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Identification of novel driver tumor suppressors through functional interrogation of putative passenger mutations in colorectal cancer.

ABSTRACT: Cancer genome sequencing efforts are leading to the identification of genetic mutations in many types of malignancy. However, the majority of these genetic alterations have been considered random passengers that do not directly contribute to tumorigenesis. We have previously conducted a soft agar-based short hairpin RNA (shRNA) screen within colorectal cancer (CRC) candidate driver genes (CAN-genes) using a karyotypically diploid hTERT- and CDK4-immortalized human colonic epithelial cell (HCEC) model and discovered that depletion of 65 of the 151 CAN-genes enhanced anchorage-independent growth in HCECs with ectopic expression of K-Ras(V12) and/or TP53 knockdown. We now constructed an interaction map of the confirmed CAN-genes with CRC non-CAN-genes and screened for functional tumor suppressors. Remarkably, depletion of 15 out of 25 presumed passenger genes that interact with confirmed CAN-genes (60%) promoted soft agar growth in HCECs with TP53 knockdown compared to only 7 out of 55 (12.5%) of presumed passenger genes that do not interact. We have thus demonstrated a pool of driver mutations among the putative CRC passenger/incidental mutations, establishing the importance of employing biological filters, in addition to bioinformatics, to identify driver mutations.

SUBMITTER: Zhang L

PROVIDER: S-EPMC5228351 | biostudies-literature | 2013 Feb

REPOSITORIES: biostudies-literature

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Identification of novel driver tumor suppressors through functional interrogation of putative passenger mutations in colorectal cancer.

Zhang Lu L Komurov Kakajan K Wright Woodring E WE Shay Jerry W JW

International journal of cancer 20120721 3

Cancer genome sequencing efforts are leading to the identification of genetic mutations in many types of malignancy. However, the majority of these genetic alterations have been considered random passengers that do not directly contribute to tumorigenesis. We have previously conducted a soft agar-based short hairpin RNA (shRNA) screen within colorectal cancer (CRC) candidate driver genes (CAN-genes) using a karyotypically diploid hTERT- and CDK4-immortalized human colonic epithelial cell (HCEC) ...[more]

PMID: 22753261

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Project description:BackgroundAPOBEC-driven mutagenesis and functional positive selection of mutated genes may synergistically drive the higher frequency of some hotspot driver mutations compared to other mutations within the same gene, as we reported for FGFR3 S249C. Only a few APOBEC-associated driver hotspot mutations have been identified in bladder cancer (BCa). Here, we systematically looked for and characterised APOBEC-associated hotspots in BCa.MethodsWe analysed 602 published exome-sequenced BCas, for part of which gene expression data were also available. APOBEC-associated hotspots were identified by motif-mapping, mutation signature fitting and APOBEC-mediated mutagenesis comparison. Joint analysis of DNA hairpin stability and gene expression was performed to predict driver or passenger hotspots. Aryl hydrocarbon receptor (AhR) activity was calculated based on its target genes expression. Effects of AhR knockout/inhibition on BCa cell viability were analysed.ResultsWe established a panel of 44 APOBEC-associated hotspot mutations in BCa, which accounted for about half of the hotspot mutations. Fourteen of them overlapped with the hotspots found in other cancer types with high APOBEC activity. They mostly occurred in the DNA lagging-strand templates and the loop of DNA hairpins. APOBEC-associated hotspots presented systematically a higher prevalence than the other mutations within each APOBEC-target gene, independently of their functional impact. A combined analysis of DNA loop stability and gene expression allowed to distinguish known passenger from known driver hotspot mutations in BCa, including loss-of-function mutations affecting tumour suppressor genes, and to predict new candidate drivers, such as AHR Q383H. We further characterised AHR Q383H as an activating driver mutation associated with high AhR activity in luminal tumours. High AhR activity was also found in tumours presenting amplifications of AHR and its co-receptor ARNT. We finally showed that BCa cells presenting those different genetic alterations were sensitive to AhR inhibition.ConclusionsOur study identified novel potential drivers within APOBEC-associated hotspot mutations in BCa reinforcing the importance of APOBEC mutagenesis in BCa. It could allow a better understanding of BCa biology and aetiology and have clinical implications such as AhR as a potential therapeutic target. Our results also challenge the dogma that all hotspot mutations are drivers and mostly gain-of-function mutations affecting oncogenes.

Project description:IntroductionBesides genetic and epigenetic alterations that lead to carcinogenesis and development of colorectal cancer (CRC), intestinal microbiomes are recently recognized to play a critical role in CRC progression. The abundant species associated with human CRC have been proposed for their roles in promoting tumorigenesis. However, a recent "driver-passenger" model suggests that these CRC-associated species with high relative abundances may be passenger bacteria that take advantage of the tumor environment instead of initiating CRC, whereas the driver species that initiate CRC have been replaced by passenger bacteria due to the alteration of the intestinal niche.MethodsHere, to reveal potential driver and passenger bacteria during CRC progression, we compare the gut mucosal microbiomes of 75 triplet-paired CRC samples collected from on-tumor site, adjacent-tumor site, and off-tumor site, and 26 healthy controls.ResultsOur analyses revealed potential driver bacteria in four genera and two families, and potential passenger bacteria in 14 genera or families. Bacillus, Bradyrhizobium, Methylobacterium, Streptomyces, Intrasporangiaceae and Sinobacteraceae were predicted to be potential driver bacteria. Moreover, 14 potential passenger bacteria were identified and divided into five groups. Group I passenger bacteria contain Fusobacterium, Campylobacter, Streptococcus, Schwartzia, and Parvimonas. Group II passenger bacteria contain Dethiosulfatibacter, Selenomonas, Peptostreptococus, Leptotrichia. Group III passenger bacteria contain Granulicatella. Group IV passenger bacteria contain Shewanella, Mogibacterium, and Eikenella. Group V passenger bacteria contain Anaerococus. Co-occurrence network analysis reveals a low correlation relationship between driver and passenger bacteria in CRC patients compared with healthy controls.DiscussionThese driver and passenger species may serve as bio-marker species for screening cohorts with high risk to initiate CRC or patients with CRC, respectively. Further functional studies will help understand the roles of driver and passenger bacteria in CRC initiation and development.

Project description:MotivationNext-generation sequencing technologies have accelerated the discovery of single nucleotide variants in the human genome, stimulating the development of predictors for classifying which of these variants are likely functional in disease, and which neutral. Recently, we proposed CScape, a method for discriminating between cancer driver mutations and presumed benign variants. For the neutral class, this method relied on benign germline variants found in the 1000 Genomes Project database. Discrimination could, therefore, be influenced by the distinction of germline versus somatic, rather than neutral versus disease driver. This motivates this article in which we consider predictive discrimination between recurrent and rare somatic single point mutations based solely on using cancer data, and the distinction between these two somatic classes and germline single point mutations.ResultsFor somatic point mutations in coding and non-coding regions of the genome, we propose CScape-somatic, an integrative classifier for predictively discriminating between recurrent and rare variants in the human cancer genome. In this study, we use purely cancer genome data and investigate the distinction between minimal occurrence and significantly recurrent somatic single point mutations in the human cancer genome. We show that this type of predictive distinction can give novel insight, and may deliver more meaningful prediction in both coding and non-coding regions of the cancer genome. Tested on somatic mutations, CScape-somatic outperforms alternative methods, reaching 74% balanced accuracy in coding regions and 69% in non-coding regions, whereas even higher accuracy may be achieved using thresholds to isolate high-confidence predictions.Availability and implementationPredictions and software are available at http://CScape-somatic.biocompute.org.uk/.Contactmark.f.rogers.phd@gmail.com or C.Campbell@bristol.ac.uk.Supplementary informationSupplementary data are available at Bioinformatics online.

Project description:BackgroundIn somatic cancer genomes, delineating genuine driver mutations against a background of multiple passenger events is a challenging task. The difficulty of determining function from sequence data and the low frequency of mutations are increasingly hindering the search for novel, less common cancer drivers. The accumulation of extensive amounts of data on somatic point and copy number alterations necessitates the development of systematic methods for driver mutation analysis.ResultsWe introduce a framework for detecting driver mutations via functional network analysis, which is applied to individual genomes and does not require pooling multiple samples. It probabilistically evaluates 1) functional network links between different mutations in the same genome and 2) links between individual mutations and known cancer pathways. In addition, it can employ correlations of mutation patterns in pairs of genes. The method was used to analyze genomic alterations in two TCGA datasets, one for glioblastoma multiforme and another for ovarian carcinoma, which were generated using different approaches to mutation profiling. The proportions of drivers among the reported de novo point mutations in these cancers were estimated to be 57.8% and 16.8%, respectively. The both sets also included extended chromosomal regions with synchronous duplications or losses of multiple genes. We identified putative copy number driver events within many such segments. Finally, we summarized seemingly disparate mutations and discovered a functional network of collagen modifications in the glioblastoma. In order to select the most efficient network for use with this method, we used a novel, ROC curve-based procedure for benchmarking different network versions by their ability to recover pathway membership.ConclusionsThe results of our network-based procedure were in good agreement with published gold standard sets of cancer genes and were shown to complement and expand frequency-based driver analyses. On the other hand, three sequence-based methods applied to the same data yielded poor agreement with each other and with our results. We review the difference in driver proportions discovered by different sequencing approaches and discuss the functional roles of novel driver mutations. The software used in this work and the global network of functional couplings are publicly available at http://research.scilifelab.se/andrej_alexeyenko/downloads.html.

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Identification of novel driver tumor suppressors through functional interrogation of putative passenger mutations in colorectal cancer.

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Identification of novel driver tumor suppressors through functional interrogation of putative passenger mutations in colorectal cancer.

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