Home LiteratureArticle Details
PMID: 32695678 Published · epublish English Journal Article Review

Integrated Multi-Omics Analyses in Oncology: A Review of Machine Learning Methods and Tools.

Frontiers in oncology ·Vol. 10 ·2020-00-00 ·Pages 1030

Nicora G, Vitali F, Dagliati A, Geifman N, Bellazzi R

Abstract

In recent years, high-throughput sequencing technologies provide unprecedented opportunity to depict cancer samples at multiple molecular levels. The integration and analysis of these multi-omics datasets is a crucial and critical step to gain actionable knowledge in a precision medicine framework. This paper explores recent data-driven methodologies that have been developed and applied to respond major challenges of stratified medicine in oncology, including patients' phenotyping, biomarker discovery, and drug repurposing. We systematically retrieved peer-reviewed journals published from 2014 to 2019, select and thoroughly describe the tools presenting the most promising innovations regarding the integration of heterogeneous data, the machine learning methodologies that successfully tackled the complexity of multi-omics data, and the frameworks to deliver actionable results for clinical practice. The review is organized according to the applied methods: Deep learning, Network-based methods, Clustering, Features Extraction, and Transformation, Factorization. We provide an overview of the tools available in each methodological group and underline the relationship among the different categories. Our analysis revealed how multi-omics datasets could be exploited to drive precision oncology, but also current limitations in the development of multi-omics data integration.

Keywords
cancer machine learning multi-omics oncology systematic review tools
Authors & Affiliations
5 authors, click to expand affiliations / ORCID
Nicora Giovanna
Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.
Vitali Francesca
Center for Innovation in Brain Science, University of Arizona, Tucson, AZ, United States. | Department of Neurology, College of Medicine, University of Arizona, Tucson, AZ, United States. | Center for Biomedical Informatics and Biostatistics, University of Arizona, Tucson, AZ, United States.
Dagliati Arianna
Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy. | Centre for Health Informatics, The University of Manchester, Manchester, United Kingdom. | The Manchester Molecular Pathology Innovation Centre, The University of Manchester, Manchester, United Kingdom.
Geifman Nophar
Centre for Health Informatics, The University of Manchester, Manchester, United Kingdom. | The Manchester Molecular Pathology Innovation Centre, The University of Manchester, Manchester, United Kingdom.
Bellazzi Riccardo
Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.
References (74)
74 references, click to expand
  1. Classification of breast cancer subtypes by combining gene expression and DNA methylation data.
    J Integr Bioinform. 2014 Jun 13;11(2):236 PMID: 24953305
  2. Drug repositioning: a machine-learning approach through data integration.
    J Cheminform. 2013 Jun 22;5(1):30 PMID: 23800010
  3. R.JIVE for exploration of multi-source molecular data.
    Bioinformatics. 2016 Sep 15;32(18):2877-9 PMID: 27273669
  4. ICan: an integrated co-alteration network to identify ovarian cancer-related genes.
    PLoS One. 2015 Mar 24;10(3):e0116095 PMID: 25803614
  5. Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis.
    Bioinformatics. 2009 Nov 15;25(22):2906-12 PMID: 19759197
  6. Characterization of heterogeneous redox responses in hepatocellular carcinoma patients using network analysis.
    EBioMedicine. 2019 Feb;40:471-487 PMID: 30606699
  7. Integrative analysis of genomic and epigenomic regulation of the transcriptome in liver cancer.
    Nat Commun. 2017 Oct 10;8(1):839 PMID: 29018224
  8. MOLI: multi-omics late integration with deep neural networks for drug response prediction.
    Bioinformatics. 2019 Jul 15;35(14):i501-i509 PMID: 31510700
  9. Robust Selection Algorithm (RSA) for Multi-Omic Biomarker Discovery; Integration with Functional Network Analysis to Identify miRNA Regulated Pathways in Multiple Cancers.
    PLoS One. 2015 Oct 27;10(10):e0140072 PMID: 26505200
  10. Detection of multiple perturbations in multi-omics biological networks.
    Biometrics. 2018 Dec;74(4):1351-1361 PMID: 29772079
  11. PREDICT: a method for inferring novel drug indications with application to personalized medicine.
    Mol Syst Biol. 2011 Jun 07;7:496 PMID: 21654673
  12. PATIENT-SPECIFIC DATA FUSION FOR CANCER STRATIFICATION AND PERSONALISED TREATMENT.
    Pac Symp Biocomput. 2016;21:321-32 PMID: 26776197
  13. Intertumoral Heterogeneity within Medulloblastoma Subgroups.
    Cancer Cell. 2017 Jun 12;31(6):737-754.e6 PMID: 28609654
  14. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin.
    Cell. 2014 Aug 14;158(4):929-944 PMID: 25109877
  15. An Improved Method for Prediction of Cancer Prognosis by Network Learning.
    Genes (Basel). 2018 Oct 02;9(10): PMID: 30279327
  16. Consistency and overfitting of multi-omics methods on experimental data.
    Brief Bioinform. 2020 Jul 15;21(4):1277-1284 PMID: 31281919
  17. netDx: interpretable patient classification using integrated patient similarity networks.
    Mol Syst Biol. 2019 Mar 14;15(3):e8497 PMID: 30872331
  18. Multiomics analysis on DNA methylation and the expression of both messenger RNA and microRNA in lung adenocarcinoma.
    J Cell Physiol. 2019 May;234(5):7579-7586 PMID: 30370535
  19. From 'omics' to complex disease: a systems biology approach to gene-environment interactions in cancer.
    Cancer Cell Int. 2010 Apr 26;10:11 PMID: 20420667
  20. Clusternomics: Integrative context-dependent clustering for heterogeneous datasets.
    PLoS Comput Biol. 2017 Oct 16;13(10):e1005781 PMID: 29036190
  21. Unsupervised multiple kernel learning for heterogeneous data integration.
    Bioinformatics. 2018 Mar 15;34(6):1009-1015 PMID: 29077792
  22. Deep Learning-Based Multi-Omics Data Integration Reveals Two Prognostic Subtypes in High-Risk Neuroblastoma.
    Front Genet. 2018 Oct 18;9:477 PMID: 30405689
  23. Multi-Omics Analysis Reveals a HIF Network and Hub Gene EPAS1 Associated with Lung Adenocarcinoma.
    EBioMedicine. 2018 Jun;32:93-101 PMID: 29859855
  24. Affinity network fusion and semi-supervised learning for cancer patient clustering.
    Methods. 2018 Aug 1;145:16-24 PMID: 29807109
  25. Driver network as a biomarker: systematic integration and network modeling of multi-omics data to derive driver signaling pathways for drug combination prediction.
    Bioinformatics. 2019 Oct 1;35(19):3709-3717 PMID: 30768150
  26. Distinct co-expression networks using multi-omic data reveal novel interventional targets in HPV-positive and negative head-and-neck squamous cell cancer.
    Sci Rep. 2018 Oct 15;8(1):15254 PMID: 30323202
  27. A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data.
    Biostatistics. 2018 Jan 1;19(1):71-86 PMID: 28541380
  28. Detecting the potential cancer association or metastasis by multi-omics data analysis.
    Genet Mol Res. 2016 Aug 19;15(3): PMID: 27706596
  29. Integrative multi-omics module network inference with Lemon-Tree.
    PLoS Comput Biol. 2015 Feb 13;11(2):e1003983 PMID: 25679508
  30. A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia.
    Nat Commun. 2018 Jan 3;9(1):42 PMID: 29298978
  31. A comparative study of multi-omics integration tools for cancer driver gene identification and tumour subtyping.
    Brief Bioinform. 2019 Nov 27;: PMID: 31774481
  32. Integrative Data Analysis of Multi-Platform Cancer Data with a Multimodal Deep Learning Approach.
    IEEE/ACM Trans Comput Biol Bioinform. 2015 Jul-Aug;12(4):928-37 PMID: 26357333
  33. Network-based integration of multi-omics data for prioritizing cancer genes.
    Bioinformatics. 2018 Jul 15;34(14):2441-2448 PMID: 29547932
  34. UK Biobank: from concept to reality.
    Pharmacogenomics. 2005 Sep;6(6):639-46 PMID: 16143003
  35. Similarity network fusion for aggregating data types on a genomic scale.
    Nat Methods. 2014 Mar;11(3):333-7 PMID: 24464287
  36. A Selective Review of Multi-Level Omics Data Integration Using Variable Selection.
    High Throughput. 2019 Jan 18;8(1): PMID: 30669303
  37. Making multi-omics data accessible to researchers.
    Sci Data. 2019 Oct 31;6(1):251 PMID: 31672978
  38. Fast dimension reduction and integrative clustering of multi-omics data using low-rank approximation: application to cancer molecular classification.
    BMC Genomics. 2015 Dec 01;16:1022 PMID: 26626453
  39. A multiomics analysis of S100 protein family in breast cancer.
    Oncotarget. 2018 Jun 26;9(49):29064-29081 PMID: 30018736
  40. Module Analysis Captures Pancancer Genetically and Epigenetically Deregulated Cancer Driver Genes for Smoking and Antiviral Response.
    EBioMedicine. 2018 Jan;27:156-166 PMID: 29331675
  41. Multi-omics Biomarker Pipeline Reveals Elevated Levels of Protein-glutamine Gamma-glutamyltransferase 4 in Seminal Plasma of Prostate Cancer Patients.
    Mol Cell Proteomics. 2019 Sep;18(9):1807-1823 PMID: 31249104
  42. mixOmics: An R package for 'omics feature selection and multiple data integration.
    PLoS Comput Biol. 2017 Nov 3;13(11):e1005752 PMID: 29099853
  43. Bayesian joint analysis of heterogeneous genomics data.
    Bioinformatics. 2014 May 15;30(10):1370-6 PMID: 24489367
  44. Gene network inference by fusing data from diverse distributions.
    Bioinformatics. 2015 Jun 15;31(12):i230-9 PMID: 26072487
  45. A multivariate approach to the integration of multi-omics datasets.
    BMC Bioinformatics. 2014 May 29;15:162 PMID: 24884486
  46. DriverDBv3: a multi-omics database for cancer driver gene research.
    Nucleic Acids Res. 2020 Jan 8;48(D1):D863-D870 PMID: 31701128
  47. JOINT AND INDIVIDUAL VARIATION EXPLAINED (JIVE) FOR INTEGRATED ANALYSIS OF MULTIPLE DATA TYPES.
    Ann Appl Stat. 2013 Mar 1;7(1):523-542 PMID: 23745156
  48. Deep Learning-Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer.
    Clin Cancer Res. 2018 Mar 15;24(6):1248-1259 PMID: 28982688
  49. Compact Integration of Multi-Network Topology for Functional Analysis of Genes.
    Cell Syst. 2016 Dec 21;3(6):540-548.e5 PMID: 27889536
  50. An individualized prognostic signature and multi‑omics distinction for early stage hepatocellular carcinoma patients with surgical resection.
    Oncotarget. 2016 Apr 26;7(17):24097-110 PMID: 27006471
  51. Integrating Clinical and Multiple Omics Data for Prognostic Assessment across Human Cancers.
    Sci Rep. 2017 Dec 5;7(1):16954 PMID: 29209073
  52. Integrating different data types by regularized unsupervised multiple kernel learning with application to cancer subtype discovery.
    Bioinformatics. 2015 Jun 15;31(12):i268-75 PMID: 26072491
  53. A Network-Based Data Integration Approach to Support Drug Repurposing and Multi-Target Therapies in Triple Negative Breast Cancer.
    PLoS One. 2016 Sep 15;11(9):e0162407 PMID: 27632168
  54. Constructing module maps for integrated analysis of heterogeneous biological networks.
    Nucleic Acids Res. 2014 Apr;42(7):4208-19 PMID: 24497192
  55. Integrated Genomic Characterization of Pancreatic Ductal Adenocarcinoma.
    Cancer Cell. 2017 Aug 14;32(2):185-203.e13 PMID: 28810144
  56. Multi-omics integration for neuroblastoma clinical endpoint prediction.
    Biol Direct. 2018 Apr 3;13(1):5 PMID: 29615097
  57. Recent Advances of Deep Learning in Bioinformatics and Computational Biology.
    Front Genet. 2019 Mar 26;10:214 PMID: 30972100
  58. Reconstruction of pathway modification induced by nicotinamide using multi-omic network analyses in triple negative breast cancer.
    Sci Rep. 2017 Jun 14;7(1):3466 PMID: 28615672
  59. SALMON: Survival Analysis Learning With Multi-Omics Neural Networks on Breast Cancer.
    Front Genet. 2019 Mar 08;10:166 PMID: 30906311
  60. More Is Better: Recent Progress in Multi-Omics Data Integration Methods.
    Front Genet. 2017 Jun 16;8:84 PMID: 28670325
  61. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.
    CA Cancer J Clin. 2018 Nov;68(6):394-424 PMID: 30207593
  62. NEMO: cancer subtyping by integration of partial multi-omic data.
    Bioinformatics. 2019 Sep 15;35(18):3348-3356 PMID: 30698637
  63. A review on machine learning principles for multi-view biological data integration.
    Brief Bioinform. 2018 Mar 1;19(2):325-340 PMID: 28011753
  64. Robust pathway-based multi-omics data integration using directed random walks for survival prediction in multiple cancer studies.
    Biol Direct. 2019 Apr 29;14(1):8 PMID: 31036036
  65. Integration of Multi-omics Data for Gene Regulatory Network Inference and Application to Breast Cancer.
    IEEE/ACM Trans Comput Biol Bioinform. 2018 Aug 23;: PMID: 30137012
  66. A novel approach for data integration and disease subtyping.
    Genome Res. 2017 Dec;27(12):2025-2039 PMID: 29066617
  67. Insights into Impact of DNA Copy Number Alteration and Methylation on the Proteogenomic Landscape of Human Ovarian Cancer via a Multi-omics Integrative Analysis.
    Mol Cell Proteomics. 2019 Aug 9;18(8 suppl 1):S52-S65 PMID: 31227599
  68. iOmicsPASS: network-based integration of multiomics data for predictive subnetwork discovery.
    NPJ Syst Biol Appl. 2019 Jul 9;5:22 PMID: 31312515
  69. Personalization of Logical Models With Multi-Omics Data Allows Clinical Stratification of Patients.
    Front Physiol. 2019 Jan 24;9:1965 PMID: 30733688
  70. GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function.
    Genome Biol. 2008;9 Suppl 1:S4 PMID: 18613948
  71. LinkedOmics: analyzing multi-omics data within and across 32 cancer types.
    Nucleic Acids Res. 2018 Jan 4;46(D1):D956-D963 PMID: 29136207
  72. A community effort to assess and improve drug sensitivity prediction algorithms.
    Nat Biotechnol. 2014 Dec;32(12):1202-12 PMID: 24880487
  73. Multi-Omics Factor Analysis-a framework for unsupervised integration of multi-omics data sets.
    Mol Syst Biol. 2018 Jun 20;14(6):e8124 PMID: 29925568
  74. Pan-cancer analysis identifies telomerase-associated signatures and cancer subtypes.
    Mol Cancer. 2019 Jun 10;18(1):106 PMID: 31179925
Article Info
Journal
Frontiers in oncology
Abbr.
Front Oncol
ISSN
2234-943X
DOI
10.3389/fonc.2020.01030
Published
2020-00-00
Epub
2020-00-30
Pages
1030
Language
English
Region
Switzerland
NLM ID
101568867
PMCID
PMC7338582
Grants
Medical Research Council · MR/N00583X/1 · United Kingdom
External Links
PubMed source DOI: 10.3389/fonc.2020.01030 PMC Full Text View journal details
Analysis Services
Analysis Services

Contact

No. 2 Wenbo Road, Zhangqiu District, Jinan, Shandong

Qilu Normal University · Genelibs Bioinformatics Lab

750 Shunhua Rd, Jinan

2F, Bldg F, University Science Park

Tel: 0531-88819269

WeChat Official Account

Follow our WeChat subscription account for real-time updates and the latest in medical and biological research.


Business Email

E-mail: [email protected]

Copyright © 2026.Genelibs Jinan Honghui Biotechnology Co., Ltd. 鲁ICP备20007159号-2