations in BCa, which accounted for about half of the hotspot mutations. Fourteen of them overlapped with the hotspots found in other cancer types with high APOBEC activity. They mostly occurred in the DNA lagging-strand templates and the loop of DNA hairpins. APOBEC-associated hotspots presented systematically a higher prevalence than the other mutations within each APOBEC-target gene, independently of their functional impact. A combined analysis of DNA loop stability and gene expression allowed to distinguish known passenger from known driver hotspot mutations in BCa, including loss-of-function mutations affecting tumour suppressor genes, and to predict new candidate drivers, such as AHR Q383H. We further characterised AHR Q383H as an activating driver mutation associated with high AhR activity in luminal tumours. High AhR activity was also found in tumours presenting amplifications of AHR and its co-receptor ARNT. We finally showed that BCa cells presenting those different genetic alterations were sensitive to AhR inhibition.

CONCLUSIONS

Our study identified novel potential drivers within APOBEC-associated hotspot mutations in BCa reinforcing the importance of APOBEC mutagenesis in BCa. It could allow a better understanding of BCa biology and aetiology and have clinical implications such as AhR as a potential therapeutic target. Our results also challenge the dogma that all hotspot mutations are drivers and mostly gain-of-function mutations affecting oncogenes.

The evolution of metapopulation dynamics and the number of stem cells in intestinal crypts and other tissue structures in multicellular bodies

Carcinogenesis is a process of somatic evolution. Previous models of stem and transient amplifying cells in epithelial proliferating units like colonic crypts showed that intermediate numbers of stem cells in a crypt should optimally prevent progression to cancer. If a stem cell population is too small, it is easy for a mutator mutation to drift to fixation. If it is too large, it is easy for selection to drive cell fitness enhancing carcinogenic mutations to fixation. Here, we show that a multiscale microsimulation, that captures both within-crypt and between-crypt evolutionary dynamics, leads to a different conclusion. Epithelial tissues are metapopulations of crypts. We measured time to initiation of a neoplasm, implemented as inactivation of both alleles of a tumor suppressor gene. In our model, time to initiation is dependent on the spread of mutator clones in the crypts. The proportion of selectively beneficial and deleterious mutations in somatic cells is unknown and so was explored with a parameter. When the majority of non-neutral mutations are deleterious, the fitness of mutator clones tends to decline. When crypts are maintained by few stem cells, intercrypt competition tends to remove crypts with fixed mutators. When there are many stem cells within a crypt, there is virtually no crypt turnover, but mutator clones are suppressed by within-crypt competition. If the majority of non-neutral mutations are beneficial to the clone, then these results are reversed and intermediate-sized crypts provide the most protection against initiation. These results highlight the need to understand the dynamics of turnover and the mechanisms that control homeostasis, both at the level of stem cells within proliferative units and at the tissue level of competing proliferative units. Determining the distribution of fitness effects of somatic mutations will also be crucial to understanding the dynamics of tumor initiation and progression.

Defining relative mutational difficulty to understand cancer formation

Most mutations in human cancer are low-frequency missense mutations, whose functional status remains hard to predict. Here, we show that depending on the type of nucleotide change and the surrounding sequences, the tendency to generate each type of nucleotide mutations varies greatly, even by several hundred folds. Therefore, a cancer-promoting mutation may appear only in a small number of cancer cases, if the underlying nucleotide change is too difficult to generate. We propose a method that integrates both the original mutation counts and their relative mutational difficulty. Using this method, we can accurately predict the functionality of hundreds of low-frequency missense mutations in p53, PTEN, and INK4A. Many loss-of-function p53 mutations with dominant negative effects were identified, and the functional importance of several regions in p53 structure were highlighted by this analysis. Our study not only established relative mutational difficulties for different types of mutations in human cancer, but also showed that by incorporating such a parameter, we can bring new angles to understanding cancer formation.

Clonal evolution driven by superdriver mutations

Background

Tumors are widely recognized to progress through clonal evolution by sequentially acquiring selectively advantageous genetic alterations that significantly contribute to tumorigenesis and thus are termned drivers. Some cancer drivers, such as TP53 point mutation or EGFR copy number gain, provide exceptional fitness gains, which, in time, can be sufficient to trigger the onset of cancer with little or no contribution from additional genetic alterations. These key alterations are called superdrivers.

Results

In this study, we employ a Wright-Fisher model to study the interplay between drivers and superdrivers in tumor progression. We demonstrate that the resulting evolutionary dynamics follow global clonal expansions of superdrivers with periodic clonal expansions of drivers. We find that the waiting time to the accumulation of a set of superdrivers and drivers in the tumor cell population can be approximated by the sum of the individual waiting times.

Conclusions

Our results suggest that superdriver dynamics dominate over driver dynamics in tumorigenesis. Furthermore, our model allows studying the interplay between superdriver and driver mutations both empirically and theoretically.

Clinical and genomic characterization of neutral tumor evolution in Head and Neck Squamous Cell Carcinoma

Recent studies suggest that a significant proportion of cancers undergo neutral tumor evolution. We applied neutral evolution model in HNSCC patients from The Cancer Genome Atlas (TCGA). To ensure the accuracy of classification results, a sample with the purity of tumor <0.7 was excluded. A tumor sample was considered to evolve neutrally if R² ≥ 0.98. We found that about 16% of HNSCC patients undergo neutral tumor evolution. We showed that neutral evolution HNSCC patients have better prognosis and higher activities of immune response pathways, and the numbers of co-occurring mutation events and significantly positive selection mutations are significantly less than non-neutral evolution HNSCC patients. In conclusion, we described a comprehensive clinical and genomic characteristics of neutral tumor evolution in Head and Neck Squamous Cell Carcinoma (HNSCC), and provided evidence that the evolution history of HNSCC has both clinical and biological implications.

Nonmalignant Formalin-Fixed Paraffin-Embedded Tissues as a Source to Study Germline Variants and Cancer Predisposition: A Systematic Review

Background: Archived formalin-fixed paraffin-embedded (FFPE) specimens from nonmalignant tissues derived from cancer patients are a vast and potentially valuable resource for high-quality genotyping analyses and could have a role in establishing inherited cancer risk. Methods: We systematically searched PubMed, Ovid MEDLINE, and Scopus databases for all articles that compared genotyping performance of DNA from nonmalignant FFPE tissue with blood DNA derived from cancer patients irrespective of tumor type. Two independent researchers screened the retrieved studies, removed duplicates, excluded irrelevant studies, and extracted genotyping data from the eligible studies. These studies included, but were not limited to, genotyping technique, reported call rate, and concordance. Results: Thirteen studies were reviewed, in which DNA from nonmalignant FFPE tissues derived from cancer patients was successfully purified and genotyped. All these studies used different approaches for genotyping of DNA from nonmalignant FFPE tissues to amplify single nucleotide polymorphisms (SNPs) and to estimate of loss of heterozygosity. The concordance between genotypes from nonmalignant FFPE tissues and blood derived from cancer patients was observed to be high, whereas the call rate of the tested SNPs was not reported in all included studies. Conclusion: This review illustrates that DNA from nonmalignant FFPE tissues derived from cancer patients can serve as an alternative and reliable source for assessment of germline DNA for various purposes, including assessment of cancer predisposition.

IDH1 and IDH2 mutations in lung adenocarcinomas: Evidences of subclonal evolution

Background

Selective IDH1 and IDH2 inhibitors have been approved for targeted therapy of acute myeloid leukemia. Clinical trials for solid tumors with IDH1 and IDH2 (IDH1/2) mutations are ongoing. Reports of IDH1/2‐mutated non–small cell lung cancers (NSCLCs), however, are limited.

Methods

We evaluated IDH1/2 mutations in 1,924 NSCLC specimens (92% adenocarcinoma) using a next‐generation sequencing assay.

Results

Retrospective quality assessments identified false detection of IDH1 c.395G>A (p.R132H) resulting from cytosine deamination (C:G→T:A) artifact in one specimen. IDH1/2 mutations were detected in 9 (0.5%) adenocarcinomas taken by fine‐needle aspiration (n = 3), thoracentesis (n = 2) or core biopsy (n = 4). All nine adenocarcinomas showed high‐grade features. Extensive clear cell change, however, was not observed. High expression (50% or greater) of PD‐L1 was observed in two of five specimens examined. IDH1/2 mutations were associated with old age, smoking history, and coexisting KRAS mutation. Lower than expected variant allele frequency of IDH1/2 mutants and coexistence of IDH1/2 mutations with known trunk drivers in the BRAF, EGFR, and KRAS genes suggest they could be branching drivers leading to subclonal evolution in lung adenocarcinomas. Multiregional analysis of an adenocarcinoma harboring two IDH2 mutations revealed parallel evolution originating from a KRAS‐mutated lineage, further supporting subclonal evolution promoted by IDH1/2 mutations.

Conclusions

IDH1/2 mutations in NSCLCs are uncommon. They occur in adenocarcinomas with high‐grade features and may be branching drivers leading to subclonal evolution. Accumulation of more IDH1/2‐mutated NSCLCs is needed to clarify their clinicopathological characteristics and implications for targeted therapy.

Measuring the distribution of fitness effects in somatic evolution by combining clonal dynamics with dN/dS ratios

The distribution of fitness effects (DFE) defines how new mutations spread through an evolving population. The ratio of non-synonymous to synonymous mutations (dN/dS) has become a popular method to detect selection in somatic cells. However the link, in somatic evolution, between dN/dS values and fitness coefficients is missing. Here we present a quantitative model of somatic evolutionary dynamics that determines the selective coefficients of individual driver mutations from dN/dS estimates. We then measure the DFE for somatic mutant clones in ostensibly normal oesophagus and skin. We reveal a broad distribution of fitness effects, with the largest fitness increases found for TP53 and NOTCH1 mutants (proliferative bias 1-5%). This study provides the theoretical link between dN/dS values and selective coefficients in somatic evolution, and measures the DFE of mutations in human tissues.

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.

Tumorigenesis as the Paradigm of Quasi-neutral Molecular Evolution

In the absence of both positive and negative selections, coding sequences evolve at a neutral rate (R = 1). Such a high genomic rate is generally not achievable due to the prevalence of negative selection against codon substitutions. Remarkably, somatic evolution exhibits the seemingly neutral rate R ∼ 1 across normal and cancerous tissues. Nevertheless, R ∼ 1 may also mean that positive and negative selections are both strong, but equal in intensity. We refer to this regime as quasi-neutral. Indeed, individual genes in cancer cells often evolve at a much higher, or lower, rate than R ∼ 1. Here, we show that 1) quasi-neutrality is much more likely when populations are small (N < 50); 2) stem-cell populations in single normal tissue niches, from which tumors likely emerge, have a small N (usually <50) but selection at this stage is measurable and strong; 3) when N dips below 50, selection efficacy decreases precipitously; and 4) notably, N is smaller in the stem-cell niche of the small intestine than in the colon. Hence, the ∼70-fold higher rate of phenotypic evolution (observed as cancer risk) in the latter can be explained by the greater efficacy of selection, which then leads to the fixation of more advantageous and fewer deleterious mutations in colon cancers. In conclusion, quasi-neutral evolution sheds a new light on a general evolutionary principle that helps to explain aspects of cancer evolution.

Clonal selection confers distinct evolutionary trajectories in BRAF-driven cancers

Molecular determinants governing the evolution of tumor subclones toward phylogenetic branches or fixation remain unknown. Using sequencing data, we model the propagation and selection of clones expressing distinct categories of BRAF mutations to estimate their evolutionary trajectories. We show that strongly activating BRAF mutations demonstrate hard sweep dynamics, whereas mutations with less pronounced activation of the BRAF signaling pathway confer soft sweeps or are subclonal. We use clonal reconstructions to estimate the strength of "driver" selection in individual tumors. Using tumors cells and human-derived murine xenografts, we show that tumor sweep dynamics can significantly affect responses to targeted inhibitors of BRAF/MEK or DNA damaging agents. Our study uncovers patterns of distinct BRAF clonal evolutionary dynamics and nominates therapeutic strategies based on the identity of the BRAF mutation and its clonal composition.

An analysis of genetic heterogeneity in untreated cancers

Genetic intratumoural heterogeneity is a natural consequence of imperfect DNA replication. Any two randomly selected cells, whether normal or cancerous, are therefore genetically different. Here, we review the different forms of genetic heterogeneity in cancer and re-analyse the extent of genetic heterogeneity within seven types of untreated epithelial cancers, with particular regard to its clinical relevance. We find that the homogeneity of predicted functional mutations in driver genes is the rule rather than the exception. In primary tumours with multiple samples, 97% of driver-gene mutations in 38 patients were homogeneous. Moreover, among metastases from the same primary tumour, 100% of the driver mutations in 17 patients were homogeneous. With a single biopsy of a primary tumour in 14 patients, the likelihood of missing a functional driver-gene mutation that was present in all metastases was 2.6%. Furthermore, all functional driver-gene mutations detected in these 14 primary tumours were present among all their metastases. Finally, we found that individual metastatic lesions responded concordantly to targeted therapies in 91% of 44 patients. These analyses indicate that the cells within the primary tumours that gave rise to metastases are genetically homogeneous with respect to functional driver-gene mutations, and we suggest that future efforts to develop combination therapies have the potential to be curative.

Estimating the Frequency of Single Point Driver Mutations across Common Solid Tumours

For cancers, such as common solid tumours, variants in the genome give a selective growth advantage to certain cells. It has recently been argued that the mean count of coding single nucleotide variants acting as disease-drivers in common solid tumours is frequently small in size, but significantly variable by cancer type (hypermutation is excluded from this study). In this paper we investigate this proposal through the use of integrative machine-learning-based classifiers we have proposed recently for predicting the disease-driver status of single nucleotide variants (SNVs) in the human cancer genome. We find that predicted driver counts are compatible with this proposal, have similar variabilities by cancer type and, to a certain extent, the drivers are identifiable by these machine learning methods. We further discuss predicted driver counts stratified by stage of disease and driver counts in non-coding regions of the cancer genome, in addition to driver-genes.