The increase in available high-throughput molecular data creates computational challenges for the identification of cancer genes. Genetic as well as non-genetic causes contribute to tumorigenesis, and this necessitates the development of predictive models to effectively integrate different data modalities while being interpretable. We introduce EMOGI, an explainable machine learning method based on graph convolutional networks to predict cancer genes by combining multiomics pan-cancer data—such as mutations, copy number changes, DNA methylation and gene expression—together with protein–protein interaction (PPI) networks. EMOGI was on average more accurate than other methods across different PPI networks and datasets. We used layer-wise relevance propagation to stratify genes according to whether their classification was driven by the interactome or any of the omics levels, and to identify important modules in the PPI network. We propose 165 novel cancer genes that do not necessarily harbour recurrent alterations but interact with known cancer genes, and we show that they correspond to essential genes from loss-of-function screens. We believe that our method can open new avenues in precision oncology and be applied to predict biomarkers for other complex diseases.
Subscribe to Journal
Get full journal access for 1 year
only $8.25 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Garraway, L. A. & Lander, E. S. Lessons from the cancer genome. Cell 153, 17–37 (2013).
Lawrence, M. S. et al. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 505, 495–501 (2014).
Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013).
Vogelstein, B. et al. Cancer genome landscapes. Science 340, 1546–1558 (2013).
Zhang, J. et al. International cancer genome consortium data portal-a one-stop shop for cancer genomics data. Database 2011, bar026 (2011).
Cancer Genome Atlas Research Network, J. N. et al. The cancer genome atlas pan-cancer analysis project. Nat. Genet. 45, 1113–20 (2013).
Campbell, P. J. et al. Pan-cancer analysis of whole genomes. Nature 578, 82–93 (2020).
Repana, D. et al. The network of cancer genes (NCG): a comprehensive catalogue of known and candidate cancer genes from cancer sequencing screens. Genome Biol. 20, 1–12 (2019).
Sondka, Z. et al. The COSMIC cancer gene census: describing genetic dysfunction across all human cancers. Nat. Rev. Cancer 18, 696–705 (2018).
Lawrence, M. S. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214–218 (2013).
Leiserson, M. D. et al. Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes. Nat. Genet. 47, 106–114 (2015).
Silverbush, D. et al. Simultaneous integration of multi-omics data improves the identification of cancer driver modules. Cell Syst. 8, 456–466.e5 (2019).
Bailey, M. H. et al. Comprehensive characterization of cancer driver genes and mutations. Cell 173, 371–385.e18 (2018).
Tokheim, C. J., Papadopoulos, N., Kinzler, K. W., Vogelstein, B. & Karchin, R. Evaluating the evaluation of cancer driver genes. Proc. Natl Acad. Sci. USA 113, 14330–14335 (2016).
Bell, C. C. & Gilan, O. Principles and mechanisms of non-genetic resistance in cancer. Brit. J. Cancer 122, 465–472 (2019).
Bradner, J. E., Hnisz, D. & Young, R. A. Transcriptional addiction in cancer. Cell 168, 629–643 (2017).
Baylin, S. B. & Jones, P. A. Epigenetic determinants of cancer. Cold Spring Harb. Perspect. Biol. 8, a019505 (2016).
Gazzoli, I., Loda, M., Garber, J., Syngal, S. & Kolodner, R. D. A hereditary nonpolyposis colorectal carcinoma case associated with hypermethylation of the MLH1 gene in normal tissue and loss of heterozygosity of the unmethylated allele in the resulting microsatellite instability-high tumor. Cancer Res. 62, 3925–3928 (2002).
Poi, M. J., Knobloch, T. J. & Li, J. Deletion of RDINK4/ARF enhancer: a novel mutation to ‘inactivate’ the INK4-ARF locus. DNA Repair 57, 50–55 (2017).
Huang, F. W. et al. Highly recurrent TERT promoter mutations in human melanoma. Science 339, 957–959 (2013).
Beroukhim, R. et al. The landscape of somatic copy-number alteration across human cancers. Nature 463, 899–905 (2010).
Dang, C. V. MYC on the path to cancer. Cell 149, 22–35 (2012).
Schuijers, J. et al. Transcriptional dysregulation of MYC reveals common enhancer-docking mechanism. Cell Rep. 23, 349–360 (2018).
Cowen, L., Ideker, T., Raphael, B. J. & Sharan, R. Network propagation: a universal amplifier of genetic associations. Nat. Rev. Genet. 18, 551–562 (2017).
Reyna, M. A., Leiserson, M. D. & Raphael, B. J. Hierarchial HotNet: identifying hierarchies of altered subnetworks. Bioinformatics 34, i972–i980 (2018).
Rappoport, N. & Shamir, R. Multi-omic and multi-view clustering algorithms: Review and cancer benchmark. Nucl. Acids Res. 46, 10546–10562 (2018).
Collier, O., Stoven, V. & Vert, J.-P. LOTUS: a single- and multitask machine learning algorithm for the prediction of cancer driver genes. PLoS Comput. Biol. 15, e1007381 (2019).
Eraslan, G., Avsec, Ž., Gagneur, J. & Theis, F. J. Deep learning: new computational modelling techniques for genomics. Nat. Rev. Genet. 20, 389–403 (2019).
Bruna, J., Zaremba, W., Szlam, A. & LeCun, Y. Spectral networks and locally connected networks on graphs. In International Conference on Learning Representations 2014 (OpenReview, 2013).
Perozzi, B., Al-Rfou, R. & Skiena, S. DeepWalk: online learning of social representations. In Proc. 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 701–710 (ACM, 2014).
Kipf, T. N. & Welling, M. Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations 2017 1–10 (OpenReview, 2016)..
Bach, S. et al. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS ONE 10, 1–46 (2015).
Gilpin, L. H. et al. Explaining explanations: an overview of interpretability of machine learning. In Proc. 2018 IEEE 5th International Conference on Data Science and Advanced Analytics 80–89 (IEEE, 2019).
Jamieson, C. Bad blood promotes tumour progression. Nature 549, 465–466 (2017).
Patani, H. et al. Transition to naïve human pluripotency mirrors pan-cancer DNA hypermethylation. Nat. Commun. 11, 1–17 (2020).
Page, L., Brin, S., Motwani, R. & Winograd, T. The PageRank Citation Ranking: Bringing Order to the Web (Stanford Univ. InfoLab, 1998).
Chakravarty, D. et al. OncoKB: a precision oncology knowledge base. JCO Precis. Oncol. 1, 1–16 (2017).
Liu, Y., Sun, J. & Zhao, M. ONGene: a literature-based database for human oncogenes. J. Genet. Genom. 44, 119–121 (2017).
Fodde, R. The APC gene in colorectal cancer. Eur. J. Cancer 38, 867–871 (2002).
Khan, M. A., Chen, H. C., Zhang, D. & Fu, J. Twist: a molecular target in cancer therapeutics. Tumor Biol. 34, 2497–2506 (2013).
Patwardhan, D., Mani, S., Passemard, S., Gressens, P. & El Ghouzzi, V. STIL balancing primary microcephaly and cancer. Cell Death Dis. 9, 65 (2018).
Jinesh, G. G., Sambandam, V., Vijayaraghavan, S., Balaji, K. & Mukherjee, S. Molecular genetics and cellular events of K-Ras-driven tumorigenesis. Oncogene 37, 839–846 (2018).
Chen, H. Z., Tsai, S. Y. & Leone, G. Emerging roles of E2Fs in cancer: an exit from cell cycle control. Nat. Rev. Cancer 9, 785–797 (2009).
Nevins, J. R. The Rb/E2F pathway and cancer. Human Mol. Genet. 10, 699–703 (2001).
Li, Y. & Seto, E. HDACs and HDAC inhibitors in cancer development and therapy. Cold Spring Harb. Perspect. Med. https://doi.org/10.1101/cshperspect.a026831 (2016).
Luo, R. X., Postigo, A. A. & Dean, D. C. Rb interacts with histone deacetylase to repress transcription. Cell 92, 463–473 (1998).
Tsherniak, A. et al. Defining a cancer dependency map. Cell 170, 564–576.e16 (2017).
Kluger, Y., Basri, R., Chang, J. T. & Gerstein, M. Spectral biclustering of microarray data: coclustering genes and conditions. Genome Res. 13, 703–716 (2003).
Suvà, M. L., Riggi, N. & Bernstein, B. E. Epigenetic reprogramming in cancer. Science 340, 1567–1570 (2013).
Keita, M. et al. Global methylation profiling in serous ovarian cancer is indicative for distinct aberrant DNA methylation signatures associated with tumor aggressiveness and disease progression. Gynecol. Oncol. 128, 356–363 (2013).
Webber, B. R. et al. DNA methylation of Runx1 regulatory regions correlates with transition from primitive to definitive hematopoietic potential in vitro and in vivo. Blood 122, 2978–2986 (2013).
Bissell, M. J. & Hines, W. C. Why don’t we get more cancer? A proposed role of the microenvironment in restraining cancer progression. Nat. Med. 17, 320–329 (2011).
Yu, Y. et al. The inhibitory effects of COL1A2 on colorectal cancer cell proliferation, migration, and invasion. J. Cancer 9, 2953–2962 (2018).
Sigismund, S., Avanzato, D. & Lanzetti, L. Emerging functions of the EGFR in cancer. Mol. Oncol. 12, 3–20 (2018).
Oh, E.-S., Seiki, M., Gotte, M. & Chung, J. Cell adhesion in cancer. Int. J. Cell Biol. 2012, 965618 (2012).
Xing, P. et al. Roles of low-density lipoprotein receptor-related protein 1 in tumors. Chinese J. Cancer https://doi.org/10.1186/s40880-015-0064-0 (2016).
Pu, X. et al. Caspase-3 and caspase-8 expression in breast cancer: caspase-3 is associated with survival. Apoptosis 22, 357–368 (2017).
Schramek, D. et al. Direct in vivo RNAi screen unveils myosin IIa as a tumor suppressor of squamous cell carcinomas. Science 343, 309–313 (2014).
Wang, B. et al. MYH9 Promotes growth and metastasis via activation of MAPK/AKT signaling in colorectal cancer. J. Cancer 10, 874–884 (2019).
Chen, R., Zhao, W. Q., Fang, C., Yang, X. & Ji, M. Histone methyltransferase SETD2: a potential tumor suppressor in solid cancers. J. Cancer 11, 3349–3356 (2020).
Klink, B. U., Gatsogiannis, C., Hofnagel, O., Wittinghofer, A. & Raunser, S. Structure of the human BBSome core complex. eLife 9, e53910 (2020).
Yang, K. et al. Integrative analysis reveals CRHBP inhibits renal cell carcinoma progression by regulating inflammation and apoptosis. Cancer Gene Ther. 27, 607–618 (2020).
Deng, L., Meng, T., Chen, L., Wei, W. & Wang, P. The role of ubiquitination in tumorigenesis and targeted drug discovery. Signal Transduct. Target. Ther. 5, 11 (2020).
Li, Y., Lu, W., He, X., Schwartz, A. L. & Bu, G. LRP6 expression promotes cancer cell proliferation and tumorigenesis by altering β-catenin subcellular distribution. Oncogene 23, 9129–9135 (2004).
Ding, Y. et al. Caprin-2 enhances canonical Wnt signaling through regulating LRP5/6 phosphorylation. J. Cell Biol. 182, 865–872 (2008).
Tombran-Tink, J. & Barnstable, C. J. PEDF: A multifaceted neurotrophic factor. Nat. Rev. Neurosci. 4, 628–636 (2003).
Lytle, N. K., Barber, A. G. & Reya, T. Stem cell fate in cancer growth, progression and therapy resistance. Nat. Rev. Cancer 18, 669–680 (2018).
Schaefer, M. H., Serrano, L. & Andrade-Navarro, M. A. Correcting for the study bias associated with protein–protein interaction measurements reveals differences between protein degree distributions from different cancer types. Front. Genet. 6, 00260 (2015).
Mourikis, T. P. et al. Patient-specific cancer genes contribute to recurrently perturbed pathways and establish therapeutic vulnerabilities in esophageal adenocarcinoma. Nat. Commun. 10, 3101 (2019).
Shi, J. et al. YWHAZ promotes ovarian cancer metastasis by modulating glycolysis. Oncol. Rep. 41, 1101–1112 (2019).
Vellingiri, B. et al. Understanding the role of the transcription factor sp1 in ovarian cancer: from theory to practice. Int. J. Mol. Sci. 21, 1153 (2020).
Wee, Y., Liu, Y., Lu, J., Li, X. & Zhao, M. Identification of novel prognosis-related genes associated with cancer using integrative network analysis. Sci. Rep. 8, 3233 (2018).
Priestley, P. et al. Pan-cancer whole-genome analyses of metastatic solid tumours. Nature 575, 210–216 (2019).
Wang, Q. et al. Data descriptor: unifying cancer and normal RNA sequencing data from different sources. Sci. Data 5, 1–8 (2018).
Mermel, C. H. et al. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol. 12, R41 (2011).
Frankish, A. et al. GENCODE reference annotation for the human and mouse genomes. Nucl. Acids Res. 47, D766–D773 (2019).
Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).
Kamburov, A. et al. ConsensusPathDB: toward a more complete picture of cell biology. Nucl. Acids Res. 39, D712–D717 (2011).
Szklarczyk, D. et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucl. Acids Res. 47, D607–D613 (2019).
Razick, S., Magklaras, G. & Donaldson, I. M. iRefIndex: a consolidated protein interaction database with provenance. BMC Bioinformatics 9, 405 (2008).
Khurana, E., Fu, Y., Chen, J. & Gerstein, M. Interpretation of genomic variants using a unified biological network approach. PLoS Comput. Biol. 9, e1002886 (2013).
Huang, J. K. et al. Systematic evaluation of molecular networks for discovery of disease genes. Cell Syst. 6, 484–495.e5 (2018).
Kim, J. & et al. DigSee: disease gene search engine with evidence sentences (version cancer). Nucl. Acids Res. 41, W510–W517 (2013).
Kanehisa, M. & Goto, S. KEGG: kyoto encyclopedia of genes and genomes. Nucl. Acids Res. 28, 27–30 (2000).
McKusick, V. A. Mendelian inheritance in man and its online version, OMIM. Am. J. Human Genet. 80, 588–604 (2007).
Liberzon, A. et al. The molecular signatures database hallmark gene set collection. Cell Syst. 1, 417–425 (2015).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
Niepert, M., Ahmed, M. & Kutzkov, K. Learning Convolutional Neural Networks for Graphs. In International Conference on Learning Representations (ICLR, 2016).
Defferrard, M., Bresson, X. & Vandergheynst, P. Convolutional neural networks on graphs with fast localized spectral filtering. In Advances in Neural Information Processing Systems 29 1–14 (NeurIPS, 2016).
Li, Q., Han, Z. & Wu, X.-M. Deeper insights into graph convolutional networks for semi-supervised learning. Preprint at https://arxiv.org/abs/1801.07606 (2018).
Shindjalova, R., Prodanova, K. & Svechtarov, V. Modeling data for tilted implants in grafted with bio-oss maxillary sinuses using logistic regression. In AIP Conference Proceedings Vol. 1631, 58–62 (2014).
Liu, S. H. et al. DriverDBv3: a multi-omics database for cancer driver gene research. Nucl. Acids Res. 48, D863–D870 (2020).
Lapuschkin, S. et al. Unmasking clever hans predictors and assessing what machines really learn. Nat. Commun. 10, 1096 (2019).
Tarjan, R. Depth-first search and linear graph algorithms. SIAM J. Comput. 1, 146–160 (1972).
Schulte-Sasse, R. EMOGI Code Release (Zenodo, 2021).
Schulte-Sasse, R., Budach, S., Hnisz, D. & Marsico, A. EMOGI—Integration of Multi-Omics Data with Graph Convolutional Networks Identifies New Cancer Genes and their Associated Molecular Mechanisms (CodeOcean, 2021).
We thank M. Vingron, R. Herwig and G. Barel for fruitful discussions, M. Vingron and C. Marr for proofreading the manuscript, and IMPRS for Computational Biology and Scientific Computing funding to R.S.-S. and S.B.
The authors declare no competing interests.
Peer review information Nature Machine Intelligence thanks Joel Nulsen, Kevin Y. Yip and the other anonymous reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Schulte-Sasse, R., Budach, S., Hnisz, D. et al. Integration of multiomics data with graph convolutional networks to identify new cancer genes and their associated molecular mechanisms. Nat Mach Intell 3, 513–526 (2021). https://doi.org/10.1038/s42256-021-00325-y
Integration of multiomics data with graph convolutional networks to identify new cancer genes and their associated molecular mechanisms
Nature Machine Intelligence (2021)