Abstract

Mini Review

Advances in deep learning-based cancer outcome prediction using multi-omics data

Andrew Zhou, Charlie Zhang and Okyaz Eminaga*

Published: 01 May, 2023 | Volume 7 - Issue 1 | Pages: 010-013

Cancer prognosis reflects a complex biological process measured by multiple types of omics data. Deep learning frameworks have been proposed to integrate multi-omics data and predict patient outcomes in different cancer types, potentially revolutionizing cancer prognosis with superior performance. This minireview summarizes the advances in the strategies for multi-omics data integration and the performance of different deep learning models in prognosis prediction of diverse cancer types using multi-omics data published in the past 18 months. The challenges and limitations of deep learning models for predicting cancer outcomes based on multi-omics data are discussed.

Read Full Article HTML DOI: 10.29328/journal.apb.1001020 Cite this Article Read Full Article PDF

Keywords:

Deep learning; Cancer; Outcome prediction; Multi-omics

References

  1. Arjmand B, Hamidpour SK, Tayanloo-Beik A, Goodarzi P, Aghayan HR, Adibi H, Larijani B. Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer. Front Genet. 2022 Jan 27;13:824451. doi: 10.3389/fgene.2022.824451. PMID: 35154283; PMCID: PMC8829119.
  2. Lobato-Delgado B, Priego-Torres B, Sanchez-Morillo D. Combining Molecular, Imaging, and Clinical Data Analysis for Predicting Cancer Prognosis. Cancers (Basel). 2022 Jun 30;14(13):3215. doi: 10.3390/cancers14133215. PMID: 35804988; PMCID: PMC9265023.
  3. Vahabi N, Michailidis G. Unsupervised Multi-Omics Data Integration Methods: A Comprehensive Review. Front Genet. 2022 Mar 22;13:854752. doi: 10.3389/fgene.2022.854752. PMID: 35391796; PMCID: PMC8981526.
  4. Tsimenidis S, Vrochidou E, Papakostas GA. Omics Data and Data Representations for Deep Learning-Based Predictive Modeling. Int J Mol Sci. 2022 Oct 14;23(20):12272. doi: 10.3390/ijms232012272. PMID: 36293133; PMCID: PMC9603455.
  5. Ding DY, Li S, Narasimhan B, Tibshirani R. Cooperative learning for multiview analysis. Proc Natl Acad Sci U S A. 2022 Sep 20;119(38):e2202113119. doi: 10.1073/pnas.2202113119. Epub 2022 Sep 12. PMID: 36095183; PMCID: PMC9499553.
  6. Benkirane H, Pradat Y, Michiels S, Cournède PH. CustOmics: A versatile deep-learning based strategy for multi-omics integration. PLoS Comput Biol. 2023 Mar 6;19(3):e1010921. doi: 10.1371/journal.pcbi.1010921. PMID: 36877736; PMCID: PMC10019780.
  7. Uno H, Cai T, Pencina MJ, D'Agostino RB, Wei LJ. On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Stat Med. 2011 May 10;30(10):1105-17. doi: 10.1002/sim.4154. Epub 2011 Jan 13. PMID: 21484848; PMCID: PMC3079915.
  8. Sokolova M, Lapalme G. A systematic analysis of performance measures for classification tasks. Information Processing & Management. 2009; 45:427-437. doi:https://doi.org/10.1016/j.ipm.2009.03.002
  9. Choi SR, Lee M. Estimating the Prognosis of Low-Grade Glioma with Gene Attention Using Multi-Omics and Multi-Modal Schemes. Biology (Basel). 2022 Oct 5;11(10):1462. doi: 10.3390/biology11101462. PMID: 36290366; PMCID: PMC9598836.
  10. Pan X, Burgman B, Wu E, Huang JH, Sahni N, Stephen Yi S. i-Modern: Integrated multi-omics network model identifies potential therapeutic targets in glioma by deep learning with interpretability. Comput Struct Biotechnol J. 2022 Jun 30;20:3511-3521. doi: 10.1016/j.csbj.2022.06.058. PMID: 35860408; PMCID: PMC9284388.
  11. Tian J, Zhu M, Ren Z, Zhao Q, Wang P, He CK, Zhang M, Peng X, Wu B, Feng R, Fu M. Deep learning algorithm reveals two prognostic subtypes in patients with gliomas. BMC Bioinformatics. 2022 Oct 11;23(1):417. doi: 10.1186/s12859-022-04970-x. PMID: 36221066; PMCID: PMC9552440.
  12. Hu J, Yu W, Dai Y, Liu C, Wang Y, Wu Q. A Deep Neural Network for Gastric Cancer Prognosis Prediction Based on Biological Information Pathways. J Oncol. 2022 Sep 9;2022:2965166. doi: 10.1155/2022/2965166. PMID: 36117847; PMCID: PMC9481367.
  13. Xu J, Yao Y, Xu B, Li Y, Su Z. Unsupervised learning of cross-modal mappings in multi-omics data for survival stratification of gastric cancer. Future Oncol. 2022 Jan;18(2):215-230. doi: 10.2217/fon-2021-1059. Epub 2021 Dec 2. PMID: 34854737.
  14. Chen S, Zang Y, Xu B, Lu B, Ma R, Miao P, Chen B. An Unsupervised Deep Learning-Based Model Using Multiomics Data to Predict Prognosis of Patients with Stomach Adenocarcinoma. Comput Math Methods Med. 2022 Oct 27;2022:5844846. doi: 10.1155/2022/5844846. PMID: 36339684; PMCID: PMC9633210.
  15. Wei Z, Han D, Zhang C, Wang S, Liu J, Chao F, Song Z, Chen G. Deep Learning-Based Multi-Omics Integration Robustly Predicts Relapse in Prostate Cancer. Front Oncol. 2022 Jun 23;12:893424. doi: 10.3389/fonc.2022.893424. PMID: 35814412; PMCID: PMC9259796.
  16. Wang C, Lue W, Kaalia R, Kumar P, Rajapakse JC. Network-based integration of multi-omics data for clinical outcome prediction in neuroblastoma. Sci Rep. 2022 Sep 14;12(1):15425. doi: 10.1038/s41598-022-19019-5. PMID: 36104347; PMCID: PMC9475034.
  17. Ju J, Wismans LV, Mustafa DAM, Reinders MJT, van Eijck CHJ, Stubbs AP, Li Y. Robust deep learning model for prognostic stratification of pancreatic ductal adenocarcinoma patients. iScience. 2021 Nov 10;24(12):103415. doi: 10.1016/j.isci.2021.103415. PMID: 34901786; PMCID: PMC8637475.
  18. Hira MT, Razzaque MA, Angione C, Scrivens J, Sawan S, Sarker M. Integrated multi-omics analysis of ovarian cancer using variational autoencoders. Sci Rep. 2021 Mar 18;11(1):6265. doi: 10.1038/s41598-021-85285-4. Erratum in: Sci Rep. 2021 Aug 11;11(1):16671. PMID: 33737557; PMCID: PMC7973750.
  19. Mathema VB, Sen P, Lamichhane S, Orešič M, Khoomrung S. Deep learning facilitates multi-data type analysis and predictive biomarker discovery in cancer precision medicine. Comput Struct Biotechnol J. 2023 Jan 31;21:1372-1382. doi: 10.1016/j.csbj.2023.01.043. PMID: 36817954; PMCID: PMC9929204.
  20. Nicora G, Vitali F, Dagliati A, Geifman N, Bellazzi R. Integrated Multi-Omics Analyses in Oncology: A Review of Machine Learning Methods and Tools. Front Oncol. 2020 Jun 30;10:1030. doi: 10.3389/fonc.2020.01030. PMID: 32695678; PMCID: PMC7338582.

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