ICI efficacy information portal: a knowledgebase for responder prediction to immune checkpoint inhibitors.


Journal

NAR cancer
ISSN: 2632-8674
Titre abrégé: NAR Cancer
Pays: England
ID NLM: 101769553

Informations de publication

Date de publication:
Mar 2023
Historique:
received: 01 10 2022
revised: 20 12 2022
accepted: 11 02 2023
entrez: 7 3 2023
pubmed: 8 3 2023
medline: 8 3 2023
Statut: epublish

Résumé

Immune checkpoint inhibitors (ICIs) have led to durable responses in cancer patients, yet their efficacy varies significantly across cancer types and patients. To stratify patients based on their potential clinical benefits, there have been substantial research efforts in identifying biomarkers and computational models that can predict the efficacy of ICIs, and it has become difficult to keep track of all of them. It is also difficult to compare findings of different studies since they involve different cancer types, ICIs, and various other details. To make it easy to access the latest information about ICI efficacy, we have developed a knowledgebase and a corresponding web-based portal (https://iciefficacy.org/). Our knowledgebase systematically records information about latest publications related to ICI efficacy, predictors proposed, and datasets used to test them. All information recorded is checked carefully by a manual curation process. The web-based portal provides functions to browse, search, filter, and sort the information. Digests of method details are provided based on the original descriptions in the publications. Evaluation results of the effectiveness of the predictors reported in the publications are summarized for quick overviews. Overall, our resource provides centralized access to the burst of information produced by the vibrant research on ICI efficacy.

Identifiants

pubmed: 36879684
doi: 10.1093/narcan/zcad012
pii: zcad012
pmc: PMC9984987
doi:

Types de publication

Journal Article

Langues

eng

Pagination

zcad012

Subventions

Organisme : NCI NIH HHS
ID : P30 CA030199
Pays : United States
Organisme : NIA NIH HHS
ID : R21 AG075483
Pays : United States

Informations de copyright

© The Author(s) 2023. Published by Oxford University Press on behalf of NAR Cancer.

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Auteurs

Jiamin Chen (J)

Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR.

Daniel Rebibo (D)

Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA.

Jianquan Cao (J)

Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR.
School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR.

Simon Yat-Man Mok (SY)

Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR.

Neel Patel (N)

Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR.
Department of Mathematics, Indian Institute of Technology, Hauz Khas, New Delhi, India.

Po-Cheng Tseng (PC)

Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR.

Zhenghao Zhang (Z)

Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR.

Kevin Y Yip (KY)

Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR.
Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA.

Classifications MeSH