Home LiteratureArticle Details
PMID: 36304921 Published · epublish English Journal Article Review

Multi-omics analysis: Paving the path toward achieving precision medicine in cancer treatment and immuno-oncology.

Frontiers in molecular biosciences ·Vol. 9 ·2022-00-00 ·Pages 962743

Raufaste-Cazavieille V, Santiago R, Droit A

Abstract

The acceleration of large-scale sequencing and the progress in high-throughput computational analyses, defined as omics, was a hallmark for the comprehension of the biological processes in human health and diseases. In cancerology, the omics approach, initiated by genomics and transcriptomics studies, has revealed an incredible complexity with unsuspected molecular diversity within a same tumor type as well as spatial and temporal heterogeneity of tumors. The integration of multiple biological layers of omics studies brought oncology to a new paradigm, from tumor site classification to pan-cancer molecular classification, offering new therapeutic opportunities for precision medicine. In this review, we will provide a comprehensive overview of the latest innovations for multi-omics integration in oncology and summarize the largest multi-omics dataset available for adult and pediatric cancers. We will present multi-omics techniques for characterizing cancer biology and show how multi-omics data can be combined with clinical data for the identification of prognostic and treatment-specific biomarkers, opening the way to personalized therapy. To conclude, we will detail the newest strategies for dissecting the tumor immune environment and host-tumor interaction. We will explore the advances in immunomics and microbiomics for biomarker identification to guide therapeutic decision in immuno-oncology.

Keywords
cancer immunology immunomics machine learning microbiome multi-omics precision medicine
Authors & Affiliations
3 authors, click to expand affiliations / ORCID
Raufaste-Cazavieille Virgile
CHU de Québec Research Center, Université Laval, Québec, QC, Canada.
Santiago Raoul
CHU de Québec Research Center, Université Laval, Québec, QC, Canada. | Division of Pediatric Hematology-Oncology, Centre Hospitalier Universitaire de L'Université Laval, Charles Bruneau Cancer Center, Québec, QC, Canada.
Droit Arnaud
CHU de Québec Research Center, Université Laval, Québec, QC, Canada.
Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References (155)
155 references, click to expand
  1. Determining cell type abundance and expression from bulk tissues with digital cytometry.
    Nat Biotechnol. 2019 Jul;37(7):773-782 PMID: 31061481
  2. Multi-omics analysis of genomics, epigenomics and transcriptomics for molecular subtypes and core genes for lung adenocarcinoma.
    BMC Cancer. 2021 Mar 9;21(1):257 PMID: 33750346
  3. Comparison and evaluation of integrative methods for the analysis of multilevel omics data: a study based on simulated and experimental cancer data.
    Brief Bioinform. 2019 Mar 25;20(2):671-681 PMID: 29688321
  4. The Application of Bayesian Methods in Cancer Prognosis and Prediction.
    Cancer Genomics Proteomics. 2022 Jan-Feb;19(1):1-11 PMID: 34949654
  5. Intestinal Akkermansia muciniphila predicts clinical response to PD-1 blockade in patients with advanced non-small-cell lung cancer.
    Nat Med. 2022 Feb;28(2):315-324 PMID: 35115705
  6. Deep learning-based classification of mesothelioma improves prediction of patient outcome.
    Nat Med. 2019 Oct;25(10):1519-1525 PMID: 31591589
  7. 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
  8. Multi-omics approaches in cancer research with applications in tumor subtyping, prognosis, and diagnosis.
    Comput Struct Biotechnol J. 2021 Jan 22;19:949-960 PMID: 33613862
  9. Fecal microbiota transplant promotes response in immunotherapy-refractory melanoma patients.
    Science. 2021 Feb 5;371(6529):602-609 PMID: 33303685
  10. Prospects and challenges of multi-omics data integration in toxicology.
    Arch Toxicol. 2020 Feb;94(2):371-388 PMID: 32034435
  11. Integrative multi-omics analysis of muscle-invasive bladder cancer identifies prognostic biomarkers for frontline chemotherapy and immunotherapy.
    Commun Biol. 2020 Dec 17;3(1):784 PMID: 33335285
  12. Tumor Microbiome Diversity and Composition Influence Pancreatic Cancer Outcomes.
    Cell. 2019 Aug 8;178(4):795-806.e12 PMID: 31398337
  13. Relating the metatranscriptome and metagenome of the human gut.
    Proc Natl Acad Sci U S A. 2014 Jun 3;111(22):E2329-38 PMID: 24843156
  14. Tumor and Microenvironment Evolution during Immunotherapy with Nivolumab.
    Cell. 2017 Nov 2;171(4):934-949.e16 PMID: 29033130
  15. B cells sustain inflammation and predict response to immune checkpoint blockade in human melanoma.
    Nat Commun. 2019 Sep 13;10(1):4186 PMID: 31519915
  16. 16S rRNA gene sequencing for bacterial identification in the diagnostic laboratory: pluses, perils, and pitfalls.
    J Clin Microbiol. 2007 Sep;45(9):2761-4 PMID: 17626177
  17. Reducing variability of breast cancer subtype predictors by grounding deep learning models in prior knowledge.
    Comput Biol Med. 2021 Nov;138:104850 PMID: 34536702
  18. IFN-γ-related mRNA profile predicts clinical response to PD-1 blockade.
    J Clin Invest. 2017 Aug 1;127(8):2930-2940 PMID: 28650338
  19. Evaluation of integrative clustering methods for the analysis of multi-omics data.
    Brief Bioinform. 2020 Mar 23;21(2):541-552 PMID: 31220206
  20. Large-scale benchmark study of survival prediction methods using multi-omics data.
    Brief Bioinform. 2021 May 20;22(3): PMID: 32823283
  21. ImmuneDB, a Novel Tool for the Analysis, Storage, and Dissemination of Immune Repertoire Sequencing Data.
    Front Immunol. 2018 Sep 21;9:2107 PMID: 30298069
  22. Changes in the composition of the human fecal microbiome after bacteriotherapy for recurrent Clostridium difficile-associated diarrhea.
    J Clin Gastroenterol. 2010 May-Jun;44(5):354-60 PMID: 20048681
  23. Metagenes and molecular pattern discovery using matrix factorization.
    Proc Natl Acad Sci U S A. 2004 Mar 23;101(12):4164-9 PMID: 15016911
  24. Learning the parts of objects by non-negative matrix factorization.
    Nature. 1999 Oct 21;401(6755):788-91 PMID: 10548103
  25. Tumour heterogeneity poses a significant challenge to cancer biomarker research.
    Br J Cancer. 2017 Jul 25;117(3):367-375 PMID: 28618431
  26. 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
  27. Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells.
    Cancer Cell. 2020 Nov 9;38(5):672-684.e6 PMID: 33096023
  28. Computational Oncology in the Multi-Omics Era: State of the Art.
    Front Oncol. 2020 Apr 07;10:423 PMID: 32318338
  29. Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine.
    Brief Bioinform. 2022 Jul 18;23(4): PMID: 35788277
  30. Roles of the immune system in cancer: from tumor initiation to metastatic progression.
    Genes Dev. 2018 Oct 1;32(19-20):1267-1284 PMID: 30275043
  31. State of the Field in Multi-Omics Research: From Computational Needs to Data Mining and Sharing.
    Front Genet. 2020 Dec 10;11:610798 PMID: 33362867
  32. Deep learning.
    Nature. 2015 May 28;521(7553):436-44 PMID: 26017442
  33. Negative association of antibiotics on clinical activity of immune checkpoint inhibitors in patients with advanced renal cell and non-small-cell lung cancer.
    Ann Oncol. 2018 Jun 1;29(6):1437-1444 PMID: 29617710
  34. Cellular Barcoding Identifies Clonal Substitution as a Hallmark of Local Recurrence in a Surgical Model of Head and Neck Squamous Cell Carcinoma.
    Cell Rep. 2018 Nov 20;25(8):2208-2222.e7 PMID: 30463016
  35. Involvement of PD-L1 on tumor cells in the escape from host immune system and tumor immunotherapy by PD-L1 blockade.
    Proc Natl Acad Sci U S A. 2002 Sep 17;99(19):12293-7 PMID: 12218188
  36. Meta-analysis of tumor- and T cell-intrinsic mechanisms of sensitization to checkpoint inhibition.
    Cell. 2021 Feb 4;184(3):596-614.e14 PMID: 33508232
  37. A comparative study of multi-omics integration tools for cancer driver gene identification and tumour subtyping.
    Brief Bioinform. 2020 Dec 1;21(6):1920-1936 PMID: 31774481
  38. B cells and tertiary lymphoid structures promote immunotherapy response.
    Nature. 2020 Jan;577(7791):549-555 PMID: 31942075
  39. Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data.
    Genome Med. 2019 May 24;11(1):34 PMID: 31126321
  40. Diversity of 16S rRNA genes within individual prokaryotic genomes.
    Appl Environ Microbiol. 2010 Jun;76(12):3886-97 PMID: 20418441
  41. PD-L1 degradation pathway and immunotherapy for cancer.
    Cell Death Dis. 2020 Nov 6;11(11):955 PMID: 33159034
  42. 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
  43. EPIC: A Tool to Estimate the Proportions of Different Cell Types from Bulk Gene Expression Data.
    Methods Mol Biol. 2020;2120:233-248 PMID: 32124324
  44. Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases.
    Am J Epidemiol. 2017 Oct 1;186(7):753-761 PMID: 28978193
  45. Gene expression markers of Tumor Infiltrating Leukocytes.
    J Immunother Cancer. 2017 Feb 21;5:18 PMID: 28239471
  46. Integrative multi-omics module network inference with Lemon-Tree.
    PLoS Comput Biol. 2015 Feb 13;11(2):e1003983 PMID: 25679508
  47. Extracting Intercellular Signaling Network of Cancer Tissues using Ligand-Receptor Expression Patterns from Whole-tumor and Single-cell Transcriptomes.
    Sci Rep. 2017 Aug 18;7(1):8815 PMID: 28821810
  48. ARID1A mutation plus CXCL13 expression act as combinatorial biomarkers to predict responses to immune checkpoint therapy in mUCC.
    Sci Transl Med. 2020 Jun 17;12(548): PMID: 32554706
  49. A machine learning framework that integrates multi-omics data predicts cancer-related LncRNAs.
    BMC Bioinformatics. 2021 Jun 16;22(1):332 PMID: 34134612
  50. Combining metagenomics, metatranscriptomics and viromics to explore novel microbial interactions: towards a systems-level understanding of human microbiome.
    Comput Struct Biotechnol J. 2015 Jun 09;13:390-401 PMID: 26137199
  51. MALDI-TOF mass spectrometry: an emerging technology for microbial identification and diagnosis.
    Front Microbiol. 2015 Aug 05;6:791 PMID: 26300860
  52. Biomarker discovery by integrated joint non-negative matrix factorization and pathway signature analyses.
    Sci Rep. 2018 Jun 27;8(1):9743 PMID: 29950679
  53. Cancer statistics for the year 2020: An overview.
    Int J Cancer. 2021 Apr 5;: PMID: 33818764
  54. Co-occurring genomic alterations in non-small-cell lung cancer biology and therapy.
    Nat Rev Cancer. 2019 Sep;19(9):495-509 PMID: 31406302
  55. Integrative clustering of multi-level 'omic data based on non-negative matrix factorization algorithm.
    PLoS One. 2017 May 1;12(5):e0176278 PMID: 28459819
  56. Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas.
    Cell Rep. 2018 Apr 3;23(1):194-212.e6 PMID: 29617660
  57. The Influence of the Gut Microbiome on Cancer, Immunity, and Cancer Immunotherapy.
    Cancer Cell. 2018 Apr 9;33(4):570-580 PMID: 29634945
  58. CRI iAtlas: an interactive portal for immuno-oncology research.
    F1000Res. 2020 Aug 24;9:1028 PMID: 33214875
  59. The cancer microbiome atlas: a pan-cancer comparative analysis to distinguish tissue-resident microbiota from contaminants.
    Cell Host Microbe. 2021 Feb 10;29(2):281-298.e5 PMID: 33382980
  60. A Review of Integrative Imputation for Multi-Omics Datasets.
    Front Genet. 2020 Oct 15;11:570255 PMID: 33193667
  61. Genomic analyses identify molecular subtypes of pancreatic cancer.
    Nature. 2016 Mar 3;531(7592):47-52 PMID: 26909576
  62. Measurement of tumor mutational burden (TMB) in routine molecular diagnostics: in silico and real-life analysis of three larger gene panels.
    Int J Cancer. 2019 May 1;144(9):2303-2312 PMID: 30446996
  63. MiXCR: software for comprehensive adaptive immunity profiling.
    Nat Methods. 2015 May;12(5):380-1 PMID: 25924071
  64. The microbiome and human cancer.
    Science. 2021 Mar 26;371(6536): PMID: 33766858
  65. What is Machine Learning? A Primer for the Epidemiologist.
    Am J Epidemiol. 2019 Dec 31;188(12):2222-2239 PMID: 31509183
  66. timeOmics: an R package for longitudinal multi-omics data integration.
    Bioinformatics. 2021 Sep 23;: PMID: 34554215
  67. Discovery of multi-dimensional modules by integrative analysis of cancer genomic data.
    Nucleic Acids Res. 2012 Oct;40(19):9379-91 PMID: 22879375
  68. Progress in cancer mortality, incidence, and survival: a global overview.
    Eur J Cancer Prev. 2020 Sep;29(5):367-381 PMID: 32740162
  69. Predicting cancer outcomes from histology and genomics using convolutional networks.
    Proc Natl Acad Sci U S A. 2018 Mar 27;115(13):E2970-E2979 PMID: 29531073
  70. Intratumor Heterogeneity: The Rosetta Stone of Therapy Resistance.
    Cancer Cell. 2020 Apr 13;37(4):471-484 PMID: 32289271
  71. Generalized Bayesian Factor Analysis for Integrative Clustering with Applications to Multi-Omics Data.
    Proc Int Conf Data Sci Adv Anal. 2018 Oct;2018:109-119 PMID: 31106307
  72. Machine Learning and Integrative Analysis of Biomedical Big Data.
    Genes (Basel). 2019 Jan 28;10(2): PMID: 30696086
  73. The European MAPPYACTS Trial: Precision Medicine Program in Pediatric and Adolescent Patients with Recurrent Malignancies.
    Cancer Discov. 2022 May 2;12(5):1266-1281 PMID: 35292802
  74. International network of cancer genome projects.
    Nature. 2010 Apr 15;464(7291):993-8 PMID: 20393554
  75. A modern era of personalized medicine in the diagnosis, prognosis, and treatment of prostate cancer.
    Comput Biol Med. 2020 Nov;126:104020 PMID: 33039808
  76. IOBR: Multi-Omics Immuno-Oncology Biological Research to Decode Tumor Microenvironment and Signatures.
    Front Immunol. 2021 Jul 02;12:687975 PMID: 34276676
  77. Essential guidelines for computational method benchmarking.
    Genome Biol. 2019 Jun 20;20(1):125 PMID: 31221194
  78. Circulating microbiome DNA: An emerging paradigm for cancer liquid biopsy.
    Cancer Lett. 2021 Aug 28;521:82-87 PMID: 34461180
  79. Understanding the tumor immune microenvironment (TIME) for effective therapy.
    Nat Med. 2018 May;24(5):541-550 PMID: 29686425
  80. Pan-cancer genome and transcriptome analyses of 1,699 paediatric leukaemias and solid tumours.
    Nature. 2018 Mar 15;555(7696):371-376 PMID: 29489755
  81. PIntMF: Penalized Integrative Matrix Factorization method for multi-omics data.
    Bioinformatics. 2021 Nov 26;: PMID: 34849583
  82. Multi-omic and multi-view clustering algorithms: review and cancer benchmark.
    Nucleic Acids Res. 2018 Nov 16;46(20):10546-10562 PMID: 30295871
  83. Association of Chlamydia pneumoniae immunoglobulin A seropositivity and risk of lung cancer.
    Cancer Epidemiol Biomarkers Prev. 2000 Nov;9(11):1263-6 PMID: 11097237
  84. Pan-Cancer Analysis Reveals Differential Susceptibility of Bidirectional Gene Promoters to DNA Methylation, Somatic Mutations, and Copy Number Alterations.
    Int J Mol Sci. 2018 Aug 05;19(8): PMID: 30081598
  85. Network-based integrative clustering of multiple types of genomic data using non-negative matrix factorization.
    Comput Biol Med. 2020 Mar;118:103625 PMID: 31999549
  86. Machine learning analysis of gene expression data reveals novel diagnostic and prognostic biomarkers and identifies therapeutic targets for soft tissue sarcomas.
    PLoS Comput Biol. 2019 Feb 20;15(2):e1006826 PMID: 30785874
  87. Monitoring immune-checkpoint blockade: response evaluation and biomarker development.
    Nat Rev Clin Oncol. 2017 Nov;14(11):655-668 PMID: 28653677
  88. Improved grading and survival prediction of human astrocytic brain tumors by artificial neural network analysis of gene expression microarray data.
    Mol Cancer Ther. 2008 May;7(5):1013-24 PMID: 18445660
  89. Integrated Multi-Omics Analyses in Oncology: A Review of Machine Learning Methods and Tools.
    Front Oncol. 2020 Jun 30;10:1030 PMID: 32695678
  90. Evaluation and comparison of multi-omics data integration methods for cancer subtyping.
    PLoS Comput Biol. 2021 Aug 12;17(8):e1009224 PMID: 34383739
  91. Quantification of the heterogeneity of prognostic cellular biomarkers in ewing sarcoma using automated image and random survival forest analysis.
    PLoS One. 2014 Sep 22;9(9):e107105 PMID: 25243408
  92. Module Analysis Captures Pancancer Genetically and Epigenetically Deregulated Cancer Driver Genes for Smoking and Antiviral Response.
    EBioMedicine. 2018 Jan;27:156-166 PMID: 29331675
  93. Deep learning based tissue analysis predicts outcome in colorectal cancer.
    Sci Rep. 2018 Feb 21;8(1):3395 PMID: 29467373
  94. Systematic benchmarking of omics computational tools.
    Nat Commun. 2019 Mar 27;10(1):1393 PMID: 30918265
  95. Pan-cancer single-cell landscape of tumor-infiltrating T cells.
    Science. 2021 Dec 17;374(6574):abe6474 PMID: 34914499
  96. 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
  97. Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression.
    Genome Biol. 2016 Oct 20;17(1):218 PMID: 27765066
  98. Molecular mechanisms linking environmental toxicants to cancer development: Significance for protective interventions with polyphenols.
    Semin Cancer Biol. 2022 May;80:118-144 PMID: 32044471
  99. The Immune Landscape of Cancer.
    Immunity. 2018 Apr 17;48(4):812-830.e14 PMID: 29628290
  100. Tumour heterogeneity and resistance to cancer therapies.
    Nat Rev Clin Oncol. 2018 Feb;15(2):81-94 PMID: 29115304
  101. Bayesian integrative model for multi-omics data with missingness.
    Bioinformatics. 2018 Nov 15;34(22):3801-3808 PMID: 30184058
  102. 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
  103. Application of artificial neural network-based survival analysis on two breast cancer datasets.
    AMIA Annu Symp Proc. 2007 Oct 11;:130-4 PMID: 18693812
  104. Nonnegative matrix factorization: an analytical and interpretive tool in computational biology.
    PLoS Comput Biol. 2008 Jul 25;4(7):e1000029 PMID: 18654623
  105. Influence of the Gut Microbiome, Diet, and Environment on Risk of Colorectal Cancer.
    Gastroenterology. 2020 Jan;158(2):322-340 PMID: 31586566
  106. Cross-Cohort Analysis Identifies a TEAD4-MYCN Positive Feedback Loop as the Core Regulatory Element of High-Risk Neuroblastoma.
    Cancer Discov. 2018 May;8(5):582-599 PMID: 29510988
  107. Association between composition of the human gastrointestinal microbiome and development of fatty liver with choline deficiency.
    Gastroenterology. 2011 Mar;140(3):976-86 PMID: 21129376
  108. Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade.
    Cell Rep. 2017 Jan 3;18(1):248-262 PMID: 28052254
  109. Deciphering intratumor heterogeneity and temporal acquisition of driver events to refine precision medicine.
    Genome Biol. 2014 Aug 27;15(8):453 PMID: 25222836
  110. Multi-omics Data Integration, Interpretation, and Its Application.
    Bioinform Biol Insights. 2020 Jan 31;14:1177932219899051 PMID: 32076369
  111. Pattern discovery and cancer gene identification in integrated cancer genomic data.
    Proc Natl Acad Sci U S A. 2013 Mar 12;110(11):4245-50 PMID: 23431203
  112. The Complex Interaction between the Tumor Micro-Environment and Immune Checkpoints in Breast Cancer.
    Cancers (Basel). 2019 Aug 19;11(8): PMID: 31430935
  113. The role of the microbiome in cancer development and therapy.
    CA Cancer J Clin. 2017 Jul 8;67(4):326-344 PMID: 28481406
  114. Cold Tumors: A Therapeutic Challenge for Immunotherapy.
    Front Immunol. 2019 Feb 08;10:168 PMID: 30800125
  115. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge.
    Contemp Oncol (Pozn). 2015;19(1A):A68-77 PMID: 25691825
  116. Shotgun metagenomics, from sampling to analysis.
    Nat Biotechnol. 2017 Sep 12;35(9):833-844 PMID: 28898207
  117. Whole genome, transcriptome and methylome profiling enhances actionable target discovery in high-risk pediatric cancer.
    Nat Med. 2020 Nov;26(11):1742-1753 PMID: 33020650
  118. A non-negative matrix factorization method for detecting modules in heterogeneous omics multi-modal data.
    Bioinformatics. 2016 Jan 1;32(1):1-8 PMID: 26377073
  119. Non-invasive biomarkers for monitoring the immunotherapeutic response to cancer.
    J Transl Med. 2020 Dec 9;18(1):471 PMID: 33298096
  120. Re-expression of major histocompatibility complex (MHC) class I molecules on malignant tumor cells and its effect on host-tumor interaction.
    Bioessays. 1989 Jul;11(1):22-6 PMID: 2673229
  121. The Cancer Genome Atlas Pan-Cancer analysis project.
    Nat Genet. 2013 Oct;45(10):1113-20 PMID: 24071849
  122. Mechanisms of immune escape in the cancer immune cycle.
    Int Immunopharmacol. 2020 Sep;86:106700 PMID: 32590316
  123. SMGR: a joint statistical method for integrative analysis of single-cell multi-omics data.
    NAR Genom Bioinform. 2022 Jul 27;4(3):lqac056 PMID: 35910046
  124. Integrative analysis of genomic, epigenomic and transcriptomic data identified molecular subtypes of esophageal carcinoma.
    Aging (Albany NY). 2021 Feb 26;13(5):6999-7019 PMID: 33638948
  125. Bench pressing with genomics benchmarkers.
    Nat Methods. 2020 Mar;17(3):255-258 PMID: 32080620
  126. A primer on learning in Bayesian networks for computational biology.
    PLoS Comput Biol. 2007 Aug;3(8):e129 PMID: 17784779
  127. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors.
    Science. 2018 Jan 5;359(6371):91-97 PMID: 29097494
  128. Temporal shifts in the skin microbiome associated with disease flares and treatment in children with atopic dermatitis.
    Genome Res. 2012 May;22(5):850-9 PMID: 22310478
  129. Cancer immune escape: MHC expression in primary tumours versus metastases.
    Immunology. 2019 Dec;158(4):255-266 PMID: 31509607
  130. Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer.
    Nat Commun. 2021 Jan 5;12(1):124 PMID: 33402734
  131. MOLI: multi-omics late integration with deep neural networks for drug response prediction.
    Bioinformatics. 2019 Jul 15;35(14):i501-i509 PMID: 31510700
  132. Elements of cancer immunity and the cancer-immune set point.
    Nature. 2017 Jan 18;541(7637):321-330 PMID: 28102259
  133. The effect of diet on the human gut microbiome: a metagenomic analysis in humanized gnotobiotic mice.
    Sci Transl Med. 2009 Nov 11;1(6):6ra14 PMID: 20368178
  134. Extensive Remodeling of the Immune Microenvironment in B Cell Acute Lymphoblastic Leukemia.
    Cancer Cell. 2020 Jun 8;37(6):867-882.e12 PMID: 32470390
  135. Omics-Based Strategies in Precision Medicine: Toward a Paradigm Shift in Inborn Errors of Metabolism Investigations.
    Int J Mol Sci. 2016 Sep 14;17(9): PMID: 27649151
  136. SALMON: Survival Analysis Learning With Multi-Omics Neural Networks on Breast Cancer.
    Front Genet. 2019 Mar 08;10:166 PMID: 30906311
  137. Structural learning and integrative decomposition of multi-view data.
    Biometrics. 2019 Dec;75(4):1121-1132 PMID: 31254385
  138. Multi-omics integration in biomedical research - A metabolomics-centric review.
    Anal Chim Acta. 2021 Jan 2;1141:144-162 PMID: 33248648
  139. Cancer Classification at the Crossroads.
    Cancers (Basel). 2020 Apr 15;12(4): PMID: 32326638
  140. The Future of Blood Testing Is the Immunome.
    Front Immunol. 2021 Mar 15;12:626793 PMID: 33790897
  141. DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network.
    BMC Med Res Methodol. 2018 Feb 26;18(1):24 PMID: 29482517
  142. 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
  143. Trends in extreme learning machines: a review.
    Neural Netw. 2015 Jan;61:32-48 PMID: 25462632
  144. The human microbiome and metabolomics: Current concepts and applications.
    Crit Rev Food Sci Nutr. 2017 Nov 2;57(16):3565-3576 PMID: 27767329
  145. The Application of Deep Learning in Cancer Prognosis Prediction.
    Cancers (Basel). 2020 Mar 05;12(3): PMID: 32150991
  146. Tracking genomic cancer evolution for precision medicine: the lung TRACERx study.
    PLoS Biol. 2014 Jul 08;12(7):e1001906 PMID: 25003521
  147. xCell: digitally portraying the tissue cellular heterogeneity landscape.
    Genome Biol. 2017 Nov 15;18(1):220 PMID: 29141660
  148. Integrative Tumor and Immune Cell Multi-omic Analyses Predict Response to Immune Checkpoint Blockade in Melanoma.
    Cell Rep Med. 2020 Nov 17;1(8):100139 PMID: 33294860
  149. Intra-tumour heterogeneity: a looking glass for cancer?
    Nat Rev Cancer. 2012 Apr 19;12(5):323-34 PMID: 22513401
  150. 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
  151. DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data.
    Genome Med. 2021 Jul 14;13(1):112 PMID: 34261540
  152. From big data analysis to personalized medicine for all: challenges and opportunities.
    BMC Med Genomics. 2015 Jun 27;8:33 PMID: 26112054
  153. TIMER2.0 for analysis of tumor-infiltrating immune cells.
    Nucleic Acids Res. 2020 Jul 2;48(W1):W509-W514 PMID: 32442275
  154. Intratumoral heterogeneity and genetic characteristics of prostate cancer.
    Int J Cancer. 2020 Jun 15;146(12):3369-3378 PMID: 32159858
  155. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups.
    Nature. 2012 Apr 18;486(7403):346-52 PMID: 22522925
Article Info
Journal
Frontiers in molecular biosciences
Abbr.
Front Mol Biosci
ISSN
2296-889X
Published
2022-00-00
Epub
2022-00-11
Pages
962743
Language
English
Region
Switzerland
NLM ID
101653173
PMCID
PMC9595279
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]