Identifying Epistasis in Cancer Genomes: A Delicate Affair

Prevalence and Associations of Co-occurrence of NFE2L2 Mutations and Chromosome 3q26 Amplification in Lung Cancer

Background   NFE2L2 (nuclear factor erythroid-2-related factor-2) encodes a basic leucine zipper (bZIP) transcription factor and exhibits variations in various tumor types, including lung cancer. In this study, we comprehensively investigated the impact of simultaneous mutations on the survival of NFE2L2 -mutant lung cancer patients within specific subgroups. Methods  A cohort of 1,103 lung cancer patients was analyzed using hybridization capture-based next-generation sequencing. Results  The NFE2L2 gene had alterations in 3.0% (33/1,103) of lung cancer samples, including 1.5% (15/992) in adenocarcinoma and 16.2% (18/111) in squamous cell carcinoma. Thirty-four variations were found, mainly in exons 2 (27/34). New variations in exon 2 (p.D21H, p.V36_E45del, p.F37_E45del, p.R42P, p.E67Q, and p.L76_E78delinsQ) were identified. Some patients had copy number amplifications. Co-occurrence with TP53 (84.8%), CDKN2A (33.3%), KMT2B (33.3%), LRP1B (33.3%), and PIK3CA (27.3%) mutations was common. Variations of NFE2L2 displayed the tightest co-occurrence with IRF2 , TERC , ATR , ZMAT3 , and SOX2 ( p  < 0.001). In The Cancer Genome Atlas Pulmonary Squamous Carcinoma project, patients with NFE2L2 variations and 3q26 amplification had longer median survival (63.59 vs. 32.04 months, p  = 0.0459) and better overall survival. Conclusions   NFE2L2 mutations display notable heterogeneity in lung cancer. The coexistence of NFE2L2 mutations and 3q26 amplification warrants in-depth exploration of their potential clinical implications and treatment approaches for affected patients.

A latent variable model for evaluating mutual exclusivity and co-occurrence between driver mutations in cancer

A key challenge in cancer genomics is understanding the functional relationships and dependencies between combinations of somatic mutations that drive cancer development. Such driver mutations frequently exhibit patterns of mutual exclusivity or co-occurrence across tumors, and many methods have been developed to identify such dependency patterns from bulk DNA sequencing data of a cohort of patients. However, while mutual exclusivity and co-occurrence are described as properties of driver mutations, existing methods do not explicitly disentangle functional, driver mutations from neutral, passenger mutations. In particular, nearly all existing methods evaluate mutual exclusivity or co-occurrence at the gene level, marking a gene as mutated if any mutation - driver or passenger - is present. Since some genes have a large number of passenger mutations, existing methods either restrict their analyses to a small subset of suspected driver genes - limiting their ability to identify novel dependencies - or make spurious inferences of mutual exclusivity and co-occurrence involving genes with many passenger mutations. We introduce DIALECT, an algorithm to identify dependencies between pairs of driver mutations from somatic mutation counts. We derive a latent variable mixture model for drivers and passengers that combines existing probabilistic models of passenger mutation rates with a latent variable describing the unknown status of a mutation as a driver or passenger. We use an expectation maximization (EM) algorithm to estimate the parameters of our model, including the rates of mutually exclusivity and co-occurrence between drivers. We demonstrate that DIALECT more accurately infers mutual exclusivity and co-occurrence between driver mutations compared to existing methods on both simulated mutation data and somatic mutation data from 5 cancer types in The Cancer Genome Atlas (TCGA).

Genetic interactions reveal distinct biological and therapeutic implications in breast cancer

Co-occurrence and mutual exclusivity of genomic alterations may reflect the existence of genetic interactions, potentially shaping distinct biological phenotypes and impacting therapeutic response in breast cancer. However, our understanding of them remains limited. Herein, we investigate a large-scale multi-omics cohort (n = 873) and a real-world clinical sequencing cohort (n = 4,405) including several clinical trials with detailed treatment outcomes and perform functional validation in patient-derived organoids, tumor fragments, and in vivo models. Through this comprehensive approach, we construct a network comprising co-alterations and mutually exclusive events and characterize their therapeutic potential and underlying biological basis. Notably, we identify associations between TP53mut-AURKAamp and endocrine therapy resistance, germline BRCA1mut-MYCamp and improved sensitivity to PARP inhibitors, and TP53mut-MYBamp and immunotherapy resistance. Furthermore, we reveal that precision treatment strategies informed by co-alterations hold promise to improve patient outcomes. Our study highlights the significance of genetic interactions in guiding genome-informed treatment decisions beyond single driver alterations.

Combinatorial in vivo genome editing identifies widespread epistasis during lung tumorigenesis

Lung adenocarcinoma, the most common subtype of lung cancer, is genomically complex, with tumors containing tens to hundreds of non-synonymous mutations. However, little is understood about how genes interact with each other to enable tumorigenesis in vivo , largely due to a lack of methods for investigating genetic interactions in a high-throughput and multiplexed manner. Here, we employed a novel platform to generate tumors with all pairwise inactivation of ten tumor suppressor genes within an autochthonous mouse model of oncogenic KRAS-driven lung cancer. By quantifying the fitness of tumors with every single and double mutant genotype, we show that most tumor suppressor genetic interactions exhibited negative epistasis, with diminishing returns on tumor fitness. In contrast, Apc inactivation showed positive epistasis with the inactivation of several other genes, including dramatically synergistic effects on tumor fitness in combination with Lkb1 or Nf1 inactivation. This approach has the potential to expand the scope of genetic interactions that may be functionally characterized in vivo , which could lead to a better understanding of how complex tumor genotypes impact each step of carcinogenesis.

Conceptual breakthroughs of the long noncoding RNA functional system and its endogenous regulatory role in the cancerous regime

Long noncoding RNAs (lncRNAs) derived from noncoding regions in the human genome were once regarded as junks with no biological significance, but recent studies have shown that these molecules are highly functional, prompting an explosion of studies on their biology. However, these recent efforts have only begun to recognize the biological significance of a small fraction (< 1%) of the lncRNAs. The basic concept of these lncRNA functions remains controversial. This controversy arises primarily from conventional biased observations based on limited datasets. Fortunately, emerging big data provides a promising path to circumvent conventional bias to understand an unbiased big picture of lncRNA biology and advance the fundamental principles of lncRNA biology. This review focuses on big data studies that break through the critical concepts of the lncRNA functional system and its endogenous regulatory roles in all cancers. lncRNAs have unique functional systems distinct from proteins, such as transcriptional initiation and regulation, and they abundantly interact with mitochondria and consume less energy. lncRNAs, rather than proteins as traditionally thought, function as the most critical endogenous regulators of all cancers. lncRNAs regulate the cancer regulatory regime by governing the endogenous regulatory network of all cancers. This is accomplished by dominating the regulatory network module and serving as a key hub and top inducer. These critical conceptual breakthroughs lay a blueprint for a comprehensive functional picture of the human genome. They also lay a blueprint for combating human diseases that are regulated by lncRNAs.

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

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

Novel Alzheimer's disease genes and epistasis identified using machine learning GWAS platform

Alzheimer's disease (AD) is a complex genetic disease, and variants identified through genome-wide association studies (GWAS) explain only part of its heritability. Epistasis has been proposed as a major contributor to this 'missing heritability', however, many current methods are limited to only modelling additive effects. We use VariantSpark, a machine learning approach to GWAS, and BitEpi, a tool for epistasis detection, to identify AD associated variants and interactions across two independent cohorts, ADNI and UK Biobank. By incorporating significant epistatic interactions, we captured 10.41% more phenotypic variance than logistic regression (LR). We validate the well-established AD loci, APOE, and identify two novel genome-wide significant AD associated loci in both cohorts, SH3BP4 and SASH1, which are also in significant epistatic interactions with APOE. We show that the SH3BP4 SNP has a modulating effect on the known pathogenic APOE SNP, demonstrating a possible protective mechanism against AD. SASH1 is involved in a triplet interaction with pathogenic APOE SNP and ACOT11, where the SASH1 SNP lowered the pathogenic interaction effect between ACOT11 and APOE. Finally, we demonstrate that VariantSpark detects disease associations with 80% fewer controls than LR, unlocking discoveries in well annotated but smaller cohorts.

Oncogenic context shapes the fitness landscape of tumor suppression

Tumors acquire alterations in oncogenes and tumor suppressor genes in an adaptive walk through the fitness landscape of tumorigenesis. However, the interactions between oncogenes and tumor suppressor genes that shape this landscape remain poorly resolved and cannot be revealed by human cancer genomics alone. Here, we use a multiplexed, autochthonous mouse platform to model and quantify the initiation and growth of more than one hundred genotypes of lung tumors across four oncogenic contexts: KRAS G12D, KRAS G12C, BRAF V600E, and EGFR L858R. We show that the fitness landscape is rugged-the effect of tumor suppressor inactivation often switches between beneficial and deleterious depending on the oncogenic context-and shows no evidence of diminishing-returns epistasis within variants of the same oncogene. These findings argue against a simple linear signaling relationship amongst these three oncogenes and imply a critical role for off-axis signaling in determining the fitness effects of inactivating tumor suppressors.

Fully accessible fitness landscape of oncogene-negative lung adenocarcinoma

Cancer genomes are almost invariably complex with genomic alterations cooperating during each step of carcinogenesis. In cancers that lack a single dominant oncogene mutation, cooperation between the inactivation of multiple tumor suppressor genes can drive tumor initiation and growth. Here, we shed light on how the sequential acquisition of genomic alterations generates oncogene-negative lung tumors. We couple tumor barcoding with combinatorial and multiplexed somatic genome editing to characterize the fitness landscapes of three tumor suppressor genes NF1, RASA1, and PTEN, the inactivation of which jointly drives oncogene-negative lung adenocarcinoma initiation and growth. The fitness landscape was surprisingly accessible, with each additional mutation leading to growth advantage. Furthermore, the fitness landscapes remained fully accessible across backgrounds with the inactivation of additional tumor suppressor genes. These results suggest that while predicting cancer evolution will be challenging, acquiring the multiple alterations that drive the growth of oncogene-negative tumors can be facilitated by the lack of constraints on mutational order.

Gene-network analysis predicts clinical response to immunotherapy in patients affected by NSCLC

OBJECTIVES

Predictive biomarkers of response to immune checkpoint inhibitors (ICIs) have been extensively studied in non-small cell lung cancer (NSCLC) with controversial results. Recently, gene-network analysis emerged as a new tool to address tumor biology and behavior, representing a potential tool to evaluate response to therapies.

METHODS

Clinical data and genetic profiles of 644 advanced NSCLCs were retrieved from cBioPortal and the Cancer Genome Atlas (TCGA); 243 ICI-treated NSCLCs were used to identify an immunotherapy response signatures via mutated gene network analysis and K-means unsupervised clustering. Signatures predictive values were tested in an external dataset of 242 cases and assessed versus a control group of 159 NSCLCs treated with standard chemotherapy.

RESULTS

At least two mutations in the coding sequence of genes belonging to the chromatin remodelling pathway (A signature), and/or at least two mutations of genes involved in cell-to-cell signalling pathways (B signature), showed positive prediction in ICI-treated advanced NSCLC. Signatures performed best when combined for patients undergoing first-line immunotherapy, and for those receiving combined ICIs.

CONCLUSIONS

Alterations in genes related to chromatin remodelling complexes and cell-to-cell crosstalk may force dysfunctional immune evasion, explaining susceptibility to immunotherapy. Therefore, exploring mutated gene networks could be valuable for determining essential biological interactions, contributing to treatment personalization.

Gene Interactions in Human Disease Studies-Evidence Is Mounting

Despite monumental advances in molecular technology to generate genome sequence data at scale, there is still a considerable proportion of heritability in most complex diseases that remains unexplained. Because many of the discoveries have been single-nucleotide variants with small to moderate effects on disease, the functional implication of many of the variants is still unknown and, thus, we have limited new drug targets and therapeutics. We, and many others, posit that one primary factor that has limited our ability to identify novel drug targets from genome-wide association studies may be due to gene interactions (epistasis), gene-environment interactions, network/pathway effects, or multiomic relationships. We propose that many of these complex models explain much of the underlying genetic architecture of complex disease. In this review, we discuss the evidence from multiple research avenues, ranging from pairs of alleles to multiomic integration studies and pharmacogenomics, that supports the need for further investigation of gene interactions (or epistasis) in genetic and genomic studies of human disease. Our goal is to catalog the mounting evidence for epistasis in genetic studies and the connections between genetic interactions and human health and disease that could enable precision medicine of the future.

Leveraging the genetic correlation between traits improves the detection of epistasis in genome-wide association studies

Epistasis, commonly defined as the interaction between genetic loci, is known to play an important role in the phenotypic variation of complex traits. As a result, many statistical methods have been developed to identify genetic variants that are involved in epistasis, and nearly all of these approaches carry out this task by focusing on analyzing one trait at a time. Previous studies have shown that jointly modeling multiple phenotypes can often dramatically increase statistical power for association mapping. In this study, we present the "multivariate MArginal ePIstasis Test" (mvMAPIT)-a multioutcome generalization of a recently proposed epistatic detection method which seeks to detect marginal epistasis or the combined pairwise interaction effects between a given variant and all other variants. By searching for marginal epistatic effects, one can identify genetic variants that are involved in epistasis without the need to identify the exact partners with which the variants interact-thus, potentially alleviating much of the statistical and computational burden associated with conventional explicit search-based methods. Our proposed mvMAPIT builds upon this strategy by taking advantage of correlation structure between traits to improve the identification of variants involved in epistasis. We formulate mvMAPIT as a multivariate linear mixed model and develop a multitrait variance component estimation algorithm for efficient parameter inference and P-value computation. Together with reasonable model approximations, our proposed approach is scalable to moderately sized genome-wide association studies. With simulations, we illustrate the benefits of mvMAPIT over univariate (or single-trait) epistatic mapping strategies. We also apply mvMAPIT framework to protein sequence data from two broadly neutralizing anti-influenza antibodies and approximately 2,000 heterogeneous stock of mice from the Wellcome Trust Centre for Human Genetics. The mvMAPIT R package can be downloaded at https://github.com/lcrawlab/mvMAPIT.

Prognostic Values of Core Genes in Pilocytic Astrocytom

BACKGROUND

Pilocytic astrocytoma (PA) is the most common primary brain tumor in children and adolescents. Treatment strategy largely depends on its key genes and molecular mutations. This study aimed to identify potential biomarkers of PA closely related to its prognosis.

METHODS

The gene expression profiles (series numbers GSE50161, GSE66354, and GSE86574) of PA and normal brain tissues were downloaded from the Gene Expression Omnibus database. The Gene Expression Omnibus2R was used to identify differentially expressed genes. The overlapping differentially expressed genes were subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database. A protein-protein interaction network was constructed using Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape. The Gene Expression Profiling Interactive Analysis 2 (GEPIA2) tool analyzed the impact of hub genes on PA prognosis based on the Kaplan-Meier curves.

RESULTS

Compared with normal brain tissues (n = 36), a total of 37 upregulated and 144 downregulated genes were identified in PA (n = 40). In the protein-protein interaction network construction and GEPIA2 survival analysis, 2 of the top 10 hub genes were significantly associated with decreased overall survival of PA patients, namely Gamma-aminobutyric acid A receptor alpha 2 (hazard ratio  =  2.8, P  <  0.01) and regulating synaptic membrane exocytosis protein 1) (hazard ratio =  3.2, P  <  0.01).

CONCLUSIONS

This bioinformatics analysis reveals that low expression of Gamma-aminobutyric acid A receptor alpha 2 and regulating synaptic membrane exocytosis protein 1 is associated with a favorable prognosis for PA patients. These 2 hub genes could be novel biomarkers for prognosis assessment, furthermore a key element for treatment decisions in the future.

Associations Between Cancer Predisposition Mutations and Clonal Hematopoiesis in Patients With Solid Tumors

PURPOSE

Clonal hematopoiesis (CH), the expansion of clones in the hematopoietic system, has been linked to different internal and external features such as aging, genetic ancestry, smoking, and oncologic treatment. However, the interplay between mutations in known cancer predisposition genes and CH has not been thoroughly examined in patients with solid tumors.

METHODS

We used prospective tumor-blood paired sequencing data from 46,906 patients who underwent Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT) testing to interrogate the associations between CH and rare pathogenic or likely pathogenic (P/LP) germline variants.

RESULTS

We observed an enrichment of CH-positive patients among those carrying P/LP germline mutations and identified a significant association between P/LP germline variants in ATM and CH. Germline and CH comutation patterns in ATM, TP53, and CHEK2 suggested biallelic inactivation as a potential mediator of clonal expansion. Moreover, we observed that CH-PPM1D mutations, similar to somatic tumor-associated PPM1D mutations, were depleted in patients with P/LP germline mutations in the DNA damage response (DDR) genes ATM, CHEK2, and TP53. Patients with solid tumors and harboring P/LP germline mutations, CH mutations, and mosaicism chromosomal alterations might be at an increased risk of developing secondary leukemia while germline variants in TP53 were identified as an independent risk factor (hazard ratio, 36; P < .001) for secondary leukemias.

CONCLUSION

Our results suggest a close relationship between inherited variants and CH mutations within the DDR genes in patients with solid tumors. Associations identified in this study might translate into enhanced clinical surveillance for CH and associated comorbidities in patients with cancer harboring these germline mutations.

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

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

Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications

Since the rise of next-generation sequencing technologies, the catalogue of mutations in cancer has been continuously expanding. To address the complexity of the cancer-genomic landscape and extract meaningful insights, numerous computational approaches have been developed over the last two decades. In this review, we survey the current leading computational methods to derive intricate mutational patterns in the context of clinical relevance. We begin with mutation signatures, explaining first how mutation signatures were developed and then examining the utility of studies using mutation signatures to correlate environmental effects on the cancer genome. Next, we examine current clinical research that employs mutation signatures and discuss the potential use cases and challenges of mutation signatures in clinical decision-making. We then examine computational studies developing tools to investigate complex patterns of mutations beyond the context of mutational signatures. We survey methods to identify cancer-driver genes, from single-driver studies to pathway and network analyses. In addition, we review methods inferring complex combinations of mutations for clinical tasks and using mutations integrated with multi-omics data to better predict cancer phenotypes. We examine the use of these tools for either discovery or prediction, including prediction of tumor origin, treatment outcomes, prognosis, and cancer typing. We further discuss the main limitations preventing widespread clinical integration of computational tools for the diagnosis and treatment of cancer. We end by proposing solutions to address these challenges using recent advances in machine learning.

Oncogenic RAS promotes leukemic transformation of CUX1-deficient cells

-7/del(7q) is prevalent across subtypes of myeloid neoplasms. CUX1, located on 7q22, encodes a homeodomain-containing transcription factor, and, like -7/del(7q), CUX1 inactivating mutations independently carry a poor prognosis. As with loss of 7q, CUX1 mutations often occur early in disease pathogenesis. We reported that CUX1 deficiency causes myelodysplastic syndrome in mice but was insufficient to drive acute myeloid leukemia (AML). Given the known association between -7/del(7q) and RAS pathway mutations, we mined cancer genome databases and explicitly linked CUX1 mutations with oncogenic RAS mutations. To determine if activated RAS and CUX1 deficiency promote leukemogenesis, we generated mice bearing NrasG12D and CUX1-knockdown which developed AML, not seen in mice with either mutation alone. Oncogenic RAS imparts increased self-renewal on CUX1-deficient hematopoietic stem/progenitor cells (HSPCs). Reciprocally, CUX1 knockdown amplifies RAS signaling through reduction of negative regulators of RAS/PI3K signaling. Double mutant HSPCs were responsive to PIK3 or MEK inhibition. Similarly, low expression of CUX1 in primary AML samples correlates with sensitivity to the same inhibitors, suggesting a potential therapy for malignancies with CUX1 inactivation. This work demonstrates an unexpected convergence of an oncogene and tumor suppressor gene on the same pathway.

Fully accessible fitness landscape of oncogene-negative lung adenocarcinoma

Cancer genomes are almost invariably complex with genomic alterations cooperating during each step of carcinogenesis. In cancers that lack a single dominant oncogene mutation, cooperation between the inactivation of multiple tumor suppressor genes can drive tumor initiation and growth. Here, we shed light on how the sequential acquisition of genomic alterations generates oncogene-negative lung tumors. We couple tumor barcoding with combinatorial and multiplexed somatic genome editing to characterize the fitness landscapes of three tumor suppressor genes NF1, RASA1, and PTEN, the inactivation of which jointly drives oncogene-negative lung adenocarcinoma initiation and growth. The fitness landscape was surprisingly accessible, with each additional mutation leading to growth advantage. Furthermore, the fitness landscapes remained fully accessible across backgrounds with additional tumor suppressor mutations. These results suggest that while predicting cancer evolution will be challenging, acquiring the multiple alterations required for the growth of oncogene-negative tumors can be facilitated by the lack of constraints on mutational order.

Dimensionality reduction approach for many-objective epistasis analysis

In epistasis analysis, single-nucleotide polymorphism-single-nucleotide polymorphism interactions (SSIs) among genes may, alongside other environmental factors, influence the risk of multifactorial diseases. To identify SSI between cases and controls (i.e. binary traits), the score for model quality is affected by different objective functions (i.e. measurements) because of potential disease model preferences and disease complexities. Our previous study proposed a multiobjective approach-based multifactor dimensionality reduction (MOMDR), with the results indicating that two objective functions could enhance SSI identification with weak marginal effects. However, SSI identification using MOMDR remains a challenge because the optimal measure combination of objective functions has yet to be investigated. This study extended MOMDR to the many-objective version (i.e. many-objective MDR, MaODR) by integrating various disease probability measures based on a two-way contingency table to improve the identification of SSI between cases and controls. We introduced an objective function selection approach to determine the optimal measure combination in MaODR among 10 well-known measures. In total, 6 disease models with and 40 disease models without marginal effects were used to evaluate the general algorithms, namely those based on multifactor dimensionality reduction, MOMDR and MaODR. Our results revealed that the MaODR-based three objective function model, correct classification rate, likelihood ratio and normalized mutual information (MaODR-CLN) exhibited the higher 6.47% detection success rates (Accuracy) than MOMDR and higher 17.23% detection success rates than MDR through the application of an objective function selection approach. In a Wellcome Trust Case Control Consortium, MaODR-CLN successfully identified the significant SSIs (P < 0.001) associated with coronary artery disease. We performed a systematic analysis to identify the optimal measure combination in MaODR among 10 objective functions. Our combination detected SSIs-based binary traits with weak marginal effects and thus reduced spurious variables in the score model. MOAI is freely available at https://sites.google.com/view/maodr/home.

Epistasis and evolutionary dependencies in human cancers

Cancer evolution is driven by the concerted action of multiple molecular alterations, which emerge and are selected during tumor progression. An alteration is selected when it provides an advantage to the tumor cell. However, the advantage provided by a specific alteration depends on the tumor lineage, cell epigenetic state, and presence of additional alterations. In this case, we say that an evolutionary dependency exists between an alteration and what influences its selection. Epistatic interactions between altered genes lead to evolutionary dependencies (EDs), by favoring or vetoing specific combinations of events. Large-scale cancer genomics studies have discovered examples of such dependencies, and showed that they influence tumor progression, disease phenotypes, and therapeutic response. In the past decade, several algorithmic approaches have been proposed to infer EDs from large-scale genomics datasets. These methods adopt diverse strategies to address common challenges and shed new light on cancer evolutionary trajectories. Here, we review these efforts starting from a simple conceptualization of the problem, presenting the tackled and still unmet needs in the field, and discussing the implications of EDs in cancer biology and precision oncology.

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.

OMEN: Network-based Driver Gene Identification using Mutual Exclusivity

MOTIVATION

Network-based driver identification methods that can exploit mutual exclusivity typically fail to detect rare drivers because of their statistical rigor. Propagation-based methods in contrast allow recovering rare driver genes, but the interplay between network topology and high-scoring nodes often results in spurious predictions. The specificity of driver gene detection can be improved by taking into account both gene-specific and gene-set properties. Combining these requires a formalism that can adjust gene-set properties depending on the exact network context within which a gene is analyzed.

RESULTS

We developed OMEN: a logic programming framework based on random walk semantics. OMEN presents a number of novel concepts. In particular, its design is unique in that it presents an effective approach to combine both gene-specific driver properties and gene-set properties, and includes a novel method to avoid restrictive, a priori filtering of genes by exploiting the gene-set property of mutual exclusivity, expressed in terms of the functional impact scores of mutations, rather than in terms of simple binary mutation calls. Applying OMEN to a benchmark data set derived from TCGA illustrates how OMEN is able to robustly identify driver genes and modules of driver genes as proxies of driver pathways.

AVAILABILITY

The source code is freely available for download at www.github.com/DriesVanDaele/OMEN The data set is archived at https://doi.org/10.5281/zenodo.6419097 and the code at https://doi.org/10.5281/zenodo.6419764.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

EPIMUTESTR: a nearest neighbor machine learning approach to predict cancer driver genes from the evolutionary action of coding variants

Discovering rare cancer driver genes is difficult because their mutational frequency is too low for statistical detection by computational methods. EPIMUTESTR is an integrative nearest-neighbor machine learning algorithm that identifies such marginal genes by modeling the fitness of their mutations with the phylogenetic Evolutionary Action (EA) score. Over cohorts of sequenced patients from The Cancer Genome Atlas representing 33 tumor types, EPIMUTESTR detected 214 previously inferred cancer driver genes and 137 new candidates never identified computationally before of which seven genes are supported in the COSMIC Cancer Gene Census. EPIMUTESTR achieved better robustness and specificity than existing methods in a number of benchmark methods and datasets.

Cancer mutation profiles predict ICIs efficacy in patients with non-small cell lung cancer

Although immune checkpoint inhibitors (ICIs) have produced remarkable responses in non-small cell lung cancer (NSCLC) patients, receivers still have a relatively low response rate. Initial response assessment by conventional imaging and evaluation criteria is often unable to identify whether patients can achieve durable clinical benefit from ICIs. Overall, there are sparse effective biomarkers identified to screen NSCLC patients responding to this therapy. A lot of studies have reported that patients with specific gene mutations may benefit from or resist to immunotherapy. However, the single gene mutation may be not effective enough to predict the benefit from immunotherapy for patients. With the advancement in sequencing technology, further studies indicate that many mutations often co-occur and suggest a drastic transformation of tumour microenvironment phenotype. Moreover, co-mutation events have been reported to synergise to activate or suppress signalling pathways of anti-tumour immune response, which also indicates a potential target for combining intervention. Thus, the different mutation profile (especially co-mutation) of patients may be an important concern for predicting or promoting the efficacy of ICIs. However, there is a lack of comprehensive knowledge of this field until now. Therefore, in this study, we reviewed and elaborated the value of cancer mutation profile in predicting the efficacy of immunotherapy and analysed the underlying mechanisms, to provide an alternative way for screening dominant groups, and thereby, optimising individualised therapy for NSCLC patients.

Noncoding RNAs endogenously rule the cancerous regulatory realm while proteins govern the normal

Cancers evolve from normal tissues and share an endogenous regulatory realm distinctive from that of normal human tissues. Unearthing such an endogenous realm faces challenges due to heterogeneous biology data. This study computes petabyte level data and reveals the endogenous regulatory networks of normal and cancers and then unearths the most important endogenous regulators for normal and cancerous realm. In normal, proteins dominate the entire realm and trans-regulate their targets across chromosomes and ribosomal proteins serve as the most important drivers. However, in cancerous realm, noncoding RNAs dominate the whole realm and pseudogenes work as the most important regulators that cis-regulate their neighbors, in which they primarily regulate their targets within 1 million base pairs but they rarely regulate their cognates with complementary sequences as thought. Therefore, two distinctive mechanisms rule the normal and cancerous realm separately, in which noncoding RNAs endogenously regulate cancers, instead of proteins as currently conceptualized. This establishes a fundamental avenue to understand the basis of cancerous and normal physiology.

LINA: A Linearizing Neural Network Architecture for Accurate First-Order and Second-Order Interpretations

While neural networks can provide high predictive performance, it was a challenge to identify the salient features and important feature interactions used for their predictions. This represented a key hurdle for deploying neural networks in many biomedical applications that require interpretability, including predictive genomics. In this paper, linearizing neural network architecture (LINA) was developed here to provide both the first-order and the second-order interpretations on both the instance-wise and the model-wise levels. LINA combines the representational capacity of a deep inner attention neural network with a linearized intermediate representation for model interpretation. In comparison with DeepLIFT, LIME, Grad*Input and L2X, the first-order interpretation of LINA had better Spearman correlation with the ground-truth importance rankings of features in synthetic datasets. In comparison with NID and GEH, the second-order interpretation results from LINA achieved better precision for identification of the ground-truth feature interactions in synthetic datasets. These algorithms were further benchmarked using predictive genomics as a real-world application. LINA identified larger numbers of important single nucleotide polymorphisms (SNPs) and salient SNP interactions than the other algorithms at given false discovery rates. The results showed accurate and versatile model interpretation using LINA.

Tumor suppressor pathways shape EGFR-driven lung tumor progression and response to treatment

In vivo modeling combined with CRISPR/Cas9-mediated somatic genome editing has contributed to elucidating the functional importance of specific genetic alterations in human tumors. Our recent work uncovered tumor suppressor pathways that affect EGFR-driven lung tumor growth and sensitivity to tyrosine kinase inhibitors and reflect the mutational landscape and treatment outcomes in the human disease.

Genome instability drives epistatic adaptation in the human pathogen Leishmania

Chromosome and gene copy number variations often correlate with the evolution of microbial and cancer drug resistance, thus causing important human mortality. How genome instability is harnessed to generate beneficial phenotypes and how deleterious gene dosage effects are compensated remain open questions. The protist pathogen Leishmania exploits genome instability to regulate expression via gene dosage changes. Using these parasites as a unique model system, we uncover complex epistatic interactions between gene copy number variations and compensatory transcriptomic responses as key processes that harness genome instability for adaptive evolution in Leishmania. Our results propose a model of eukaryotic fitness gain that may be broadly applicable to pathogenic fungi or tumor cells known to exploit genome instability for adaptation.

How genome instability is harnessed for fitness gain despite its potential deleterious effects is largely elusive. An ideal system to address this important open question is provided by the protozoan pathogen Leishmania, which exploits frequent variations in chromosome and gene copy number to regulate expression levels. Using ecological genomics and experimental evolution approaches, we provide evidence that Leishmania adaptation relies on epistatic interactions between functionally associated gene copy number variations in pathways driving fitness gain in a given environment. We further uncover posttranscriptional regulation as a key mechanism that compensates for deleterious gene dosage effects and provides phenotypic robustness to genetically heterogenous parasite populations. Finally, we correlate dynamic variations in small nucleolar RNA (snoRNA) gene dosage with changes in ribosomal RNA 2′-O-methylation and pseudouridylation, suggesting translational control as an additional layer of parasite adaptation. Leishmania genome instability is thus harnessed for fitness gain by genome-dependent variations in gene expression and genome-independent compensatory mechanisms. This allows for polyclonal adaptation and maintenance of genetic heterogeneity despite strong selective pressure. The epistatic adaptation described here needs to be considered in Leishmania epidemiology and biomarker discovery and may be relevant to other fast-evolving eukaryotic cells that exploit genome instability for adaptation, such as fungal pathogens or cancer.

Combinatorial CRISPR/Cas9 Screening Reveals Epistatic Networks of Interacting Tumor Suppressor Genes and Therapeutic Targets in Human Breast Cancer

The majority of cancers are driven by multiple genetic alterations, but how these changes collaborate during tumorigenesis remains largely unknown. To gain mechanistic insights into tumor-promoting genetic interactions among tumor suppressor genes (TSG), we conducted combinatorial CRISPR screening coupled with single-cell transcriptomic profiling in human mammary epithelial cells. As expected, different driver gene alterations in mammary epithelial cells influenced the repertoire of tumor suppressor alterations capable of inducing tumor formation. More surprisingly, TSG interaction networks were comprised of numerous cliques-sets of three or four genes such that each TSG within the clique showed oncogenic cooperation with all other genes in the clique. Genetic interaction profiling indicated that the predominant cooperating TSGs shared overlapping functions rather than distinct or complementary functions. Single-cell transcriptomic profiling of CRISPR double knockouts revealed that cooperating TSGs that synergized in promoting tumorigenesis and growth factor independence showed transcriptional epistasis, whereas noncooperating TSGs did not. These epistatic transcriptional changes, both buffering and synergistic, affected expression of oncogenic mediators and therapeutic targets, including CDK4, SRPK1, and DNMT1. Importantly, the epistatic expression alterations caused by dual inactivation of TSGs in this system, such as PTEN and TP53, were also observed in patient tumors, establishing the relevance of these findings to human breast cancer. An estimated 50% of differentially expressed genes in breast cancer are controlled by epistatic interactions. Overall, our study indicates that transcriptional epistasis is a central aspect of multigenic breast cancer progression and outlines methodologies to uncover driver gene epistatic networks in other human cancers. SIGNIFICANCE: This study provides a roadmap for moving beyond discovery and development of therapeutic strategies based on single driver gene analysis to discovery based on interactions between multiple driver genes.See related commentary by Fong et al., p. 6078.

A Network-Centric Framework for the Evaluation of Mutual Exclusivity Tests on Cancer Drivers

One of the key concepts employed in cancer driver gene identification is that of mutual exclusivity (ME); a driver mutation is less likely to occur in case of an earlier mutation that has common functionality in the same molecular pathway. Several ME tests have been proposed recently, however the current protocols to evaluate ME tests have two main limitations. Firstly the evaluations are mostly with respect to simulated data and secondly the evaluation metrics lack a network-centric view. The latter is especially crucial as the notion of common functionality can be achieved through searching for interaction patterns in relevant networks. We propose a network-centric framework to evaluate the pairwise significances found by statistical ME tests. It has three main components. The first component consists of metrics employed in the network-centric ME evaluations. Such metrics are designed so that network knowledge and the reference set of known cancer genes are incorporated in ME evaluations under a careful definition of proper control groups. The other two components are designed as further mechanisms to avoid confounders inherent in ME detection on top of the network-centric view. To this end, our second objective is to dissect the side effects caused by mutation load artifacts where mutations driving tumor subtypes with low mutation load might be incorrectly diagnosed as mutually exclusive. Finally, as part of the third main component, the confounding issue stemming from the use of nonspecific interaction networks generated as combinations of interactions from different tissues is resolved through the creation and use of tissue-specific networks in the proposed framework. The data, the source code and useful scripts are available at: https://github.com/abu-compbio/NetCentric.

Clonal architecture predicts clinical outcomes and drug sensitivity in acute myeloid leukemia

The impact of clonal heterogeneity on disease behavior or drug response in acute myeloid leukemia remains poorly understood. Using a cohort of 2,829 patients, we identify features of clonality associated with clinical features and drug sensitivities. High variant allele frequency for 7 mutations (including NRAS and TET2) associate with dismal prognosis; elevated GATA2 variant allele frequency correlates with better outcomes. Clinical features such as white blood cell count and blast percentage correlate with the subclonal abundance of mutations such as TP53 and IDH1. Furthermore, patients with cohesin mutations occurring before NPM1, or transcription factor mutations occurring before splicing factor mutations, show shorter survival. Surprisingly, a branched pattern of clonal evolution is associated with superior clinical outcomes. Finally, several mutations (including NRAS and IDH1) predict drug sensitivity based on their subclonal abundance. Together, these results demonstrate the importance of assessing clonal heterogeneity with implications for prognosis and actionable biomarkers for therapy.

Gene network modeling via TopNet reveals functional dependencies between diverse tumor-critical mediator genes

Malignant cell transformation and the underlying reprogramming of gene expression require the cooperation of multiple oncogenic mutations. This cooperation is reflected in the synergistic regulation of non-mutant downstream genes, so-called cooperation response genes (CRGs). CRGs affect diverse hallmark features of cancer cells and are not known to be functionally connected. However, they act as critical mediators of the cancer phenotype at an unexpectedly high frequency >50%, as indicated by genetic perturbations. Here, we demonstrate that CRGs function within a network of strong genetic interdependencies that are critical to the malignant state. Our network modeling methodology, TopNet, takes the approach of incorporating uncertainty in the underlying gene perturbation data and can identify non-linear gene interactions. In the dense space of gene connectivity, TopNet reveals a sparse topological gene network architecture, effectively pinpointing functionally relevant gene interactions. Thus, among diverse potential applications, TopNet has utility for identification of non-mutant targets for cancer intervention.

Malignant cell transformation requires the cooperation of multiple oncogenic mutations. Here, we demonstrate that non-mutated genes function within a network of strong genetic interdependencies that are critical to the malignant state. Our network modeling methodology, TopNet, reveals a sparse topological gene network architecture, effectively pinpointing functionally relevant gene interactions.

Evolutionary selection of alleles in the melanophilin gene that impacts on prostate organ function and cancer risk

Background and objectives

Several hundred inherited genetic variants or SNPs that alter the risk of cancer have been identified through genome-wide association studies. In populations of European ancestry, these variants are mostly present at relatively high frequencies. To gain insight into evolutionary origins, we screened a series of genes and SNPs linked to breast or prostate cancer for signatures of historical positive selection.

Methodology

We took advantage of the availability of the 1000 genome data and we performed genomic scans for positive selection in five different Caucasian populations as well as one African reference population. We then used prostate organoid cultures to provide a possible functional explanation for the interplay between the action of evolutionary forces and the disease risk association.

Results

Variants in only one gene showed genomic signatures of positive, evolutionary selection within Caucasian populations melanophilin (MLPH). Functional depletion of MLPH in prostate organoids, by CRISPR/Cas9 mutation, impacted lineage commitment of progenitor cells promoting luminal versus basal cell differentiation and on resistance to androgen deprivation.

Conclusions and implications

The MLPH variants influencing prostate cancer risk may have been historically selected for their adaptive benefit on skin pigmentation but MLPH is highly expressed in the prostate and the derivative, positively selected, alleles decrease the risk of prostate cancer. Our study suggests a potential functional mechanism via which MLPH and its genetic variants could influence risk of prostate cancer, as a serendipitous consequence of prior evolutionary benefits to another tissue.

Lay Summary

We screened a limited series of genomic variants associated with breast and prostate cancer risk for signatures of historical positive selection. Variants within the melanophilin (MLPH) gene fell into this category. Depletion of MLPH in prostate organoid cultures, suggested a potential functional mechanism for impacting on cancer risk, as a serendipitous consequence of prior evolutionary benefits to another tissue.

A systematic analysis of genetic interactions and their underlying biology in childhood cancer

Childhood cancer is a major cause of child death in developed countries. Genetic interactions between mutated genes play an important role in cancer development. They can be detected by searching for pairs of mutated genes that co-occur more (or less) often than expected. Co-occurrence suggests a cooperative role in cancer development, while mutual exclusivity points to synthetic lethality, a phenomenon of interest in cancer treatment research. Little is known about genetic interactions in childhood cancer. We apply a statistical pipeline to detect genetic interactions in a combined dataset comprising over 2,500 tumors from 23 cancer types. The resulting genetic interaction map of childhood cancers comprises 15 co-occurring and 27 mutually exclusive candidates. The biological explanation of most candidates points to either tumor subtype, pathway epistasis or cooperation while synthetic lethality plays a much smaller role. Thus, other explanations beyond synthetic lethality should be considered when interpreting genetic interaction test results.

Loss of a 7q gene, CUX1, disrupts epigenetically driven DNA repair and drives therapy-related myeloid neoplasms

Therapy-related myeloid neoplasms (t-MNs) are high-risk late effects with poorly understood pathogenesis in cancer survivors. It has been postulated that, in some cases, hematopoietic stem and progenitor cells (HSPCs) harboring mutations are selected for by cytotoxic exposures and transform. Here, we evaluate this model in the context of deficiency of CUX1, a transcription factor encoded on chromosome 7q and deleted in half of t-MN cases. We report that CUX1 has a critical early role in the DNA repair process in HSPCs. Mechanistically, CUX1 recruits the histone methyltransferase EHMT2 to DNA breaks to promote downstream H3K9 and H3K27 methylation, phosphorylated ATM retention, subsequent γH2AX focus formation and propagation, and, ultimately, 53BP1 recruitment. Despite significant unrepaired DNA damage sustained in CUX1-deficient murine HSPCs after cytotoxic exposures, they continue to proliferate and expand, mimicking clonal hematopoiesis in patients postchemotherapy. As a consequence, preexisting CUX1 deficiency predisposes mice to highly penetrant and rapidly fatal therapy-related erythroleukemias. These findings establish the importance of epigenetic regulation of HSPC DNA repair and position CUX1 as a gatekeeper in myeloid transformation.

Migrations of cancer cells through the lens of phylogenetic biogeography

Malignant cells leave their initial tumor of growth and disperse to other tissues to form metastases. Dispersals also occur in nature when individuals in a population migrate from their area of origin to colonize other habitats. In cancer, phylogenetic biogeography is concerned with the source and trajectory of cell movements. We examine the suitability of primary features of organismal biogeography, including genetic diversification, dispersal, extinction, vicariance, and founder effects, to describe and reconstruct clone migration events among tumors. We used computer-simulated data to compare fits of seven biogeographic models and evaluate models’ performance in clone migration reconstruction. Models considering founder effects and dispersals were often better fit for the clone phylogenetic patterns, especially for polyclonal seeding and reseeding of metastases. However, simpler biogeographic models produced more accurate estimates of cell migration histories. Analyses of empirical datasets of basal-like breast cancer had model fits consistent with the patterns seen in the analysis of computer-simulated datasets. Our analyses reveal the powers and pitfalls of biogeographic models for modeling and inferring clone migration histories using tumor genome variation data. We conclude that the principles of molecular evolution and organismal biogeography are useful in these endeavors but that the available models and methods need to be applied judiciously.

KEAP1 and TP53 frame genomic, evolutionary and immunological subtypes of lung adenocarcinoma with different sensitivity to immunotherapy

INTRODUCTION

The connection between driver mutations and efficacy of immune-checkpoint inhibitors (ICIs) is the focus of intense investigations. In lung adenocarcinoma (LUAD), KEAP1/STK11 alterations have been tied to immunoresistance. Nevertheless, the heterogeneity characterizing immunotherapy efficacy suggests the contribution of still unappreciated events.

METHODS

Somatic interaction analysis of top-ranking mutant genes in LUAD was carried out in the AARC project GENIE (N=6208). Mutational processes, intratumor heterogeneity, evolutionary trajectories, immunological features and cancer-associated signatures were investigated exploiting multiple datasets (AACR GENIE, TCGA, TRACERx). The impact of the proposed subtyping on survival outcomes was assessed in two independent cohorts of ICI-treated patients: the tissue-based sequencing cohort (Rome/MSKCC/DFCI, tNGS cohort, N=343) and the blood-based sequencing cohort (OAK/POPLAR trials, bNGS cohort, N=304).

RESULTS

Observing the neutral interaction between KEAP1 and TP53, KEAP1/TP53-based subtypes were dissected at the molecular and clinical level. KEAP1 single-mutant (KEAP1 SM) and KEAP1/TP53 double-mutant (KEAP1/TP53 DM) LUAD share a transcriptomic profile characterized by AKR gene overexpression, which are under the control of a productive super-enhancer with NEF2L2-binding signals. Nevertheless, KEAP1 SM and KEAP1/TP53 DM tumors differ by mutational repertoire, degree of intratumor heterogeneity, evolutionary trajectories, pathway-level signatures and immune microenvironment composition. In both cohorts (bNGS and tNGS), KEAP1 SM tumors had the shortest survival, the KEAP1/TP53 DM subgroup had intermediate prognosis matching that of pure TP53 LUAD, whereas the longest survival was noticed in the double-wild-type group.

CONCLUSIONS

Our data provide a framework for genomically-informed immunotherapy, highlighting the importance of multi-modal data integration to achieve a clinically exploitable taxonomy.

Extreme value theory as a framework for understanding mutation frequency distribution in cancer genomes

Currently, the population dynamics of preclonal cancer cells before clonal expansion of tumors has not been sufficiently addressed thus far. By focusing on preclonal cancer cell population as a Darwinian evolutionary system, we formulated and analyzed the observed mutation frequency among tumors (MFaT) as a proxy for the hypothesized sequence read frequency and beneficial fitness effect of a cancer driver mutation. Analogous to intestinal crypts, we assumed that sample donor patients are separate culture tanks where proliferating cells follow certain population dynamics described by extreme value theory (EVT). To validate this, we analyzed three large-scale cancer genome datasets, each harboring > 10000 tumor samples and in total involving > 177898 observed mutation sites. We clarified the necessary premises for the application of EVT in the strong selection and weak mutation (SSWM) regime in relation to cancer genome sequences at scale. We also confirmed that the stochastic distribution of MFaT is likely of the Fréchet type, which challenges the well-known Gumbel hypothesis of beneficial fitness effects. Based on statistical data analysis, we demonstrated the potential of EVT as a population genetics framework to understand and explain the stochastic behavior of driver-mutation frequency in cancer genomes as well as its applicability in real cancer genome sequence data.

The within-host evolution of antimicrobial resistance in Mycobacterium tuberculosis

Tuberculosis (TB) has been responsible for the greatest number of human deaths due to an infectious disease in general, and due to antimicrobial resistance (AMR) in particular. The etiological agents of human TB are a closely-related group of human-adapted bacteria that belong to the Mycobacterium tuberculosis complex (MTBC). Understanding how MTBC populations evolve within-host may allow for improved TB treatment and control strategies. In this review, we highlight recent works that have shed light on how AMR evolves in MTBC populations within individual patients. We discuss the role of heteroresistance in AMR evolution, and review the bacterial, patient and environmental factors that likely modulate the magnitude of heteroresistance within-host. We further highlight recent works on the dynamics of MTBC genetic diversity within-host, and discuss how spatial substructures in patients' lungs, spatiotemporal heterogeneity in antimicrobial concentrations and phenotypic drug tolerance likely modulates the dynamics of MTBC genetic diversity in patients during treatment. We note the general characteristics that are shared between how the MTBC and other bacterial pathogens evolve in humans, and highlight the characteristics unique to the MTBC.

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

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

A Functional Taxonomy of Tumor Suppression in Oncogenic KRAS-Driven Lung Cancer

Cancer genotyping has identified a large number of putative tumor suppressor genes. Carcinogenesis is a multistep process, but the importance and specific roles of many of these genes during tumor initiation, growth, and progression remain unknown. Here we use a multiplexed mouse model of oncogenic KRAS-driven lung cancer to quantify the impact of 48 known and putative tumor suppressor genes on diverse aspects of carcinogenesis at an unprecedented scale and resolution. We uncover many previously understudied functional tumor suppressors that constrain cancer in vivo. Inactivation of some genes substantially increased growth, whereas the inactivation of others increases tumor initiation and/or the emergence of exceptionally large tumors. These functional in vivo analyses revealed an unexpectedly complex landscape of tumor suppression that has implications for understanding cancer evolution, interpreting clinical cancer genome sequencing data, and directing approaches to limit tumor initiation and progression. SIGNIFICANCE: Our high-throughput and high-resolution analysis of tumor suppression uncovered novel genetic determinants of oncogenic KRAS-driven lung cancer initiation, overall growth, and exceptional growth. This taxonomy is consistent with changing constraints during the life history of cancer and highlights the value of quantitative in vivo genetic analyses in autochthonous cancer models.This article is highlighted in the In This Issue feature, p. 1601.

Co-occurrence and mutual exclusivity: what cross-cancer mutation patterns can tell us

Cancer is the dysregulated proliferation of cells caused by acquired mutations in key driver genes. The most frequently mutated driver genes promote tumorigenesis in various organisms, cell types, and genetic backgrounds. However, recent cancer genomics studies also point to the existence of context-dependent driver gene functions, where specific mutations occur predominately or even exclusively in certain tumor types or genetic backgrounds. Here, we review examples of co-occurring and mutually exclusive driver gene mutation patterns across cancer genomes and discuss their underlying biology. While co-occurring driver genes typically activate collaborating oncogenic pathways, we identify two distinct biological categories of incompatibilities among the mutually exclusive driver genes depending on whether the mutated drivers trigger the same or divergent tumorigenic pathways. Finally, we discuss possible therapeutic avenues emerging from the study of incompatible driver gene mutations.

Immune evasion in HPV- head and neck precancer-cancer transition is driven by an aneuploid switch involving chromosome 9p loss

An aneuploid-immune paradox encompasses somatic copy-number alterations (SCNAs), unleashing a cytotoxic response in experimental precancer systems, while conversely being associated with immune suppression and cytotoxic-cell depletion in human tumors, especially head and neck cancer (HNSC). We present evidence from patient samples and cell lines that alterations in chromosome dosage contribute to an immune hot-to-cold switch during human papillomavirus-negative (HPV-) head and neck tumorigenesis. Overall SCNA (aneuploidy) level was associated with increased CD3+ and CD8+ T cell microenvironments in precancer (mostly CD3+, linked to trisomy and aneuploidy), but with T cell-deficient tumors. Early lesions with 9p21.3 loss were associated with depletion of cytotoxic T cell infiltration in TP53 mutant tumors; and with aneuploidy were associated with increased NK-cell infiltration. The strongest driver of cytotoxic T cell and Immune Score depletion in oral cancer was 9p-arm level loss, promoting profound decreases of pivotal IFN-γ-related chemokines (e.g., CXCL9) and pathway genes. Chromosome 9p21.3 deletion contributed mainly to cell-intrinsic senescence suppression, but deletion of the entire arm was necessary to diminish levels of cytokine, JAK-STAT, and Hallmark NF-κB pathways. Finally, 9p arm-level loss and JAK2-PD-L1 codeletion (at 9p24) were predictive markers of poor survival in recurrent HPV- HNSC after anti-PD-1 therapy; likely amplified by independent aneuploidy-induced immune-cold microenvironments observed here. We hypothesize that 9p21.3 arm-loss expansion and epistatic interactions allow oral precancer cells to acquire properties to overcome a proimmunogenic aneuploid checkpoint, transform and invade. These findings enable distinct HNSC interception and precision-therapeutic approaches, concepts that may apply to other CN-driven neoplastic, immune or aneuploid diseases, and immunotherapies.

CBFB cooperates with p53 to maintain TAp73 expression and suppress breast cancer

The CBFB gene is frequently mutated in several types of solid tumors. Emerging evidence suggests that CBFB is a tumor suppressor in breast cancer. However, our understanding of the tumor suppressive function of CBFB remains incomplete. Here, we analyze genetic interactions between mutations of CBFB and other highly mutated genes in human breast cancer datasets and find that CBFB and TP53 mutations are mutually exclusive, suggesting a functional association between CBFB and p53. Integrated genomic studies reveal that TAp73 is a common transcriptional target of CBFB and p53. CBFB cooperates with p53 to maintain TAp73 expression, as either CBFB or p53 loss leads to TAp73 depletion. TAp73 re-expression abrogates the tumorigenic effect of CBFB deletion. Although TAp73 loss alone is insufficient for tumorigenesis, it enhances the tumorigenic effect of NOTCH3 overexpression, a downstream event of CBFB loss. Immunohistochemistry shows that p73 loss is coupled with higher proliferation in xenografts. Moreover, TAp73 loss-of-expression is a frequent event in human breast cancer tumors and cell lines. Together, our results significantly advance our understanding of the tumor suppressive functions of CBFB and reveal a mechanism underlying the communication between the two tumor suppressors CBFB and p53.

Bioinformatics analysis of potential core genes for glioblastoma

Background: Glioblastoma (GBM) has a high degree of malignancy, aggressiveness and recurrence rate. However, there are limited options available for the treatment of GBM, and they often result in poor prognosis and unsatisfactory outcomes.

Materials and methods: In order to identify potential core genes in GBM that may provide new therapeutic insights, we analyzed three gene chips (GSE2223, GSE4290 and GSE50161) screened from the GEO database. Differentially expressed genes (DEG) from the tissues of GBM and normal brain were screened using GEO2R. To determine the functional annotation and pathway of DEG, Gene Ontology (GO) and KEGG pathway enrichment analysis were conducted using DAVID database. Protein interactions of DEG were visualized using PPI network on Cytoscape software. Next, 10 Hub nodes were screened from the differentially expressed network using MCC algorithm on CytoHubba software and subsequently identified as Hub genes. Finally, the relationship between Hub genes and the prognosis of GBM patients was described using GEPIA2 survival analysis web tool.

Results: A total of 37 up-regulated and 187 down-regulated genes were identified through microarray analysis. Amongst the 10 Hub genes selected, SV2B appeared to be the only gene associated with poor prognosis in glioblastoma based on the survival analysis.

Conclusion: Our study suggests that high expression of SV2B is associated with poor prognosis in GBM patients. Whether SV2B can be used as a new therapeutic target for GBM requires further validation.

Genetic Determinants of EGFR-Driven Lung Cancer Growth and Therapeutic Response In Vivo

In lung adenocarcinoma, oncogenic EGFR mutations co-occur with many tumor suppressor gene alterations; however, the extent to which these contribute to tumor growth and response to therapy in vivo remains largely unknown. By quantifying the effects of inactivating 10 putative tumor suppressor genes in a mouse model of EGFR-driven Trp53-deficient lung adenocarcinoma, we found that Apc, Rb1, or Rbm10 inactivation strongly promoted tumor growth. Unexpectedly, inactivation of Lkb1 or Setd2-the strongest drivers of growth in a KRAS-driven model-reduced EGFR-driven tumor growth. These results are consistent with mutational frequencies in human EGFR- and KRAS-driven lung adenocarcinomas. Furthermore, KEAP1 inactivation reduced the sensitivity of EGFR-driven tumors to the EGFR inhibitor osimertinib, and mutations in genes in the KEAP1 pathway were associated with decreased time on tyrosine kinase inhibitor treatment in patients. Our study highlights how the impact of genetic alterations differs across oncogenic contexts and that the fitness landscape shifts upon treatment. SIGNIFICANCE: By modeling complex genotypes in vivo, this study reveals key tumor suppressors that constrain the growth of EGFR-mutant tumors. Furthermore, we uncovered that KEAP1 inactivation reduces the sensitivity of these tumors to tyrosine kinase inhibitors. Thus, our approach identifies genotypes of biological and therapeutic importance in this disease.This article is highlighted in the In This Issue feature, p. 1601.

High Tumor Mutation Burden and Other Immunotherapy Response Predictors in Breast Cancers: Associations and Therapeutic Opportunities

BACKGROUND

The recent development of effective immunotherapies with immune checkpoint inhibitors for the treatment of cancer has rekindled the interest for the immune system and its activation for an anti-cancer response. At the same time, it has become evident that not all types of cancers respond equally to these treatments, and even within the same tumor type only a subset of patients derive clinical benefit. Biomarkers predictive of response to immunotherapy have been sought and in certain occasions incorporated in the indication for treatment. These include expression of PD-L1 and defects in DNA mismatch repair (MMR).

OBJECTIVE

Tumor mutation burden (TMB) has been associated with response to immune checkpoint inhibitors. The current investigation examines TMB as a biomarker of response to immunotherapy in breast cancer.

PATIENTS AND METHODS

Publicly available data from the breast cancer study of The Cancer Genome Atlas (TCGA) and the METABRIC study were analyzed. Parameters examined included the TMB and specific mutations that may impact on TMB. In addition, correlations with breast cancer sub-types were investigated.

RESULTS

The percentage of breast cancers with high TMB (more than 192 mutations per sample) was low (3.5-4.6%) in luminal and triple-negative cancers and higher (14.1%) in the HER2-positive subset. Almost all cancers with high TMB had defects in MMR proteins or the replicative polymerases POLE and POLD1.

CONCLUSIONS

Small sub-sets of breast cancers with high TMB exist and may present an opportunity for effective immunotherapeutic targeting.

Multiple mutations within individual oncogenes

Recent studies of the cancer genome have identified numerous patients harboring multiple mutations (MM) within individual oncogenes. These MM (de novo MM) in cis synergistically activate the mutated oncogene and promote tumorigenesis, indicating a positive epistatic interaction between mutations. The relatively frequent de novo MM suggest that intramolecular positive epistasis is widespread in oncogenes. Studies also suggest that negative and higher-order epistasis affects de novo MM. Comparison of de novo MM and MM associated with drug-resistant secondary mutations (secondary MM) revealed several similarities with respect to allelic configuration, mutational selection and functionality of individual mutations. Conversely, they have several differences, most notably the difference in drug sensitivities. Secondary MM usually confer resistance to molecularly targeted therapies, whereas several de novo MM are associated with increased sensitivity, implying that both can be useful as therapeutic biomarkers. Unlike secondary MM in which specific secondary resistant mutations are selected, minor (infrequent) functionally weak mutations are convergently selected in de novo MM, which may provide an explanation as to why such mutations accumulate in cancer. The third type of MM is MM from different subclones. This type of MM is associated with parallel evolution, which may contribute to relapse and treatment failure. Collectively, MM within individual oncogenes are diverse, but all types of MM are associated with cancer evolution and therapeutic response. Further evaluation of oncogenic MM is warranted to gain a deeper understanding of cancer genetics and evolution.

Comprehensive Analysis of SWI/SNF Inactivation in Lung Adenocarcinoma Cell Models

Simple Summary

Mammalian SWI/SNF complexes regulate gene expression by reorganizing the way DNA is packaged into chromatin. SWI/SNF subunits are recurrently altered in tumors at multiple levels, including DNA mutations as well as alteration of the levels of RNA and protein. Cancer cell lines are often used to study SWI/SNF function, but their patterns of SWI/SNF alterations can be complex. Here, we present a comprehensive characterization of DNA mutations and RNA and protein expression of SWI/SNF members in 38 lung adenocarcinoma (LUAD) cell lines. We show that over 85% of our cell lines harbored at least one alteration in one SWI/SNF subunit. In addition, over 75% of our cell lines lacked expression of at least one SWI/SNF subunit at the protein level. Our catalog will help researchers choose an appropriate cell line model to study SWI/SNF function in LUAD.

Abstract

Mammalian SWI/SNF (SWitch/Sucrose Non-Fermentable) complexes are ATP-dependent chromatin remodelers whose subunits have emerged among the most frequently mutated genes in cancer. Studying SWI/SNF function in cancer cell line models has unveiled vulnerabilities in SWI/SNF-mutant tumors that can lead to the discovery of new therapeutic drugs. However, choosing an appropriate cancer cell line model for SWI/SNF functional studies can be challenging because SWI/SNF subunits are frequently altered in cancer by various mechanisms, including genetic alterations and post-transcriptional mechanisms. In this work, we combined genomic, transcriptomic, and proteomic approaches to study the mutational status and the expression levels of the SWI/SNF subunits in a panel of 38 lung adenocarcinoma (LUAD) cell lines. We found that the SWI/SNF complex was mutated in more than 76% of our LUAD cell lines and there was a high variability in the expression of the different SWI/SNF subunits. These results underline the importance of the SWI/SNF complex as a tumor suppressor in LUAD and the difficulties in defining altered and unaltered cell models for the SWI/SNF complex. These findings will assist researchers in choosing the most suitable cellular models for their studies of SWI/SNF to bring all of its potential to the development of novel therapeutic applications.

Inference of mutability landscapes of tumors from single cell sequencing data

One of the hallmarks of cancer is the extremely high mutability and genetic instability of tumor cells. Inherent heterogeneity of intra-tumor populations manifests itself in high variability of clone instability rates. Analogously to fitness landscapes, the instability rates of clonal populations form their mutability landscapes. Here, we present MULAN (MUtability LANdscape inference), a maximum-likelihood computational framework for inference of mutation rates of individual cancer subclones using single-cell sequencing data. It utilizes the partial information about the orders of mutation events provided by cancer mutation trees and extends it by inferring full evolutionary history and mutability landscape of a tumor. Evaluation of mutation rates on the level of subclones rather than individual genes allows to capture the effects of genomic interactions and epistasis. We estimate the accuracy of our approach and demonstrate that it can be used to study the evolution of genetic instability and infer tumor evolutionary history from experimental data. MULAN is available at https://github.com/compbel/MULAN.