Cancer is a heterogeneous disease that results from genetic alteration of cell cycle and proliferation controls. Identifying mutations that drive cancer, understanding cancer type specificities, and delineating how driver mutations interact with each other to establish disease is vital for identifying therapeutic vulnerabilities. Such cancer specific patterns and gene co-occurrences can be identified by studying tumor genome sequences, and networks have proven effective in uncovering relationships between sequences. We present two network-based approaches to identify driver gene patterns among tumor samples. The first approach relies on analysis using the Directed Weighted All Nearest Neighbors (DiWANN) model, which is a variant of sequence similarity network, and the second approach uses bipartite network analysis. A data reduction framework was implemented to extract the minimal relevant information for the sequence similarity network analysis, where a transformed reference sequence is generated for constructing the driver gene network. This data reduction process combined with the efficiency of the DiWANN network model, greatly lowered the computational cost (in terms of execution time and memory usage) of generating the networks enabling us to work at a much larger scale than previously possible. The DiWANN network helped us identify cancer types in which samples were more closely connected to each other suggesting they are less heterogeneous and potentially susceptible to a common drug. The bipartite network analysis provided insight into gene associations and co-occurrences. We identified genes that were broadly mutated in multiple cancer types and mutations exclusive to only a few. Additionally, weighted one-mode gene projections of the bipartite networks revealed a pattern of occurrence of driver genes in different cancers. Our study demonstrates that network-based approaches can be an effective tool in cancer genomics. The analysis identifies co-occurring and exclusive driver genes and mutations for specific cancer types, providing a better understanding of the driver genes that lead to tumor initiation and evolution.

Aneuploidy, the presence of an aberrant number of chromosomes, has been associated with tumorigenesis for over a century. More recently, advances in karyotyping techniques have revealed its high prevalence in cancer: About 90% of solid tumors and 50-70% of hematopoietic cancers exhibit chromosome gains or losses. When analyzed at the level of specific chromosomes, there are strong patterns that are observed in cancer karyotypes both pan-cancer and for specific cancer types. These specific aneuploidy patterns correlate strongly with outcomes for tumor initiation, progression, metastasis formation, immune evasion and resistance to therapeutic treatment. Despite their prominence, understanding the basis underlying aneuploidy patterns in cancer has been challenging. Advances in genetic engineering and bioinformatic analyses now offer insights into the genetic determinants of aneuploidy pattern selection. Overall, there is substantial evidence that expression changes of particular genes can act as the positive selective forces for adaptation through aneuploidy. Recent findings suggest that multiple genes contribute to the selection of specific aneuploid chromosomes in cancer; however, further research is necessary to identify the most impactful driver genes. Determining the genetic basis and accompanying vulnerabilities of specific aneuploidy patterns is an essential step in selectively targeting these hallmarks of tumors.

OBJECTIVE: To evaluate the incidence, clinical laboratory characteristics, and gene mutation spectrum of Ph-negative MPN patients with atypical variants of JAK2, MPL, or CALR.
METHODS: We collected a total of 359 Ph-negative MPN patients with classical mutations in driver genes JAK2, MPL, or CALR, and divided them into two groups based on whether they had additional atypical variants of driver genes JAK2, MPL, or CALR: 304 patients without atypical variants of driver genes and 55 patients with atypical variants of driver genes. We analyzed the relevant characteristics of these patients.
RESULTS: This study included 359 patients with Ph-negative MPNs with JAK2, MPL, or CALR classical mutations and found that 55 (15%) patients had atypical variants of JAK2, MPL, or CALR. Among them, 28 cases (51%) were male, and 27 (49%) were female, with a median age of 64 years (range, 21-83). The age of ET patients with atypical variants was higher than that of ET patients without atypical variants [70 (28-80) vs. 61 (19-82), p = 0.03]. The incidence of classical MPL mutations in ET patients with atypical variants was higher than in ET patients without atypical variants [13.3% (2/15) vs. 0% (0/95), p = 0.02]. The number of gene mutations in patients with atypical variants of driver genes PV, ET, and Overt-PMF is more than in patients without atypical variants of PV, ET, and Overt-PMF [PV: 3 (2-6) vs. 2 (1-7), p < 0.001; ET: 4 (2-8) vs. 2 (1-7), p < 0.05; Overt-PMF: 5 (2-9) vs. 3 (1-8), p < 0.001]. The incidence of SH2B3 and ASXL1 mutations were higher in MPN patients with atypical variants than in those without atypical variants (SH2B3: 16% vs. 6%, p < 0.01; ASXL1: 24% vs. 13%, p < 0.05).
CONCLUSIONS: These data indicate that classical mutations of JAK2, MPL, and CALR may not be completely mutually exclusive with atypical variants of JAK2, MPL, and CALR. In this study, 30 different atypical variants of JAK2, MPL, and CALR were identified, JAK2 G127D being the most common (42%, 23/55). Interestingly, JAK2 G127D only co-occurred with JAK2V617F mutation. The incidence of atypical variants of JAK2 in Ph-negative MPNs was much higher than that of the atypical variants of MPL and CALR. The significance of these atypical variants will be further studied in the future.

Cancer is a complex and evolutionary disease mainly driven by the accumulation of genetic variations in genes. Identifying cancer driver genes is important. However, most related studies have focused on the population level. Cancer is a disease with high heterogeneity. Thus, the discovery of driver genes at the individual level is becoming more valuable but is a great challenge. Although there have been some computational methods proposed to tackle this challenge, few can cover all patient samples well, and there is still room for performance improvement. In this study, to identify individual-level driver genes more efficiently, we propose the PDGCN method. PDGCN integrates multiple types of data features, including mutation, expression, methylation, copy number data, and system-level gene features, along with network structural features extracted using Node2vec in order to construct a sample-gene interaction network. Prediction is performed using a graphical convolutional neural network model with a conditional random field layer, which is able to better combine the network structural features with biological attribute features. Experiments on the ACC (Adrenocortical Cancer) and KICH (Kidney Chromophobe) datasets from TCGA (The Cancer Genome Atlas) demonstrated that the method performs better compared to other similar methods. It can identify not only frequently mutated driver genes, but also rare candidate driver genes and novel biomarker genes. The results of the survival and enrichment analyses of these detected genes demonstrate that the method can identify important driver genes at the individual level.

Cancer is a complex gene mutation disease that derives from the accumulation of mutations during somatic cell evolution. With the advent of high-throughput technology, a large amount of omics data has been generated, and how to find cancer-related driver genes from a large number of omics data is a challenge. In the early stage, the researchers developed many frequency-based driver genes identification methods, but they could not identify driver genes with low mutation rates well. Afterwards, researchers developed network-based methods by fusing multi-omics data, but they rarely considered the connection among features. In this paper, after analyzing a large number of methods for integrating multi-omics data, a hierarchical weak consensus model for fusing multiple features is proposed according to the connection among features. By analyzing the connection between PPI network and co-mutation hypergraph network, this paper firstly proposes a new topological feature, called co-mutation clustering coefficient (CMCC). Then, a hierarchical weak consensus model is used to integrate CMCC, mRNA and miRNA differential expression scores, and a new driver genes identification method HWC is proposed. In this paper, the HWC method and current 7 state-of-the-art methods are compared on three types of cancers. The comparison results show that HWC has the best identification performance in statistical evaluation index, functional consistency and the partial area under ROC curve.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s13755-024-00279-6.

The vast amount of available sequencing data allows the scientific community to explore different genetic alterations that may drive cancer or favor cancer progression. Software developers have proposed a myriad of predictive tools, allowing researchers and clinicians to compare and prioritize driver genes and mutations and their relative pathogenicity. However, there is little consensus on the computational approach or a golden standard for comparison. Hence, benchmarking the different tools depends highly on the input data, indicating that overfitting is still a massive problem. One of the solutions is to limit the scope and usage of specific tools. However, such limitations force researchers to walk on a tightrope between creating and using high-quality tools for a specific purpose and describing the complex alterations driving cancer. While the knowledge of cancer development increases daily, many bioinformatic pipelines rely on single nucleotide variants or alterations in a vacuum without accounting for cellular compartments, mutational burden or disease progression. Even within bioinformatics and computational cancer biology, the research fields work in silos, risking overlooking potential synergies or breakthroughs. Here, we provide an overview of databases and datasets for building or testing predictive cancer driver tools. Furthermore, we introduce predictive tools for driver genes, driver mutations, and the impact of these based on structural analysis. Additionally, we suggest and recommend directions in the field to avoid silo-research, moving towards integrative frameworks.

Cancer is widely regarded as a complex disease primarily driven by genetic mutations. A critical concern and significant obstacle lies in discerning driver genes amid an extensive array of passenger genes.
We present a new method termed DriverMP for effectively prioritizing altered genes on a cancer-type level by considering mutated gene pairs. It is designed to first apply nonsilent somatic mutation data, protein‒protein interaction network data, and differential gene expression data to prioritize mutated gene pairs, and then individual mutated genes are prioritized based on prioritized mutated gene pairs. Application of this method in 10 cancer datasets from The Cancer Genome Atlas demonstrated its great improvements over all the compared state-of-the-art methods in identifying known driver genes. Then, a comprehensive analysis demonstrated the reliability of the novel driver genes that are strongly supported by clinical experiments, disease enrichment, or biological pathway analysis.
The new method, DriverMP, which is able to identify driver genes by effectively integrating the advantages of multiple kinds of cancer data, is available at https://github.com/LiuYangyangSDU/DriverMP. In addition, we have developed a novel driver gene database for 10 cancer types and an online service that can be freely accessed without registration for users. The DriverMP method, the database of novel drivers, and the user-friendly online server are expected to contribute to new diagnostic and therapeutic opportunities for cancers.

According to the Cancer Genome Atlas (TCGA), gastric cancers are classified into four molecular subtypes: Epstein-Barr virus-positive (EBV+), tumors with microsatellite instability (MSI), tumors with chromosomal instability (CIN), and genomically stable (GS) tumors. However, the gastric cancer (GC) with chromosomal instability remains insufficiently described and does not have effective markers for molecular and histological verification and diagnosis. The CIN subtype of GC is characterized by chromosomal instability, which is manifested by an increased frequency of aneuploidies and/or structural chromosomal rearrangements in tumor cells. Structural rearrangements in the CIN subtype of GC are not accidental and are commonly detected in chromosomal loci, being abnormal because of specific structural organization. The causes of CIN are still being discussed; however, according to recent data, aberrations in the TP53 gene may cause CIN development or worsen its phenotype. Clinically, patients with the CIN subtype of GC demonstrate poor survival, but receive the maximum benefit from adjuvant chemotherapy. In the review, we consider the molecular mechanisms and possible causes of chromosomal instability in GC, the common rearrangements of chromosomal loci and their impact on the development and clinical course of the disease, as well as the driver genes, their functions, and perspectives on their targeting in the CIN subtype of GC.

BACKGROUND: The identification of cancer driver genes and key molecular pathways has been the focus of large-scale cancer genome studies. Network-based methods detect significantly perturbed subnetworks as putative cancer pathways by incorporating genomics data with the topological information of PPI networks. However, commonly used PPI networks have distinct topological structures, making the results of the same method vary widely when applied to different networks. Furthermore, emerging context-specific PPI networks often have incomplete topological structures, which pose serious challenges for existing subnetwork detection algorithms.
METHODS: In this paper, we propose a novel method, referred to as MultiFDRnet, to address the above issues. The basic idea is to model a set of PPI networks as a multiplex network to preserve the topological structure of individual networks, while introducing dependencies among them, and, then, to detect significantly perturbed subnetworks on the modeled multiplex network using all the structural information simultaneously.
RESULTS: To illustrate the effectiveness of the proposed approach, an extensive benchmark analysis was conducted on both simulated and real cancer data. The experimental results showed that the proposed method is able to detect significantly perturbed subnetworks jointly supported by multiple PPI networks and to identify novel modular structures in context-specific PPI networks.

High-risk endometrial cancer has poor prognosis and is increasing in incidence. However, understanding of the molecular mechanisms which drive this disease is limited. We used genetically engineered mouse models (GEMM) to determine the functional consequences of missense and loss of function mutations in Fbxw7, Pten and Tp53, which collectively occur in nearly 90% of high-risk endometrial cancers. We show that Trp53 deletion and missense mutation cause different phenotypes, with the latter a substantially stronger driver of endometrial carcinogenesis. We also show that Fbxw7 missense mutation does not cause endometrial neoplasia on its own, but potently accelerates carcinogenesis caused by Pten loss or Trp53 missense mutation. By transcriptomic analysis, we identify LEF1 signalling as upregulated in Fbxw7/FBXW7-mutant mouse and human endometrial cancers, and in human isogenic cell lines carrying FBXW7 mutation, and validate LEF1 and the additional Wnt pathway effector TCF7L2 as novel FBXW7 substrates. Our study provides new insights into the biology of high-risk endometrial cancer and suggests that targeting LEF1 may be worthy of investigation in this treatment-resistant cancer subgroup.