Candidate Cancer Driver 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.

BRD4 modulates vulnerability of triple-negative breast cancer to targeting of integrin-dependent signaling pathways

PurposeStemming from a myriad of genetic and epigenetic alterations, triple-negative breast cancer (TNBC) is tied to poor clinical outcomes and aspires for individualized therapies. Here we investigated the therapeutic potential of co-inhibiting integrin-dependent signaling pathway and BRD4, a transcriptional and epigenetic mediator, for TNBC.MethodsTwo independent patient cohorts were subjected to bioinformatic and IHC examination for clinical association of candidate cancer drivers. The efficacy and biological bases for co-targeting these drivers were interrogated using cancer cell lines, a protein kinase array, chemical inhibitors, RNAi/CRISPR/Cas9 approaches, and a 4 T1-Balb/c xenograft model.ResultsWe found that amplification of the chromosome 8q24 region occurred in nearly 20% of TNBC tumors, and that it coincided with co-upregulation or amplification of c-Myc and FAK, a key effector of integrin-dependent signaling. This co-upregulation at the mRNA or protein level correlated with a poor patient survival (p < 0.0109 or p < 0.0402, respectively). Furthermore, we found that 14 TNBC cell lines exhibited high vulnerabilities to the combination of JQ1 and VS-6063, potent pharmacological antagonists of the BRD4/c-Myc and integrin/FAK-dependent pathways, respectively. We also observed a cooperative inhibitory effect of JQ1 and VS-6063 on tumor growth and infiltration of Ly6G+ myeloid-derived suppressor cells in vivo. Finally, we found that JQ1 and VS-6063 cooperatively induced apoptotic cell death by altering XIAP, Bcl2/Bcl-xl and Bim levels, impairing c-Src/p130Cas-, PI3K/Akt- and RelA-associated signaling, and were linked to EMT-inducing transcription factor Snail- and Slug-dependent regulation.ConclusionBased on our results, we conclude that the BRD4/c-Myc- and integrin/FAK-dependent pathways act in concert to promote breast cancer cell survival and poor clinical outcomes. As such, they represent promising targets for a synthetic lethal-type of therapy against TNBC.

Parent-child signals identify candidate cancer driver genes

Parent-child signals identify candidate cancer driver genes

Candidate Cancer Driver Mutations in Distal Regulatory Elements and Long-Range Chromatin Interaction Networks

A comprehensive catalog of cancer driver mutations is essential for understanding tumorigenesis and developing therapies. Exome-sequencing studies have mapped many protein-coding drivers, yet few non-coding drivers are known because genome-wide discovery is challenging. We developed a driver discovery method, ActiveDriverWGS, and analyzed 120,788 cis-regulatory modules (CRMs) across 1,844 whole tumor genomes from the ICGC-TCGA PCAWG project. We found 30 CRMs with enriched SNVs and indels (FDR< 0.05). These frequently mutated regulatory elements (FMREs) were ubiquitously active in human tissues, showed long-range chromatin interactions and mRNA abundance associations with target genes, and were enriched in motif-rewiring mutations and structural variants. Genomic deletion of one FMRE in human cells caused proliferative deficiencies and transcriptional deregulation of cancer genes CCNB1IP1, CDH1, and CDKN2B, validating observations in FMRE-mutated tumors. Pathway analysis revealed further sub-significant FMREs at cancer genes and processes, indicating an unexplored landscape of infrequent driver mutations in the non-coding genome.

Identification of candidate cancer drivers by integrative Epi-DNA and Gene Expression (iEDGE) data analysis

The emergence of large-scale multi-omics data warrants method development for data integration. Genomic studies from cancer patients have identified epigenetic and genetic regulators – such as methylation marks, somatic mutations, and somatic copy number alterations (SCNAs), among others – as predictive features of cancer outcome. However, identification of “driver genes” associated with a given alteration remains a challenge. To this end, we developed a computational tool, iEDGE, to model cis and trans effects of (epi-)DNA alterations and identify potential cis driver genes, where cis and trans genes denote those genes falling within and outside the genomic boundaries of a given (epi-)genetic alteration, respectively. iEDGE first identifies the cis and trans gene expression signatures associated with the presence/absence of a particular epi-DNA alteration across samples. It then applies tests of statistical mediation to determine the cis genes predictive of the trans gene expression. Finally, cis and trans effects are annotated by pathway enrichment analysis to gain insights into the underlying regulatory networks. We used iEDGE to perform integrative analysis of SCNAs and gene expression data from breast cancer and 18 additional cancer types included in The Cancer Genome Atlas (TCGA). Notably, cis gene drivers identified by iEDGE were found to be significantly enriched for known driver genes from multiple compendia of validated oncogenes and tumor suppressors, suggesting that the remainder are of equal importance. Furthermore, predicted drivers were enriched for functionally relevant cancer genes with amplification-driven dependencies, which are of potential prognostic and therapeutic value. All the analyses results are accessible at https://montilab.bu.edu/iEDGE. In summary, integrative analysis of SCNAs and gene expression using iEDGE successfully identified known cancer driver genes and putative cancer therapeutic targets across 19 cancer types in the TCGA. The proposed method can easily be applied to the integration of gene expression profiles with other epi-DNA assays in a variety of disease contexts.

Experimental identification of cancer driver alterations in the era of pan-cancer genomics.

Rapidly accumulating data from large‐scale cancer genomics studies have been generating important information about genes and their somatic alterations underlying cell transformation, cancer onset and tumor progression. However, these events are usually defined by using computational techniques, whereas the understanding of their actual functional roles and impact typically warrants validation by experimental means. Critical information has been obtained from targeted genetic perturbation (gene knockout) studies conducted in animals, yet these investigations are cost‐prohibitive and time‐consuming. In addition, the 3R principles (replacement, reduction, refinement) have been set in place to reduce animal use burden and are increasingly observed in many areas of biomedical research. Consequently, the focus has shifted to new designs of innovative cell‐based experimental models of cell immortalization and transformation in which the critical cancer driver events can be introduced by mutagenic insult and studied functionally, at the level of critical phenotypic readouts. From these efforts, primary cell‐based selective barrier‐bypass models of cell immortalization have emerged as an attractive system that allows studies of the functional relevance of acquired mutations as well as their role as candidate cancer driver events. In this review, we provide an overview of various experimental systems linking carcinogen exposure‐driven cell transformation with the study of cancer driver events. We further describe the advantages and disadvantages of the currently available cell‐based models while outlining future directions for in vitro modeling and functional testing of cancer driver events.

Identifying Candidate Druggable Targets in Canine Cancer Cell Lines Using Whole-Exome Sequencing.

Cancer cell culture has been a backbone in cancer research, in which analysis of human cell line mutational profiles often correlates with oncogene addiction and drug sensitivity. We have conducted whole-exome sequence analyses on 33 canine cancer cell lines from 10 cancer types to identify somatic variants that contribute to pathogenesis and therapeutic sensitivity. A total of 66,344 somatic variants were identified. Mutational load ranged from 15.79 to 129.37 per Mb, and 13.2% of variants were located in protein-coding regions (PCR) of 5,085 genes. PCR somatic variants were identified in 232 genes listed in the Cancer Gene Census (COSMIC). Cross-referencing variants with human driving mutations on cBioPortal identified 61 variants as candidate cancer drivers in 30 cell lines. The most frequently mutated cancer driver was TP53 (15 mutations in 12 cell lines). No drivers were identified in three cell lines. We identified 501 non-COSMIC genes with PCR variants that functionally annotate with COSMIC genes. These genes frequently mapped to the KEGG MAPK and PI3K-AKT pathways. We evaluated the cell lines for ERK1/2 and AKT(S473) phosphorylation and sensitivity to the MEK1/2 inhibitor, trametinib. Twelve of the 33 cell lines were trametinib-sensitive (IC50 < 32 nmol/L), all 12 exhibited constitutive or serum-activated ERK1/2 phosphorylation, and 8 carried MAPK pathway cancer driver variants: NF1(2), BRAF(3), N/KRAS(3). This functionally annotated database of canine cell line variants will inform hypothesis-driven preclinical research to support the use of companion animals in clinical trials to test novel combination therapies.

Systematic discovery of the functional impact of somatic genome alterations in individual tumors through tumor-specific causal inference.

Cancer is mainly caused by somatic genome alterations (SGAs). Precision oncology involves identifying and targeting tumor-specific aberrations resulting from causative SGAs. We developed a novel tumor-specific computational framework that finds the likely causative SGAs in an individual tumor and estimates their impact on oncogenic processes, which suggests the disease mechanisms that are acting in that tumor. This information can be used to guide precision oncology. We report a tumor-specific causal inference (TCI) framework, which estimates causative SGAs by modeling causal relationships between SGAs and molecular phenotypes (e.g., transcriptomic, proteomic, or metabolomic changes) within an individual tumor. We applied the TCI algorithm to tumors from The Cancer Genome Atlas (TCGA) and estimated for each tumor the SGAs that causally regulate the differentially expressed genes (DEGs) in that tumor. Overall, TCI identified 634 SGAs that are predicted to cause cancer-related DEGs in a significant number of tumors, including most of the previously known drivers and many novel candidate cancer drivers. The inferred causal relationships are statistically robust and biologically sensible, and multiple lines of experimental evidence support the predicted functional impact of both the well-known and the novel candidate drivers that are predicted by TCI. TCI provides a unified framework that integrates multiple types of SGAs and molecular phenotypes to estimate which genome perturbations are causally influencing one or more molecular/cellular phenotypes in an individual tumor. By identifying major candidate drivers and revealing their functional impact in an individual tumor, TCI sheds light on the disease mechanisms of that tumor, which can serve to advance our basic knowledge of cancer biology and to support precision oncology that provides tailored treatment of individual tumors.

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.

NcdDetect2: improved models of the site-specific mutation rate in cancer and driver detection with robust significance evaluation.

MotivationUnderstanding the mutational processes that act during cancer development is a key topic of cancer biology. Nevertheless, much remains to be learned, as a complex interplay of processes with dependencies on a range of genomic features creates highly heterogeneous cancer genomes. Accurate driver detection relies on unbiased models of the mutation rate that also capture rate variation from uncharacterized sources.ResultsHere, we analyse patterns of observed-to-expected mutation counts across 505 whole cancer genomes, and find that genomic features missing from our mutation-rate model likely operate on a megabase length scale. We extend our site-specific model of the mutation rate to include the additional variance from these sources, which leads to robust significance evaluation of candidate cancer drivers. We thus present ncdDetect v.2, with greatly improved cancer driver detection specificity. Finally, we show that ranking candidates by their posterior mean value of their effect sizes offers an equivalent and more computationally efficient alternative to ranking by their P-values.Availability and implementationncdDetect v.2 is implemented as an R-package and is freely available at http://github.com/TobiasMadsen/ncdDetect2Supplementary information Supplementary data are available at Bioinformatics online.

Modeling cancer driver events in vitro using barrier bypass-clonal expansion assays and massively parallel sequencing

The information on candidate cancer driver alterations available from public databases is often descriptive and of limited mechanistic insight, which poses difficulties for reliable distinction between true driver and passenger events. To address this challenge, we performed in-depth analysis of whole-exome sequencing data from cell lines generated by a barrier bypass-clonal expansion (BBCE) protocol. The employed strategy is based on carcinogen-driven immortalization of primary mouse embryonic fibroblasts and recapitulates early steps of cell transformation. Among the mutated genes were almost 200 COSMIC Cancer Gene Census genes, many of which were recurrently affected in the set of 25 immortalized cell lines. The alterations affected pathways regulating DNA damage response and repair, transcription and chromatin structure, cell cycle and cell death, as well as developmental pathways. The functional impact of the mutations was strongly supported by the manifestation of several known cancer hotspot mutations among the identified alterations. We identified a new set of genes encoding subunits of the BAF chromatin remodeling complex that exhibited Ras-mediated dependence on PRC2 histone methyltransferase activity, a finding that is similar to what has been observed for other BAF subunits in cancer cells. Among the affected BAF complex subunits, we determined Smarcd2 and Smarcc1 as putative driver candidates not yet fully identified by large-scale cancer genome sequencing projects. In addition, Ep400 displayed characteristics of a driver gene in that it showed a mutually exclusive mutation pattern when compared with mutations in the Trrap subunit of the TIP60 complex, both in the cell line panel and in a human tumor data set. We propose that the information generated by deep sequencing of the BBCE cell lines coupled with phenotypic analysis of the mutant cells can yield mechanistic insights into driver events relevant to human cancer development.

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