Predicting patient outcomes after treatment with immune checkpoint blockade: A review of biomarkers derived from diverse data modalities.

Journal

Cell genomics
ISSN: 2666-979X
Titre abrégé: Cell Genom
Pays: United States
ID NLM: 9918284260106676

Informations de publication

Date de publication:
16 Nov 2023
Historique:
received: 07 02 2023
revised: 12 07 2023
accepted: 24 10 2023
medline: 8 1 2024
pubmed: 8 1 2024
entrez: 8 1 2024
Statut: aheadofprint

Résumé

Immune checkpoint blockade (ICB) therapy targeting cytotoxic T-lymphocyte-associated protein 4, programmed death 1, and programmed death ligand 1 has shown durable remission and clinical success across different cancer types. However, patient outcomes vary among disease indications. Studies have identified prognostic biomarkers associated with immunotherapy response and patient outcomes derived from diverse data types, including next-generation bulk and single-cell DNA, RNA, T cell and B cell receptor sequencing data, liquid biopsies, and clinical imaging. Owing to inter- and intra-tumor heterogeneity and the immune system's complexity, these biomarkers have diverse efficacy in clinical trials of ICB. Here, we review the genetic and genomic signatures and image features of ICB studies for pan-cancer applications and specific indications. We discuss the advantages and disadvantages of computational approaches for predicting immunotherapy effectiveness and patient outcomes. We also elucidate the challenges of immunotherapy prognostication and the discovery of novel immunotherapy targets.

Identifiants

DOI: 10.1016/j.xgen.2023.100444 PMID: 38190106
pubmed: 38190106
pii: S2666-979X(23)00279-3
doi: 10.1016/j.xgen.2023.100444
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

100444

Informations de copyright

Copyright © 2023. Published by Elsevier Inc.

Déclaration de conflit d'intérêts

Declaration of interests C.J. Wu holds equity in BioNTech, Inc., and receives research support from Pharmacyclics. F.M. is a co-founder of and has equity in Harbinger Health, has equity in Zephyr AI, and serves as a consultant for both companies. She is also on the board of directors of Exscientia Plc. F.M. declares that none of these relationships are directly or indirectly related to the content of this manuscript.

Auteurs

Yang Liu (Y)

Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02115, USA.

Jennifer Altreuter (J)

Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02115, USA.

Sudheshna Bodapati (S)

Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02115, USA.

Simona Cristea (S)

Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02115, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.

Cheryl J Wong (CJ)

Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02115, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA 20115, USA.

Catherine J Wu (CJ)

Harvard Medical School, Boston, MA 02115, USA; The Eli and Edythe Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02115, USA.

Franziska Michor (F)

Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02115, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA 20115, USA; The Eli and Edythe Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA; Center for Cancer Evolution, Dana-Farber Cancer Institute, Boston, MA 02138, USA; The Ludwig Center at Harvard, Boston, MA 02115, USA. Electronic address: michor@jimmy.harvard.edu.

Classifications MeSH