Universal Patterns of Selection in Cancer and Somatic Tissues

Measuring Clonal Evolution in Cancer with Genomics

Cancers originate from somatic cells in the human body that have accumulated genetic alterations. These mutations modify the phenotype of the cells, allowing them to escape the homeostatic regulation that maintains normal cell number. Viewed through the lens of evolutionary biology, the transformation of normal cells into malignant cells is evolution in action. Evolution continues throughout cancer growth, progression, treatment resistance, and disease relapse, driven by adaptation to changes in the cancer's environment, and intratumor heterogeneity is an inevitable consequence of this evolutionary process. Genomics provides a powerful means to characterize tumor evolution, enabling quantitative measurement of evolving clones across space and time. In this review, we discuss concepts and approaches to quantify and measure this evolutionary process in cancer using genomics.

A generalized theory of age-dependent carcinogenesis

The Multi-Stage Model of Carcinogenesis (MMC), developed in the 1950 s-70s, postulated carcinogenesis as a Darwinian somatic selection process. The cellular organization of tissues was then poorly understood, with almost nothing known about cancer drivers and stem cells. The MMC paradigm was later confirmed, and cancer incidence was explained as a function of mutation occurrence. However, the MMC has never been tested for its ability to account for the discrepancies in the number of driver mutations and the organization of the stem cell compartments underlying different cancers that still demonstrate nearly universal age-dependent incidence patterns. Here we demonstrate by Monte Carlo modeling the impact of key somatic evolutionary parameters on the MMC performance, revealing that two additional major mechanisms, aging-dependent somatic selection and life history-dependent evolution of species-specific tumor suppressor mechanisms, need to be incorporated into the MMC to make it capable of generalizing cancer incidence across tissues and species.

Editorial note

This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).

The Molecular Chaperone Heat Shock Protein 70 Controls Liver Cancer Initiation and Progression by Regulating Adaptive DNA Damage and Mitogen-Activated Protein Kinase/Extracellular Signal-Regulated Kinase Signaling Pathways

Delineating the mechanisms that drive hepatic injury and hepatocellular carcinoma (HCC) progression is critical for development of novel treatments for recurrent and advanced HCC but also for the development of diagnostic and preventive strategies. Heat shock protein 70 (HSP70) acts in concert with several cochaperones and nucleotide exchange factors and plays an essential role in protein quality control that increases survival by protecting cells against environmental stressors. Specifically, the HSP70-mediated response has been implicated in the pathogenesis of cancer, but the specific mechanisms by which HSP70 may support malignant cell transformation remains to be fully elucidated. Here, we show that genetic ablation of HSP70 markedly impairs HCC initiation and progression by distinct but overlapping pathways. This includes the potentiation of the carcinogen-induced DNA damage response, at the tumor initiation stage, to increase the p53-dependent surveillance response leading to the cell cycle exit or death of genomically damaged differentiated pericentral hepatocytes, and this may also prevent their conversion into more proliferating HCC progenitor cells. Subsequently, activation of a mitogen-activated protein kinase/extracellular signal-regulated kinase (MAPK/ERK) negative feedback pathway diminishes oncogenic signals, thereby attenuating premalignant cell transformation and tumor progression. Modulation of HSP70 function may be a strategy for interfering with oncogenic signals driving liver cell transformation and tumor progression, thus providing an opportunity for human cancer control.

Finding driver mutations in cancer: Elucidating the role of background mutational processes

Identifying driver mutations in cancer is notoriously difficult. To date, recurrence of a mutation in patients remains one of the most reliable markers of mutation driver status. However, some mutations are more likely to occur than others due to differences in background mutation rates arising from various forms of infidelity of DNA replication and repair machinery, endogenous, and exogenous mutagens. We calculated nucleotide and codon mutability to study the contribution of background processes in shaping the observed mutational spectrum in cancer. We developed and tested probabilistic pan-cancer and cancer-specific models that adjust the number of mutation recurrences in patients by background mutability in order to find mutations which may be under selection in cancer. We showed that mutations with higher mutability values had higher observed recurrence frequency, especially in tumor suppressor genes. This trend was prominent for nonsense and silent mutations or mutations with neutral functional impact. In oncogenes, however, highly recurring mutations were characterized by relatively low mutability, resulting in an inversed U-shaped trend. Mutations not yet observed in any tumor had relatively low mutability values, indicating that background mutability might limit mutation occurrence. We compiled a dataset of missense mutations from 58 genes with experimentally validated functional and transforming impacts from various studies. We found that mutability of driver mutations was lower than that of passengers and consequently adjusting mutation recurrence frequency by mutability significantly improved ranking of mutations and driver mutation prediction. Even though no training on existing data was involved, our approach performed similarly or better to the state-of-the-art methods.

Passenger mutations accurately classify human tumors

Determining the cancer type and molecular subtype has important clinical implications. The primary site is however unknown for some malignancies discovered in the metastatic stage. Moreover liquid biopsies may be used to screen for tumoral DNA, which upon detection needs to be assigned to a site-of-origin. Classifiers based on genomic features are a promising approach to prioritize the tumor anatomical site, type and subtype. We examined the predictive ability of causal (driver) somatic mutations in this task, comparing it against global patterns of non-selected (passenger) mutations, including features based on regional mutation density (RMD). In the task of distinguishing 18 cancer types, the driver mutations-mutated oncogenes or tumor suppressors, pathways and hotspots-classified 36% of the patients to the correct cancer type. In contrast, the features based on passenger mutations did so at 92% accuracy, with similar contribution from the RMD and the trinucleotide mutation spectra. The RMD and the spectra covered distinct sets of patients with predictions. In particular, introducing the RMD features into a combined classification model increased the fraction of diagnosed patients by 50 percentage points (at 20% FDR). Furthermore, RMD was able to discriminate molecular subtypes and/or anatomical site of six major cancers. The advantage of passenger mutations was upheld under high rates of false negative mutation calls and with exome sequencing, even though overall accuracy decreased. We suggest whole genome sequencing is valuable for classifying tumors because it captures global patterns emanating from mutational processes, which are informative of the underlying tumor biology.

Clonal approaches to understanding the impact of mutations on hematologic disease development

Interrogation of hematopoietic tissue at the clonal level has a rich history spanning over 50 years, and has provided critical insights into both normal and malignant hematopoiesis. Characterization of chromosomes identified some of the first genetic links to cancer with the discovery of chromosomal translocations in association with many hematological neoplasms. The unique accessibility of hematopoietic tissue and the ability to clonally expand hematopoietic progenitors in vitro has provided fundamental insights into the cellular hierarchy of normal hematopoiesis, as well as the functional impact of driver mutations in disease. Transplantation assays in murine models have enabled cellular assessment of the functional consequences of somatic mutations in vivo. Most recently, next-generation sequencing-based assays have shown great promise in allowing multi-"omic" characterization of single cells. Here, we review how clonal approaches have advanced our understanding of disease development, focusing on the acquisition of somatic mutations, clonal selection, driver mutation cooperation, and tumor evolution.

Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational principles for interpreting cancer drivers

At the root of the so-called precision medicine or precision oncology, which is our focus here, is the hypothesis that cancer treatment would be considerably better if therapies were guided by a tumor’s genomic alterations. This hypothesis has sparked major initiatives focusing on whole-genome and/or exome sequencing, creation of large databases, and developing tools for their statistical analyses—all aspiring to identify actionable alterations, and thus molecular targets, in a patient. At the center of the massive amount of collected sequence data is their interpretations that largely rest on statistical analysis and phenotypic observations. Statistics is vital, because it guides identification of cancer-driving alterations. However, statistics of mutations do not identify a change in protein conformation; therefore, it may not define sufficiently accurate actionable mutations, neglecting those that are rare. Among the many thematic overviews of precision oncology, this review innovates by further comprehensively including precision pharmacology, and within this framework, articulating its protein structural landscape and consequences to cellular signaling pathways. It provides the underlying physicochemical basis, thereby also opening the door to a broader community.

Undifferentiated Sarcomas Develop through Distinct Evolutionary Pathways

Undifferentiated sarcomas (USARCs) of adults are diverse, rare, and aggressive soft tissue cancers. Recent sequencing efforts have confirmed that USARCs exhibit one of the highest burdens of structural aberrations across human cancer. Here, we sought to unravel the molecular basis of the structural complexity in USARCs by integrating DNA sequencing, ploidy analysis, gene expression, and methylation profiling. We identified whole genome duplication as a prevalent and pernicious force in USARC tumorigenesis. Using mathematical deconvolution strategies to unravel the complex copy-number profiles and mutational timing models we infer distinct evolutionary pathways of these rare cancers. In addition, 15% of tumors exhibited raised mutational burdens that correlated with gene expression signatures of immune infiltration, and good prognosis.

On fitness: how do mutations shape the biology of cancer?

The theory of evolution by natural selection shapes our understanding of the living world. While natural selection has given rise to all the intricacies of life on the planet, those responsible for treating cancer have a darker view of adaptation and selection. Revolutionary changes in DNA sequencing technology have allowed us to survey the complexities that constitute the cancer genome, while advances in genetic engineering are allowing us to functionally interrogate these alterations. These approaches are providing new insights into how mutations influence cancer biology. It is possible that with time, this new knowledge will allow us to take control of the evolutionary processes that shape the disease, to develop more effective treatments.

Large-scale compound screens and pharmacogenomic interactions in cancer

In the last decade, we have witnessed tremendous advances in our understanding of the landscape of the molecular alterations that underpin many of the most prevalent cancers, in the use of automated high throughput platforms for high-throughput drug screens in cancer cells, in the creation of more clinically relevant cancer cell models, in the application of CRISPR genetic screens for novel target identification, and lastly in the development of more useful computational approaches in the pursuit of biomarkers of drug response.

Error-Prone Replication through UV Lesions by DNA Polymerase θ Protects against Skin Cancers

Cancers from sun-exposed skin accumulate "driver" mutations, causally implicated in oncogenesis. Because errors incorporated during translesion synthesis (TLS) opposite UV lesions would generate these mutations, TLS mechanisms are presumed to underlie cancer development. To address the role of TLS in skin cancer formation, we determined which DNA polymerase is responsible for generating UV mutations, analyzed the relative contributions of error-free TLS by Polη and error-prone TLS by Polθ to the replication of UV-damaged DNA and to genome stability, and examined the incidence of UV-induced skin cancers in Polθ-/-, Polη-/-, and Polθ-/- Polη-/- mice. Our findings that the incidence of skin cancers rises in Polθ-/- mice and is further exacerbated in Polθ-/- Polη-/- mice compared with Polη-/- mice support the conclusion that error-prone TLS by Polθ provides a safeguard against tumorigenesis and suggest that cancer formation can ensue in the absence of somatic point mutations.

HSP90: Enabler of Cancer Adaptation

The stability and function of many oncogenic mutant proteins depend on heat shock protein 90 (HSP90). This unique activity has inspired the exploration of HSP90 as an anticancer target for over two decades. Unfortunately, while clinical trials of highly optimized HSP90 inhibitors have demonstrated modest benefit for patients with advanced cancers, most commonly stabilization of disease, no HSP90 inhibitor has demonstrated sufficient efficacy to achieve FDA approval to date. This review discusses potential reasons for the limited success of these agents and how our increasingly sophisticated understanding of HSP90 suggests alternative, potentially more effective strategies for targeting it to treat cancers. First, we focus on insights gained from model organisms that suggest a fundamental role for HSP90 in supporting the adaptability and heterogeneity of cancers, key factors underlying their ability to evolve and acquire drug resistance. Second, we examine how HSP90’s role in promoting the stability of mutant proteins might be targeted in genetically unstable tumor cells to reveal their aberrant, foreign proteome to the immune system. Both of these emerging aspects of HSP90 biology suggest that the most effective use of HSP90 inhibitors may not be at high doses with the intent to kill cancer cells, but rather in combination with other molecularly targeted therapies at modest, non-heat shock-inducing exposures that limit the adaptive capacity of cancers.

Epigenetic therapy in immune-oncology

DNA methylation inhibitors have become the mainstay for treatment of certain haematological malignancies. In addition to their abilities to reactivate genes, including tumour suppressors, that have acquired DNA methylation during carcinogenesis, they induce the expression of thousands of transposable elements including endogenous retroviruses and latent cancer testis antigens normally silenced by DNA methylation in most somatic cells. This results in a state of viral mimicry in which treated cells mount an innate immune response by turning on viral defence genes and potentially expressing neoantigens. Furthermore, these changes mediated by DNA methylation inhibitors can also alter the function of immune cells relevant to acquired immunity. Additionally, other inhibitors of epigenetic processes, such as histone deacetylases, methylases and demethylases, can elicit similar effects either individually or in combinations with DNA methylation inhibitors. These findings together with rapid development of immunotherapies open new avenues for cancer treatment.

Mechanisms of DNA-reactive and epigenetic chemical carcinogens: applications to carcinogenicity testing and risk assessment

Chemicals with carcinogenic activity in either animals or humans produce increases in neoplasia through diverse mechanisms. One mechanism is reaction with nuclear DNA. Other mechanisms consist of epigenetic effects involving either modifications of regulatory macromolecules or perturbation of cellular regulatory processes. The basis for distinguishing between carcinogens that have either DNA reactivity or an epigenetic activity as their primary mechanism of action is detailed in this review. In addition, important applications of information on these mechanisms of action to carcinogenicity testing and human risk assessment are discussed.

The landscape of selection in 551 esophageal adenocarcinomas defines genomic biomarkers for the clinic

Esophageal adenocarcinoma (EAC) is a poor-prognosis cancer type with rapidly rising incidence. Understanding of the genetic events driving EAC development is limited, and there are few molecular biomarkers for prognostication or therapeutics. Using a cohort of 551 genomically characterized EACs with matched RNA sequencing data, we discovered 77 EAC driver genes and 21 noncoding driver elements. We identified a mean of 4.4 driver events per tumor, which were derived more commonly from mutations than copy number alterations, and compared the prevelence of these mutations to the exome-wide mutational excess calculated using non-synonymous to synonymous mutation ratios (dN/dS). We observed mutual exclusivity or co-occurrence of events within and between several dysregulated EAC pathways, a result suggestive of strong functional relationships. Indicators of poor prognosis (SMAD4 and GATA4) were verified in independent cohorts with significant predictive value. Over 50% of EACs contained sensitizing events for CDK4 and CDK6 inhibitors, which were highly correlated with clinically relevant sensitivity in a panel of EAC cell lines and organoids.

Deciphering the genomic, epigenomic, and transcriptomic landscapes of pre-invasive lung cancer lesions

The molecular alterations that occur in cells before cancer is manifest are largely uncharted. Lung carcinoma in situ (CIS) lesions are the pre-invasive precursor to squamous cell carcinoma. Although microscopically identical, their future is in equipoise, with half progressing to invasive cancer and half regressing or remaining static. The cellular basis of this clinical observation is unknown. Here, we profile the genomic, transcriptomic, and epigenomic landscape of CIS in a unique patient cohort with longitudinally monitored pre-invasive disease. Predictive modeling identifies which lesions will progress with remarkable accuracy. We identify progression-specific methylation changes on a background of widespread heterogeneity, alongside a strong chromosomal instability signature. We observed mutations and copy number changes characteristic of cancer and chart their emergence, offering a window into early carcinogenesis. We anticipate that this new understanding of cancer precursor biology will improve early detection, reduce overtreatment, and foster preventative therapies targeting early clonal events in lung cancer.

Somatic mutations in early metazoan genes disrupt regulatory links between unicellular and multicellular genes in cancer

Extensive transcriptional alterations are observed in cancer, many of which activate core biological processes established in unicellular organisms or suppress differentiation pathways formed in metazoans. Through rigorous, integrative analysis of genomics data from a range of solid tumors, we show many transcriptional changes in tumors are tied to mutations disrupting regulatory interactions between unicellular and multicellular genes within human gene regulatory networks (GRNs). Recurrent point mutations were enriched in regulator genes linking unicellular and multicellular subnetworks, while copy-number alterations affected downstream target genes in distinctly unicellular and multicellular regions of the GRN. Our results depict drivers of tumourigenesis as genes that created key regulatory links during the evolution of early multicellular life, whose dysfunction creates widespread dysregulation of primitive elements of the GRN. Several genes we identified as important in this process were associated with drug response, demonstrating the potential clinical value of our approach.

Somatic mutations in early metazoan genes disrupt regulatory links between unicellular and multicellular genes in cancer

Extensive transcriptional alterations are observed in cancer, many of which activate core biological processes established in unicellular organisms or suppress differentiation pathways formed in metazoans. Through rigorous, integrative analysis of genomics data from a range of solid tumors, we show many transcriptional changes in tumors are tied to mutations disrupting regulatory interactions between unicellular and multicellular genes within human gene regulatory networks (GRNs). Recurrent point mutations were enriched in regulator genes linking unicellular and multicellular subnetworks, while copy-number alterations affected downstream target genes in distinctly unicellular and multicellular regions of the GRN. Our results depict drivers of tumourigenesis as genes that created key regulatory links during the evolution of early multicellular life, whose dysfunction creates widespread dysregulation of primitive elements of the GRN. Several genes we identified as important in this process were associated with drug response, demonstrating the potential clinical value of our approach.

Cancers arise when harmful changes happen in the genetic information of certain cells. These ‘mutations’ are different from person to person, but overall, they disrupt healthy cells in similar ways. In particular, cancer cells tend to lose features that help cells work together in the body. Researchers have suggested that cancers may emerge when cells stop being able to cooperate with each other as part of an organism.

Our bodies still rely on old genes that were present in our earliest, single-cell ancestors. However, we also have newer genes that evolved when the organisms in our lineage started to have more than one cell. A complex network of signals exists to ensure that both sets of genes work together smoothly, and previous studies have suggested that cancers may appear when this delicate balance is disrupted.

To address this question, Trigos et al. have now used a computational approach to analyse different types of tumours from over 9,000 patients. This showed that, in cancer, many mutations disrupt the genes that coordinate old and new genes. These mutations were usually small, punctual changes in the genetic sequence. However, large modifications, such as an entire gene being deleted or repeated, took place more often in the old or the new genes themselves. Therefore, different classes of mutations have specific roles when disrupting how old and new genes work in cancer.

While certain genes highlighted during this analysis were already known to be associated with cancer, others were not – including genes present during the evolution of the earliest animals on Earth. Looking more closely into how these genes can cause disease may help us better understand and fight cancer.

Nucleotide Weight Matrices Reveal Ubiquitous Mutational Footprints of AID/APOBEC Deaminases in Human Cancer Genomes

Cancer genomes accumulate nucleotide sequence variations that number in the tens of thousands per genome. A prominent fraction of these mutations is thought to arise as a consequence of the off-target activity of DNA/RNA editing cytosine deaminases. These enzymes, collectively called activation induced deaminase (AID)/APOBECs, deaminate cytosines located within defined DNA sequence contexts. The resulting changes of the original C:G pair in these contexts (mutational signatures) provide indirect evidence for the participation of specific cytosine deaminases in a given cancer type. The conventional method used for the analysis of mutable motifs is the consensus approach. Here, for the first time, we have adopted the frequently used weight matrix (sequence profile) approach for the analysis of mutagenesis and provide evidence for this method being a more precise descriptor of mutations than the sequence consensus approach. We confirm that while mutational footprints of APOBEC1, APOBEC3A, APOBEC3B, and APOBEC3G are prominent in many cancers, mutable motifs characteristic of the action of the humoral immune response somatic hypermutation enzyme, AID, are the most widespread feature of somatic mutation spectra attributable to deaminases in cancer genomes. Overall, the weight matrix approach reveals that somatic mutations are significantly associated with at least one AID/APOBEC mutable motif in all studied cancers.

Drug Sensitivity Screening and Targeted Pathway Analysis Reveal a Multi-Driver Proliferative Mechanism and Suggest a Strategy of Combination Targeted Therapy for Colorectal Cancer Cells

Treatment of colorectal cancer mostly relies on traditional therapeutic approaches, such as surgery and chemotherapy. Limited options of targeted therapy for colorectal cancer narrowly focus on blocking cancer-generic targets VEGFR and EGFR. Identifying the oncogenic drivers, understanding their contribution to proliferation, and finding inhibitors to block such drivers are the keys to developing targeted therapy for colorectal cancer. In this study, ten colorectal cancer cell lines were screened against a panel of protein kinase inhibitors blocking key oncogenic signaling pathways. The results show that four of the 10 cell lines did not respond to any kinase inhibitors significantly, the other six were mildly inhibited by AZD-6244, BMS-754807, and/or dasatinib. Mechanistic analyses demonstrate that these inhibitors independently block the MAP kinase pathway, IR/IGF-1R/AKT pathway, and Src kinases, suggesting a multi-driver nature of proliferative signaling in these cells. Most of these cell lines were potently and synergistically inhibited by pair-wise combinations of these drugs. Furthermore, seven of the 10 cell lines were inhibited by the triple combination of AZD-6244/BMS-754807/dasatinib with IC₅₀’s between 10 and 84 nM. These results suggest that combination targeted therapy may be an effective strategy against colorectal cancer.

Mutational and Antigenic Landscape in Tumor Progression and Cancer Immunotherapy

Evolving neoplasms accumulate non-synonymous mutations at a high rate, potentially enabling the expression of antigenic epitopes that can be recognized by the immune system. Since they are not covered by central tolerance, such tumor neoantigens (TNAs) should be under robust immune control as they surge. However, genetic defects that impair cancer cell eradication by the immune system coupled with the establishment of local immunosuppression can enable TNA accumulation, which is generally associated with improved clinical sensitivity to various immunotherapies. Here, we explore how tumor-intrinsic factors and immunological processes shape the mutational and antigenic landscape of evolving neoplasms to influence clinical responses to immunotherapy, and propose strategies to achieve robust immunological control of the disease despite disabled immunosurveillance.

Integration of Genomic and Transcriptional Features in Pancreatic Cancer Reveals Increased Cell Cycle Progression in Metastases

We integrated clinical, genomic, and transcriptomic data from 224 primaries and 95 metastases from 289 patients to characterize progression of pancreatic ductal adenocarcinoma (PDAC). Driver gene alterations and mutational and expression-based signatures were preserved, with truncations, inversions, and translocations most conserved. Cell cycle progression (CCP) increased with sequential inactivation of tumor suppressors, yet remained higher in metastases, perhaps driven by cell cycle regulatory gene variants. Half of the cases were hypoxic by expression markers, overlapping with molecular subtypes. Paired tumor heterogeneity showed cancer cell migration by Halstedian progression. Multiple PDACs arising synchronously and metachronously in the same pancreas were actually intra-parenchymal metastases, not independent primary tumors. Established clinical co-variates dominated survival analyses, although CCP and hypoxia may inform clinical practice.

Evolutionary Stem Cell Poker and Cancer Risks: The Paradox of The Large And Small Intestines

Purpose of review

Recent studies demonstrate that normal human tissues accumulate substantial numbers of somatic mutations with aging, to levels comparable to their corresponding cancers. If mutations cause cancer, how do tissues avoid cancer when mutations are unavoidable?

Recent findings

The small intestines (SI) and colon accumulate similar numbers of replication errors, but SI adenocarcinoma is much rarer than colorectal cancer. Both the small and large intestines are subdivided into millions of small neighborhoods (crypts) that are maintained by small numbers of stem cells. To explain the SI cancer paradox, four fundamental evolution parameters (mutation, drift, selection, and population size) are translated to crypts.

Summary

The accumulations of driver mutations in a single stem cell may be analogous to an evolutionary poker game. The rarity of SI cancer may reflect that SI crypts are smaller and have fewer stem cells than the colon, which reduces the numbers of cells at risk for mutation and perhaps selection efficiency. Tissue microarchitecture may physically modulate cancer evolution by controlling the numbers of directly competing neighboring cells. A better understanding of the SI cancer paradox may illuminate how tissues naturally avoid cancers when mutations are unavoidable.

Interleukin-22 protects intestinal stem cells against genotoxic stress

Environmental genotoxic factors pose a challenge to the genomic integrity of epithelial cells at barrier surfaces that separate host organisms from the environment. They can induce mutations that, if they occur in epithelial stem cells, contribute to malignant transformation and cancer development1-3. Genome integrity in epithelial stem cells is maintained by an evolutionarily conserved cellular response pathway, the DNA damage response (DDR). The DDR culminates in either transient cell-cycle arrest and DNA repair or elimination of damaged cells by apoptosis4,5. Here we show that the cytokine interleukin-22 (IL-22), produced by group 3 innate lymphoid cells (ILC3) and γδ T cells, is an important regulator of the DDR machinery in intestinal epithelial stem cells. Using a new mouse model that enables sporadic inactivation of the IL-22 receptor in colon epithelial stem cells, we demonstrate that IL-22 is required for effective initiation of the DDR following DNA damage. Stem cells deprived of IL-22 signals and exposed to carcinogens escaped DDR-controlled apoptosis, contained more mutations and were more likely to give rise to colon cancer. We identified metabolites of glucosinolates, a group of phytochemicals contained in cruciferous vegetables, to be a widespread source of genotoxic stress in intestinal epithelial cells. These metabolites are ligands of the aryl hydrocarbon receptor (AhR)6, and AhR-mediated signalling in ILC3 and γδ T cells controlled their production of IL-22. Mice fed with diets depleted of glucosinolates produced only very low levels of IL-22 and, consequently, the DDR in epithelial cells of mice on a glucosinolate-free diet was impaired. This work identifies a homeostatic network protecting stem cells against challenge to their genome integrity by AhR-mediated 'sensing' of genotoxic compounds from the diet. AhR signalling, in turn, ensures on-demand production of IL-22 by innate lymphocytes directly regulating components of the DDR in epithelial stem cells.

Cellular Target of a Rhodium Metalloinsertor is the DNA Base Pair Mismatch

Defects in DNA mismatch repair (MMR) are commonly found in various cancers, especially in colorectal cancers. Despite the high prevalence of MMR-deficient cancers, mismatch-targeted therapeutics are limited and diagnostic tools are indirect. Here, we examine the cytotoxic properties of a rhodium metalloinsertor, [Rh(phen)(chrysi)(PPO)]²⁺ (RhPPO) in 27 diverse colorectal cancer cell lines. Despite the low frequency of genomic mismatches and the non-covalent nature of the RhPPO-DNA lesion, RhPPO is on average five times more potent than cisplatin. Importantly, the biological target and profile for RhPPO differs from that of cisplatin. A fluorescent metalloinsertor, RhCy3, was used to demonstrate that the cellular target of RhPPO is the DNA mismatch. RhCy3 represents a direct probe for MMR-deficiency and correlates directly with the cytotoxicity of RhPPO across different cell lines. Overall, our studies clearly indicate that RhPPO and RhCy3 are promising anticancer and diagnostic probes for MMR-deficient cancers, respectively.

A sequential Monte Carlo algorithm for inference of subclonal structure in cancer

Tumors are heterogeneous in the sense that they consist of multiple subpopulations of cells, referred to as subclones, each of which is characterized by a distinct profile of genomic variations such as somatic mutations. Inferring the underlying clonal landscape has become an important topic in that it can help in understanding cancer development and progression, and thereby help in improving treatment. We describe a novel state-space model, based on the feature allocation framework and an efficient sequential Monte Carlo (SMC) algorithm, using the somatic mutation data obtained from tumor samples to estimate the number of subclones, as well as their characterization. Our approach, by design, is capable of handling any number of mutations. Via extensive simulations, our method exhibits high accuracy, in most cases, and compares favorably with existing methods. Moreover, we demonstrated the validity of our method through analyzing real tumor samples from patients from multiple cancer types (breast, prostate, and lung). Our results reveal driver mutation events specific to cancer types, and indicate clonal expansion by manual phylogenetic analysis. MATLAB code and datasets are available to download at: https://github.com/moyanre/tumor_clones.

Energy oversupply to tissues: a single mechanism possibly underlying multiple cancer risk factors

BACKGROUND AND OBJECTIVES

Several major risk factors for cancer involve vascular oversupply of energy to affected tissues. These include obesity, diabetes and chronic inflammation. Here, we propose a potential mechanistic explanation for the association between energy oversupply and cancer risk, which we call the metabolic cancer suppression hypothesis: We hypothesize that oncogenesis is normally suppressed by organismal physiology that regulates and strictly limits normal energy supply to somatic cells, and that this protection is removed by abnormal oversupply of energy.

METHODOLOGY

We evaluate this hypothesis using a computational model of somatic cell evolution to simulate experimental manipulation of the vascular energy supply to a tissue. The model simulates the evolutionary dynamics of somatic cells during oncogenesis.

RESULTS

In our simulation experiment, we found that under plausible biological assumptions, elevated energy supply to a tissue led to the evolution of elevated energy uptake by somatic cells, leading to the rapid evolution of both defining traits of cancer cells: hyperproliferation, and tissue invasion.

CONCLUSIONS AND IMPLICATIONS

Our results support the hypothesis of metabolic cancer suppression, suggesting that vascular oversupply of energetic resources to somatic cells removes normal energetic limitations on cell proliferation, and that this accelerates cellular evolution toward cancer. Various predictions of this hypothesis are amenable to empirical testing, and have promising implications for translational research toward clinical cancer prevention.

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

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

Studying Cancer Heterogeneity by Single-Cell RNA Sequencing

A major hurdle for the treatment of cancer is the incomplete understanding of its evolution through the course of its emergence, dispersal, and relapse. Genetic and epigenetic changes in combination with external cues and selective forces are the driving factors behind tumor heterogeneity. Understanding this variability within and across patients may partly explain the unpredictable outcomes of cancer treatments. Measuring the variation of gene expression levels within cells of the same tumor is a crucial part of this endeavor. Hence, the recently developed single-cell RNA-sequencing (scRNA-seq) technologies have become a valuable tool for cancer research. In practice, however, this is still challenging, especially for clinical samples. Here, we describe mcSCRB-seq (molecular crowding single-cell RNA barcoding and sequencing), a highly sensitive and powerful plate-based scRNA-seq method, which shows great capability to generate transcriptome data for cancer cells. mcSCRB-seq is not only characterized by high sensitivity due to molecular crowding and the use of unique molecular identifiers (UMIs) but also features an easy workflow and a low per-cell cost and does not require specialized equipment.

Selection Acting on Genomes

Populations evolve as mutations arise in individual organisms and, through hereditary transmission, may become "fixed" (shared by all individuals) in the population. Most mutations are lethal or have negative fitness consequences for the organism. Others have essentially no effect on organismal fitness and can become fixed through the neutral stochastic process known as random drift. However, mutations may also produce a selective advantage that boosts their chances of reaching fixation. Regions of genomes where new mutations are beneficial, rather than neutral or deleterious, tend to evolve more rapidly due to positive selection. Genes involved in immunity and defense are a well-known example; rapid evolution in these genes presumably occurs because new mutations help organisms to prevail in evolutionary "arms races" with pathogens. In recent years genome-wide scans for selection have enlarged our understanding of the genome evolution of various species. In this chapter, we will focus on methods to detect selection on the genome. In particular, we will discuss probabilistic models and how they have changed with the advent of new genome-wide data now available.

Teneurins: An Integrative Molecular, Functional, and Biomedical Overview of Their Role in Cancer

Teneurins are large transmembrane proteins originally identified in Drosophila. Their essential role in development of the central nervous system is conserved throughout species, and evidence supports their involvement in organogenesis of additional tissues. Homophilic and heterophilic interactions between Teneurin paralogues mediate cellular adhesion in crucial processes such as neuronal pathfinding and synaptic organization. At the molecular level, Teneurins are proteolytically processed into distinct subdomains that have been implicated in extracellular and intracellular signaling, and in transcriptional regulation. Phylogenetic studies have shown a high degree of intra- and interspecies conservation of Teneurin genes. Accordingly, the occurrence of genetic variants has been associated with functional and phenotypic alterations in experimental systems, and with some inherited or sporadic conditions. Recently, tumor-related variations in Teneurin gene expression have been associated with patient survival in different cancers. Although these findings were incidental and molecular mechanisms were not addressed, they suggested a potential utility of Teneurin transcript levels as biomarkers for disease prognosis. Mutations and chromosomal alterations affecting Teneurin genes have been found occasionally in tumors, but literature remains scarce. The analysis of open-access molecular and clinical datasets derived from large oncologic cohorts provides an invaluable resource for the identification of additional somatic mutations. However, Teneurin variants have not been classified in terms of pathogenic risk and their phenotypic impact remains unknown. On this basis, is it plausible to hypothesize that Teneurins play a role in carcinogenesis? Does current evidence support a tumor suppressive or rather oncogenic function for these proteins? Here, we comprehensively discuss available literature with integration of molecular evidence retrieved from open-access databases. We show that Teneurins undergo somatic changes comparable to those of well-established cancer genes, and discuss their involvement in cancer-related signaling pathways. Current data strongly suggest a functional contribution of Teneurins to human carcinogenesis.

Integrated Analysis of MicroRNA (miRNA) and mRNA Profiles Reveals Reduced Correlation between MicroRNA and Target Gene in Cancer

BACKGROUND

Accumulating evidences demonstrated that microRNA-target gene pairs were closely related to tumorigenesis and development. However, the correlation between miRNA and target gene was insufficiently understood, especially its changes between tumor and normal tissues.

OBJECTIVES

The aim of this study was to evaluate the changes of correlation of miRNAs-target pairs between normal and tumor.

MATERIALS AND METHODS

5680 mRNA and 5740 miRNA expression profiles of 11 major human cancers were downloaded from the Cancer Genome Atlas (TCGA). The 11 cancer types were bladder urothelial carcinoma, breast invasive carcinoma, head and neck squamous cell carcinoma, kidney chromophobe, kidney renal clear cell carcinoma, kidney renal papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, stomach adenocarcinoma, and thyroid carcinoma. For each cancer type, we firstly obtained differentially expressed miRNAs (DEMs) and genes (DEGs) in tumor and then acquired critical miRNA-target gene pairs by combining DEMs, DEGs and two experimentally validated miRNA-target interaction databases, miRTarBase and miRecords. We collected samples with both miRNA and mRNA expression values and performed a correlation analysis by Pearson method for miRNA-target pairs in normal and tumor, respectively.

RESULTS

We totally got 4743 critical miRNA-target pairs across 11 cancer types, and 4572 of them showed weaker correlation in tumor than in normal. The average correlation coefficients of miRNA-target pairs were different greatly between normal (-0.38 ~ -0.61) and tumor (-0.04 ~ -0.26) for 11 cancer type. The pan-cancer network, which consisted of 108 edges connecting 35 miRNAs and 89 target genes, showed the interactions of pairs appeared in multicancers.

CONCLUSIONS

This comprehensive analysis revealed that correlation between miRNAs and target genes was greatly reduced in tumor and these critical pairs we got were involved in cellular adhesion, proliferation, and migration. Our research could provide opportunities for investigating cancer molecular regulatory mechanism and seeking therapeutic targets.

Rejection of immunogenic tumor clones is limited by clonal fraction

Tumors often co-exist with T cells that recognize somatically mutated peptides presented by cancer cells on major histocompatibility complex I (MHC-I). However, it is unknown why the immune system fails to eliminate immune-recognizable neoplasms before they manifest as frank disease. To understand the determinants of MHC-I peptide immunogenicity in nascent tumors, we tested the ability of thousands of MHC-I ligands to cause tumor subclone rejection in immunocompetent mice by use of a new 'PresentER' antigen presentation platform. Surprisingly, we show that immunogenic tumor antigens do not lead to immune-mediated cell rejection when the fraction of cells bearing each antigen ('clonal fraction') is low. Moreover, the clonal fraction necessary to lead to rejection of immunogenic tumor subclones depends on the antigen. These data indicate that tumor neoantigen heterogeneity has an underappreciated impact on immune elimination of cancer cells and has implications for the design of immunotherapeutics such as cancer vaccines.

Selective Pressures on Human Cancer Genes along the Evolution of Mammals

Cancer is a disease driven by both somatic mutations that increase survival and proliferation of cell lineages and the evolution of genes associated with cancer risk in populations. Several genes associated with cancer in humans, hereafter cancer genes, show evidence of germline positive selection among species. Taking advantage of a large collection of mammalian genomes, we systematically looked for signatures of germline positive selection in 430 cancer genes available in COSMIC. We identified 40 cancer genes with a robust signal of positive selection in mammals. We found evidence for fewer selective constraints—higher number of non-synonymous substitutions per non-synonymous site to the number of synonymous substitutions per synonymous site (dN/dS)—and higher incidence of positive selection—more positively selected sites—in cancer genes bearing germline and recessive mutations that predispose to cancer. This finding suggests a potential association between relaxed selection, positive selection, and risk of hereditary cancer. On the other hand, we did not find significant differences in terms of tissue or gene type. Human cancer genes under germline positive selection in mammals are significantly enriched in the processes of DNA repair, with high presence of Fanconi anaemia/Breast Cancer A (FA/BRCA) pathway components and T cell proliferation genes. We also show that the inferred positively selected sites in the two genes with the strongest signal of positive selection, i.e., BRCA2 and PTPRC, are in regions of functional relevance, which could be relevant to cancer susceptibility.

Somatic mutant clones colonize the human esophagus with age

The extent to which cells in normal tissues accumulate mutations throughout life is poorly understood. Some mutant cells expand into clones that can be detected by genome sequencing. We mapped mutant clones in normal esophageal epithelium from nine donors (age range, 20 to 75 years). Somatic mutations accumulated with age and were caused mainly by intrinsic mutational processes. We found strong positive selection of clones carrying mutations in 14 cancer genes, with tens to hundreds of clones per square centimeter. In middle-aged and elderly donors, clones with cancer-associated mutations covered much of the epithelium, with NOTCH1 and TP53 mutations affecting 12 to 80% and 2 to 37% of cells, respectively. Unexpectedly, the prevalence of NOTCH1 mutations in normal esophagus was several times higher than in esophageal cancers. These findings have implications for our understanding of cancer and aging.

Need for high-resolution Genetic Analysis in iPSC: Results and Lessons from the ForIPS Consortium

Genetic integrity of induced pluripotent stem cells (iPSCs) is essential for their validity as disease models and for potential therapeutic use. We describe the comprehensive analysis in the ForIPS consortium: an iPSC collection from donors with neurological diseases and healthy controls. Characterization included pluripotency confirmation, fingerprinting, conventional and molecular karyotyping in all lines. In the majority, somatic copy number variants (CNVs) were identified. A subset with available matched donor DNA was selected for comparative exome sequencing. We identified single nucleotide variants (SNVs) at different allelic frequencies in each clone with high variability in mutational load. Low frequencies of variants in parental fibroblasts highlight the importance of germline samples. Somatic variant number was independent from reprogramming, cell type and passage. Comparison with disease genes and prediction scores suggest biological relevance for some variants. We show that high-throughput sequencing has value beyond SNV detection and the requirement to individually evaluate each clone.

Criticality in tumor evolution and clinical outcome

How mutation and selection determine the fitness landscape of tumors and hence clinical outcome is an open fundamental question in cancer biology, crucial for the assessment of therapeutic strategies and resistance to treatment. Here we explore the mutation-selection phase diagram of 6,721 tumors representing 23 cancer types by quantifying the overall somatic point mutation load (ML) and selection (dN/dS) in the entire proteome of each tumor. We show that ML strongly correlates with patient survival, revealing two opposing regimes around a critical point. In low-ML cancers, a high number of mutations indicates poor prognosis, whereas high-ML cancers show the opposite trend, presumably due to mutational meltdown. Although the majority of cancers evolve near neutrality, deviations are observed at extreme MLs. Melanoma, with the highest ML, evolves under purifying selection, whereas in low-ML cancers, signatures of positive selection are observed, demonstrating how selection affects tumor fitness. Moreover, different cancers occupy specific positions on the ML-dN/dS plane, revealing a diversity of evolutionary trajectories. These results support and expand the theory of tumor evolution and its nonlinear effects on survival.

Gene characteristics predicting missense, nonsense and frameshift mutations in tumor samples

BACKGROUND

Because driver mutations provide selective advantage to the mutant clone, they tend to occur at a higher frequency in tumor samples compared to selectively neutral (passenger) mutations. However, mutation frequency alone is insufficient to identify cancer genes because mutability is influenced by many gene characteristics, such as size, nucleotide composition, etc. The goal of this study was to identify gene characteristics associated with the frequency of somatic mutations in the gene in tumor samples.

RESULTS

We used data on somatic mutations detected by genome wide screens from the Catalog of Somatic Mutations in Cancer (COSMIC). Gene size, nucleotide composition, expression level of the gene, relative replication time in the cell cycle, level of evolutionary conservation and other gene characteristics (totaling 11) were used as predictors of the number of somatic mutations. We applied stepwise multiple linear regression to predict the number of mutations per gene. Because missense, nonsense, and frameshift mutations are associated with different sets of gene characteristics, they were modeled separately. Gene characteristics explain 88% of the variation in the number of missense, 40% of nonsense, and 23% of frameshift mutations. Comparisons of the observed and expected numbers of mutations identified genes with a higher than expected number of mutations- positive outliers. Many of these are known driver genes. A number of novel candidate driver genes was also identified.

CONCLUSIONS

By comparing the observed and predicted number of mutations in a gene, we have identified known cancer-associated genes as well as 111 novel cancer associated genes. We also showed that adding the number of silent mutations per gene reported by genome/exome wide screens across all cancer type (COSMIC data) as a predictor substantially exceeds predicting accuracy of the most popular cancer gene predicting tool - MutsigCV.

Breaking point: the genesis and impact of structural variation in tumours

Somatic structural variants undoubtedly play important roles in driving tumourigenesis. This is evident despite the substantial technical challenges that remain in accurately detecting structural variants and their breakpoints in tumours and in spite of our incomplete understanding of the impact of structural variants on cellular function. Developments in these areas of research contribute to the ongoing discovery of structural variation with a clear impact on the evolution of the tumour and on the clinical importance to the patient. Recent large whole genome sequencing studies have reinforced our impression of each tumour as a unique combination of mutations but paradoxically have also discovered similar genome-wide patterns of single-nucleotide and structural variation between tumours. Statistical methods have been developed to deconvolute mutation patterns, or signatures, that recur across samples, providing information about the mutagens and repair processes that may be active in a given tumour. These signatures can guide treatment by, for example, highlighting vulnerabilities in a particular tumour to a particular chemotherapy. Thus, although the complete reconstruction of the full evolutionary trajectory of a tumour genome remains currently out of reach, valuable data are already emerging to improve the treatment of cancer.

Widespread Selection for Oncogenic Mutant Allele Imbalance in Cancer

Driver mutations in oncogenes encode proteins with gain-of-function properties that enhance fitness. Heterozygous mutations are thus viewed as sufficient for tumorigenesis. We describe widespread oncogenic mutant allele imbalance in 13,448 prospectively characterized cancers. Imbalance was selected for through modest dosage increases of gain-of-fitness mutations. Negative selection targeted haplo-essential effectors of the spliceosome. Loss of the normal allele comprised a distinct class of imbalance driven by competitive fitness, which correlated with enhanced response to targeted therapies. In many cancers, an antecedent oncogenic mutation drove evolutionarily dependent allele-specific imbalance. In other instances, oncogenic mutations co-opted independent copy-number changes via the evolutionary process of exaptation. Oncogenic allele imbalance is a pervasive evolutionary innovation that enhances fitness and modulates sensitivity to targeted therapy.

Analysis of 7,815 cancer exomes reveals associations between mutational processes and somatic driver mutations

Driver mutations are the genetic variants responsible for oncogenesis, but how specific somatic mutational events arise in cells remains poorly understood. Mutational signatures derive from the frequency of mutated trinucleotides in a given cancer sample, and they provide an avenue for investigating the underlying mutational processes that operate in cancer. Here we analyse somatic mutations from 7,815 cancer exomes from The Cancer Genome Atlas (TCGA) across 26 cancer types. We curate a list of 50 known cancer driver mutations by analysing recurrence in our cohort and annotations of known cancer-associated genes from the Cancer Gene Census, IntOGen database and Cancer Genome Interpreter. We then use these datasets to perform binary univariate logistic regression and establish the statistical relationship between individual driver mutations and known mutational signatures across different cancer types. Our analysis led to the identification of 39 significant associations between driver mutations and mutational signatures (P < 0.004, with a false discovery rate of < 5%). We first validate our methodology by establishing statistical links for known and novel associations between driver mutations and the mutational signature arising from Polymerase Epsilon proofreading deficiency. We then examine associations between driver mutations and mutational signatures for AID/APOBEC enzyme activity and deficient mismatch repair. We also identify negative associations (odds ratio < 1) between mutational signatures and driver mutations, and here we examine the role of aging and cigarette smoke mutagenesis in the generation of driver mutations in IDH1 and KRAS in brain cancers and lung adenocarcinomas respectively. Our study provides statistical foundations for hypothesised links between otherwise independent biological processes and we uncover previously unexplored relationships between driver mutations and mutagenic processes during cancer development. These associations give insights into how cancers acquire advantageous mutations and can provide direction to guide further mechanistic studies into cancer pathogenesis.

Cancer develops when cells acquire somatic driver mutations that confer a growth advantage. The origins underlying the development of many of these mutations remain largely unknown. Mutational signatures represent the frequency of different somatic mutations across a genome and can be used to characterise the mutational processes that have operated over time within an individual cancer. In this study, we use mutational signatures as a tool to identify associations between mutational processes and cancer-causing mutations known as drivers. We hypothesised that in some cases a driver mutation would be the underlying cause of an associated signature. In other cases, the altered trinucleotide preferences arising from a signature would have increased the likelihood of the associated driver mutation arising. We determine which scenario is most likely to be the case by examining the trinucleotide context of each driver mutation. Here we identify 39 significant associations using a cohort of 7,815 cancer exomes. We examine known and novel associations between driver mutations and mutational signatures arising from processes such as defective proofreading during DNA replication, AID/APOBEC enzyme-associated mutagenesis and deficient mismatch repair. Our study explores important relationships that can inform our understanding of the complex pathogenic history associated with cancer development.

TGF-β-associated extracellular matrix genes link cancer-associated fibroblasts to immune evasion and immunotherapy failure

The extracellular matrix (ECM) is a key determinant of cancer progression and prognosis. Here we report findings from one of the largest pan-cancer analyses of ECM gene dysregulation in cancer. We define a distinct set of ECM genes upregulated in cancer (C-ECM) and linked to worse prognosis. We found that the C-ECM transcriptional programme dysregulation is correlated with the activation of TGF-β signalling in cancer-associated fibroblasts and is linked to immunosuppression in otherwise immunologically active tumours. Cancers that activate this programme carry distinct genomic profiles, such as BRAF, SMAD4 and TP53 mutations and MYC amplification. Finally, we show that this signature is a predictor of the failure of PD-1 blockade and outperforms previously-proposed biomarkers. Thus, our findings identify a distinct transcriptional pattern of ECM genes in operation across cancers that may be potentially targeted, pending preclinical validation, using TGF-β blockade to enhance responses to immune-checkpoint blockade.

Modeling human RNA spliceosome mutations in the mouse: not all mice were created equal

Myelodysplastic syndromes (MDS) and related myelodysplastic/myeloproliferative neoplasms (MDS/MPNs) are clonal stem cell disorders, primarily affecting patients over 65 years of age. Mapping of the MDS and MDS/MPN genome identified recurrent heterozygous mutations in the RNA splicing machinery, with the SF3B1, SRSF2, and U2AF1 genes being frequently mutated. To better understand how spliceosomal mutations contribute to MDS pathogenesis in vivo, numerous groups have sought to establish conditional murine models of SF3B1, SRSF2, and U2AF1 mutations. The high degree of conservation of hematopoiesis between mice and human and the well-established phenotyping and genetic modification approaches make murine models an effective tool with which to study how a gene mutation contributes to disease pathogenesis. The murine models of spliceosomal mutations described to date recapitulate human MDS or MDS/MPN to varying extents. Reasons for the differences in phenotypes reported between alleles of the same mutation are varied, but the nature of the genetic modification itself and subsequent analysis methods are important to consider. In this review, we summarize recently reported murine models of SF3B1, SRSF2, and U2AF1 mutations, with a particular focus on the genetically engineered modifications underlying the models and the experimental approaches applied.