Impact of deleterious passenger mutations on cancer progression

Comprehensive Transcriptomic Analysis in Wild-type and ATM Knockout Lung Cancer Cells: Influence of Cisplatin on Oxidative Stress-Induced Senescence

Genetic mutations and impaired DNA repair mechanisms in cancer not only facilitate tumor progression but also reduce the effectiveness of chemotherapeutic agents, particularly cisplatin. Combination therapy has emerged as a promising strategy to overcome resistance. Comprehensive transcriptomic analyses, supported by integrated comparative bioinformatics and experimental approaches, are essential for identifying biomarkers and novel therapeutic targets underlying drug resistance. In this study, we performed overall survival and mutation analyses, examining 23 double-strand break repair proteins across more than 7,500 tumors spanning 23 distinct cancer types. Our findings identify ATM (ataxia-telangiectasia mutated) as a key protein with the highest mutation frequency. Using CRISPR/Cas9, we investigated the effects of ATM mutations on signalling pathways that influence the cellular response to cisplatin. ATM knockout enhanced cisplatin cytotoxicity by activating alternative cell death pathways, including oxidative stress-induced senescence and necroptosis. Microarray analysis revealed a regulatory interplay between ATM and NRF2 in the activation of oxidative stress-induced senescence. Specifically, ATM knockoutpromoted senescence by increasing reactive oxygen species (ROS) accumulation and downregulating NRF2 expression. To enhance combination therapy, integrating genetic profiling with advanced tools such as CRISPR/Cas9 to target oxidative stress-induced senescence may provide innovative strategies to overcome drug resistance, thereby advancing personalized cancer treatment. These approaches lay the foundation for the development of personalized cancer therapies tailored to the unique mutational landscape of individual patients, offering promising prospects for improving treatment outcomes.

Biophysical Properties of Somatic Cancer Mutations in the S4 Transmembrane Segment of the Human Voltage-Gated Proton Channel hHV1

Somatic mutations are common in cancer, with only a few driving the progression of the disease, while most are silent passengers. Some mutations may hinder or even reverse cancer progression. The voltage-gated proton channel (HV1) plays a key role in cellular pH homeostasis and shows increased expression in several malignancies. Inhibiting HV1 in cancer cells reduces invasion, migration, proton extrusion, and pH recovery, impacting tumor progression. Focusing on HVCN1, the gene coding for the human voltage-gated proton channel (hHV1), 197 mutations were identified from three databases: 134 missense mutations, 51 sense mutations, and 12 introducing stop codons. These mutations cluster in two hotspots: the central region of the N-terminus and the region coding for the S4 transmembrane domain, which contains the channel's voltage sensor. Five somatic mutations within the S4 segment (R205W, R208W, R208Q, G215E, and G215R) were selected for electrophysiological analysis and MD simulations. The findings reveal that while all mutants remain proton-selective, they all exhibit reduced effective charge displacement and proton conduction. The mutations differentially affect hHV1 kinetics, with the most pronounced effects observed in the two Arg-to-Trp substitutions. Mutation of the first voltage-sensing arginine (R1) to tryptophan (R205W) causes proton leakage in the closed state, accelerates channel activation, and diminishes the voltage dependence of gating. Except for R205W, the mutations promote the deactivated channel configuration. Altogether, these data are consistent with impairment of hHV1 function by mutations in the S4 transmembrane segment, potentially affecting pH homeostasis of tumor cells.

Oncometabolites at the crossroads of genetic, epigenetic and ecological alterations in cancer

By the time a tumor reaches clinical detectability, it contains around 108-109 cells. However, during tumor formation, significant cell loss occurs due to cell death. In some estimates, it could take up to a thousand cell generations, over a ~ 20-year life-span of a tumor, to reach clinical detectability, which would correspond to a "theoretical" generation of ~1030 cells. These rough calculations indicate that cancers are under negative selection. The fact that they thrive implies that they "evolve", and that their evolutionary trajectories are shaped by the pressure of the environment. Evolvability of a cancer is a function of its heterogeneity, which could be at the genetic, epigenetic, and ecological/microenvironmental levels [1]. These principles were summarized in a proposed classification in which Evo (evolutionary) and Eco (ecological) indexes are used to label cancers [1]. The Evo index addresses cancer cell-autonomous heterogeneity (genetic/epigenetic). The Eco index describes the ecological landscape (non-cell-autonomous) in terms of hazards to cancer survival and resources available. The reciprocal influence of Evo and Eco components is critical, as it can trigger self-sustaining loops that shape cancer evolvability [2]. Among the various hallmarks of cancer [3], metabolic alterations appear unique in that they intersect with both Evo and Eco components. This is partly because altered metabolism leads to the accumulation of oncometabolites. These oncometabolites have traditionally been viewed as mediators of non-cell-autonomous alterations in the cancer microenvironment. However, they are now increasingly recognized as inducers of genetic and epigenetic modifications. Thus, oncometabolites are uniquely positioned at the crossroads of genetic, epigenetic and ecological alterations in cancer. In this review, the mechanisms of action of oncometabolites will be summarized, together with their roles in the Evo and Eco phenotypic components of cancer evolvability. An evolutionary perspective of the impact of oncometabolites on the natural history of cancer will be presented.

Cancers adapt to their mutational load by buffering protein misfolding stress

In asexual populations that don't undergo recombination, such as cancer, deleterious mutations are expected to accrue readily due to genome-wide linkage between mutations. Despite this mutational load of often thousands of deleterious mutations, many tumors thrive. How tumors survive the damaging consequences of this mutational load is not well understood. Here, we investigate the functional consequences of mutational load in 10,295 human tumors by quantifying their phenotypic response through changes in gene expression. Using a generalized linear mixed model (GLMM), we find that high mutational load tumors up-regulate proteostasis machinery related to the mitigation and prevention of protein misfolding. We replicate these expression responses in cancer cell lines and show that the viability in high mutational load cancer cells is strongly dependent on complexes that degrade and refold proteins. This indicates that the upregulation of proteostasis machinery is causally important for high mutational burden tumors and uncovers new therapeutic vulnerabilities.

HER3 V104 mutations regulate cell signaling, growth, and drug sensitivity in cancer

HER3 is mutated in ~2%-10% of cancers depending on the cancer type. We found the HER3-V104L mutation to be activating from patient-derived mutations introduced via lentiviral transduction in HER3KO HER2 + HCC1569 breast cancer cells in which endogenous HER3 was eliminated by CRISPR/Cas9. Cells expressing HER3-V104L showed higher p-HER3 and p-ERK1/2 expression versus cells expressing wild-type HER3 or HER3-V104M. Patients whose tumor expressed the HER3 V104L variant had a reduced probability of overall survival compared to patients lacking a HER3 mutation whereas we did not find a statistically significant difference in overall survival of various cancer patients with the HER3 V104M mutation. Our data showed that HER2 inhibitors suppressed cell growth of HCC1569HER3KO cells stably expressing the HER3-V104L mutation. Cancer cell lines (SNU407, UC15 and DV90) with endogenous HER3-V104M mutation showed reduced cell proliferation and p-HER2/p-ERK1/2 expression with HER2 inhibitor treatment. Knock down of HER3 abrogated cell proliferation in the above cell lines which were overall more sensitive to the ERK inhibitor SCH779284 versus PI3K inhibitors. HER3-V104L mutation stabilized HER3 protein expression in COS7 and SNUC5 cells. COS7 cells transiently transfected with the HER3-V104L mutation in the presence of HER binding partners showed higher expression of p-HER3, p-ERK1/2 versus HER3-WT in a NRG-independent manner without any change in AKT signaling. Overall, this study shows the clinical relevance of the HER3 V104L and the V104M mutations and its response to HER2, PI3K and ERK inhibitors.

Spontaneous single-nucleotide substitutions and microsatellite mutations have distinct distributions of fitness effects

The fitness effects of new mutations determine key properties of evolutionary processes. Beneficial mutations drive evolution, yet selection is also shaped by the frequency of small-effect deleterious mutations, whose combined effect can burden otherwise adaptive lineages and alter evolutionary trajectories and outcomes in clonally evolving organisms such as viruses, microbes, and tumors. The small effect sizes of these important mutations have made accurate measurements of their rates difficult. In microbes, assessing the effect of mutations on growth can be especially instructive, as this complex phenotype is closely linked to fitness in clonally evolving organisms. Here, we perform high-throughput time-lapse microscopy on cells from mutation-accumulation strains to precisely infer the distribution of mutational effects on growth rate in the budding yeast, Saccharomyces cerevisiae. We show that mutational effects on growth rate are overwhelmingly negative, highly skewed towards very small effect sizes, and frequent enough to suggest that deleterious hitchhikers may impose a significant burden on evolving lineages. By using lines that accumulated mutations in either wild-type or slippage repair-defective backgrounds, we further disentangle the effects of 2 common types of mutations, single-nucleotide substitutions and simple sequence repeat indels, and show that they have distinct effects on yeast growth rate. Although the average effect of a simple sequence repeat mutation is very small (approximately 0.3%), many do alter growth rate, implying that this class of frequent mutations has an important evolutionary impact.

The mitochondrial calcium uniporter: Balancing tumourigenic and anti-tumourigenic responses

Increased malignancy and poor treatability associated with solid tumour cancers have commonly been attributed to mitochondrial calcium (Ca2+) dysregulation. The mitochondrial Ca2+ uniporter complex (mtCU) is the predominant mode of Ca2+ uptake into the mitochondrial matrix. The main components of mtCU are the pore-forming mitochondrial Ca2+ uniporter (MCU) subunit, MCU dominant-negative beta (MCUb) subunit, essential MCU regulator (EMRE) and the gatekeeping mitochondrial Ca2+ uptake 1 and 2 (MICU1 and MICU2) proteins. In this review, we describe mtCU-mediated mitochondrial Ca2+ dysregulation in solid tumour cancer types, finding enhanced mtCU activity observed in colorectal cancer, breast cancer, oral squamous cell carcinoma, pancreatic cancer, hepatocellular carcinoma and embryonal rhabdomyosarcoma. By contrast, decreased mtCU activity is associated with melanoma, whereas the nature of mtCU dysregulation remains unclear in glioblastoma. Furthermore, we show that numerous polymorphisms associated with cancer may alter phosphorylation sites on the pore forming MCU and MCUb subunits, which cluster at interfaces with EMRE. We highlight downstream/upstream biomolecular modulators of MCU and MCUb that alter mtCU-mediated mitochondrial Ca2+ uptake and may be used as biomarkers or to aid in the development of novel cancer therapeutics. Additionally, we provide an overview of the current small molecule inhibitors of mtCU that interact with the Asp residue of the critical Asp-Ile-Met-Glu motif or through other allosteric regulatory mechanisms to block Ca2+ permeation. Finally, we describe the relationship between MCU- and MCUb-mediating microRNAs and mitochondrial Ca2+ uptake that should be considered in the discovery of new treatment approaches for cancer.

Plasminogen missense variants and their involvement in cardiovascular and inflammatory disease

Human plasminogen (PLG), the zymogen of the fibrinolytic protease, plasmin, is a polymorphic protein with two widely distributed codominant alleles, PLG/Asp453 and PLG/Asn453. About 15 other missense or non-synonymous single nucleotide polymorphisms (nsSNPs) of PLG show major, yet different, relative abundances in world populations. Although the existence of these relatively abundant allelic variants is generally acknowledged, they are often overlooked or assumed to be non-pathogenic. In fact, at least half of those major variants are classified as having conflicting pathogenicity, and it is unclear if they contribute to different molecular phenotypes. From those, PLG/K19E and PLG/A601T are examples of two relatively abundant PLG variants that have been associated with PLG deficiencies (PD), but their pathogenic mechanisms are unclear. On the other hand, approximately 50 rare and ultra-rare PLG missense variants have been reported to cause PD as homozygous or compound heterozygous variants, often leading to a debilitating disease known as ligneous conjunctivitis. The true abundance of PD-associated nsSNPs is unknown since they can remain undetected in heterozygous carriers. However, PD variants may also contribute to other diseases. Recently, the ultra-rare autosomal dominant PLG/K311E has been found to be causative of hereditary angioedema (HAE) with normal C1 inhibitor. Two other rare pathogenic PLG missense variants, PLG/R153G and PLG/V709E, appear to affect platelet function and lead to HAE, respectively. Herein, PLG missense variants that are abundant and/or clinically relevant due to association with disease are examined along with their world distribution. Proposed molecular mechanisms are discussed when known or can be reasonably assumed.

Spontaneous single-nucleotide substitutions and microsatellite mutations have distinct distributions of fitness effects

The fitness effects of new mutations determine key properties of evolutionary processes. Beneficial mutations drive evolution, yet selection is also shaped by the frequency of small-effect deleterious mutations, whose combined effect can burden otherwise adaptive lineages and alter evolutionary trajectories and outcomes in clonally evolving organisms such as viruses, microbes, and tumors. The small effect sizes of these important mutations have made accurate measurements of their rates difficult. In microbes, assessing the effect of mutations on growth can be especially instructive, as this complex phenotype is closely linked to fitness in clonally evolving organisms. Here, we perform high-throughput time-lapse microscopy on cells from mutation-accumulation strains to precisely infer the distribution of mutational effects on growth rate in the budding yeast, Saccharomyces cerevisiae. We show that mutational effects on growth rate are overwhelmingly negative, highly skewed towards very small effect sizes, and frequent enough to suggest that deleterious hitchhikers may impose a significant burden on evolving lineages. By using lines that accumulated mutations in either wild-type or slippage repair-defective backgrounds, we further disentangle the effects of two common types of mutations, single-nucleotide substitutions and simple sequence repeat indels, and show that they have distinct effects on yeast growth rate. Although the average effect of a simple sequence repeat mutation is very small (~0.3%), many do alter growth rate, implying that this class of frequent mutations has an important evolutionary impact.

Non-Poissonian Bursts in the Arrival of Phenotypic Variation Can Strongly Affect the Dynamics of Adaptation

Modeling the rate at which adaptive phenotypes appear in a population is a key to predicting evolutionary processes. Given random mutations, should this rate be modeled by a simple Poisson process, or is a more complex dynamics needed? Here we use analytic calculations and simulations of evolving populations on explicit genotype-phenotype maps to show that the introduction of novel phenotypes can be "bursty" or overdispersed. In other words, a novel phenotype either appears multiple times in quick succession or not at all for many generations. These bursts are fundamentally caused by statistical fluctuations and other structure in the map from genotypes to phenotypes. Their strength depends on population parameters, being highest for "monomorphic" populations with low mutation rates. They can also be enhanced by additional inhomogeneities in the mapping from genotypes to phenotypes. We mainly investigate the effect of bursts using the well-studied genotype-phenotype map for RNA secondary structure, but find similar behavior in a lattice protein model and in Richard Dawkins's biomorphs model of morphological development. Bursts can profoundly affect adaptive dynamics. Most notably, they imply that fitness differences play a smaller role in determining which phenotype fixes than would be the case for a Poisson process without bursts.

Synergistic epistasis among cancer drivers can rescue early tumors from the accumulation of deleterious passengers

Epistasis among driver mutations is pervasive and explains relevant features of cancer, such as differential therapy response and convergence towards well-characterized molecular subtypes. Furthermore, a growing body of evidence suggests that tumor development could be hampered by the accumulation of slightly deleterious passenger mutations. In this work, we combined empirical epistasis networks, computer simulations, and mathematical models to explore how synergistic interactions among driver mutations affect cancer progression under the burden of slightly deleterious passengers. We found that epistasis plays a crucial role in tumor development by promoting the transformation of precancerous clones into rapidly growing tumors through a process that is analogous to evolutionary rescue. The triggering of epistasis-driven rescue is strongly dependent on the intensity of epistasis and could be a key rate-limiting step in many tumors, contributing to their unpredictability. As a result, central genes in cancer epistasis networks appear as key intervention targets for cancer therapy.

A dynamical limit to evolutionary adaptation

Natural selection makes evolutionary adaptation possible even if the overwhelming majority of new mutations are deleterious. However, in rapidly evolving populations where numerous linked mutations occur and segregate simultaneously, clonal interference and genetic hitchhiking can limit the efficiency of selection, allowing deleterious mutations to accumulate over time. This can in principle overwhelm the fitness increases provided by beneficial mutations, leading to an overall fitness decline. Here, we analyze the conditions under which evolution will tend to drive populations to higher versus lower fitness. Our analysis focuses on quantifying the boundary between these two regimes, as a function of parameters such as population size, mutation rates, and selection pressures. This boundary represents a state in which adaptation is precisely balanced by Muller's ratchet, and we show that it can be characterized by rapid molecular evolution without any net fitness change. Finally, we consider the implications of global fitness-mediated epistasis and find that under some circumstances, this can drive populations toward the boundary state, which can thus represent a long-term evolutionary attractor.

Mutational meltdown in asexual populations doomed to extinction

Asexual populations are expected to accumulate deleterious mutations through a process known as Muller's ratchet. Lynch and colleagues proposed that the ratchet eventually results in a vicious cycle of mutation accumulation and population decline that drives populations to extinction. They called this phenomenon mutational meltdown. Here, we analyze mutational meltdown using a multi-type branching process model where, in the presence of mutation, populations are doomed to extinction. We analyse the change in size and composition of the population and the time of extinction under this model.

A multiscale model of the role of microenvironmental factors in cell segregation and heterogeneity in breast cancer development

We analyzed a quantitative multiscale model that describes the epigenetic dynamics during the growth and evolution of an avascular tumor. A gene regulatory network (GRN) formed by a set of ten genes that are believed to play an important role in breast cancer development was kinetically coupled to the microenvironmental agents: glucose, estrogens, and oxygen. The dynamics of spontaneous mutations was described by a Yule-Furry master equation whose solution represents the probability that a given cell in the tissue undergoes a certain number of mutations at a given time. We assumed that the mutation rate is modified by a spatial gradient of nutrients. The tumor mass was simulated by means of cellular automata supplemented with a set of reaction diffusion equations that described the transport of microenvironmental agents. By analyzing the epigenetic state space described by the GRN dynamics, we found three attractors that were identified with cellular epigenetic states: normal, precancer and cancer. For two-dimensional (2D) and three-dimensional (3D) tumors we calculated the spatial distribution of the following quantities: (i) number of mutations, (ii) mutation of each gene and, (iii) phenotypes. Using estrogen as the principal microenvironmental agent that regulates cell proliferation process, we obtained tumor shapes for different values of estrogen consumption and supply rates. It was found that he majority of mutations occurred in cells that were located close to the 2D tumor perimeter or close to the 3D tumor surface. Also, it was found that the occurrence of different phenotypes in the tumor are controlled by estrogen concentration levels since they can change the individual cell threshold and gene expression levels. All results were consistently observed for 2D and 3D tumors.

Reuniting philosophy and science to advance cancer research

Cancers rely on multiple, heterogeneous processes at different scales, pertaining to many biomedical fields. Therefore, understanding cancer is necessarily an interdisciplinary task that requires placing specialised experimental and clinical research into a broader conceptual, theoretical, and methodological framework. Without such a framework, oncology will collect piecemeal results, with scant dialogue between the different scientific communities studying cancer. We argue that one important way forward in service of a more successful dialogue is through greater integration of applied sciences (experimental and clinical) with conceptual and theoretical approaches, informed by philosophical methods. By way of illustration, we explore six central themes: (i) the role of mutations in cancer; (ii) the clonal evolution of cancer cells; (iii) the relationship between cancer and multicellularity; (iv) the tumour microenvironment; (v) the immune system; and (vi) stem cells. In each case, we examine open questions in the scientific literature through a philosophical methodology and show the benefit of such a synergy for the scientific and medical understanding of cancer.

Intra-FCY1: a novel system to identify mutations that cause protein misfolding

Protein misfolding is a common intracellular occurrence. Most mutations to coding sequences increase the propensity of the encoded protein to misfold. These misfolded molecules can have devastating effects on cells. Despite the importance of protein misfolding in human disease and protein evolution, there are fundamental questions that remain unanswered, such as, which mutations cause the most misfolding? These questions are difficult to answer partially because we lack high-throughput methods to compare the destabilizing effects of different mutations. Commonly used systems to assess the stability of mutant proteins in vivo often rely upon essential proteins as sensors, but misfolded proteins can disrupt the function of the essential protein enough to kill the cell. This makes it difficult to identify and compare mutations that cause protein misfolding using these systems. Here, we present a novel in vivo system named Intra-FCY1 that we use to identify mutations that cause misfolding of a model protein [yellow fluorescent protein (YFP)] in Saccharomyces cerevisiae. The Intra-FCY1 system utilizes two complementary fragments of the yeast cytosine deaminase Fcy1, a toxic protein, into which YFP is inserted. When YFP folds, the Fcy1 fragments associate together to reconstitute their function, conferring toxicity in media containing 5-fluorocytosine and hindering growth. But mutations that make YFP misfold abrogate Fcy1 toxicity, thus strains possessing misfolded YFP variants rise to high frequency in growth competition experiments. This makes such strains easier to study. The Intra-FCY1 system cancels localization of the protein of interest, thus can be applied to study the relative stability of mutant versions of diverse cellular proteins. Here, we confirm this method can identify novel mutations that cause misfolding, highlighting the potential for Intra-FCY1 to illuminate the relationship between protein sequence and stability.

A dynamical limit to evolutionary adaptation

Natural selection makes evolutionary adaptation possible even if the overwhelming majority of new mutations are deleterious. However, in rapidly evolving populations where numerous linked mutations occur and segregate simultaneously, clonal interference and genetic hitchhiking can limit the efficiency of selection, allowing deleterious mutations to accumulate over time. This can in principle overwhelm the fitness increases provided by beneficial mutations, leading to an overall fitness decline. Here, we analyze the conditions under which evolution will tend to drive populations to higher versus lower fitness. Our analysis focuses on quantifying the boundary between these two regimes, as a function of parameters such as population size, mutation rates, and selection pressures. This boundary represents a state in which adaptation is precisely balanced by Muller's ratchet, and we show that it can be characterized by rapid molecular evolution without any net fitness change. Finally, we consider the implications of global fitness-mediated epistasis, and find that under some circumstances this can drive populations towards the boundary state, which can thus represent a long-term evolutionary attractor.

Next-generation sequencing methodologies to detect low-frequency mutations: "Catch me if you can"

Mutations, the irreversible changes in an organism's DNA sequence, are present in tissues at a variant allele frequency (VAF) ranging from ∼10-8 per bp for a founder mutation to ∼10-3 for a histologically normal tissue sample containing several independent clones - compared to 1%- 50% for a heterozygous tumor mutation or a polymorphism. The rarity of these events poses a challenge for accurate clinical diagnosis and prognosis, toxicology, and discovering new disease etiologies. Standard Next-Generation Sequencing (NGS) technologies report VAFs as low as 0.5% per nt, but reliably observing rarer precursor events requires additional sophistication to measure ultralow-frequency mutations. We detail the challenge; define terms used to characterize the results, which vary between laboratories and sometimes conflict between biologists and bioinformaticists; and describe recent innovations to improve standard NGS methodologies including: single-strand consensus sequence methods such as Safe-SeqS and SiMSen-Seq; tandem-strand consensus sequence methods such as o2n-Seq and SMM-Seq; and ultrasensitive parent-strand consensus sequence methods such as DuplexSeq, PacBio HiFi, SinoDuplex, OPUSeq, EcoSeq, BotSeqS, Hawk-Seq, NanoSeq, SaferSeq, and CODEC. Practical applications are also noted. Several methods quantify VAF down to 10-5 at a nt and mutation frequency (MF) in a target region down to 10-7 per nt. By expanding to > 1 Mb of sites never observed twice, thus forgoing VAF, other methods quantify MF < 10-9 per nt or < 15 errors per haploid genome. Clonal expansion cannot be directly distinguished from independent mutations by sequencing, so it is essential for a paper to report whether its MF counted only different mutations - the minimum independent-mutation frequency MFminI - or all mutations observed including recurrences - the larger maximum independent-mutation frequency MFmaxI which may reflect clonal expansion. Ultrasensitive methods reveal that, without their use, even mutations with VAF 0.5-1% are usually spurious.

Cancer genomes tolerate deleterious coding mutations through somatic copy number amplifications of wild-type regions

Cancers evolve under the accumulation of thousands of somatic mutations and chromosomal aberrations. While most coding mutations are deleterious, almost all protein-coding genes lack detectable signals of negative selection. This raises the question of how tumors tolerate such large amounts of deleterious mutations. Using 8,690 tumor samples from The Cancer Genome Atlas, we demonstrate that copy number amplifications frequently cover haploinsufficient genes in mutation-prone regions. This could increase tolerance towards the deleterious impact of mutations by creating safe copies of wild-type regions and, hence, protecting the genes therein. Our findings demonstrate that these potential buffering events are highly influenced by gene functions, essentiality, and mutation impact and that they occur early during tumor evolution. We show how cancer type-specific mutation landscapes drive copy number alteration patterns across cancer types. Ultimately, our work paves the way for the detection of novel cancer vulnerabilities by revealing genes that fall within amplifications likely selected during evolution to mitigate the effect of mutations.

Unified views on variant impact across many diseases

Genomic studies of human disorders are often performed by distinct research communities (i.e., focused on rare diseases, common diseases, or cancer). Despite underlying differences in the mechanistic origin of different disease categories, these studies share the goal of identifying causal genomic events that are critical for the clinical manifestation of the disease phenotype. Moreover, these studies face common challenges, including understanding the complex genetic architecture of the disease, deciphering the impact of variants on multiple scales, and interpreting noncoding mutations. Here, we highlight these challenges in depth and argue that properly addressing them will require a more unified vocabulary and approach across disease communities. Toward this goal, we present a unified perspective on relating variant impact to various genomic disorders.

Agent-based methods facilitate integrative science in cancer

In this opinion, we highlight agent-based modeling as a key tool for exploration of cell-cell and cell-environment interactions that drive cancer progression, therapeutic resistance, and metastasis. These biological phenomena are particularly suited to be captured at the cell-scale resolution possible only within agent-based or individual-based mathematical models. These modeling approaches complement experimental work (in vitro and in vivo systems) through parameterization and data extrapolation but also feed forward to drive new experiments that test model-generated predictions.

State-dependent evolutionary models reveal modes of solid tumour growth

Spatial properties of tumour growth have profound implications for cancer progression, therapeutic resistance and metastasis. Yet, how spatial position governs tumour cell division remains difficult to evaluate in clinical tumours. Here, we demonstrate that faster division on the tumour periphery leaves characteristic genetic patterns, which become evident when a phylogenetic tree is reconstructed from spatially sampled cells. Namely, rapidly dividing peripheral lineages branch more extensively and acquire more mutations than slower-dividing centre lineages. We develop a Bayesian state-dependent evolutionary phylodynamic model (SDevo) that quantifies these patterns to infer the differential division rates between peripheral and central cells. We demonstrate that this approach accurately infers spatially varying birth rates of simulated tumours across a range of growth conditions and sampling strategies. We then show that SDevo outperforms state-of-the-art, non-cancer multi-state phylodynamic methods that ignore differential sequence evolution. Finally, we apply SDevo to single-time-point, multi-region sequencing data from clinical hepatocellular carcinomas and find evidence of a three- to six-times-higher division rate on the tumour edge. With the increasing availability of high-resolution, multi-region sequencing, we anticipate that SDevo will be useful in interrogating spatial growth restrictions and could be extended to model non-spatial factors that influence tumour progression.

SMITH: spatially constrained stochastic model for simulation of intra-tumour heterogeneity

MOTIVATION

Simulations of cancer evolution are highly useful to study the effects of selection and mutation rates on cellular fitness. However, most methods are either lattice-based and cannot simulate realistically sized tumours, or they omit spatial constraints and lack the clonal dynamics of real-world tumours.

RESULTS

Stochastic model of intra-tumour heterogeneity (SMITH) is an efficient and explainable model of cancer evolution that combines a branching process with a new confinement mechanism limiting clonal growth based on the size of the individual clones as well as the overall tumour population. We demonstrate how confinement is sufficient to induce the rich clonal dynamics observed in spatial models and cancer samples across tumour types, while allowing for a clear geometric interpretation and simulation of 1 billion cells within a few minutes on a desktop PC.

AVAILABILITY AND IMPLEMENTATION

SMITH is implemented in C# and freely available at https://bitbucket.org/schwarzlab/smith. For visualizations, we provide the accompanying Python package PyFish at https://bitbucket.org/schwarzlab/pyfish.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Pan-cancer clinical impact of latent drivers from double mutations

Here, we discover potential 'latent driver' mutations in cancer genomes. Latent drivers have low frequencies and minor observable translational potential. As such, to date they have escaped identification. Their discovery is important, since when paired in cis, latent driver mutations can drive cancer. Our comprehensive statistical analysis of the pan-cancer mutation profiles of ~60,000 tumor sequences from the TCGA and AACR-GENIE cohorts identifies significantly co-occurring potential latent drivers. We observe 155 same gene double mutations of which 140 individual components are cataloged as latent drivers. Evaluation of cell lines and patient-derived xenograft response data to drug treatment indicate that in certain genes double mutations may have a prominent role in increasing oncogenic activity, hence obtaining a better drug response, as in PIK3CA. Taken together, our comprehensive analyses indicate that same-gene double mutations are exceedingly rare phenomena but are a signature for some cancer types, e.g., breast, and lung cancers. The relative rarity of doublets can be explained by the likelihood of strong signals resulting in oncogene-induced senescence, and by doublets consisting of non-identical single residue components populating the background mutational load, thus not identified.

Tumour Genetic Heterogeneity in Relation to Oral Squamous Cell Carcinoma and Anti-Cancer Treatment

Oral squamous cell carcinoma (SCC) represents more than 90% of all oral cancers and is the most frequent SCC of the head and neck region. It may affect any oral mucosal subsite but most frequently the tongue, followed by the floor of the mouth. The use of tobacco and betel nut, either smoked or chewed, and abuse of alcohol are the main risk factors for oral SCC. Oral SCC is characterized by considerable genetic heterogeneity and diversity, which together have a significant impact on the biological behaviour, clinical course, and response to treatment and on the generally poor prognosis of this carcinoma. Characterization of spatial and temporal tumour-specific molecular profiles and of person-specific resource availability and environmental and biological selective pressures could assist in personalizing anti-cancer treatment for individual patients, with the aim of improving treatment outcomes. In this narrative review, we discuss some of the events in cancer evolution and the functional significance of driver-mutations in carcinoma-related genes in general and elaborate on mechanisms mediating resistance to anti-cancer treatment.

DeepSom: a CNN-based approach to somatic variant calling in WGS samples without a matched normal

MOTIVATION

Somatic mutations are usually called by analyzing the DNA sequence of a tumor sample in conjunction with a matched normal. However, a matched normal is not always available, for instance, in retrospective analysis or diagnostic settings. For such cases, tumor-only somatic variant calling tools need to be designed. Previously proposed approaches demonstrate inferior performance on whole-genome sequencing (WGS) samples.

RESULTS

We present the convolutional neural network-based approach called DeepSom for detecting somatic single nucleotide polymorphism and short insertion and deletion variants in tumor WGS samples without a matched normal. We validate DeepSom by reporting its performance on five different cancer datasets. We also demonstrate that on WGS samples DeepSom outperforms previously proposed methods for tumor-only somatic variant calling.

AVAILABILITY AND IMPLEMENTATION

DeepSom is available as a GitHub repository at https://github.com/heiniglab/DeepSom.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Most cancers carry a substantial deleterious load due to Hill-Robertson interference

Cancer genomes exhibit surprisingly weak signatures of negative selection^1,2. This may be because selective pressures are relaxed or because genome-wide linkage prevents deleterious mutations from being removed (Hill-Robertson interference)³. By stratifying tumors by their genome-wide mutational burden, we observe negative selection (dN/dS ~ 0.56) in low mutational burden tumors, while remaining cancers exhibit dN/dS ratios ~1. This suggests that most tumors do not remove deleterious passengers. To buffer against deleterious passengers, tumors upregulate heat shock pathways as their mutational burden increases. Finally, evolutionary modeling finds that Hill-Robertson interference alone can reproduce patterns of attenuated selection and estimates the total fitness cost of passengers to be 46% per cell on average. Collectively, our findings suggest that the lack of observed negative selection in most tumors is not due to relaxed selective pressures, but rather the inability of selection to remove deleterious mutations in the presence of genome-wide linkage.

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.

Small extracellular vesicle DNA-mediated horizontal gene transfer as a driving force for tumor evolution: Facts and riddles

The basis of the conventional gene-centric view on tumor evolution is that vertically inherited mutations largely define the properties of tumor cells. In recent years, however, accumulating evidence shows that both the tumor cells and their microenvironment may acquire external, non-vertically inherited genetic properties via horizontal gene transfer (HGT), particularly through small extracellular vesicles (sEVs). Many phases of sEV-mediated HGT have been described, such as DNA packaging into small vesicles, their release, uptake by recipient cells, and incorporation of sEV-DNA into the recipient genome to modify the phenotype and properties of cells. Recent techniques in sEV separation, genome sequencing and editing, as well as the identification of new secretion mechanisms, shed light on a number of additional details of this phenomenon. Here, we discuss the key features of this form of gene transfer and make an attempt to draw relevant conclusions on the contribution of HGT to tumor evolution.

Diffusion approximations in population genetics and the rate of Muller's ratchet

The Wright-Fisher binomial model of allele frequency change is often approximated by a scaling limit in which selection, mutation and drift all decrease at the same 1/N rate. This construction restricts the applicability of the resulting 'Wright-Fisher diffusion equation' to the weak selection, weak mutation regime of evolution. We argue that diffusion approximations of the Wright-Fisher model can be used more generally, for instance in cases where genetic drift is much weaker than selection. One important example of this regime is Muller's ratchet phenomenon, whereby deleterious mutations slowly but irreversibly accumulate through rare stochastic fluctuations. Using a modified diffusion equation we derive improved analytical estimates for the mean click time of the ratchet.

Population genetics of clonally transmissible cancers

Populations of cancer cells are subject to the same core evolutionary processes as asexually reproducing, unicellular organisms. Transmissible cancers are particularly striking examples of these processes. These unusual cancers are clonal lineages that can spread through populations via physical transfer of living cancer cells from one host individual to another, and they have achieved long-term success in the colonization of at least eight different host species. Population genetic theory provides a useful framework for understanding the shift from a multicellular sexual animal into a unicellular asexual clone and its long-term effects on the genomes of these cancers. In this Review, we consider recent findings from transmissible cancer research with the goals of developing an evolutionarily informed perspective on transmissible cancers, examining possible implications for their long-term fate and identifying areas for future research on these exceptional lineages.

In silico Prediction of Deleterious Single Nucleotide Polymorphism in S100A4 Metastatic Gene: Potential Early Diagnostic Marker

S100A4 protein overexpression has been reported in different types of cancer and plays a key role by interacting with the tumor suppressor protein Tp53. Single nucleotide polymorphisms (SNP) in S100A4 could directly influence the biomolecular interaction with the tumor suppressor protein Tp53 due to their aberrant conformations. Hence, the study was designed to predict the deleterious SNP and its effect on the S100A4 protein structure and function. Twenty-one SNP data sets were screened for nonsynonymous mutations and subsequently subjected to deleterious mutation prediction using different computational tools. The screened deleterious mutations were analyzed for their changes in functionality and their interaction with the tumor suppressor protein Tp53 by protein-protein docking analysis. The structural effects were studied using the 3DMissense mutation tool to estimate the solvation energy and torsion angle of the screened mutations on the predicted structures. In our study, 21 deleterious nonsynonymous mutations were screened, including F72V, E74G, L5P, D25E, N65S, A28V, A8D, S20L, L58P, and K26N were found to be remarkably conserved by exhibiting the interaction either with the EF-hand 1 or EF-hand 2 domain. The solvation and torsion values significantly deviated for the mutant-type structures with S20L, N65S, and F72L mutations and showed a marked reduction in their binding affinity with the Tp53 protein. Hence, these deleterious mutations might serve as prospective targets for diagnosing and developing personalized treatments for cancer and other related diseases.

Tumour immunotherapy: lessons from predator-prey theory

With the burgeoning use of immune-based treatments for cancer, never has there been a greater need to understand the tumour microenvironment within which immune cells function and how it can be perturbed to inhibit tumour growth. Yet, current challenges in identifying optimal combinations of immunotherapies and engineering new cell-based therapies highlight the limitations of conventional paradigms for the study of the tumour microenvironment. Ecology has a rich history of studying predator-prey dynamics to discern factors that drive prey to extinction. Here, we describe the basic tenets of predator-prey theory as applied to 'predation' by immune cells and the 'extinction' of cancer cells. Our synthesis reveals fundamental mechanisms by which antitumour immunity might fail in sometimes counterintuitive ways and provides a fresh yet evidence-based framework to better understand and therapeutically target the immune-cancer interface.

Inferring Potential Cancer Driving Synonymous Variants

Synonymous single nucleotide variants (sSNVs) are often considered functionally silent, but a few cases of cancer-causing sSNVs have been reported. From available databases, we collected four categories of sSNVs: germline, somatic in normal tissues, somatic in cancerous tissues, and putative cancer drivers. We found that screening sSNVs for recurrence among patients, conservation of the affected genomic position, and synVep prediction (synVep is a machine learning-based sSNV effect predictor) recovers cancer driver variants (termed proposed drivers) and previously unknown putative cancer genes. Of the 2.9 million somatic sSNVs found in the COSMIC database, we identified 2111 proposed cancer driver sSNVs. Of these, 326 sSNVs could be further tagged for possible RNA splicing effects, RNA structural changes, and affected RBP motifs. This list of proposed cancer driver sSNVs provides computational guidance in prioritizing the experimental evaluation of synonymous mutations found in cancers. Furthermore, our list of novel potential cancer genes, galvanized by synonymous mutations, may highlight yet unexplored cancer mechanisms.

Influence of T Cell-Mediated Immune Surveillance on Somatic Mutation Occurrences in Melanoma

Background

Neoantigens are presented on the cancer cell surface by peptide-restricted human leukocyte antigen (HLA) proteins and can subsequently activate cognate T cells. It has been hypothesized that the observed somatic mutations in tumors are shaped by immunosurveillance.

Methods

We investigated all somatic mutations identified in The Cancer Genome Atlas (TCGA) Skin Cutaneous Melanoma (SKCM) samples. By applying a computational algorithm, we calculated the binding affinity of the resulting neo-peptides and their corresponding wild-type peptides with the major histocompatibility complex (MHC) Class I complex. We then examined the relationship between binding affinity alterations and mutation frequency.

Results

Our results show that neoantigens derived from recurrent mutations tend to have lower binding affinities with the MHC Class I complex compared to peptides from non-recurrent mutations. Tumor samples harboring recurrent SKCM mutations exhibited lower immune infiltration levels, indicating a relatively colder immune microenvironment.

Conclusions

These results suggested that the occurrences of somatic mutations in melanoma have been shaped by immunosurveillance. Mutations that lead to neoantigens with high MHC class I binding affinity are more likely to be eliminated and thus are less likely to be present in tumors.

A phenotype-structured model to reproduce the avascular growth of a tumor and its interaction with the surrounding environment

We here propose a one-dimensional spatially explicit phenotype-structured model to analyze selected aspects of avascular tumor progression. In particular, our approach distinguishes viable and necrotic cell fractions. The metabolically active part of the disease is, in turn, differentiated according to a continuous trait, that identifies cell variants with different degrees of motility and proliferation potential. A parabolic partial differential equation (PDE) then governs the spatio-temporal evolution of the phenotypic distribution of active cells within the host tissue. In this respect, active tumor agents are allowed to duplicate, move upon haptotactic and pressure stimuli, and eventually undergo necrosis. The mutual influence between the emerging malignancy and its environment (in terms of molecular landscape) is implemented by coupling the evolution law of the viable tumor mass with a parabolic PDE for oxygen kinetics and a differential equation that accounts for local consumption of extracellular matrix (ECM) elements. The resulting numerical realizations reproduce tumor growth and invasion in a number scenarios that differ for cell properties (i.e., individual migratory behavior, duplication and mutation potential) and environmental conditions (i.e., level of tissue oxygenation and homogeneity in the initial matrix profile). In particular, our simulations show that, in all cases, more mobile cell variants occupy the front edge of the tumor, whereas more proliferative clones are selected at the more internal regions. A necrotic core constantly occupies the bulk of the mass due to nutrient deprivation. This work may eventually suggest some biomedical strategies to partially reduce tumor aggressiveness, i.e., to enhance necrosis of malignant tissue and to promote the presence of more proliferative cell phenotypes over more invasive ones.

Effects of Selection at Linked Sites on Patterns of Genetic Variability

Patterns of variation and evolution at a given site in a genome can be strongly influenced by the effects of selection at genetically linked sites. In particular, the recombination rates of genomic regions correlate with their amount of within-population genetic variability, the degree to which the frequency distributions of DNA sequence variants differ from their neutral expectations, and the levels of adaptation of their functional components. We review the major population genetic processes that are thought to lead to these patterns, focusing on their effects on patterns of variability: selective sweeps, background selection, associative overdominance, and Hill–Robertson interference among deleterious mutations. We emphasize the difficulties in distinguishing among the footprints of these processes and disentangling them from the effects of purely demographic factors such as population size changes. We also discuss how interactions between selective and demographic processes can significantly affect patterns of variability within genomes.

Optimizing the evaluation of gene-targeted panels for tumor mutational burden estimation

Though whole exome sequencing (WES) is the gold-standard for measuring tumor mutational burden (TMB), the development of gene-targeted panels enables cost-effective TMB estimation. With the growing number of panels in clinical trials, developing a statistical method to effectively evaluate and compare the performance of different panels is necessary. The mainstream method uses R-squared value to measure the correlation between the panel-based TMB and WES-based TMB. However, the performance of a panel is usually overestimated via R-squared value based on the long-tailed TMB distribution of the dataset. Herein, we propose angular distance, a measurement used to compute the extent of the estimated bias. Our extensive in silico analysis indicates that the R-squared value reaches a plateau after the panel size reaches 0.5 Mb, which does not adequately characterize the performance of the panels. In contrast, the angular distance is still sensitive to the changes in panel sizes when the panel size reaches 6 Mb. In particular, R-squared values between the hypermutation-included dataset and the non-hypermutation dataset differ widely across many cancer types, whereas the angular distances are highly consistent. Therefore, the angular distance is more objective and logical than R-squared value for evaluating the accuracy of TMB estimation for gene-targeted panels.

Clonal Architecture and Evolutionary Dynamics in Acute Myeloid Leukemias

Acute myeloid leukemias (AML) results from the accumulation of genetic and epigenetic alterations, often in the context of an aging hematopoietic environment. The development of high-throughput sequencing-and more recently, of single-cell technologies-has shed light on the intratumoral diversity of leukemic cells. Taking AML as a model disease, we review the multiple sources of genetic, epigenetic, and functional heterogeneity of leukemic cells and discuss the definition of a leukemic clone extending its definition beyond genetics. After introducing the two dimensions contributing to clonal diversity, namely, richness (number of leukemic clones) and evenness (distribution of clone sizes), we discuss the mechanisms at the origin of clonal emergence (mutation rate, number of generations, and effective size of the leukemic population) and the causes of clonal dynamics. We discuss the possible role of neutral drift, but also of cell-intrinsic and -extrinsic influences on clonal fitness. After reviewing available data on the prognostic role of genetic and epigenetic diversity of leukemic cells on patients' outcome, we discuss how a better understanding of AML as an evolutionary process could lead to the design of novel therapeutic strategies in this disease.

Bridging Tumorigenesis and Therapy Resistance With a Non-Darwinian and Non-Lamarckian Mechanism of Adaptive Evolution

Although neo-Darwinian (and less often Lamarckian) dynamics are regularly invoked to interpret cancer's multifarious molecular profiles, they shine little light on how tumorigenesis unfolds and often fail to fully capture the frequency and breadth of resistance mechanisms. This uncertainty frames one of the most problematic gaps between science and practice in modern times. Here, we offer a theory of adaptive cancer evolution, which builds on a molecular mechanism that lies outside neo-Darwinian and Lamarckian schemes. This mechanism coherently integrates non-genetic and genetic changes, ecological and evolutionary time scales, and shifts the spotlight away from positive selection towards purifying selection, genetic drift, and the creative-disruptive power of environmental change. The surprisingly simple use-it or lose-it rationale of the proposed theory can help predict molecular dynamics during tumorigenesis. It also provides simple rules of thumb that should help improve therapeutic approaches in cancer.

Interacting evolutionary pressures drive mutation dynamics and health outcomes in aging blood

Age-related clonal hematopoiesis (ARCH) is characterized by age-associated accumulation of somatic mutations in hematopoietic stem cells (HSCs) or their pluripotent descendants. HSCs harboring driver mutations will be positively selected and cells carrying these mutations will rise in frequency. While ARCH is a known risk factor for blood malignancies, such as Acute Myeloid Leukemia (AML), why some people who harbor ARCH driver mutations do not progress to AML remains unclear. Here, we model the interaction of positive and negative selection in deeply sequenced blood samples from individuals who subsequently progressed to AML, compared to healthy controls, using deep learning and population genetics. Our modeling allows us to discriminate amongst evolutionary classes with high accuracy and captures signatures of purifying selection in most individuals. Purifying selection, acting on benign or mildly damaging passenger mutations, appears to play a critical role in preventing disease-predisposing clones from rising to dominance and is associated with longer disease-free survival. Through exploring a range of evolutionary models, we show how different classes of selection shape clonal dynamics and health outcomes thus enabling us to better identify individuals at a high risk of malignancy.

Personalized Medicine in Gynecologic Cancer: Fact or Fiction?

Personalized medicine in gynecologic oncology is an evolving field. In recent years, tumor profiling and large databases such as TCGA and NCI-Match have provided us with enormous amounts of molecular data. Several therapies that capitalize on novel genetic and immune discoveries including VEGF inhibitors, PARP inhibitors, and cancer vaccinations are discussed in this article. Additionally, we have seen direct to consumer marketing play an important role in cancer care and prevention as patients have increased ability to access genetic testing. This presents a unique challenge to gynecologic oncology providers as we learn to navigate the world of personalized medicine.

Non-coding driver mutations in human cancer

Tumour formation involves random mutagenic events and positive evolutionary selection acting on a subset of such events, referred to as driver mutations. A decade of careful surveying of tumour DNA using exome-based analyses has revealed a multitude of protein-coding somatic driver mutations, some of which are clinically actionable. Today, a transition towards whole-genome analysis is well under way, technically enabling the discovery of potential driver mutations occurring outside protein-coding sequences. Mutations are abundant in this vast non-coding space, which is more than 50 times larger than the coding exome, but reliable identification of selection signals in non-coding DNA remains a challenge. In this Review, we discuss recent findings in the field, where the emerging landscape is one in which non-coding driver mutations appear to be relatively infrequent. Nevertheless, we highlight several notable discoveries. We consider possible reasons for the relative absence of non-coding driver events, as well as the difficulties associated with detecting signals of positive selection in non-coding DNA.

CanDriS: posterior profiling of cancer-driving sites based on two-component evolutionary model

Current cancer genomics databases have accumulated millions of somatic mutations that remain to be further explored. Due to the over-excess mutations unrelated to cancer, the great challenge is to identify somatic mutations that are cancer-driven. Under the notion that carcinogenesis is a form of somatic-cell evolution, we developed a two-component mixture model: while the ground component corresponds to passenger mutations, the rapidly evolving component corresponds to driver mutations. Then, we implemented an empirical Bayesian procedure to calculate the posterior probability of a site being cancer-driven. Based on these, we developed a software CanDriS (Cancer Driver Sites) to profile the potential cancer-driving sites for thousands of tumor samples from the Cancer Genome Atlas and International Cancer Genome Consortium across tumor types and pan-cancer level. As a result, we identified that approximately 1% of the sites have posterior probabilities larger than 0.90 and listed potential cancer-wide and cancer-specific driver mutations. By comprehensively profiling all potential cancer-driving sites, CanDriS greatly enhances our ability to refine our knowledge of the genetic basis of cancer and might guide clinical medication in the upcoming era of precision medicine. The results were displayed in a database CandrisDB (http://biopharm.zju.edu.cn/candrisdb/).

Normal tissue architecture determines the evolutionary course of cancer

Cancer growth can be described as a caricature of the renewal process of the tissue of origin, where the tissue architecture has a strong influence on the evolutionary dynamics within the tumor. Using a classic, well-studied model of tumor evolution (a passenger-driver mutation model) we systematically alter spatial constraints and cell mixing rates to show how tissue structure influences functional (driver) mutations and genetic heterogeneity over time. This approach explores a key mechanism behind both inter-patient and intratumoral tumor heterogeneity: competition for space. Time-varying competition leads to an emergent transition from Darwinian premalignant growth to subsequent invasive neutral tumor growth. Initial spatial constraints determine the emergent mode of evolution (Darwinian to neutral) without a change in cell-specific mutation rate or fitness effects. Driver acquisition during the Darwinian precancerous stage may be modulated en route to neutral evolution by the combination of two factors: spatial constraints and limited cellular mixing. These two factors occur naturally in ductal carcinomas, where the branching topology of the ductal network dictates spatial constraints and mixing rates.

Mechanisms of stearoyl CoA desaturase inhibitor sensitivity and acquired resistance in cancer

The lipogenic enzyme stearoyl CoA desaturase (SCD) plays a key role in tumor lipid metabolism and membrane architecture. SCD is often up-regulated and a therapeutic target in cancer. Here, we report the unexpected finding that median expression of SCD is low in glioblastoma relative to normal brain due to hypermethylation and unintentional monoallelic co-deletion with phosphatase and tensin homolog (PTEN) in a subset of patients. Cell lines from this subset expressed undetectable SCD, yet retained residual SCD enzymatic activity. Unexpectedly, these lines evolved to survive independent of SCD through unknown mechanisms. Cell lines that escaped such genetic and epigenetic alterations expressed higher levels of SCD and were highly dependent on SCD for survival. Last, we identify that SCD-dependent lines acquire resistance through a previously unknown FBJ murine osteosarcoma viral oncogene homolog B (FOSB)-mediated mechanism. Accordingly, FOSB inhibition blunted acquired resistance and extended survival of tumor-bearing mice treated with SCD inhibitor.

Inferring Tumor Proliferative Organization from Phylogenetic Tree Measures in a Computational Model

We use a computational modeling approach to explore whether it is possible to infer a solid tumor's cellular proliferative hierarchy under the assumptions of the cancer stem cell hypothesis and neutral evolution. We work towards inferring the symmetric division probability for cancer stem cells, since this is believed to be a key driver of progression and therapeutic response. Motivated by the advent of multiregion sampling and resulting opportunities to infer tumor evolutionary history, we focus on a suite of statistical measures of the phylogenetic trees resulting from the tumor's evolution in different regions of parameter space and through time. We find strikingly different patterns in these measures for changing symmetric division probability which hinge on the inclusion of spatial constraints. These results give us a starting point to begin stratifying tumors by this biological parameter and also generate a number of actionable clinical and biological hypotheses regarding changes during therapy, and through tumor evolutionary time. [Cancer; evolution; phylogenetics.].

Integrating Quantitative Assays with Biologically Based Mathematical Modeling for Predictive Oncology

We provide an overview on the use of biological assays to calibrate and initialize mechanism-based models of cancer phenomena. Although artificial intelligence methods currently dominate the landscape in computational oncology, mathematical models that seek to explicitly incorporate biological mechanisms into their formalism are of increasing interest. These models can guide experimental design and provide insights into the underlying mechanisms of cancer progression. Historically, these models have included a myriad of parameters that have been difficult to quantify in biologically relevant systems, limiting their practical insights. Recently, however, there has been much interest calibrating biologically based models with the quantitative measurements available from (for example) RNA sequencing, time-resolved microscopy, and in vivo imaging. In this contribution, we summarize how a variety of experimental methods quantify tumor characteristics from the molecular to tissue scales and describe how such data can be directly integrated with mechanism-based models to improve predictions of tumor growth and treatment response.

Evolving insights: how DNA repair pathways impact cancer evolution

Viewing cancer as a large, evolving population of heterogeneous cells is a common perspective. Because genomic instability is one of the fundamental features of cancer, this intrinsic tendency of genomic variation leads to striking intratumor heterogeneity and functions during the process of cancer formation, development, metastasis, and relapse. With the increased mutation rate and abundant diversity of the gene pool, this heterogeneity leads to cancer evolution, which is the major obstacle in the clinical treatment of cancer. Cells rely on the integrity of DNA repair machineries to maintain genomic stability, but these machineries often do not function properly in cancer cells. The deficiency of DNA repair could contribute to the generation of cancer genomic instability, and ultimately promote cancer evolution. With the rapid advance of new technologies, such as single-cell sequencing in recent years, we have the opportunity to better understand the specific processes and mechanisms of cancer evolution, and its relationship with DNA repair. Here, we review recent findings on how DNA repair affects cancer evolution, and discuss how these mechanisms provide the basis for critical clinical challenges and therapeutic applications.

Mutation-selection balance and compensatory mechanisms in tumour evolution

Intratumour heterogeneity and phenotypic plasticity, sustained by a range of somatic aberrations, as well as epigenetic and metabolic adaptations, are the principal mechanisms that enable cancers to resist treatment and survive under environmental stress. A comprehensive picture of the interplay between different somatic aberrations, from point mutations to whole-genome duplications, in tumour initiation and progression is lacking. We posit that different genomic aberrations generally exhibit a temporal order, shaped by a balance between the levels of mutations and selective pressures. Repeat instability emerges first, followed by larger aberrations, with compensatory effects leading to robust tumour fitness maintained throughout the tumour progression. A better understanding of the interplay between genetic aberrations, the microenvironment, and epigenetic and metabolic cellular states is essential for early detection and prevention of cancer as well as development of efficient therapeutic strategies.