Cancer driver genes are either oncogenes or tumour suppressor genes that are classically activated or inactivated, respectively, by driver mutations. Alternative splicing-which produces various mature mRNAs and, eventually, protein variants from a single gene-may also result in driving neoplastic transformation because of the different and often opposed functions of the variants of driver genes. The present review analyses the different alternative splicing events that result in driving neoplastic transformation, with an emphasis on their molecular mechanisms. To do this, we collected a list of 568 gene drivers of cancer and revised the literature to select those involved in the alternative splicing of other genes as well as those in which its pre-mRNA is subject to alternative splicing, with the result, in both cases, of producing an oncogenic isoform. Thirty-one genes fall into the first category, which includes splicing factors and components of the spliceosome and splicing regulators. In the second category, namely that comprising driver genes in which alternative splicing produces the oncogenic isoform, 168 genes were found. Then, we grouped them according to the molecular mechanisms responsible for alternative splicing yielding oncogenic isoforms, namely, mutations in cis splicing-determining elements, other causes involving non-mutated cis elements, changes in splicing factors, and epigenetic and chromatin-related changes. The data given in the present review substantiate the idea that aberrant splicing may regulate the activation of proto-oncogenes or inactivation of tumour suppressor genes and details on the mechanisms involved are given for more than 40 driver genes.

Common diseases manifest differentially between patients, but the genetic origin of this variation remains unclear. To explore possible involvement of gene transcriptional-variation, we produce a DNA methylation-oriented, driver-gene-wide dataset of regulatory elements in human glioblastomas and study their effect on inter-patient gene expression variation.
In 175 of 177 analyzed gene regulatory domains, transcriptional enhancers and silencers are intermixed. Under experimental conditions, DNA methylation induces enhancers to alter their enhancing effects or convert into silencers, while silencers are affected inversely. High-resolution mapping of the association between DNA methylation and gene expression in intact genomes reveals methylation-related regulatory units (average size = 915.1 base-pairs). Upon increased methylation of these units, their target-genes either increased or decreased in expression. Gene-enhancing and silencing units constitute cis-regulatory networks of genes. Mathematical modeling of the networks highlights indicative methylation sites, which signified the effect of key regulatory units, and add up to make the overall transcriptional effect of the network. Methylation variation in these sites effectively describe inter-patient expression variation and, compared with DNA sequence-alterations, appears as a major contributor of gene-expression variation among glioblastoma patients.
We describe complex cis-regulatory networks, which determine gene expression by summing the effects of positive and negative transcriptional inputs. In these networks, DNA methylation induces both enhancing and silencing effects, depending on the context. The revealed mechanism sheds light on the regulatory role of DNA methylation, explains inter-individual gene-expression variation, and opens the way for monitoring the driving forces behind deferential courses of cancer and other diseases.

We propose a statistical framework to analyze radiological magnetic resonance imaging (MRI) and genomic data to identify the underlying radiogenomic associations in lower grade gliomas (LGG). We devise a novel imaging phenotype by dividing the tumor region into concentric spherical layers that mimics the tumor evolution process. MRI data within each layer is represented by voxel-intensity-based probability density functions which capture the complete information about tumor heterogeneity. Under a Riemannian-geometric framework these densities are mapped to a vector of principal component scores which act as imaging phenotypes. Subsequently, we build Bayesian variable selection models for each layer with the imaging phenotypes as the response and the genomic markers as predictors. Our novel hierarchical prior formulation incorporates the interior-to-exterior structure of the layers, and the correlation between the genomic markers. We employ a computationally-efficient Expectation-Maximization-based strategy for estimation. Simulation studies demonstrate the superior performance of our approach compared to other approaches. With a focus on the cancer driver genes in LGG, we discuss some biologically relevant findings. Genes implicated with survival and oncogenesis are identified as being associated with the spherical layers, which could potentially serve as early-stage diagnostic markers for disease monitoring, prior to routine invasive approaches. We provide a R package that can be used to deploy our framework to identify radiogenomic associations.

The crosstalk between cancer and the tumour immune microenvironment (TIME) has attracted significant interest in the latest years because of its impact on cancer evolution and response to treatment. Despite this, cancer-specific tumour-TIME interactions and their mechanistic insights are still poorly understood.
Here, we compute the significant interactions occurring between cancer-specific genetic drivers and five anti- and pro-tumour TIME features in 32 cancer types using Lasso regularised ordinal regression. Focusing on head and neck squamous cancer (HNSC), we rebuild the functional networks linking specific TIME driver alterations to the TIME state they associate with.
The 477 TIME drivers that we identify are multifunctional genes whose alterations are selected early in cancer evolution and recur across and within cancer types. Tumour suppressors and oncogenes have an opposite effect on the TIME and the overall anti-tumour TIME driver burden is predictive of response to immunotherapy. TIME driver alterations predict the immune profiles of HNSC molecular subtypes, and perturbations in keratinization, apoptosis and interferon signalling underpin specific driver-TIME interactions.
Overall, our study delivers a comprehensive resource of TIME drivers, gives mechanistic insights into their immune-regulatory role, and provides an additional framework for patient prioritisation to immunotherapy. The full list of TIME drivers and associated properties are available at http://www.network-cancer-genes.org .

BACKGROUND: Cancer is a life-threatening disease in humans; yet, cancer genes are frequently reported to be under positive selection. This suggests an evolutionary-genetic paradox in which cancer evolves as a secondary product of selection in human beings. However, systematic investigation of the evolution of cancer driver genes is sparse.
RESULTS: Using comparative genomics analysis, population genetics analysis and computational molecular evolutionary analysis, the evolution of 568 cancer driver genes of 66 cancer types were evaluated at two levels, selection on the early evolution of humans (long timescale selection in the human lineage during primate evolution, i.e., millions of years), and recent selection in modern human populations (~ 100,000 years). Results showed that eight cancer genes covering 11 cancer types were under positive selection in the human lineage (long timescale selection). And 35 cancer genes covering 47 cancer types were under positive selection in modern human populations (recent selection). Moreover, SNPs associated with thyroid cancer in three thyroid cancer driver genes (CUX1, HERC2 and RGPD3) were under positive selection in East Asian and European populations, consistent with the high incidence of thyroid cancer in these populations.
CONCLUSIONS: These findings suggest that cancer can be evolved, in part, as a by-product of adaptive changes in humans. Different SNPs at the same locus can be under different selection pressures in different populations, and thus should be under consideration during precision medicine, especially for targeted medicine in specific populations.

Identifying cancer driver genes plays a curial role in the development of precision oncology and cancer therapeutics. Although a plethora of methods have been developed to tackle this problem, the complex cancer mechanisms and intricate interactions between genes still make the identification of cancer driver genes challenging. In this work, we propose a novel machine learning method of heterophilic graph diffusion convolutional networks (called HGDCs) to boost cancer-driver gene identification. Specifically, HGDC first introduces graph diffusion to generate an auxiliary network for capturing the structurally similar nodes in a biomolecular network. Then, HGDC designs an improved message aggregation and propagation scheme to adapt to the heterophilic setting of biomolecular networks, alleviating the problem of driver gene features being smoothed by its neighboring dissimilar genes. Finally, HGDC uses a layer-wise attention classifier to predict the probability of one gene being a cancer driver gene. In the comparison experiments with other existing state-of-the-art methods, our HGDC achieves outstanding performance in identifying cancer driver genes. The experimental results demonstrate that HGDC not only effectively identifies well-known driver genes on different networks but also novel candidate cancer genes. Moreover, HGDC can effectively prioritize cancer driver genes for individual patients. Particularly, HGDC can identify patient-specific additional driver genes, which work together with the well-known driver genes to cooperatively promote tumorigenesis.

The development and progression of cancer arise due to the accumulation of mutations in driver genes. Correctly identifying the driver genes that lead to cancer development can significantly assist the drug design, cancer diagnosis and treatment. Most computer methods detect cancer drivers based on gene-gene networks by assuming that driver genes tend to work together, form protein complexes and enrich pathways. However, they ignore that microribonucleic acid (RNAs; miRNAs) regulate the expressions of their targeted genes and are related to human diseases. In this work, we propose a graph convolution network (GCN) approach called GM-GCN to identify the cancer driver genes based on a gene-miRNA network. First, we constructed a gene-miRNA network, where the nodes are miRNAs and their targeted genes. The edges connecting miRNA and genes indicate the regulatory relationship between miRNAs and genes. We prepared initial attributes for miRNA and genes according to their biological properties and used a GCN model to learn the gene feature representations in the network by aggregating the features of their neighboring miRNA nodes. And then, the learned features were passed through a 1D convolution module for feature dimensionality change. We employed the learned and original gene features to optimize model parameters. Finally, the gene features learned from the network and the initial input gene features were fed into a logistic regression model to predict whether a gene is a driver gene. We applied our model and state-of-the-art methods to predict cancer drivers for pan-cancer and individual cancer types. Experimental results show that our model performs well in terms of the area under the receiver operating characteristic curve and the area under the precision-recall curve compared to state-of-the-art methods that work on gene networks. The GM-GCN is freely available via https://github.com/weiba/GM-GCN.

BACKGROUND: Correctly identifying the driver genes that promote cell growth can significantly assist drug design, cancer diagnosis and treatment. The recent large-scale cancer genomics projects have revealed multi-omics data from thousands of cancer patients, which requires to design effective models to unlock the hidden knowledge within the valuable data and discover cancer drivers contributing to tumorigenesis.
RESULTS: In this work, we propose a graph convolution network-based method called MRNGCN that integrates multiple gene relationship networks to identify cancer driver genes. First, we constructed three gene relationship networks, including the gene-gene, gene-outlying gene and gene-miRNA networks. Then, genes learnt feature presentations from the three networks through three sharing-parameter heterogeneous graph convolution network (HGCN) models with the self-attention mechanism. After that, these gene features pass a convolution layer to generate fused features. Finally, we utilized the fused features and the original feature to optimize the model by minimizing the node and link prediction losses. Meanwhile, we combined the fused features, the original features and the three features learned from every network through a logistic regression model to predict cancer driver genes.
CONCLUSIONS: We applied the MRNGCN to predict pan-cancer and cancer type-specific driver genes. Experimental results show that our model performs well in terms of the area under the ROC curve (AUC) and the area under the precision-recall curve (AUPRC) compared to state-of-the-art methods. Ablation experimental results show that our model successfully improved the cancer driver identification by integrating multiple gene relationship networks.

Clonal hematopoiesis of indeterminate potential (CHIP), defined by the age-related ontogenesis of expanded leukemogenic variants indicative of a genetically distinct clonal leukocyte population, is associated with risk of hematologic malignancy and cardiovascular disease. In experimental models, recapitulation of CHIP promotes kidney interstitial fibrosis with direct tissue infiltration of donor macrophages. We tested the hypothesis that CHIP is associated with kidney function decline in the general population.
Cohort study.
12,004 individuals from 3 community-based cohorts in the TOPMed Consortium.
CHIP status from whole-genome sequences obtained from DNA extracted from peripheral blood.
Risk of 30% decline in estimated glomerular filtration rate (eGFR) and percent eGFR decline per year during the follow-up period.
Cox proportional hazards models for 30% eGFR decline end point and generalized estimating equations for annualized relative change in eGFR with meta-analysis. Study-specific estimates were combined using fixed-effect meta-analysis.
The median baseline eGFR was 84mL/min/1.73m2. The prevalence of CHIP was 6.6%, 9.0%, and 12.2% in persons aged 50-60, 60-70, and>70 years, respectively. Over a median follow-up period of 8 years, for the 30% eGFR outcome 205 events occurred among 1,002 CHIP carriers (2.1 events per 100 person-years) and 2,041 events in persons without CHIP (1.7 events per 100 person-years). In meta-analysis, CHIP was associated with greater risk of a 30% eGFR decline (17% [95% CI, 1%-36%] higher; P=0.04). Differences were not observed between those with baseline eGFR above or below 60mL/min/1.73m2, of age above or below 60 years, or with or without diabetes.
Small number of participants with moderate-to-advanced kidney disease and restricted set of CHIP driver variants.
We report an association between CHIP and eGFR decline in 3 general population cohorts without known kidney disease. Further studies are needed to investigate this novel condition and its potential impact among individuals with overt kidney disease.

BACKGROUND: Recent advances in next-generation sequencing technologies have helped investigators generate massive amounts of cancer genomic data. A critical challenge in cancer genomics is identification of a few cancer driver genes whose mutations cause tumor growth. However, the majority of existing computational approaches underuse the co-occurrence mutation information of the individuals, which are deemed to be important in tumorigenesis and tumor progression, resulting in high rate of false positive.
RESULTS: To make full use of co-mutation information, we present a random walk algorithm referred to as DriverRWH on a weighted gene mutation hypergraph model, using somatic mutation data and molecular interaction network data to prioritize candidate driver genes. Applied to tumor samples of different cancer types from The Cancer Genome Atlas, DriverRWH shows significantly better performance than state-of-art prioritization methods in terms of the area under the curve scores and the cumulative number of known driver genes recovered in top-ranked candidate genes. Besides, DriverRWH discovers several potential drivers, which are enriched in cancer-related pathways. DriverRWH recovers approximately 50% known driver genes in the top 30 ranked candidate genes for more than half of the cancer types. In addition, DriverRWH is also highly robust to perturbations in the mutation data and gene functional network data.
CONCLUSIONS: DriverRWH is effective among various cancer types in prioritizes cancer driver genes and provides considerable improvement over other tools with a better balance of precision and sensitivity. It can be a useful tool for detecting potential driver genes and facilitate targeted cancer therapies.