For example, Moonlight identified GAS7 as a hypermethylated tumor suppressor in lung cancer and as an hypomethylated oncogene in head-and-neck squamous cell tumors. Moonlight also identified BCL2 as a tumor suppressor in prostate adenocarcinoma with promoter hypermethylation, deletion, and associated with increased apoptosis (Supplementary Data7, 9). Specifically, we selected breast-invasive carcinoma from TCGA for illustrative purposes. Introduction. FOXM1 and FOXQ1 are promising prognostic biomarkers and novel targets of tumor-suppressive miR-342 in human colorectal cancer. The rationale behind this two-step process is that gene expression alone may lead to a large number of candidate genes that are not necessary driving the cancer phenotype. Curr. To capitalize on our discovery of dual-role cancer driver genes, we next employed Connectivity Map71 to search for candidate compounds that could target cancer driver genes revealed by Moonlight (Methods). Discovery of Candidate DNA Methylation Cancer Driver Genes PGBD5 promotes site-specific oncogenic mutations in human tumors. Huberts, D. H. E. W. & van der Klei, I. J. Moonlighting proteins: an intriguing mode of multitasking. To evaluate the performance of Moonlight, we compared its machine-learning approach to two state-of-the-art methods for the detection of cancer driver genes: 20/20+66 and OncodriveRole67. Because epigenetic changes cooperate with chromatin accessibility to influence transcriptional activities, we also investigated if cancer driver genes predicted by Moonlight showed molecular changes at the level of chromatin accessibility. We looked at FOXA1 chromatin signal and we observed an association with open states of chromatin. Indeed, a therapy that has a positive effect on a subject could be completely inefficient on another tumor type due to the opposite behavior of the target protein. For example, APC is a large driver gene, but only those mutations that truncate the encoded protein within its N-terminal 1600 amino acids are driver gene mutations. Rep. 5, 14301 (2015). We also reported overall higher chromatin peaks signal for oncogenes when compared with tumor suppressors (Fig. Candidate driver genes in CNAs can instead be identified by comparison with gene expression data: Only genes whose expression is altered . Nat. http://bioconductor.org/packages/MoonlightR/, https://gdc.cancer.gov/resources-tcga-users/tcga-code-tables/tcga-study-abbreviations, http://karchinlab.org/data/Protocol/pancan-mutation-set-from-Tokheim-2016.txt.gz, https://gdc.cancer.gov/about-data/publications/mc3-2017, ftp://ftp.sanger.ac.uk/pub/project/cancerrxgene/releases/current_release/sanger1018_brainarray_ensemblgene_rma.txt.gz, ftp://ftp.sanger.ac.uk/pub/project/cancerrxgene/releases/current_release/Cell_Lines_Details.xlsx, Description of Additional Supplementary Files, http://creativecommons.org/licenses/by/4.0/, Experimental analysis of bladder cancer-associated mutations in EP300 identifies EP300-R1627W as a driver mutation, LSM2 is associated with a poor prognosis and promotes cell proliferation, migration, and invasion in skin cutaneous melanoma, COMMD3 loss drives invasive breast cancer growth by modulating copper homeostasis, Unraveling the Drivers of Tumorigenesis in the Context of Evolution: Theoretical Models and Bioinformatics Tools. PLoS Comput. In summary, Moonlight provides a platform for multi-omics integration and utilizes a wealth of prior knowledge (Fig. Cell 171, 14371452 (2017). Interpreting pathways to discover cancer driver genes with - Nature Google Scholar. Science 363, 11501151 (2019). FoxM1 is a promising candidate target in the treatment of breast cancer. 7c; Supplementary Data13). Interestingly, Moonlight also predicted this gene to be a tumor suppressor in bladder urothelial carcinoma with good survival prognosis (log-rank test p=0.022, Fig. DriverRWH: discovering cancer driver genes by random walk on a gene We downloaded a second list from Vogelstein et al.2, where 54 OCGs and 71 TSGs were validated and recorded. A compendium of mutational cancer driver genes - Nature Moonlight identified the cell cycle kinase CDK4 as an oncogene in glioblastoma multiforme, with the highest normalized peak score (1164). A.C., C.O. We defined these 77 genes as critical drug genes, of which 18 were tumor suppressors and 59 oncogenes (Supplementary Data12). In this review, we summarize the biological function of key driver genes and pharmaceutical targets in PDAC. ActiveDriver and e-Driver identify driver genes detecting genes with mutations that might also have an impact on protein function. However, systematic investigation of the evolution of cancer driver genes is sparse. Recently, it was shown that knockdown of RRM2 led to intrinsic apoptosis in head-and-neck squamous cell carcinoma and non-small cell lung cancer cell lines, confirming our findings30. Zhou, W., Laird, P. W. & Shen, H. Comprehensive characterization, annotation and innovative use of infinium DNA methylation BeadChip probes. For each gene, we compute the logarithm of the probability that gene i belongs to class j according to our prediction. Clin. Sci. SOX17 regulates uterine epithelial-stromal cross-talk acting via a distal enhancer upstream of Ihh. Targeting the Clear Cell Sarcoma Oncogenic Driver Fusion Gene EWSR1::ATF1 by HDAC Inhibition. Within these GDSC cell lines, we observed that 41% of the oncogenes upregulated in TCGAs breast-invasive carcinoma tumors had high expression. BMC Bioinforma. Rep. 3, 2650 (2013). While these approaches are able to identify well-known cancer genes, they have difficulties when it comes to the prediction of new TSG/OCG candidates85. Lastly, we compare the obtained results in each run with a set of random classifications. To accomplish this, we performed Pattern Regulation Analysis (Methods), enabling the identification of genes with two distinct patterns. Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. Machine learning identifies stemness features associated with oncogenic dedifferentiation. The research progression was like a seedbranches kept growing off of it." by Jackie Swift Scientists have long attempted to puzzle out the enigma that is cancer. Rep. 5, 15029 (2015). In the first approach, PRA takes in two objects: (i) URAs output, and (ii) selection of a subset of the BP provided by the end user. Thank you for visiting nature.com. Martelotto, L. G. et al. We supplied the output of the Moonlight Upstream Regulatory Analysis (Methods) to this model to score the biological processes. Of these, 521 oncogenes were associated with poor prognosis, whereas 50 tumor suppressors with good prognosis. For the 776 biological mediators in breast cancer, this analysis revealed 365 compounds targeting 77 genes. To identify DMRs, we used the Wilcoxon test followed by multiple testing using the BH method to estimate the false discovery rate. Cui, X. et al. We investigated if cancer driver genes predicted by Moonlight showed molecular changes at the copy-number level. The MC3 effort provided consensus calls of variants from seven software packages: MuTect, MuSE, VarScan2, Radia, Pindel, Somatic Sniper, and Indelocator100. Res. Clin. Nature Communications (Nat Commun) BCL2s dual role is related not only to its expression but also to the localization of its protein products60. We used level-3 data. Volume 3 Issue 7 | Cancer Research Communications | American This analysis, using Fishers test, allows for the identification of gene sets (with biological functions linked to cancer studies) that are significantly enriched in the regulated genes. 4a). Martignetti, L, Calzone, L, Bonnet, E, Barillot, E. & Zinovyev, A. ROMA: representation and quantification of module activity from target expression data. Nucleic Acids Res. Google Scholar. The genome is oriented horizontally from top to the bottom, and GISTIC q-values at each locus are plotted from the left to right on a log scale. Categories were considered significantly enriched with permutation P<0.05. We considered DEGs significant if the log fold change |logFC| >1 and FDR <0.01. Cell Rep. 24, 304311 (2018). Cell 173, 305320 (2018). Hoadley, K. A. et al. Mohammadizadeh, F., Hani, M., Ranaee, M. & Bagheri, M. Role of cyclin D1 in breast carcinoma. Commun. 22, 49474957 (2016). extended the original 20/20 rule2 in an ML approach allowing the integration of multiple ratiometric features of positive selection in 20/20+66 to predict oncogenes and TSGs from small somatic variants. The values generated from the differential expression analysis (DEA) analysis were sorted in ascending order and corrected using the BenjaminiHochberg (BH) procedure for multiple-testing correction. c Heatmap showing the top 50 TSG and OCG (by Moonlight Gene Z-score) predicted in breast cancer as mediators of apoptosis and proliferation (columns) and expression profiles of 50 breast-cancer cell lines from the Genomics of Drug Sensitivity in Cancer (GDSC) database (rows). Tamborero, D. et al. A red square indicates the presence of a relationship between compound and target. In this study, we used a dozen of computational driver gene identification approaches including online resources, offline and online tools to explore most potential breast cancer driver . L.C. Furthermore, the majority of the current methods use only mutation data to detect cancer drivers, limiting the knowledge of the related molecular mechanisms. To understand the molecular mechanisms that underlie CDGs, we focused our analysis on a subset of specific BPs.We used the function TCGAanalyze_DEA from TCGAbiolinks to create a merged list of all DEGs. Publishers note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Of the 3123 oncogenic mediators within the comprehensive set of 18 cancer types, the Moonlight pipeline identified 1076 tumor-suppressor-like and 1896 oncogene-like mediators (Fig. We inspected the 50 cancer driver genes for breast cancer with the highest Moonlight Gene Z-scores (Methods), of which Moonlight identified 14 tumor suppressors (Fig. This allowed for the extraction of the DNA methylation level-3 data following the TCGA pipeline used to create data from the Illumina Infinium HumanMethylation450 (HM450) array. "The compendium of driver genes provides cancer researchers, both in the clinical and basic research setting, with crucial knowledge and it has an important impact on clinical decision-making . Biochim. Interuniversity Institute of Bioinformatics in Brussels (IB)2, Brussels, Belgium, Antonio Colaprico,Catharina Olsen&Gianluca Bontempi, Machine Learning Group, Universit Libre de Bruxelles (ULB), Brussels, Belgium, Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL, 33136, USA, Antonio Colaprico,Gabriel J. Odom,Tiago C. Silva&Xi Steven Chen, Center for Medical Genetics, Reproduction and Genetics, Reproduction Genetics and Regenerative Medicine, Vrije Universiteit Brussel, UZ Brussel, Laarbeeklaan 101, 1090, Brussels, Belgium, Brussels Interuniversity Genomics High Throughput core (BRIGHTcore), VUB-ULB, Laarbeeklaan 101, 1090, Brussels, Belgium, Division of Oncology, Department of Medicine, Washington University in St. Louis, St. Louis, MO, 63110, USA, McDonnell Genome Institute, Washington University, St. Louis, MO, 63108, USA, Department of Biostatistics, Stempel College of Public Health, Florida International University, Miami, FL, 33199, USA, Computational Biology Laboratory, and Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center, Strandboulevarden 49, 2100, Copenhagen, Denmark, Thilde Terkelsen,Andr V. Olsen&Elena Papaleo, Department of Genetics, Ribeiro Preto Medical School, University of Sao Paulo, Ribeiro Preto, Brazil, Institut Curie, 26 rue dUlm, F-75248, Paris, France, Laura Cantini,Andrei Zinovyev&Emmanuel Barillot, Mines ParisTech, Fontainebleau, F-77300, France, Computational Systems Biology Team, Institut de Biologie de lEcole Normale Suprieure, CNRS UMR8197, INSERM U1024, Ecole Normale Suprieure, Paris Sciences et Lettres Research University, 75005, Paris, France, Department of Neurosurgery, Brain Tumor Center, Henry Ford Health System, Detroit, MI, USA, Institute of Molecular Bioimaging and Physiology of the National Research Council (IBFM-CNR), Milan, Italy, Gloria Bertoli,Isabella Castiglioni&Claudia Cava, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, 33136, USA, Translational Disease System Biology, Faculty of Health and Medical Science, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark, You can also search for this author in Cancer 18, 669680 (2018). [version 2; peer review: 1 approved, 2 approved with reservations]. J. Pathol. As we look to the future of driver-gene discovery in cancer, tools like Moonlight will become essential for the integration of biological processes across many data molecular substrates. CAS assessed the performance and accuracy of the method. e Circos plots for molecular subtypes of Moonlight genes predicted using expert knowledge paired with PRA using two selected BPs, such as apoptosis and cell proliferation. For the intra-tumoral genomic and transcriptomic heterogeneity case study, we used Breast invasive carcinoma (BRCA) from TCGA as deposited in the GDC Data Portal. Google Scholar. and Gl.B curated the BPs data sets and scored the data. Clinicopathological and prognostic significance of SDC1 overexpression in breast cancer. These findings confirmed the hypothesis that (i) a mechanism of activation for oncogenes is related to open chromatin in the promoter region, and (ii) distant chromatin peaks and open chromatin in intron regions are associated with tumor suppressors79. Oncotarget 9, 842852 (2018). Moonlight results support this finding. Hahn, W. C. & Weinberg, R. A. Modelling the molecular circuitry of cancer. Google Scholar. Due to a strong dependency on the biological context, cancer driver genes and their roles in specific tissues are elusive to annotate, and their discovery is often complicated. Cell. 6c), while liver hepatocellular carcinoma and headneck squamous cell carcinoma had poorer performance. Analysis of TCGA datasets have mostly focused on somatic mutations and translocations, with less emphasis placed on gene amplifications. Iorio, F. et al. Nat. 2023 Apr 25;24 (1):215. doi: 10.1186/s12864-023-09301-9. Mounir, M. et al. 44, 17711777 (2000). One example of a biological process associated with cancer progression is increased cell proliferation. Proc. 35, 86858690 (2014). 19, 53 (2017). Google Scholar. A census of human cancer genes. CDK4/6 inhibition is more active against the glioblastoma proneural subtype. In a recent review, cancer progression was summarized across four different steps: cancer initiation, tumor propagation, metastasis to distant organs, and drug resistance to chemotherapy1. MapSplice: accurate mapping of RNA-seq reads for splice junction discovery. Functional Enrichment Analysis (Methods) revealed that these genes were significantly enriched in 32 biological processes (Fig. In addition, we recently demonstrated the flexibility of Moonlight in pinpointing context-specific gene programs that are differentially expressed in varied scenarios from the TCGA Pan-Cancer Atlas Initiative. ADS Among BRCA samples, 1097 were TP and 114 NT. Row colors indicate TSGs (yellow) and OCGs (green). Stovall, D. B. et al. Gastroenterology 154, 965975 (2018). The -omics data sets (gene expression, methylation, copy number, chromatin accessibility, clinical, and mutation) analyzed during this study are publicly available in the repository https://portal.gdc.cancer.gov/ and can be downloaded directly by using the TCGAbiolinks R package as described in the Methods section. In particular, we downloaded, normalized, and filtered RNA-seq raw counts of 1211 BRCA cases as a legacy archive, using the reference of hg19, using the R/Bioconductor package TCGAbiolinks following the above pipeline. But driver genes may also contain passenger gene mutations. The estimate of p for log-loss evaluation is obtained by computing, The estimate of p for the AUC evaluation is obtained computing. 1a). Biol. This suggests that further investigation in long-range regulation within the intron region of tumor suppressors can inform us of the mechanism to re-activate silent tumor-suppressor genes. Romagosa, C. et al. In particular, among 151 dual-role genes detected by Moonlight one interesting gene, ANGPTL4, was predicted to be an oncogene in kidney cancers with associated promoter peaks as well as a tumor suppressor in prostate adenocarcinoma with hypermethylation in the promoter region (Supplementary Data7, 8; Methods). We repeat this procedure 100 times for each of the ten repetitions. Also, Moonlight identified more mutations in intron regions than in promoter regions for tumor-suppressor genes. Notably, LSM1, predicted by Moonlight as an oncogene and reported as an oncogene in breast cancer35, showed the highest peak in the promoter region (followed by ERBB2, PSMD3, and PRR15). The main concept behind Moonlight relies on the observation that the classical approach to experimentally validated cancer driver genes consists in the modulation of their expression in cellular assays, together with the quantification of process markers, such as cellular proliferation, apoptosis, and invasion. Genet. b Moonlight pipeline for discovery of tumor suppressors, oncogenes, and dual-role genes. This suggests that the chromatin signature influences transcriptional reprogramming, in which activated genes associated with new open chromatin sitesespecially in transcription factorsplay an important role. The machine-learning approach predicted four genes as candidate dual-role genes: BCL2, CDKN2A, KIT, and SOCS1 (Methods; Fig. Moreover, we identified deletions in tumor suppressors, such as DACT2 and TGFBR3 (Fig. Oncotarget 6, 4420744221 (2015). Research shows novel drug pairing could beat pancreatic cancer Even more critically, the existence of different cancer subtypes may affect patterns of mutations associated with drug resistance in rare cases. However, we have limited ability to differentiate driver DNA methylation (DNAme) changes from passenger events. Google Scholar. The orange line represents the significance threshold (FDR=0.25). (B) Somatic mutations per sample are plotted for each sample and cancer type. A pan-cancer analysis of enhancer expression in nearly 9000 patient samples. Moonlight also showed that the anti-apoptotic BCL2 is a dual-role gene. We calculated the pairwise mutual information between the DEGs and all the genes filtered for each cancer type, considering only tumor samples. Harari, D. & Yarden, Y. Molecular mechanisms underlying ErbB2/HER2 action in breast cancer. In particular, this initiative employed many computational tools to identify 299 cancer driver genes and >3400 driver mutations12. If the gene exhibits significant evidence after additional data integration, we define the genes that Moonlight discovered as cancer driver genes. CAS Oncogene alterations in carcinomas of the uterine cervix: overexpression of the epidermal growth factor receptor is associated with poor prognosis. Article Furthermore, Connectivity Map also identified potential drugs to target the 151 dual-role genes identified by the expert-based Moonlight approach. Preprocessing steps included background correction, dye-bias normalization, and calculation of beta values. Clin. Its application to somatic mutations of more than 28,000 tumours . Background Identifying the complete repertoire of genes that drive cancer in individual patients is crucial for precision oncology. PubMed Challenges in identifying cancer genes by analysis of exome sequencing data. We identify 299 driver genes with implications regarding their anatomical sites and cancer/cell types. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Colaprico, A. et al. Sanchez-Vega, F. et al. The most common cancer driver genes have been identified. Another example is apoptosis, which is generally downregulated in association with cancer progression. Color code is according to TCGA BRCA molecular subtypes. Sci. The images or other third party material in this article are included in the articles Creative Commons license, unless indicated otherwise in a credit line to the material. These loci were significantly enriched for known cancer driver genes, including genes not detected through analysis of focal copy-number events, and were often lineage specific. 5, 1542 (2016). wrote the paper with input from all other authors. 19, 34163428 (2013). We thus selected apoptosis and cell proliferation as main gene programs to detect cancer driver genes. In Fig. Sci. Data with intermediate results and code to generate specific analysis are available from the corresponding author, Dr. Antonio Colaprico, and will be uploaded to GitHub [https://github.com/torongs82/] upon request. e-Driver identifies protein regions (domains and disordered sites) enriched with somatic modifications that could influence protein function. Recent findings support the dual-role behavior of these four genes. Nat. 12, 323 (2011). Optimal methods could effectively improve the accuracy of predicting and identifying candidate driver genes. Tokheim, C. J., Papadopoulos, N., Kinzler, K. W., Vogelstein, B. Oncotarget 8, 111444111455 (2017). The second part of the pipelines tool provides pattern recognition analysis (PRA) that incorporates two approaches. 3a). Breast cancer is the most common malignancy in women. Med. Benchmarking mutation effect prediction algorithms using functionally validated cancer-related missense mutations. To better understand the hallmarks of cancer, such as proliferation and apoptosis, it is critical to accurately identify cancer driver genes. For breast cancer, we found that 231 (30%) of the predicted oncogenic mediators experienced epigenetic changes. & Wade, P. A. GATA3 in breast cancer: tumor suppressor or oncogene? 1 Altmetric Metrics Abstract Background Recent advances in next-generation sequencing technologies have helped investigators generate massive amounts of cancer genomic data. Tumour-promoting role of SOCS1 in colorectal cancer cells. MET in gastric cancerdiscarding a 10% cutoff rule. Commun. Krmer, A., Green, J., Pollard, J. However, GATA3 has also been recently reported as a tumor suppressor for breast cancer in certain contexts47, which intrigued us. Genes with a mean expression of less than 25% of the quantile expression distribution were considered lowly expressed in cell lines while genes with a mean expression of more than 75% were considered highly expressed. Article ADHFE1 is a breast cancer oncogene and induces metabolic reprogramming. Moonlight detected RRM2 as an oncogene. Of these genes, 54 tumor suppressors showed hypermethylation while 80 oncogenes showed hypomethylation. 12, 480 (2011). This measure penalizes strongly confident misclassifications. It is known that intron retention is a widespread mechanism of tumor-suppressor inactivation78, which was consistent with our observation. Cancer Drivers Actionability Database (2014.12) This database contains data on the interactions with therapeutic agents an driver genes contained in Cancer Drivers Database (2014.12). A next generation connectivity map: L1000 platform and the first 1,000,000 profiles.

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