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

Autor:	Chen J; Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR., Rebibo D; Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA., 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., Mok SY; Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR., 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., Tseng PC; Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR., Zhang Z; Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR., 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.
Jazyk:	angličtina
Zdroj:	NAR cancer [NAR Cancer] 2023 Mar 03; Vol. 5 (1), pp. zcad012. Date of Electronic Publication: 2023 Mar 03 (Print Publication: 2023).
DOI:	10.1093/narcan/zcad012
Abstrakt:	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. (© The Author(s) 2023. Published by Oxford University Press on behalf of NAR Cancer.)
Databáze:	MEDLINE
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