» Articles » PMID: 36304921

Multi-omics Analysis: Paving the Path Toward Achieving Precision Medicine in Cancer Treatment and Immuno-oncology

Overview
Specialty Biology
Date 2022 Oct 28
PMID 36304921
Authors
Affiliations
Soon will be listed here.
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.

Citing Articles

Multi-omics approaches for understanding gene-environment interactions in noncommunicable diseases: techniques, translation, and equity issues.

Alemu R, Sharew N, Arsano Y, Ahmed M, Tekola-Ayele F, Mersha T Hum Genomics. 2025; 19(1):8.

PMID: 39891174 PMC: 11786457. DOI: 10.1186/s40246-025-00718-9.


Advancing precision cancer immunotherapy drug development, administration, and response prediction with AI-enabled Raman spectroscopy.

Chadokiya J, Chang K, Sharma S, Hu J, Lill J, Dionne J Front Immunol. 2025; 15():1520860.

PMID: 39850874 PMC: 11753970. DOI: 10.3389/fimmu.2024.1520860.


Contemporary Update on Clinical and Experimental Prostate Cancer Biomarkers: A Multi-Omics-Focused Approach to Detection and Risk Stratification.

Hachem S, Yehya A, El Masri J, Mavingire N, Johnson J, Dwead A Biology (Basel). 2024; 13(10).

PMID: 39452071 PMC: 11504278. DOI: 10.3390/biology13100762.


Integrative Analysis of Multi-Omics Data to Identify Deregulated Molecular Pathways and Druggable Targets in Chronic Lymphocytic Leukemia.

Mavridou D, Psatha K, Aivaliotis M J Pers Med. 2024; 14(8).

PMID: 39202022 PMC: 11355716. DOI: 10.3390/jpm14080831.


Applications of Multimodal Artificial Intelligence in Non-Hodgkin Lymphoma B Cells.

Isavand P, Aghamiri S, Amin R Biomedicines. 2024; 12(8).

PMID: 39200217 PMC: 11351272. DOI: 10.3390/biomedicines12081753.


References
1.
Newman A, Steen C, Liu C, Gentles A, Chaudhuri A, Scherer F . Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat Biotechnol. 2019; 37(7):773-782. PMC: 6610714. DOI: 10.1038/s41587-019-0114-2. View

2.
Zhao Y, Gao Y, Xu X, Zhou J, Wang H . Multi-omics analysis of genomics, epigenomics and transcriptomics for molecular subtypes and core genes for lung adenocarcinoma. BMC Cancer. 2021; 21(1):257. PMC: 7942004. DOI: 10.1186/s12885-021-07888-4. View

3.
Pucher B, Zeleznik O, Thallinger G . Comparison and evaluation of integrative methods for the analysis of multilevel omics data: a study based on simulated and experimental cancer data. Brief Bioinform. 2018; 20(2):671-681. DOI: 10.1093/bib/bby027. View

4.
Chu J, Sun N, Hu W, Chen X, Yi N, Shen Y . The Application of Bayesian Methods in Cancer Prognosis and Prediction. Cancer Genomics Proteomics. 2021; 19(1):1-11. PMC: 8717957. DOI: 10.21873/cgp.20298. View

5.
Derosa L, Routy B, Thomas A, Iebba V, Zalcman G, Friard S . Intestinal Akkermansia muciniphila predicts clinical response to PD-1 blockade in patients with advanced non-small-cell lung cancer. Nat Med. 2022; 28(2):315-324. PMC: 9330544. DOI: 10.1038/s41591-021-01655-5. View