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. 2023 Mar 3;5(1):zcad012.
doi: 10.1093/narcan/zcad012. eCollection 2023 Mar.

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

Jiamin Chen  1 , Daniel Rebibo  2 , Jianquan Cao  1   3 , Simon Yat-Man Mok  1 , Neel Patel  1   4 , Po-Cheng Tseng  1 , Zhenghao Zhang  1 , Kevin Y Yip  1   2
Affiliations

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

Jiamin Chen et al. NAR Cancer. .

Abstract

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.

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Figures

Graphical Abstract
Graphical Abstract
ICI efficacy information portal provides centralized access to latest research on efficacy of cancer immune checkpoint inhibition therapies, with a manually curated database of publications, biomarkers/prediction models, and datasets.
Figure 1.
Figure 1.
Display of information on our web portal. (A) Data summary and entry points of the three main sections, for publications, predictors and datasets, respectively. (B) Overview lists of publications (upper left), predictors (upper right), and datasets (lower left), and the filter function (lower right). In the filtering example, only publications that propose a predictor for liver cancers are shown, in reverse chronological order of publication year.
Figure 2.
Figure 2.
Extraction of information from the publication of the EaSIeR predictor. (A) The extraction workflow for the predictor page. Red dashed lines correspond to the publication subsection(s) in which the information is derived. (B) The extraction workflow for the citation information page.
Figure 3.
Figure 3.
Extraction of information from the publication of the Radiomics-based model for glioblastoma.
Figure 4.
Figure 4.
Extraction of information from the publication of the exosomal PD-L1 predictor.
Figure 5.
Figure 5.
Extraction of information from the publication of the Van Allen et al. dataset.
See this image and copyright information in PMC

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