Spatial omics techniques and data analysis for cancer immunotherapy applications

Yue Zhang; Ren Yuan Lee; Chin Wee Tan; Xue Guo; Willa W-Y Yim; Jeffrey Ct Lim; Felicia Yt Wee; W U Yang; Malvika Kharbanda; Jia-Ying J Lee; Nye Thane Ngo; Wei Qiang Leow; Lit-Hsin Loo; Tony Kh Lim; Radoslaw M Sobota; Mai Chan Lau; Melissa J Davis; Joe Yeong

doi:10.1016/j.copbio.2024.103111

Spatial omics techniques and data analysis for cancer immunotherapy applications

Curr Opin Biotechnol. 2024 Jun:87:103111. doi: 10.1016/j.copbio.2024.103111. Epub 2024 Mar 22.

Authors

Yue Zhang¹, Ren Yuan Lee², Chin Wee Tan³, Xue Guo⁴, Willa W-Y Yim⁴, Jeffrey Ct Lim⁴, Felicia Yt Wee⁴, W U Yang⁴, Malvika Kharbanda⁵, Jia-Ying J Lee⁶, Nye Thane Ngo⁷, Wei Qiang Leow⁷, Lit-Hsin Loo⁶, Tony Kh Lim⁷, Radoslaw M Sobota⁴, Mai Chan Lau⁸, Melissa J Davis⁹, Joe Yeong¹⁰

Affiliations

¹ Duke-NUS Medical School, Singapore 169856, Singapore.
² Yong Loo Lin School of Medicine, National University of Singapore, 169856 Singapore; Singapore Thong Chai Medical Institution, Singapore 169874, Singapore.
³ Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia; Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Victoria 3010, Australia; Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, Queensland 4102, Australia.
⁴ Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore.
⁵ Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia; Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Victoria 3010, Australia; immunoGENomics Cancer Institute (SAiGENCI), Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia 5005, Australia.
⁶ Bioinformatics Institute (BII), Agency for Science, Technology and Research (A⁎STAR), Singapore 138671, Singapore.
⁷ Department of Anatomical Pathology, Singapore General Hospital, Singapore 169856, Singapore.
⁸ Bioinformatics Institute (BII), Agency for Science, Technology and Research (A⁎STAR), Singapore 138671, Singapore; Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A⁎STAR), Singapore 138648, Singapore.
⁹ Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia; Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Victoria 3010, Australia; Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, Queensland 4102, Australia; immunoGENomics Cancer Institute (SAiGENCI), Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia 5005, Australia; Department of Clinical Pathology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Victoria 3010, Australia.
¹⁰ Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore; Bioinformatics Institute (BII), Agency for Science, Technology and Research (A⁎STAR), Singapore 138671, Singapore. Electronic address: yeongps@imcb.a-star.edu.sg.

PMID: 38520821
DOI: 10.1016/j.copbio.2024.103111

Abstract

In-depth profiling of cancer cells/tissues is expanding our understanding of the genomic, epigenomic, transcriptomic, and proteomic landscape of cancer. However, the complexity of the cancer microenvironment, particularly its immune regulation, has made it difficult to exploit the potential of cancer immunotherapy. High-throughput spatial omics technologies and analysis pipelines have emerged as powerful tools for tackling this challenge. As a result, a potential revolution in cancer diagnosis, prognosis, and treatment is on the horizon. In this review, we discuss the technological advances in spatial profiling of cancer around and beyond the central dogma to harness the full benefits of immunotherapy. We also discuss the promise and challenges of spatial data analysis and interpretation and provide an outlook for the future.

Publication types

Review

MeSH terms

Data Analysis
Genomics / methods
Humans
Immunotherapy* / methods
Neoplasms* / genetics
Neoplasms* / therapy
Proteomics / methods
Tumor Microenvironment