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Background Molecular testing for alterations in oncogenic driver genes and targeted therapies have become standard procedures for non‐small cell lung cancer (NSCLC) patients. However, little evidence has shed light on the pattern of co‐existence of driver genes in NSCLC, and whether they may have different tumor features affecting immunotherapy is still unclarified. Methods Genomic alterations in 14 lung cancer‐related genes were conducted in 3440 Chinese NSCLC patients using next‐generation sequencing. Meanwhile, tumor mutational burden and immunotherapy dataset from the Memorial sloan kettering cancer center (MSKCC) and lung adenocarcinoma dataset from The Cancer Genome Atlas (TCGA) were utilized for analyzing the impact of the co‐occurring alterations on patients’ survival following immunotherapy. Results In this cohort, 90.17% of patients had at least one somatic alteration in the 14 genes, including 51% of co‐occurring alterations. TP53 and epidermal growth factor receptor (EGFR) were the most prevalent genes (54.74% and 53.55%, respectively), followed by KRAS, ERBB2, ALK, PIK3CA, ROS1, RET, MET, BRAF, KIT, FGFR1, PDGFRA, and NRAS. The prevalence of TP53, EGFR, and ERBB2 in our cohort were significantly higher than that from the TCGA database, whereas KRAS, BRAF, and PDGFRA were significantly lower than the latter. Furthermore, the patients who harbored multiple alterations (8.86%, 31/350) in eight driver genes survived longer and have a higher tumor mutation burden compared to the patients with a single alteration. Similar result was found between the patients with co‐occurring alteration of EGFR and other driver genes and the patients with single EGFR alteration. Meanwhile, we found a distinct immune cell infiltration feature between patients with single and multiple driver gene alterations, as well as between patients with only EGFR alteration and co‐occurring groups. Conclusion This study identified a unique driver gene feature and found patients harboring co‐occurring alterations of EGFR and other driver genes may benefit from immunotherapy, which may provide more therapeutic selections for EGFR‐mutated NSCLC patients and merit additional investigation., This study identified a unique driver gene feature and found patients harboring co‐occurring alterations of epidermal growth factor receptor (EGFR) and other driver genes may benefit from immunotherapy, which may provide more therapeutic selections for EGFR‐mutated non‐small cell lung cancer patients and merit additional investigation.

Background As one of the deadliest diseases in the world, cancer is driven by a few somatic mutations that disrupt the normal growth of cells, and leads to abnormal proliferation and tumor development. The vast majority of somatic mutations did not affect the occurrence and development of cancer; thus, identifying the mutations responsible for tumor occurrence and development is one of the main targets of current cancer treatments. Results To effectively identify driver genes, we adopted a semi-local centrality measure and gene mutation effect function to assess the effect of gene mutations on changes in gene expression patterns. Firstly, we calculated the mutation score for each gene. Secondly, we identified differentially expressed genes (DEGs) in the cohort by comparing the expression profiles of tumor samples and normal samples, and then constructed a local network for each mutation gene using DEGs and mutant genes according to the protein–protein interaction network. Finally, we calculated the score of each mutant gene according to the objective function. The top-ranking mutant genes were selected as driver genes. We name the proposed method as mutations effect and network centrality. Conclusions Four types of cancer data in The Cancer Genome Atlas were tested. The experimental data proved that our method was superior to the existing network-centric method, as it was able to quickly and easily identify driver genes and rare driver factors.

Graphical abstract, Colorectal cancer (CRC) results from the uncontrolled growth of cells in the colon, rectum, or appendix. The 5-year relative survival rate for patients with CRC is 65% and is correlated with the stage at diagnosis (being 91% for stage I at diagnosis versus 12% for stage IV). This study aimed to identify CRC driver genes to assist in the design of a cancer panel to detect gene mutations during clinical early-stage screening and identify genes for use in prognostic assessments and the evaluation of appropriate treatment options. First, we utilized bioinformatics approaches to analyze 354 paired sequencing profiles from The Cancer Genome Atlas (TCGA) to identify CRC driver genes and analyzed the sequencing profiles of 38 patients with >5 years of follow-up data to search for prognostic genes. The results revealed eight driver genes and ten prognostic genes. Next, the presence of the identified gene mutations was verified using tissue and blood samples from Taiwanese CRC patients. The results showed that the set identified gene mutations provide high coverage for driver gene screening, and APC, TP53, PIK3CA, and FAT4 could be detected in blood as ctDNA test targets. We further found that BCL7A gene mutation was correlated with prognosis in CRC (log-rank p-value = 0.02), and that mutations of BCL7A could be identified in ctDNA samples. These findings may be of value in clinical early cancer detection, disease monitoring, drug development, and treatment efforts in the future.

Background Malignant melanoma of the female genital tract is relatively uncommon and accounts for 3–7% of all melanoma localizations. This study aimed to identify driver genes in melanoma of the female genital tract with the purpose of enhancing understanding of disease pathogenesis and identifying potential new therapeutic targets to develop effective therapies. Methods KIT (CD117) and BRAF expression were detected immunohistochemically. Polymerase Chain Reaction (PCR) and Sanger sequencing techniques were performed to identify the mutational status of BRAF, NRAS, KRAS, NF1, KIT, PDGFRA and SF3B1 on 19 melanomas of the female genital tract, paired with 25 cutaneous melanomas, 18 acral melanomas and 11 melanomas of nasal cavity. Results Somatic variant analysis identified KRAS (6/19; 32%) as the most commonly mutated gene, followed by KIT (4/19; 21%), SF3B1 (3/19; 16%) and NRAS (1/19; 5%). None of the cases were found to harbor BRAF, NF1 and PDGFRA mutations in melanomas of the female genital tract. However, none of the cases were found to harbor SF3B1 and KIT mutations in cutaneous melanomas, acral melanomas and melanomas of nasal cavity. Recurrent KIT mutations, as well as mutations in the less frequently mutated genes NRAS and SF3B1, were exclusively detected in vulvovaginal melanomas, but not in tumors arising in the cervix. However, recurrent KRAS mutations were detected in similar frequencies in tumors of the vulva, vagina, and cervix. Additionally, recurrent KRAS and KIT mutations occurred predominantly in polygonal and epithelioid cell types of melanoma in the female genital tract. Immunohistochemistry revealed moderate or strong cytoplasmic CD117 expression in 6 of the 19 cases (31.6%). Conclusions We observed that gynecologic melanoma harbored distinct mutation rates in the KIT, BRAF, SF3B1, KRAS, and NRAS genes. Our findings support the notion that gynecologic melanoma is a distinct entity from non-gynecologic melanoma, and these findings offer insights into future therapeutic options for these patients.

Background: Aberrant DNA methylation occurs commonly during carcinogenesis and is of clinical value in human cancers. However, knowledge of the impact of DNA methylation changes on lung carcinogenesis and progression remains limited. Methods: Genome-wide DNA methylation profiles were surveyed in 18 pairs of tumors and adjacent normal tissues from non-small cell lung cancer (NSCLC) patients using Reduced Representation Bisulfite Sequencing (RRBS). An integrated epigenomic-transcriptomic landscape of lung cancer was depicted using the multi-omics data integration method. Results: We discovered a large number of hypermethylation events pre-marked by poised promoter in embryonic stem cells, being a hallmark of lung cancer. These hypermethylation events showed a high conservation across cancer types. Eight novel driver genes with aberrant methylation (e.g., PCDH17 and IRX1) were identified by integrated analysis of DNA methylome and transcriptome data. Methylation level of the eight genes measured by pyrosequencing can distinguish NSCLC patients from lung tissues with high sensitivity and specificity in an independent cohort. Their tumor-suppressive roles were further experimentally validated in lung cancer cells, which depend on promoter hypermethylation. Similarly, 13 methylation-driven ncRNAs (including 8 lncRNAs and 5 miRNAs) were identified, some of which were co-regulated with their host genes by the same promoter hypermethylation. Finally, by analyzing the transcription factor (TF) binding motifs, we uncovered sets of TFs driving the expression of epigenetically regulated genes and highlighted the epigenetic regulation of gene expression of TCF21 through DNA methylation of EGR1 binding motifs. Conclusions: We discovered several novel methylation driver genes of diagnostic and therapeutic relevance in lung cancer. Our findings revealed that DNA methylation in TF binding motifs regulates target gene expression by affecting the binding ability of TFs. Our study also provides a valuable epigenetic resource for identifying DNA methylation-based diagnostic biomarkers, developing cancer drugs for epigenetic therapy and studying cancer pathogenesis.

Background A retrospective analysis verified the role of gene mutations in brain metastasis in patients with non‐small cell lung cancer (NSCLC). Methods Data from 552 patients with advanced NSCLC treated from January 2015 to June 2017 in the Affiliated Cancer Hospital of Zhengzhou University were retrospectively analyzed. Next‐generation sequencing was used to detect mutations in eight reported driver genes and various risk factors were evaluated. Results Of the 552 patients with advanced NSCLC, 153 (27.7%) had brain metastases. The univariate analysis showed that age (P = .008), gender (P = .016), smoking history (P = .010), lymph node metastasis (P = .003), and three driver genes, positive epidermal growth factor receptor (EGFR) mutation (P = .001), positive anaplastic lymphoma kinase (ALK) gene fusion (P = .021), and positive rearranged during transfection (RET) gene fusion (P = .003), were the factors influencing the incidence of brain metastasis. Logistic multivariate regression analysis revealed that positive EGFR mutation (P = .012), positive ALK gene fusion (P = .015), positive RET gene fusion (P = .003), pathological type (P = .009), lymph node N2‐3 metastasis (P, The incidence of brain metastasis in non‐small cell lung cancer (NSCLC) patients is 30%‐40%. Brain metastasis (BM) in NSCLC correlated with age, pathological type and lymph node N2‐3 metastasis. Epidermal growth factor receptor, anaplastic lymphoma kinase, and rearranged during transfection (RET) mutations were associated with BM in NSCLC patients.

Significance Recently, investigators have shown that only a few driver gene mutational events appear to be needed for cancer to occur. However, the reason that some mutational events precede others in the same cancer and the explanation for tissue-specific differences in this timing, remain mysterious. We here combine mathematical modeling with epidemiologic studies and sequencing data to address these questions. We suggest that the first driver event in cancers generally occurred at early ages and provide estimates for the fitness of different types of drivers during tumor evolution, showing how they vary with the tissue of origin., Cancer is driven by the sequential accumulation of genetic and epigenetic changes in oncogenes and tumor suppressor genes. The timing of these events is not well understood. Moreover, it is currently unknown why the same driver gene change appears as an early event in some cancer types and as a later event, or not at all, in others. These questions have become even more topical with the recent progress brought by genome-wide sequencing studies of cancer. Focusing on mutational events, we provide a mathematical model of the full process of tumor evolution that includes different types of fitness advantages for driver genes and carrying-capacity considerations. The model is able to recapitulate a substantial proportion of the observed cancer incidence in several cancer types (colorectal, pancreatic, and leukemia) and inherited conditions (Lynch and familial adenomatous polyposis), by changing only 2 tissue-specific parameters: the number of stem cells in a tissue and its cell division frequency. The model sheds light on the evolutionary dynamics of cancer by suggesting a generalized early onset of tumorigenesis followed by slow mutational waves, in contrast to previous conclusions. Formulas and estimates are provided for the fitness increases induced by driver mutations, often much larger than previously described, and highly tissue dependent. Our results suggest a mechanistic explanation for why the selective fitness advantage introduced by specific driver genes is tissue dependent.

Background Cancer as a kind of genomic alteration disease each year deprives many people’s life. The biggest challenge to overcome cancer is to identify driver genes that promote the cancer development from a huge amount of passenger mutations that have no effect on the selective growth advantage of cancer. In order to solve those problems, some researchers have started to focus on identification of driver genes by integrating networks with other biological information. However, more efforts should be needed to improve the prediction performance. Methods Considering the facts that driver genes have impact on expression of their downstream genes, they likely interact with each other to form functional modules and those modules should tend to be expressed similarly in the same tissue. We proposed a novel model named by DyTidriver to identify driver genes through involving the gene dysregulated expression, tissue-specific expression and variation frequency into the human functional interaction network (e.g. human FIN). Results This method was applied on 974 breast, 316 prostate and 230 lung cancer patients. The consequence shows our method outperformed other five existing methods in terms of Fscore, Precision and Recall values. The enrichment and cociter analysis illustrate DyTidriver can not only identifies the driver genes enriched in some significant pathways but also has the capability to figure out some unknown driver genes. Conclusion The final results imply that driver genes are those that impact more dysregulated genes and express similarly in the same tissue.

Somatic copy-number alterations (SCNAs) are major contributors to cancer development that are pervasive and highly heterogeneous in human cancers. However, the driver roles of SCNAs in cancer are insufficiently characterized. We combined network propagation and linear regression models to design an integrative strategy to identify driver SCNAs and dissect the functional roles of SCNAs by integrating profiles of copy number and gene expression in lower-grade glioma (LGG). We applied our strategy to 511 LGG patients and identified 98 driver genes that dysregulated 29 cancer hallmark signatures, forming 143 active gene-hallmark pairs. We found that these active gene-hallmark pairs could stratify LGG patients into four subtypes with significantly different survival times. The two new subtypes with similar poorest prognoses were driven by two different gene sets (one including EGFR, CDKN2A, CDKN2B, INFA8, and INFA5, and the other including CDK4, AVIL, and DTX3), respectively. The SCNAs of the two gene sets could disorder the same cancer hallmark signature in a mutually exclusive manner (including E2F_TARGETS and G2M_CHECKPOINT). Compared with previous methods, our strategy could not only capture the known cancer genes and directly dissect the functional roles of their SCNAs in LGG, but also discover the functions of new driver genes in LGG, such as IFNA5, IFNA8, and DTX3. Additionally, our method can be applied to a variety of cancer types to explore the pathogenesis of driver SCNAs and improve the treatment and diagnosis of cancer.

In this study, we mined out hepatocellular carcinoma (HCC) driver genes from MEDLINE literatures by bioinformatics methods of pathway crosstalk and protein interaction network. Furthermore, the relationship between driver genes and their clinicopathological characteristics, as well as classification effectiveness was verified in the public databases. We identified 560 human genes reported to be associated with HCC in 1074 published articles. Functional analysis revealed that biological processes and biochemical pathways relating to tumor pathogenesis, cancer disease, tumor cell molecule, and hepatic disease were enriched in these genes. Pathway crosstalk analysis indicated that significant pathways could be divided into three modules: cancer disease, virus infection, and tumor signaling pathway. The HCC-related protein-protein interaction network comprised 10,212 nodes, and 56,400 edges were mined out to identify 18 modules corresponding to 14 driver genes. We verified that these 14 driver genes have high classification effectiveness to distinguish cancer samples from normal samples and the classification effectiveness was better than that of randomly selected genes. Present study provided pathway crosstalk and protein interaction network for understanding potential tumorigenesis genes underlying HCC. The 14 driver genes identified from this study are of great translational value in HCC diagnosis and treatment, as well as in clinical study on the pathogenesis of HCC.

Recent advances in sequencing technologies and the discovery of non-coding RNAs (ncRNAs) have provided new insights in the molecular pathogenesis of cancers. Several studies have implicated the role of ncRNAs, including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and recently discovered circular RNAs (circRNAs) in tumorigenesis and metastasis. Unlike linear RNAs, circRNAs are highly stable and closed-loop RNA molecules. It has been established that circRNAs regulate gene expression by controlling the functions of miRNAs and RNA-binding protein (RBP) or by translating into proteins. The circRNA–miRNA–mRNA regulatory axis is associated with human diseases, such as cancers, Alzheimer’s disease, and diabetes. In this study, we explored the interaction among circRNAs, miRNAs, and their target genes in various cancers using state-of-the-art bioinformatics tools. We identified differentially expressed circRNAs, miRNAs, and mRNAs on multiple cancers from publicly available data. Furthermore, we identified many crucial drivers and tumor suppressor genes in the circRNA–miRNA–mRNA regulatory axis in various cancers. Together, this study data provide a deeper understanding of the circRNA–miRNA–mRNA regulatory mechanisms in cancers.

Circulating tumor DNA (ctDNA) is a promising biomarker for accurate monitoring and less invasive assessment of tumor burden and treatment response. Here, targeted next-generation sequencing (NGS) with a designed gene panel of 176 cancer-relevant genes was used to assess mutations in 90 ctDNA samples from 90 patients with multiple types of liver disease and 10 healthy donor samples for control. Using our ctDNA detection panel, we identified mutations in 98.89% (89/90) of patient plasma biopsy samples, and 19 coding variants located in 10 cancer-related genes [ACVR2A, PCLO, TBCK, adhesion G protein-coupled receptor (ADGRV1), COL1A1, GABBR1, MUC16, MAGEC1, FASLG, and JAK1] were identified in 96.7% of patients (87/90). The 10 top mutated genes were tumor protein p53 (TP53), ACVR2A, ADGRV1, MUC16, TBCK, PCLO, COL11A1, titin (TTN), DNAH9, and GABBR1. TTN and TP53 and TTN and DNAH9 mutations tended to occur together in hepatocellular carcinoma samples. Most importantly, we found that most of those variants were insertions (frameshift insertions) and deletions (frameshift deletions and in-frame deletions), such as insertion variants in ACVR2A, PCLO, and TBCK; such mutations were detected in almost 95% of patients. Our study demonstrated that the targeted NGS-based ctDNA mutation profiling was a useful tool for hepatocellular carcinoma (HCC) monitoring and could potentially be used to guide treatment decisions in HCC.

Background: Obstructive Sleep Apnea (OSA) occurs in 7% of the adult population. The relationship between neurodegenerative diseases such as dementia and sleep disorders have long attracted clinical attention, however, no comprehensive data exists elucidating common gene expression between the two diseases. The objective of this study was to (1) demonstrate the practicability and feasibility of utilizing a systems biology approach called network-based identification of common driver genes (NICD) to identify common genomic features between two associated diseases and (2) utilize this approach to identify genes associated with both OSA and dementia. Methods: This study utilized 2 public databases (PCNet, DisGeNET) and a permutation assay in order to identify common genes between two co-morbid but mutually exclusive diseases. These genes were then linked to their mechanistic pathways through Enrichr, producing a list of genes that were common between the two different diseases. Results: 42 common genes were identified between OSA and dementia which were primarily linked to the G-coupled protein receptor (GPCR) and olfactory pathways. No single nucleotide polymorphisms (SNPs) were identified. Conclusions: This study demonstrates the viability of using publicly available databases and permutation assays along with canonical pathway linkage to identify common gene drivers as potential mechanistic targets for comorbid diseases.

Gastric cancer (GC) pathogenesis is complex and heterogeneous, reflecting morphological, molecular and genetic diversity. Diffuse gastric cancer (DGC) and intestinal gastric cancer (IGC) are the major histological types. GC may be sporadic or hereditary; sporadic GC is related to environmental and genetic low‐risk factors and hereditary GC is caused by inherited high‐risk mutations, so far identified only for the diffuse histotype. DGC phenotypic heterogeneity challenges the current understanding of molecular mechanisms underlying carcinogenesis. The definition of a DGC‐specific mutational profile remains controversial, possibly reflecting the heterogeneity of DGC‐related histological subtypes [signet‐ring cell carcinoma (SRCC) and poorly cohesive carcinoma not otherwise specified (PCC‐NOS)]. Indeed, DGC and DGC‐related subtypes may present specific mutational profiles underlying the particularly aggressive behaviour and dismal prognosis of DGC vs IGC and PCC‐NOS vs SRCC. In this systematic review, we revised the histological presentations, molecular classifications and approved therapies for gastric cancer, with a focus on DGC. We then analysed results from the most relevant studies, reporting mutational analysis data specifying mutational frequencies, and their relationship with DGC and IGC histological types, and with specific DGC subtypes (SRCC and PCC‐NOS). We aimed at identifying histology‐associated mutational profiles with an emphasis in DGC and its subtypes (DGC vs IGC; sporadic vs hereditary DGC; and SRCC vs PCC‐NOS). We further used these mutational profiles to identify the most commonly affected molecular pathways and biological functions, and explored the clinical trials directed specifically to patients with DGC. This systematic analysis is expected to expose a DGC‐specific molecular profile and shed light into potential targets for therapeutic intervention, which are currently missing., The main diffuse gastric cancer (DGC) histological subtypes are signet‐ring cell carcinoma (SRCC) and poorly cohesive carcinoma not otherwise specified (PCC‐NOS). Clinical trials, targeted therapies and predictive markers of therapy response directed to any of these entities are residual. This systematic review exposes the lack of integrated knowledge in these clinical entities and uses their mutational profiles to rethink therapeutic intervention.

Identifying personalized driver genes is essential for discovering critical biomarkers and developing effective personalized therapies of cancers. However, few methods consider weights for different types of mutations and efficiently distinguish driver genes over a larger number of passenger genes. We propose MinNetRank (Minimum used for Network-based Ranking), a new method for prioritizing cancer genes that sets weights for different types of mutations, considers the incoming and outgoing degree of interaction network simultaneously, and uses minimum strategy to integrate multi-omics data. MinNetRank prioritizes cancer genes among multi-omics data for each sample. The sample-specific rankings of genes are then integrated into a population-level ranking. When evaluating the accuracy and robustness of prioritizing driver genes, our method almost always significantly outperforms other methods in terms of precision, F1 score, and partial area under the curve (AUC) on six cancer datasets. Importantly, MinNetRank is efficient in discovering novel driver genes. SP1 is selected as a candidate driver gene only by our method (ranked top three), and SP1 RNA and protein differential expression between tumor and normal samples are statistically significant in liver hepatocellular carcinoma. The top seven genes stratify patients into two subtypes exhibiting statistically significant survival differences in five cancer types. These top seven genes are associated with overall survival, as illustrated by previous researchers. MinNetRank can be very useful for identifying cancer driver genes, and these biologically relevant marker genes are associated with clinical outcome. The R package of MinNetRank is available at https://github.com/weitinging/MinNetRank.

High-throughput sequencing technologies have identified millions of genetic mutations in multiple human diseases. However, the interpretation of the pathogenesis of these mutations and the discovery of driver genes that dominate disease progression is still a major challenge. Combining functional features such as protein post-translational modification (PTM) with genetic mutations is an effective way to predict such alterations. Here, we present PTMsnp, a web server that implements a Bayesian hierarchical model to identify driver genetic mutations targeting PTM sites. PTMsnp accepts genetic mutations in a standard variant call format or tabular format as input and outputs several interactive charts of PTM-related mutations that potentially affect PTMs. Additional functional annotations are performed to evaluate the impact of PTM-related mutations on protein structure and function, as well as to classify variants relevant to Mendelian disease. A total of 4,11,574 modification sites from 33 different types of PTMs and 1,776,848 somatic mutations from TCGA across 33 different cancer types are integrated into the web server, enabling identification of candidate cancer driver genes based on PTM. Applications of PTMsnp to the cancer cohorts and a GWAS dataset of type 2 diabetes identified a set of potential drivers together with several known disease-related genes, indicating its reliability in distinguishing disease-related mutations and providing potential molecular targets for new therapeutic strategies. PTMsnp is freely available at: http://ptmsnp.renlab.org.

It is rare that stage IV colon cancer is cured with chemotherapy. Here we report the long-term survival of a patient who presented with highly advanced disease characterized by a papillary architecture as well as porta hepatis lymph nodes but responded extremely well to FOLFIRI/bevacizumab. His original tumor underwent comprehensive genomic testing that included whole genome DNA sequencing, targeted sequencing, and RNA sequencing. These genetic results suggest the patient’s tumor harbored mutations in APC, KRAS, and TP53 as well as in PIK3CB. Moreover, the RNA-seq data suggested that the tumor belonged to the consensus molecular subtype 4, the “inflamed, immune phenotype,” with increased angiogenesis. Deep sequencing of highly responsive cancers may yield molecular insights into mechanisms underpinning a remarkable response.

Background Neuroblastoma is the most common pediatric malignancy with heterogeneous clinical behaviors, ranging from spontaneous regression to aggressive progression. Many studies have identified aberrations related to the pathogenesis and prognosis, broadly classifying neuroblastoma patients into high- and low-risk groups, but predicting tumor progression and clinical management of high-risk patients remains a big challenge. Results We integrate gene-level expression, array-based comparative genomic hybridization and functional gene-interaction network of 145 neuroblastoma patients to detect potential driver genes. The drivers are summarized into a driver-gene score (DGscore) for each patient, and we then validate its clinical relevance in terms of association with patient survival. Focusing on a subset of 48 clinically defined high-risk patients, we identify 193 recurrent regions of copy number alterations (CNAs), resulting in 274 altered genes whose copy-number gain or loss have parallel impact on the gene expression. Using a network enrichment analysis, we detect four common driver genes, ERCC6, HECTD2, KIAA1279, EMX2, and 66 patient-specific driver genes. Patients with high DGscore, thus carrying more copy-number-altered genes with correspondingly up- or down-regulated expression and functional implications, have worse survival than those with low DGscore (P = 0.006). Furthermore, Cox proportional-hazards regression analysis shows that, adjusted for age, tumor stage and MYCN amplification, DGscore is the only significant prognostic factor for high-risk neuroblastoma patients (P = 0.008). Conclusions Integration of genomic copy number alteration, expression and functional interaction-network data reveals clinically relevant and prognostic putative driver genes in high-risk neuroblastoma patients. The identified putative drivers are potential drug targets for individualized therapy. Reviewers This article was reviewed by Armand Valsesia, Susmita Datta and Aleksandra Gruca. Electronic supplementary material The online version of this article (10.1186/s13062-018-0218-5) contains supplementary material, which is available to authorized users.

A meeting report on the Third European Association for Cancer Research Conference on Cancer Genomics, held at Churchill College, Cambridge, UK, 25–28 June 2017.

// Tiejun Zhang 1 and Di Zhang 2 1 GMU-GIBH Joint School of Life Sciences, Guangzhou Medical University, Guangzhou, Guangdong 511436, China 2 School of Computer Science and Technology, Anhui University, Hefei, Anhui 230601, China Correspondence to: Di Zhang, email: zhangdi@ahu.edu.cn Keywords: driver genes, protein interaction network, integrative data Received: April 19, 2017 Accepted: June 19, 2017 Published: July 22, 2017 ABSTRACT Although numerous approaches have been proposed to discern driver from passenger, identification of driver genes remains a critical challenge in the cancer genomics field. Driver genes with low mutated frequency tend to be filtered in cancer research. In addition, the accumulation of different omics data necessitates the development of algorithmic frameworks for nominating putative driver genes. In this study, we presented a novel framework to identify driver genes through integrating multi-omics data such as somatic mutation, gene expression, and copy number alterations. We developed a computational approach to detect potential driver genes by virtue of their effect on their neighbors in network. Application to three datasets (head and neck squamous cell carcinoma (HNSC), thyroid carcinoma (THCA) and kidney renal clear cell carcinoma (KIRC)) from The Cancer Genome Atlas (TCGA), by comparing the Precision, Recall and F1 score, our method outperformed DriverNet and MUFFINN in all three datasets. In addition, our method was less affected by protein length compared with DriverNet. Lastly, our method not only identified the known cancer genes but also detected the potential rare drivers ( PTPN6 in THCA, SRC , GRB2 and PTPN6 in KIRC, MAPK1 and SMAD2 in HNSC).

// Qing-Ge Zhu 1, * , Shi-Ming Zhang 1, * , Xiao-Xiao Ding 1, * , Bing He 1 and Hu-Qin Zhang 1 1 The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an 710049, P.R. China * These authors contributed equally to the work Correspondence to: Hu-Qin Zhang, email: huqzhang@mail.xjtu.edu.cn Bing He, email: hebinghb@mail.xjtu.edu.cn Keywords: driver genes, non-small cell lung cancer, characteristics, detection methods, targeted therapies Received: February 23, 2017 Accepted: March 30, 2017 Published: April 10, 2017 ABSTRACT Lung cancer is one of the most common causes of cancer-related death in the world. The large number of lung cancer cases is non-small cell lung cancer (NSCLC), which approximately accounting for 75% of lung cancer. Over the past years, our comprehensive knowledge about the molecular biology of NSCLC has been rapidly enriching, which has promoted the discovery of driver genes in NSCLC and directed FDA-approved targeted therapies. Of course, the targeted therapies based on driver genes provide a more exact option for advanced non-small cell lung cancer, improving the survival rate of patients. Now, we will review the landscape of driver genes in NSCLC including the characteristics, detection methods, the application of target therapy and challenges.

Prostate cancer is one of the most common uro-oncological disease in men and is globally leading cause of cancer related deaths in males. The somatic mutation has a strong link in the occurrence of cancer. Mutation in the oncogenes and tumor suppressor genes that alter key cellular functions can lead to prostate cancer initiation and progression. Whole genome sequencing has identified numerous genetic alternations and further provided a detail view of the mutations in genes that drive progression of prostate cancer. TP53, SPOP, PTEN, ATM, AR, CTNNB1, FOXA1, KMT2D, BRACA2 and APC were found as frequently mutated genes in prostate cancer. Using data from cBioPortal and PubMed, this review summarizes the status and possible impact of mutations in these driver genes on survival, progression, and metastasis of prostate cancer. This study will contribute a better understanding of biological basis for clinical variability in prostate cancer patients and may provide new genetic diagnostic markers and drug targets.

Background Cancer as a worldwide problem is driven by genomic alterations. With the advent of high-throughput sequencing technology, a huge amount of genomic data generates at every second which offer many valuable cancer information and meanwhile throw a big challenge to those investigators. As the major characteristic of cancer is heterogeneity and most of alterations are supposed to be useless passenger mutations that make no contribution to the cancer progress. Hence, how to dig out driver genes that have effect on a selective growth advantage in tumor cells from those tremendously and noisily data is still an urgent task. Results Considering previous network-based method ignoring some important biological properties of driver genes and the low reliability of gene interactive network, we proposed a random walk method named as Subdyquency that integrates the information of subcellular localization, variation frequency and its interaction with other dysregulated genes to improve the prediction accuracy of driver genes. We applied our model to three different cancers: lung, prostate and breast cancer. The results show our model can not only identify the well-known important driver genes but also prioritize the rare unknown driver genes. Besides, compared with other existing methods, our method can improve the precision, recall and fscore to a higher level for most of cancer types. Conclusions The final results imply that driver genes are those prone to have higher variation frequency and impact more dysregulated genes in the common significant compartment. Availability The source code can be obtained at https://github.com/weiba/Subdyquency. Electronic supplementary material The online version of this article (10.1186/s12859-019-2847-9) contains supplementary material, which is available to authorized users.

Background Identifying cancer driver genes (CDG) is a crucial step in cancer genomic toward the advancement of precision medicine. However, driver gene discovery is a very challenging task because we are not only dealing with huge amount of data; but we are also faced with the complexity of the disease including the heterogeneity of background somatic mutation rate in each cancer patient. It is generally accepted that CDG harbor variants conferring growth advantage in the malignant cell and they are positively selected, which are critical to cancer development; whereas, non-driver genes harbor random mutations with no functional consequence on cancer. Based on this fact, function prediction based approaches for identifying CDG have been proposed to interrogate the distribution of functional predictions among mutations in cancer genomes (eLS 1–16, 2016). Assuming most of the observed mutations are passenger mutations and given the quantitative predictions for the functional impact of the mutations, genes enriched of functional or deleterious mutations are more likely to be drivers. The promises of these methods have been continually refined and can therefore be applied to increase accuracy in detecting new candidate CDGs. However, current function prediction based approaches only focus on coding mutations and lack a systematic way to pick the best mutation deleteriousness prediction algorithms for usage. Results In this study, we propose a new function prediction based approach to discover CDGs through a gene-based permutation approach. Our method not only covers both coding and non-coding regions of the genes; but it also accounts for the heterogeneous mutational context in cohort of cancer patients. The permutation model was implemented independently using seven popular deleteriousness prediction scores covering splicing regions (SPIDEX), coding regions (MetaLR, and VEST3) and pan-genome (CADD, DANN, Fathmm-MKL coding and Fathmm-MKL noncoding). We applied this new approach to somatic single nucleotide variants (SNVs) from whole-genome sequences of 119 breast and 24 lung cancer patients and compared the seven deleteriousness prediction scores for their performance in this study. Conclusion The new function prediction based approach not only predicted known cancer genes listed in the Cancer Gene Census (CGC), but also new candidate CDGs that are worth further investigation. The results showed the advantage of utilizing pan-genome deleteriousness prediction scores in function prediction based methods. Although VEST3 score, a deleteriousness prediction score for missense mutations, has the best performance in breast cancer, it was topped by CADD and Fathmm-MKL coding, two pan-genome deleteriousness prediction scores, in lung cancer. Electronic supplementary material The online version of this article (10.1186/s12920-018-0452-9) contains supplementary material, which is available to authorized users.

Uveal melanoma (UM) is the most common malignant eye tumor in adults affecting ~7,000 individuals per year worldwide. UM is a rare subtype of melanoma with distinct clinical and molecular features as compared to other melanoma subtypes. UMs lack the most typical cutaneous melanoma-associated mutations (BRAF, NRAS, and NF1) and are instead characterized by a different set of genes with oncogenic or loss-of-function mutations. By next-generation sequencing efforts on UM tumors, several driver genes have been detected. The most frequent ones are BAP1, EIF1AX, GNA11, GNAQ, and SF3B1. In many cases, mutations in these genes appear in a mutually exclusive manner, have different risk of metastasis, and are consequently of prognostic importance. The majority of UM cases are sporadic but a few percentage of the cases occurs in families with an inherited predisposition for this malignancy. In recent years, germline mutations in the BAP1 gene have been found to segregate in an autosomal dominant pattern with numerous different cancer types including UM in cancer-prone families. This cancer syndrome has been denoted as the tumor predisposition syndrome.

Objective: This study aims to establish a method for highly parallel multiplexed detection of genetic mutations in Chinese lung cancer samples through Agena iPLEX chemistry and matrix-assisted laser desorption ionization time-of-flight analysis on MassARRAY mass spectrometry platform. Objective: We reviewed the related literature and data on lung cancer treatments. We also identified 99 mutation hot spots in 13 target genes closely related to the pathogenesis, drug resistance, and metastasis of lung cancer. A total of 297 primers, composed of 99 paired forward and reverse amplification primers and 99 matched extension primers, were designed using Assay Design software. The detection method was established by analyzing eight cell lines and six lung cancer specimens. The proposed method was then validated through comparisons by using a LungCartaTM kit. The sensitivity and specificity of the proposed method were evaluated by directly sequencing EGFR and KRAS genes in 100 lung cancer cases. Results: The proposed method was able to detect multiplex genetic mutations in lung cancer cell lines. This finding was consistent with the observations on previously reported mutations. The proposed method can also detect such mutations in clinical lung cancer specimens. This result was consistent with the observations with LungCartaTM kit. However, an FGFR2 mutation was detected only through the proposed method. The measured sensitivity and specificity were 100% and 96.3%, respectively. Conclusions: The proposed MassARRAY technology-based multiplex method can detect genetic mutations in Chinese lung cancer patients. Therefore, the proposed method can be applied to detect mutations in other cancer tissues.

The genetic variations responsible for tumorigenesis are called driver mutations. In breast cancer (BC), two studies have demonstrated that germline mutations in driver genes linked to sporadic tumors may also influence BC risk. The present study evaluates the association between SNPs and SNP-SNP interaction in driver genes TTN (rs10497520), TBX3 (rs2242442), KMT2D (rs11168827), and MAP3K1 (rs702688 and rs702689) with BC risk in BRCA1/2-negative Chilean families. The SNPs were genotyped in 489 BC cases and 1078 controls by TaqMan Assay. Our data do not support an association between rs702688: A&gt, G or rs702689: G&gt, A and BC risk. The rs10497520-T allele was associated with a decreased risk in patients with family history of BC or early-onset BC (OR = 0.6, p &lt, 0.0001 and OR = 0.7, p = 0.05, respectively). rs2242442-G was associated with a protective effect and rs11168827-C was associated with increased BC risk in families with a strong history of BC (OR = 0.6, p = 0.02 and OR = 1.4, p = 0.05, respectively). As rs10497520-T and rs2242442-G seemed to protect against BC risk, we then evaluated their combined effect. Familial BC risk decreased in a dose-dependent manner with the protective allele count, reflecting an additive effect (p-trend &lt, 10&minus, 4). To our knowledge, this is the first association study of BC driver gene germline variations in a Chilean population.

Cancer is a complex disease in which cells progressively accumulate mutations disrupting their cellular processes. A fraction of these mutations drive tumourigenesis by affecting oncogenes or tumour suppressor genes, but many mutations are passengers with no clear contribution to tumour development. The advancement of DNA and RNA sequencing technologies has enabled in-depth analysis of thousands of human tumours from various tissues to perform systematic characterization of their (epi)genomes and transcriptomes in order to identify (epi)genetic changes associated with cancer. Combined with considerable progress in algorithmic development, this expansion in scale has resulted in the identification of many cancer-associated mutations, genes and pathways that are considered to be potential drivers of tumour development. However, it remains challenging to systematically identify drivers affected by complex genomic rearrangements and drivers residing in non-coding regions of the genome or in complex amplicons or deletions of copy-number driven tumours. Furthermore, functional characterization is challenging in the human context due to the lack of genetically tractable experimental model systems in which the effects of mutations can be studied in the context of their tumour microenvironment. In this respect, mouse models of human cancer provide unique opportunities for pinpointing novel driver genes and their detailed characterization. In this review, we provide an overview of approaches for complementing human studies with data from mouse models. We also discuss state-of-the-art technological developments for cancer gene discovery and validation in mice.

Familial adenomatous polyposis (FAP) and MUTYH‐associated polyposis (MAP) are inherited disorders associated with multiple colorectal adenomas that lead to a very high risk of colorectal cancer. The somatic mutations that drive adenoma development in these conditions have not been investigated comprehensively. In this study we performed analysis of paired colorectal adenoma and normal tissue DNA from individuals with FAP or MAP, sequencing 14 adenoma whole exomes (eight MAP, six FAP), 55 adenoma targeted exomes (33 MAP, 22 FAP) and germline DNA from each patient, and a further 63 adenomas by capillary sequencing (41 FAP, 22 MAP). With these data we examined the profile of mutated genes, the mutational signatures and the somatic mutation rates, observing significant diversity in the constellations of mutated driver genes in different adenomas, and loss‐of‐function mutations in WTX (9%; p

Over the last few years the increasing usage of "-omic" platforms, supported by next-generation sequencing, in the analysis of breast cancer samples has tremendously advanced our understanding of the disease. New driver and passenger mutations, rare chromosomal rearrangements and other genomic aberrations identified by whole genome and exome sequencing are providing missing pieces of the genomic architecture of breast cancer. High resolution maps of breast cancer methylomes and sequencing of the miRNA microworld are beginning to paint the epigenomic landscape of the disease. Transcriptomic profiling is giving us a glimpse into the gene regulatory networks that govern the fate of the breast cancer cell. At the same time, integrative analysis of sequencing data confirms an extensive intertumor and intratumor heterogeneity and plasticity in breast cancer arguing for a new approach to the problem. In this review, we report on the latest findings on the molecular characterization of breast cancer using NGS technologies, and we discuss their potential implications for the improvement of existing therapies.

Cancer treatment decisions rely on genetics, large data screens and clinical pharmacology. Here we point out that genetic analysis and treatment decisions may overlook critical elements in cancer development, progression and drug resistance. Two critical structural elements are missing in genetics-based decision-making: the mechanisms of oncogenic mutations and the cellular network which is rewired in cancer. These lay the foundation for the structural basis for cancer treatment decisions, which is rooted in the physical principles of the molecular conformational behavior of single molecules and their interactions. Improved tumor mutational analysis platforms and knowledge of the redundant pathways which can take over in cancer, may not only supplement known actionable findings, but forecast possible cancer progression and resistance. Such forward-looking can be powerful, endowing the oncologist with mechanistic insight and cancer prognosis, and consequently more informed treatment options. Examples include redundant pathways taking over after inhibition of EGFR constitutive activation, mutations in PIK3CA p110α and p85, and the non-hotspot AKT1 mutants conferring constitutive membrane localization.

Background Breast cancer (BC) is the most prevalent cancer in women and a major public health problem in Morocco. Several Moroccan studies have focused on studying this disease, but more are needed, especially at the genetic and molecular levels. Therefore, we investigated the potential association of several functional germline variants in the genes commonly mutated in sporadic breast cancer. Methods In this case–control study, we examined 36 single nucleotide polymorphisms (SNPs) in 13 genes (APOBEC3A, APOBEC3B, ARID1B, ATR, MAP3K1, MLL2, MLL3, NCOR1, RUNX1, SF3B1, SMAD4, TBX3, TTN), which were located in the core promoter, 5’-and 3’UTR or which were nonsynonymous SNPs to assess their potential association with inherited predisposition to breast cancer development. Additionally, we identified a ~29.5-kb deletion polymorphism between APOBEC3A and APOBEC3B and explored possible associations with BC. A total of 226 Moroccan breast cancer cases and 200 matched healthy controls were included in this study. Results The analysis showed that12 SNPs in 8 driver genes, 4 SNPs in APOBEC3B gene and 1 SNP in APOBEC3A gene were associated with BC risk and/or clinical outcome at P ≤ 0.05 level. RUNX1_rs8130963 (odds ratio (OR) = 2.25; 95 % CI 1.42-3.56; P = 0.0005; dominant model), TBX3_rs8853 (OR = 2.04; 95 % CI 1.38-3.01; P = 0.0003; dominant model), TBX3_rs1061651 (OR = 2.14; 95 % CI1.43-3.18; P = 0.0002; dominant model), TTN_rs12465459 (OR = 2.02; 95 % confidence interval 1.33-3.07; P = 0.0009; dominant model), were the most significantly associated SNPs with BC risk. A strong association with clinical outcome were detected for the genes SMAD4 _rs3819122 with tumor size (OR = 0.45; 95 % CI 0.25-0.82; P = 0.009) and TTN_rs2244492 with estrogen receptor (OR = 0.45; 95 % CI 0.25-0.82; P = 0.009). Conclusion Our results suggest that genetic variations in driver and APOBEC3 genes were associated with the risk of BC and may have impact on clinical outcome. However, the reported association between the deletion polymorphism and BC risk was not confirmed in the Moroccan population. These preliminary findings require replication in larger studies.

The extent of focal chromosomal copy number aberrations (CNAs) in cancer has been uncovered through technical innovations, and this discovery has been critical for the identification of new cancer driver genes in genomics projects such as TCGA and ICGC. Unlike constitutive copy number variations (CNVs), focal CNAs are the result of many selection events during the evolution of cancer genomes. Therefore, it is possible that a single gene in a focal CNA gives the tumor a selective growth advantage. This concept has been instrumental in the discovery of new cancer driver genes. However, focal CNAs lack a consensus definition; therefore, we propose one based on pragmatic considerations. We also describe different strategies to identify focal CNAs and procedures to distinguish them from large CNAs and CNVs.

Background Subtypes are widely found in cancer. They are characterized with different behaviors in clinical and molecular profiles, such as survival rates, gene signature and copy number aberrations (CNAs). While cancer is generally believed to have been caused by genetic aberrations, the number of such events is tremendous in the cancer tissue and only a small subset of them may be tumorigenic. On the other hand, gene expression signature of a subtype represents residuals of the subtype-specific cancer mechanisms. Using high-throughput data to link these factors to define subtype boundaries and identify subtype-specific drivers, is a promising yet largely unexplored topic. Results We report a systematic method to automate the identification of cancer subtypes and candidate drivers. Specifically, we propose an iterative algorithm that alternates between gene expression clustering and gene signature selection. We applied the method to datasets of the pediatric cerebellar tumor medulloblastoma (MB). The subtyping algorithm consistently converges on multiple datasets of medulloblastoma, and the converged signatures and copy number landscapes are also found to be highly reproducible across the datasets. Based on the identified subtypes, we developed a PCA-based approach for subtype-specific identification of cancer drivers. The top-ranked driver candidates are found to be enriched with known pathways in certain subtypes of MB. This might reveal new understandings for these subtypes. This article is an extended abstract of our ICCABS '12 paper (Chen et al. 2012), with revised methods in iterative subtyping, the use of canonical correlation analysis for driver-identification, and an extra dataset (Northcott90 dataset) for cross-validations. Discussions of the algorithm performance and of the slightly different gene lists identified are also added. Conclusions Our study indicates that subtype-signature defines the subtype boundaries, characterizes the subtype-specific processes and can be used to prioritize signature-related drivers.

The Oslo University Hospital (Norway), the K.G. Jebsen Centre for Breast Cancer Research (Norway), The Radiumhospital Foundation (Norway) and the Fritz-Bender-Foundation (Germany) designed under the conference chairmen (E. Mihich, K.S. Zänker, A.L. Borresen-Dale) and advisory committee (A. Borg, Z. Szallasi, O. Kallioniemi, H.P. Huber) a program at the cutting edge of “PERSONALIZED CANCER CARE: Risk prediction, early diagnosis, progression and therapy resistance.” The conference was held in Oslo from September 7 to 9, 2012 and the science-based presentations concerned six scientific areas: (1) Genetic profiling of patients, prediction of risk, late side effects; (2) Molecular profiling of tumors and metastases; (3) Tumor-host microenvironment interaction and metabolism; (4) Targeted therapy; (5) Translation and (6) Informed consent, ethical challenges and communication. Two satellite workshops on (i) Ion Ampliseq—a novel tool for large scale mutation detection; and (ii) Multiplex RNA ISH and tissue homogenate assays for cancer biomarker validation were additionally organized. The report concludes that individual risk prediction in carcinogenesis and/or metastatogenesis based on polygenic profiling may be useful for intervention strategies for health care and therapy planning in the future. To detect distinct and overlapping DNA sequence alterations in tumor samples and adjacent normal tissues, including point mutations, small insertions or deletions, copy number changes and chromosomal rearrangements will eventually make it possible to design personalized management plans for individualized patients. However, large individualized datasets need a new approach in bio-information technology to reduce this enormous data dimensionally to simply working hypotheses about health and disease for each individual.

Introduction The incidence rate of lung adenocarcinoma (LUAD), the predominant histological subtype of lung cancer, is elevated in Asians, particularly in female nonsmokers. The mutation patterns in LUAD in Asians might be distinct from those in LUAD in whites. Methods We profiled 271 resected LUAD tumors (mainly stage I) to characterize the genomic landscape of LUAD in Asians with a focus on female nonsmokers. Results Mutations in EGFR , KRAS , erb-b2 receptor tyrosine kinase 2 gene ( ERBB2 ), and BRAF ; gene fusions involving anaplastic lymphoma receptor tyrosine kinase gene ( ALK ), ROS1, and ret proto-oncogene ( RET ); and Met Proto-Oncogene Tyrosine Kinase ( MET ) exon 14 skipping were the major drivers in LUAD in Asians, exhibiting mutually exclusive and differing prevalence from those reported in studies of LUAD in non-Asians. In addition, we identified a novel mutational signature of XNX (the mutated base N in the middle flanked by two identical bases at the 5′ and 3′ positions) that was overrepresented in LUAD tumors in nonsmokers and negatively correlated with the overall mutational frequency. Conclusions In this cohort, approximately 85% of individuals have known driver mutations ( EGFR 59.4%, KRAS 7.4%, ALK 7.4%, ERBB2 2.6%, ROS1 2.2%, RET 2.2%, MET 1.8%, BRAF 1.1%, and NRAS 0.4%). Seventy percent of smokers and 90% of nonsmokers had defined oncogenic drivers matching the U.S. Food and Drug Administration–approved targeted therapies.

A large number of DNA copy number alterations (CNAs) exist in human breast cancers, and thus characterizing the most frequent CNAs is key to advancing therapeutics because it is likely that these regions contain breast tumor ‘drivers’ (i.e., cancer causal genes). This study aims to characterize the genomic landscape of breast cancer CNAs and identify potential subtype-specific drivers using a large set of human breast tumors and genetically engineered mouse (GEM) mammary tumors. Using a novel method called SWITCHplus, we identified subtype-specific DNA CNAs occurring at a 15 % or greater frequency, which excluded many well-known breast cancer-related drivers such as amplification of ERBB2, and deletions of TP53 and RB1. A comparison of CNAs between mouse and human breast tumors identified regions with shared subtype-specific CNAs. Additional criteria that included gene expression-to-copy number correlation, a DawnRank network analysis, and RNA interference functional studies highlighted candidate driver genes that fulfilled these multiple criteria. Numerous regions of shared CNAs were observed between human breast tumors and GEM mammary tumor models that shared similar gene expression features. Specifically, we identified chromosome 1q21-23 as a Basal-like subtype-enriched region with multiple potential driver genes including PI4KB, SHC1, and NCSTN. This step-wise computational approach based on a cross-species comparison is applicable to any tumor type for which sufficient human and model system DNA copy number data exist, and in this instance, highlights that a single region of amplification may in fact harbor multiple driver genes. Electronic supplementary material The online version of this article (doi:10.1007/s10549-015-3476-2) contains supplementary material, which is available to authorized users.

Background: Most tumors are the result of accumulated genomic alterations in somatic cells. The emerging spectrum of alterations in tumors is complex and the identification of relevant genes and pathways remains a challenge. Furthermore, key cancer genes are usually found amplified or deleted in chromosomal regions containing many other genes. Point mutations, on the other hand, provide exquisite information about amino acid changes that could be implicated in the oncogenic process. Current large-scale genomic projects provide high throughput genomic data in a large number of well-characterized tumor samples. Methods: We define a Bayesian approach designed to identify candidate cancer genes by integrating copy number and point mutation information. Our method exploits the concept that small and recurrent alterations in tumors are more informative in the search for cancer genes. Thus, the algorithm (Mutations with Common Focal Alterations, or MutComFocal) seeks focal copy number alterations and recurrent point mutations within high throughput data from large panels of tumor samples. Results: We apply MutComFocal to Diffuse Large B-cell Lymphoma (DLBCL) data from four different high throughput studies, totaling 78 samples assessed for copy number alterations by single nucleotide polymorphism (SNP) array analysis and 65 samples assayed for protein changing point mutations by whole exome/whole transcriptome sequencing. In addition to recapitulating known alterations, MutComFocal identifies ARID1B, ROBO2 and MRS1 as candidate tumor suppressors and KLHL6, IL31 and LRP1 as putative oncogenes in DLBCL. Conclusions: We present a Bayesian approach for the identification of candidate cancer genes by integrating data collected in large number of cancer patients, across different studies. When trained on a well-studied dataset, MutComFocal is able to identify most of the reported characterized alterations. The application of MutComFocal to large-scale cancer data provides the opportunity to pinpoint the key functional genomic alterations in tumors.