Computational immunogenomic approaches to predict response to cancer immunotherapies.


Journal

Nature reviews. Clinical oncology
ISSN: 1759-4782
Titre abrégé: Nat Rev Clin Oncol
Pays: England
ID NLM: 101500077

Informations de publication

Date de publication:
31 Oct 2023
Historique:
accepted: 03 10 2023
medline: 1 11 2023
pubmed: 1 11 2023
entrez: 1 11 2023
Statut: aheadofprint

Résumé

Cancer immunogenomics is an emerging field that bridges genomics and immunology. The establishment of large-scale genomic collaborative efforts along with the development of new single-cell transcriptomic techniques and multi-omics approaches have enabled characterization of the mutational and transcriptional profiles of many cancer types and helped to identify clinically actionable alterations as well as predictive and prognostic biomarkers. Researchers have developed computational approaches and machine learning algorithms to accurately obtain clinically useful information from genomic and transcriptomic sequencing data from bulk tissue or single cells and explore tumours and their microenvironment. The rapid growth in sequencing and computational approaches has resulted in the unmet need to understand their true potential and limitations in enabling improvements in the management of patients with cancer who are receiving immunotherapies. In this Review, we describe the computational approaches currently available to analyse bulk tissue and single-cell sequencing data from cancer, stromal and immune cells, as well as how best to select the most appropriate tool to address various clinical questions and, ultimately, improve patient outcomes.

Identifiants

pubmed: 37907723
doi: 10.1038/s41571-023-00830-6
pii: 10.1038/s41571-023-00830-6
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© 2023. Springer Nature Limited.

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Auteurs

Venkateswar Addala (V)

Cancer Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia. venkateswar.addala@qimrberghofer.edu.au.
Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia. venkateswar.addala@qimrberghofer.edu.au.

Felicity Newell (F)

Cancer Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.

John V Pearson (JV)

Cancer Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.

Alec Redwood (A)

National Centre for Asbestos Related Diseases, University of Western Australia, Perth, Western Australia, Australia.
Institute of Respiratory Health, Perth, Western Australia, Australia.
School of Biomedical Science, University of Western Australia, Perth, Western Australia, Australia.

Bruce W Robinson (BW)

National Centre for Asbestos Related Diseases, University of Western Australia, Perth, Western Australia, Australia.
Institute of Respiratory Health, Perth, Western Australia, Australia.
Department of Respiratory Medicine, Sir Charles Gairdner Hospital, Perth, Western Australia, Australia.
Medical School, University of Western Australia, Perth, Western Australia, Australia.

Jenette Creaney (J)

National Centre for Asbestos Related Diseases, University of Western Australia, Perth, Western Australia, Australia.
Institute of Respiratory Health, Perth, Western Australia, Australia.
School of Biomedical Science, University of Western Australia, Perth, Western Australia, Australia.
Department of Respiratory Medicine, Sir Charles Gairdner Hospital, Perth, Western Australia, Australia.

Nicola Waddell (N)

Cancer Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia. nic.waddell@qimrberghofer.edu.au.
Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia. nic.waddell@qimrberghofer.edu.au.

Classifications MeSH