Candidate Cancer Driver Genes Research Articles

Genomic alteration discordance in the paired primary-recurrent ovarian cancers: based on the comprehensive genomic profiling (CGP) analysis

PurposeOvarian cancer (OC) is characterized by a high recurrence rate, and homologous recombination deficiency (HRD) is an important biomarker in the clinical management of OC. We investigated the differences in clinical genomic profiles between the primary and platinum-sensitive recurrent OC (PSROC), focusing on HRD status.Materials and methodsA total of 40 formalin-fixed paraffin-embedded (FFPE) tissues of primary tumors and their first platinum-sensitive recurrence from 20 OC patients were collected, and comprehensive genomic profiling (CGP) analysis of FoundationOne®CDx (F1CDx) was applied to explore the genetic (dis)similarities of the primary and recurrent tumors.ResultsBy comparing between paired samples, we found that genomic loss of heterozygosity (gLOH) score had a high intra-patient correlation (r2 = 0.79) and that short variants (including TP53, BRCA1/2 and NOTCH1 mutations), tumor mutational burden (TMB) and microsatellite stability status remained stable. The frequency of (likely) pathological BRCA1/2 mutations was 30% (12/40) in all samples positively correlated with gLOH scores, but the proportion of gLOH-high status (score > 16%) was 50% (10/20) and 55% (11/20) in the primary and recurrent samples, respectively. An additional 20% (4/20) of patients needed attention, a quarter of which carried the pathological BRCA1 mutation but had a gLOH-low status (gLOH < 16%), and three-quarters had different gLOH status in primary-recurrent pairs. Furthermore, we observed the PSROC samples had higher gLOH scores (16.1 ± 9.24 vs. 19.4 ± 11.1, p = 0.007), more CNVs (36.1% vs. 15.1% of discordant genomic alternations), and significant enrichment of altered genes in TGF-beta signaling and Hippo signaling pathways (p < 0.05 for all) than their paired primaries. Lastly, mutational signature and oncodrive gene analyses showed that the computed mutational signature similarity in the primary and recurrent tumors were best matched the COSMI 3 signature (Aetiology of HRD) and had consistent candidate cancer driver genes of MSH2, NOTCH1 and MSH6.ConclusionThe high genetic concordance of the short variants remains stable along OC recurrence. However, the results reveal significantly higher gLOH scores in the recurrent setting than in paired primaries, supporting further clinically instantaneity HRD assay strategy.

Structure-based pathogenicity relationship identifier for predicting effects of single missense variants and discovery of higher-order cancer susceptibility clusters of mutations.

We report the structure-based pathogenicity relationship identifier (SPRI), a novel computational tool for accurate evaluation of pathological effects of missense single mutations and prediction of higher-order spatially organized units of mutational clusters. SPRI can effectively extract properties determining pathogenicity encoded in protein structures, and can identify deleterious missense mutations of germ line origin associated with Mendelian diseases, as well as mutations of somatic origin associated with cancer drivers. It compares favorably to other methods in predicting deleterious mutations. Furthermore, SPRI can discover spatially organized pathogenic higher-order spatial clusters (patHOS) of deleterious mutations, including those of low recurrence, and can be used for discovery of candidate cancer driver genes and driver mutations. We further demonstrate that SPRI can take advantage of AlphaFold2 predicted structures and can be deployed for saturation mutation analysis of the whole human proteome.

Machine learning approaches for prediction of ovarian cancer driver genes from mutational and network analysis

PurposeOvarian cancer (OC) is the most common type of gynecologic cancer in the world with a high rate of mortality. Due to manifestation of generic symptoms and absence of specific biomarkers, OC is usually diagnosed at a late stage. Machine learning models can be employed to predict driver genes implicated in causative mutations.Design/methodology/approachIn the present study, a comprehensive next generation sequencing (NGS) analysis of whole exome sequences of 47 OC patients was carried out to identify clinically significant mutations. Nine functional features of 708 mutations identified were input into a machine learning classification model by employing the eXtreme Gradient Boosting (XGBoost) classifier method for prediction of OC driver genes.FindingsThe XGBoost classifier model yielded a classification accuracy of 0.946, which was superior to that obtained by other classifiers such as decision tree, Naive Bayes, random forest and support vector machine. Further, an interaction network was generated to identify and establish correlations with cancer-associated pathways and gene ontology data.Originality/valueThe final results revealed 12 putative candidate cancer driver genes, namely LAMA3, LAMC3, COL6A1, COL5A1, COL2A1, UGT1A1, BDNF, ANK1, WNT10A, FZD4, PLEKHG5 and CYP2C9, that may have implications in clinical diagnosis.

The Genetic Landscape of Ocular Adnexa MALT Lymphoma Reveals Frequent Aberrations in NFAT and MEF2B Signaling Pathways.

A comprehensive constellation of somatic non-silent mutations and copy number (CN) variations in ocular adnexa marginal zone lymphoma (OAMZL) is unknown. By utilizing whole-exome sequencing in 69 tumors we define the genetic landscape of OAMZL. Mutations and CN changes in CABIN1 (30%), RHOA (26%), TBL1XR1 (22%), and CREBBP (17%) and inactivation of TNFAIP3 (26%) were among the most common aberrations. Candidate cancer driver genes cluster in the B-cell receptor (BCR), NFkB, NOTCH and NFAT signaling pathways. One of the most commonly altered genes is CABIN1, a calcineurin inhibitor acting as a negative regulator of the NFAT and MEF2B transcriptional activity. CABIN1 deletions enhance BCR-stimulated NFAT and MEF2B transcriptional activity, while CABIN1 mutations enhance only MEF2B transcriptional activity by impairing binding of mSin3a to CABIN1. Our data provide an unbiased identification of genetically altered genes that may play a role in the molecular pathogenesis of OAMZL and serve as therapeutic targets.

A systematic view of computational methods for identifying driver genes based on somatic mutation data.

Abnormal changes of driver genes are serious for human health and biomedical research. Identifying driver genes, exactly from enormous genes with mutations, promotes accurate diagnosis and treatment of cancer. A lot of works about uncovering driver genes have been developed over the past decades. By analyzing previous works, we find that computational methods are more efficient than traditional biological experiments when distinguishing driver genes from massive data. In this study, we summarize eight common computational algorithms only using somatic mutation data. We first group these methods into three categories according to mutation features they apply. Then, we conclude a general process of nominating candidate cancer driver genes. Finally, we evaluate three representative methods on 10 kinds of cancer derived from The Cancer Genome Atlas Program and five Chinese projects from the International Cancer Genome Consortium. In addition, we compare results of methods with various parameters. Evaluation is performed from four perspectives, including CGC, OG/TSG, Q-value and QQQuantile-Quantileplot. To sum up, we present algorithms using somatic mutation data in order to offer a systematic view of various mutation features and lay the foundation of methods based on integration of mutation information and other types of data.

Identification of cancer driver genes using Sleeping Beauty transposon mutagenesis.

Cancer genome sequencing studies have identified driver genes for a variety of different cancers and helped to understand the genetic landscape of human cancer. It is still challenging, however, to identify cancer driver genes with confidence simply from genetic data alone. In vivo forward genetic screens using Sleeping Beauty (SB) transposon mutagenesis provides another powerful genetic tool for identifying candidate cancer driver genes in wild‐type and sensitized mouse tumors. By comparing cancer driver genes identified in human and mouse tumors, cancer driver genes can be identified with additional confidence based upon comparative oncogenomics. This review describes how SB mutagenesis works in mice and focuses on studies that have identified cancer driver genes in the mouse gastrointestinal tract.

Protein dynamics analysis identifies candidate cancer driver genes and mutations in TCGA data.

Recently, it has been showed that cancer missense mutations selectively target the neighborhood of hinge residues, which are key sites in protein dynamics. Here, we show that this approach can be extended to find previously unknown candidate mutations and genes. To this aim, we developed a computational pipeline to detect significantly enriched three-dimensional (3D) clustering of missense mutations around hinge residues. The hinge residues were detected by applying a Gaussian network model. By systematically analyzing the PanCancer compendium of somatic mutations in nearly 10 000 tumors from the Cancer Genome Atlas, we identified candidate genes and mutations in addition to well known ones. For instance, we found significantly enriched 3D clustering of missense mutations in known cancer genes including CDK4, CDKN2A, TCL1A, and MAPK1. Beside these known genes, we also identified significantly enriched 3D clustering of missense mutations around hinge residues in PLA2G4A, which may lead to excessive phosphorylation of the extracellular signal-regulated kinases. Furthermore, we demonstrated that hinge-based features improves pathogenicity prediction for missense mutations. Our results show that the consideration of clustering around hinge residues can help us explain the functional role of the mutations in known cancer genes and identify candidate genes.

Parent-child signals identify candidate cancer driver genes

Background: The DREAM Challenge evaluated methods to identify molecular pathways facilitating the detection of multiple genes affecting critical interactions and processes. Dysregulation of pathways by well-known driver genes is often found in the development and progression of cancer. We used the gene interaction networks provided and the scoring rounds to test disease module identification methods to nominate candidate driver genes in these modules. Method: Our algorithm calculated the proportion of the whole network accessible in two steps from each node in a combined network, which was defined as a 2-reach gene value. Genes with high 2-reach values were used to form the center of star cover clusters. These clusters were assessed for significant modules. Within these modules we identified novel candidate driver genes, by considering the parent-child relationship of well-known driver genes. Disturbance to such driver genes or their upstream parents, can lead to disruption of highly regulated signals affecting the normal functions of cells. We explored these parents as a potential source for candidate driver genes. Results: An initial list of 57 candidate driver genes was identified from 13 significant modules. Analysis of the parent-child relationships of well-known driver genes in these modules prioritized PRKDC, YWHAB, GSK3B, and PPP1CB. Conclusion: Our method incorporated the simple m-reach topology metric in disease module identification and its relationship with known driver genes to identify candidate genes. The four genes shortlisted have been highlighted in recent publications in the literature, which supports the need for further wet lab experimental investigation.

PTMsnp: A Web Server for the Identification of Driver Mutations That Affect Protein Post-translational Modification.

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.

Parent-child signals identify candidate cancer driver genes

Parent-child signals identify candidate cancer driver genes

Comparison of different functional prediction scores using a gene-based permutation model for identifying cancer driver genes

BackgroundIdentifying 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.ResultsIn 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.ConclusionThe 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.

Genetic Landscape of Ocular Adnexa Extranodal Marginal Zone Lymphoma

Background: Ocular adnexa lymphomas (OAL) consist of a heterogeneous group of tumors that can arise in the lacrimal gland, orbit, conjunctiva and eyelid. More than 95% of these lymphomas are of B-cell origin and extranodal marginal zone lymphoma (EMZL) of mucosa-associated lymphoid tissue is the most common subtype of OAL, accounting for 55-85% of the cases. Ocular adnexa extranodal marginal zone lymphoma (OA-EMZL) patients have a persistent risk of relapses and in 4% of the cases, the disease transforms into an aggressive lymphoma. A comprehensive list of all somatic nonsilent mutations remains currently unknown thus hampering the complete understanding of the disease and the development of new diagnostic and therapeutic approaches. Previous studies analyzed only a small number of OA-EMZL patients using gene targeted sequencing. In this study we utilized whole-exome sequencing to define the genetic landscape of 69 cases of OA-EMZL. Our data provide an unbiased identification of genetically altered genes and pathways that may play a role in the molecular pathogenesis of OA-EMZL.Methods: GATK HaplotypeCaller was used for joint variant calling on all samples, while MuTect was used to detect somatic variants in seven tumor samples with available paired normal germline DNA. We focused on variants that were exonic, not synonymous, and deleterious (CADD score > 10), while excluding any variants found with a frequency > 1% in control populations. For copy number (CN) analysis we used EXCAVATOR on each sequenced exome in pooled mode against the seven paired normal samples. The output was provided to GISTIC 2.0 to determine recurrent arm-level and gene-level CN changes.Results: We identified 125 candidate cancer driver genes with the following characteristics: 1) have genomic alteration in more than 5% of the cases, 2) are present in the list of COSMIC and DLBCL driver genes, 3) are considered essential genes or tumor suppressor genes as previously defined in a functional CRISPR screen (Reddy et al., Cell 2017). Among the most frequent alterations there were mutations and CN losses of CABIN1 (32%), inactivation of TNFAIP3 (26%) and KMT2D (22%), mutations and CN changes in CARD11 (25%), RHOA (25%), CREBBP (20%) and TBL1XR1 (23%). Gene set enrichment analysis of candidate driver genes showed statistically significant enrichment for genes involved in cell junction, adhesion, cytoskeleton regulation, chromatin organization and for genes harboring at least one highly conserved NFAT binding motif TGGAAA in their promoter. In addition, we identified recurrent lesions affecting several genes belonging to the NFkB, NOTCH and B cell receptor (BCR) signaling pathways. The most frequent alterations in the NFkB pathway included inactivation of TNFAIP3 (26%), mutations and CN alterations of CARD11 (25%), MYD88 (12%), BCL10 (10%) and CRD10 (9%). Mutations affecting NOTCH signaling were found in NOTCH3 (22%), DTX1 (12%), MAML1 (12%), MAML2 (12%), DLK1 (10%) and SPEN (9%). BCR signaling genes alterations were observed in PIK3R2 (12%), VAV1 (10%), PTPN6 (7%) and BTK (4%).CABIN1 was one of the most commonly altered genes. In T cells, CABIN1 is implicated in inhibiting calcineurin-mediated signal transduction, including NFAT activation. In reactive lymph node and tonsil tissue, CABIN1 was expressed in reactive B cells (CD20+ and CD3-) localized mainly in the mantel zone area, as demonstrated by IHC and IF. We hypothesized that it may act as a negative regulator of BCR signaling in MZL. To verify this possibility we used shRNAs to knock down CABIN1 in MZL cell lines and measured NFAT activity in response to IgM stimulation using a luciferase reporter system. IgM stimulation of CABIN1-depleted MZL cells induced a more pronounced activation of NFAT signaling compared to wild-type cells.Conclusions: The genetics of OA-EMZL is characterized by somatic alterations in NFkB, NOTCH, and BCR signaling pathways. Candidate cancer driver genes are implicated in chromatin organization, cytoskeleton regulation and in adhesion, and they are also enriched for NFAT target genes. One of the most altered candidate cancer driver genes was CABIN1, which is a calcineurin inhibitor that acts as a negative regulator of NFAT activity. CABIN1 depletion in MZL cells boosts BCR-stimulated NFAT activity. Our data suggest a widespread dysregulation of NFAT signaling cascade downstream of BCR stimulation as a possible mechanism of OA-EMZL pathogenesis. DisclosuresLossos:Affimed: Research Funding.

Abstract 1546: Contribution of heterozygous loss of ribosomal protein L5 as general tumor suppressor in cancer

Abstract The association between ribosome defects and cancer became clear with the recent discovery of somatic mutations in ribosomal protein genes in several cancers, such as lymphoid leukemia’s and glioblastoma (Nat Genet. 2013 Feb;45(2):186-90 &amp; Blood. 2016 Feb 25;127(8):1007-16 &amp; Nature. 2014 Jan 23;505(7484):495-501). To further delineate the role of ribosomal proteins in human cancer, we systematically screened the TCGA database for genetic lesions in ribosomal protein genes, confirmed the contributing capacity of the most common somatic ribosomal protein defect in cancer pathogenesis by molecular interference, and explored the mode of action of this ribosomal protein defect. Systematic analysis of TCGA mutation and copy number data of respectively 4926 and 7322 patients representing 16 cancer types for defects in all 81 ribosomal protein genes. After stringent filtering, six ribosomal protein encoding genes (RPL5, RPL11, RPL23A, RPS5, RPS20 and RPSA) were significantly altered and identified as candidate cancer driver genes. RPL5 was located at a significant peak of heterozygous deletion on chromosome 1p22 and showed significant mutations and deletions in 11% of glioblastoma (GBM), 28% of melanoma, 34% of breast cancer (BRCA), and in 20% of multiple myeloma cases, thereby supporting heterozygous RPL5 inactivation to be the most common somatic ribosomal protein defect in human cancer. Human specific RPL5 doxycycline inducible knockdown (sh-hRPL5) in TP53 WT and TP53 mutant human breast cancer cell lines (MCF7 and MDA-MB-231) and in a TP53 mutant human glioblastoma cell line (U-118 MG) proved that RPL5 knockdown accelerated in vivo tumor progression in NSG mice. This acceleration was associated with reduced phosphorylation of CDK1tyr15, which is required for cell cycle progression from G2 to mitosis. Whereas RPL5 has been implicated in TP53 and MYC regulation, no consistent effects of RPL5 downregulation on these proteins could be detected in TP53 WT and TP53 homozygous R280K mutant breast cancer models (sh-hRPL5). Interestingly, partial RPL5 inactivation was found to be associated with PTEN protein suppression in these tumors. This observation was confirmed in various cell models; in normal mouse neural stem/progenitor cells and mouse bone marrow cells (RPL5+/lox by INFβ and poly I:C), and also in GBM cancer pre-disposition mouse neural stem/progenitor cell model (sh-mRPL5 TP53 -/-). RPL5 downregulation also accelerated tumor formation in the PTEN deficient GBM cell line U-118 MG, underscoring undefined PTEN-independent mechanisms of RPL5. Overall, we identified RPL5 as a new tumor suppressor that shows heterozygous inactivation in 11-34% of multiple human cancer types. Partial molecular inactivation of RPL5 supported a tumor suppressor function for RPL5 in accelerating breast cancer and glioblastoma progression in vivo, which may be, in part, due to a new function of RPL5 in regulating PTEN. Citation Format: Kim R. Kampen, Laura Fancello, Jelle Verbeeck, Kim De Keersmaecker. Contribution of heterozygous loss of ribosomal protein L5 as general tumor suppressor in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1546. doi:10.1158/1538-7445.AM2017-1546

Molecular genetics of osteosarcoma

Osteosarcoma is the predominant form of bone cancer, affecting mostly adolescents. Recent progress made in molecular genetic studies of osteosarcoma has changed our view on the cause of the disease and ongoing therapeutic approaches for patients. As we draw closer to gaining more complete catalogs of candidate cancer driver genes in common forms of cancer, the landscape of somatic mutations in osteosarcoma is emerging from its first phase. In this review, we summarize recent whole genome and/or whole exome genomic studies, and then put these findings in the context of genetic hallmarks of somatic mutations and mutational processes in human osteosarcoma. One of the lessons learned here is that the extent of somatic mutations and complexity of the osteosarcoma genome are similar to that of common forms of adult cancer. Thus, a much higher number of samples than those currently obtained are needed to complete the catalog of driver mutations in human osteosarcoma. In parallel, genetic studies in other species have revealed candidate driver genes and their roles in the genesis of osteosarcoma. This review also summarizes newly identified drivers in genetically engineered mouse models (GEMMs) and discusses our understanding of the impact of nature and number of drivers on tumor latency, subtypes, and metastatic potentials of osteosarcoma. It is becoming apparent that a synergistic team composed of three drivers (one ‘first driver’ and two ‘synergistic drivers’) may be required to generate an animal model that recapitulates aggressive osteosarcoma with a short latency. Finally, new cancer therapies are urgently needed to improve survival rate and quality of life for osteosarcoma patients. Several vulnerabilities in osteosarcoma are illustrated in this review to exemplify the opportunities for next generation molecularly targeted therapies. However, much work remains in order to complete our understanding of the somatic mutation basis of osteosarcoma, to develop reliable animal models of human disease, and to apply this information to guide new therapeutic approaches for reducing morbidity and mortality of this rare disease.

Abstract LB-330: Somatic copy number alterations at oncogenic loci show diverse correlations with gene expression

Abstract Somatic copy number alterations (SCNAs) affecting oncogenic drivers have a firmly established role in promoting cancer. However, no agreed-upon standard exists for calling locus-specific amplifications and deletions in each patient sample. Here, we report the correlative analysis of copy number amplitude and length with gene expression across 6,109 samples from The Cancer Genome Atlas (TCGA) dataset across 16 cancer types. Using specificity, sensitivity, and precision-based scores, we analyzed over 30 recurrently focally altered loci that had sufficient pan-cancer penetrance for robust statistical assessment. We were able to assign optimized amplitude and length cutoffs for nine recurrent SCNAs affecting known oncogenic drivers, using mRNA expression as a functional readout. These cutoffs captured the majority of SCNA-driven, highly-expression-altered samples. The majority of oncogenes required only amplitude cutoffs, as high amplitude samples were almost invariably focal; however, CDKN2A and PTEN uniquely required both amplitude and length cutoffs as primary predictors. For PTEN, these extended to downstream AKT activation. In contrast, the remaining 20+ assessed SCNA genes, particularly ones located peri-telomerically or in fragile sites, showed poor expression-copy number correlations. Finally, we describe several novel associations between copy number and gene expression that have implications for how SCNAs are assessed as harboring candidate cancer driver genes. Overall, our analyses identify optimized amplitude and length cutoffs as efficient predictors of gene expression changes for specific oncogenic SCNAs, yet warn against one-size-fits-all interpretations across all loci. Our results have implications for cancer data analyses and the clinic, where copy number and mutation data are increasingly used to personalize cancer therapy. Citation Format: Jason Roszik, Ching Jiun Wu, Alan E. Siroy, Alexander J. Lazar, Michael A. Davies, Scott Woodman, Lawrence N. Kwong. Somatic copy number alterations at oncogenic loci show diverse correlations with gene expression. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr LB-330.

Frequent mutations in acetylation and ubiquitination sites suggest novel driver mechanisms of cancer.

BackgroundDiscovery of cancer drivers is a major goal of cancer research. Driver genes and pathways are often predicted using mutation frequency, assuming that statistically significant recurrence of specific somatic mutations across independent samples indicates their importance in cancer. However, many mutations, including known cancer drivers, are not observed at high frequency. Fortunately, abundant information is available about functional “active sites” in proteins that can be integrated with mutations to predict cancer driver genes, even based on low frequency mutations. Further, considering active site information predicts detailed biochemical mechanisms impacted by the mutations. Post-translational modifications (PTMs) are active sites that are regulatory switches in proteins and pathways. We analyzed acetylation and ubiquitination, two important PTM types often involved in chromatin organization and protein degradation, to find proteins that are significantly affected by tumor somatic mutations.MethodsWe performed computational analyses of acetylation and ubiquitination sites in a pan-cancer dataset of 3200 tumor samples from The Cancer Genome Atlas (TCGA). These analyses were targeted at different levels of biological organization including individual genes, pathway annotated gene sets, and protein-protein interaction networks.ResultsAcetylation and ubiquitination site mutations are enriched in cancer with significantly stronger evolutionary conservation and accumulation in protein domains. Gene-focused analysis with the ActiveDriver method reveals significant co-occurrences of acetylation and ubiquitination PTMs and mutation hotspots in known oncoproteins (TP53, AKT1, IDH1) and highlights candidate cancer driver genes with PTM-related mechanisms (e.g. several histone proteins and the splicing factor SF3B1). Pathway analysis shows that PTM mutations in acetylation and ubiquitination sites accumulate in cancer-related processes such as cell cycle, apoptosis, chromatin regulation, and metabolism. Integrated mutation analysis of clinical information and protein interaction networks suggests that many PTM-specific mutations associate with decreased patient survival.ConclusionsMutation analysis of acetylation and ubiquitination PTM sites reveals their importance in cancer. As PTM networks are increasingly mapped and related enzymes are often druggable, deeper investigation of specific associated mutations may lead to the discovery of treatment-relevant cellular mechanisms.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-016-0311-2) contains supplementary material, which is available to authorized users.

Transposon Mutagenesis Screen Identifies Potential Lung Cancer Drivers and CUL3 as a Tumor Suppressor.

Non-small cell lung cancers (NSCLC) harbor thousands of passenger events that hide genetic drivers. Even highly recurrent events in NSCLC, such as mutations in PTEN, EGFR, KRAS, and ALK, are detected, at most, in only 30% of patients. Thus, many unidentified low-penetrant events are causing a significant portion of lung cancers. To detect low-penetrance drivers of NSCLC, a forward genetic screen was performed in mice using the Sleeping Beauty (SB) DNA transposon as a random mutagen to generate lung tumors in a Pten-deficient background. SB mutations coupled with Pten deficiency were sufficient to produce lung tumors in 29% of mice. Pten deficiency alone, without SB mutations, resulted in lung tumors in 11% of mice, whereas the rate in control mice was approximately 3%. In addition, thyroid cancer and other carcinomas, as well as the presence of bronchiolar and alveolar epithelialization, in mice deficient for Pten were also identified. Analysis of common transposon insertion sites identified 76 candidate cancer driver genes. These genes are frequently dysregulated in human lung cancers and implicate several signaling pathways. Cullin3 (Cul3), a member of a ubiquitin ligase complex that plays a role in the oxidative stress response pathway, was identified in the screen and evidence demonstrates that Cul3 functions as a tumor suppressor. This study identifies many novel candidate genetic drivers of lung cancer and demonstrates that CUL3 acts as a tumor suppressor by regulating oxidative stress.

Abstract 4285: Structural variant breakpoint detection in advanced colorectal cancer

Abstract Introduction Development of colorectal cancer (CRC) is accompanied by genomic alterations that drive tumor initiation and progression. Gains and losses of large chromosome segments result in DNA copy number alterations and subsequently quantitative changes in mRNA and protein expression levels. Interestingly, the accompanying chromosome breakpoints represent structural variants (SV) that may affect gene architecture and thereby normal gene function. Aim The aim of this study was to identify recurrent SV breakpoints in advanced CRC. Methods Previously a series of 352 advanced CRC samples from CAIRO and CAIRO2 clinical studies [Koopman et al. Lancet 2007; Tol et al. N Engl J Med 2009] was characterized for genome-wide DNA copy number alterations. DNA from formalin-fixed paraffin-embedded (FFPE) tumor and patient-matched normal tissue was subjected to high-resolution array-Comparative Genomic Hybridization (Agilent 180K arrays). Using these data, we now determined the prevalence of recurrent breakpoints in genes in CRC by computational analysis. In addition, multiplexed amplicon analysis involving 48 cancer-related genes (Illumina TruSeq Amplicon Cancer Panel) was applied to determine mutation frequencies. Multi-Dendrix was used to identify modules of (mutually exclusively mutated) cancer driver genes. Results We identified 748 recurrent SV breakpoints in genes (FDR&amp;lt;0.1). The highest frequency of recurrent breakpoints was detected in MACROD2, in up to ∼40% of CRC samples. Most recurrent breakpoints occurred in less than 5% of all tumors, and have not been reported before in CRC. Mutation frequencies in APC, TP53 and KRAS were conform expectations (60%, 58%, and 48%, respectively). Multi-Dendrix analysis revealed modules of cancer driver genes that included both the commonly mutated CRC cancer genes as well as genes with recurrent breakpoints, suggesting that several of the SV breakpoint genes are candidate drivers of the carcinogenic process. Conclusions We were able to pinpoint the prevalence of 748 recurrent SV breakpoint regions in genes using array-CGH data from 352 CRC samples. Moreover, our studies revealed several breakpoints in genes that are mutually exclusive with the commonly mutated APC, KRAS, and TP53 genes, and therefore represent novel candidate cancer driver genes. Further studies are required to investigate their functional and clinical significance. Citation Format: E van den Broek, O Krijgsman, D Sie, JC Haan, M Komor, J Traets, DAM Heideman, MA van de Wiel, ID Nagtegaal, CJA Punt, B Carvalho, B Ylstra, GA Meijer, RJA Fijneman. Structural variant breakpoint detection in advanced colorectal cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4285. doi:10.1158/1538-7445.AM2014-4285

Abstract 2859: Identification of driver mutations in colon cancer using Sleeping Beauty mutagenesis

Abstract Human colon tumors contain hundreds of genetic and epigenetic anomalies, of which only a handful are responsible for the growth and development of the tumor. We have developed a forward genetic screen in mice that can identify those mutations capable of causing gastrointestinal tract tumors. By comparing the mutations identified in our screen with those found in human colon tumors we can pinpoint the drivers of carcinogenesis. The forward genetic screen uses the Sleeping Beauty (SB) DNA transposon as a random mutagen capable of both activating and inactivating genes. Using Cre recombinase under the control of the Villin promoter we can selectively activate this mutagenesis in intestinal epithelial cells in the mouse. Tumors generated in these mice are analyzed for transposon insertion sites using linker mediated-PCR in combination with high-throughput sequencing. This analysis provides us with genomic sequence that can be mapped to the mouse genome. Candidate cancer driver genes are identified because they are located in areas of the genome that are recurrently mutated by the SB transposon in multiple tumors. We conducted the screen in wild-type mice and identified 77 candidate cancer genes, of which six were previously known human colon cancer genes (APC, BMPR1A, SMAD4, PTEN, FBXW7, CDK8). More importantly, we identified many novel candidate drivers that can now serve as potential targets in colon cancer therapy. We have extended our studies by conducting three more screens using mice that already contain a known driver mutation. The purpose of these new screens is to gain a better understanding of the genetic changes that cooperate with common mutations found in colon cancer. The three screens used mice that have a mutation in Apc (ApcMin), carry a dominant negative acting Trp53 (p53R270H), or are null for the tumor suppressor gene p19ARF. We have completed the screen in ApcMin mice and found an additional 30 candidate drivers of colon cancer that may specifically cooperate with the Apc mutation. The two final screens are in the process of completion and should uncover genes that cooperate with mutations in Trp53 and p19ARF. The results of these studies will greatly expand the list of potential therapeutic targets and will enhance our understanding of the genetic etiology of colon cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 2859. doi:10.1158/1538-7445.AM2011-2859