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ICI efficacy information portal: a knowledgebase for responder prediction to immune checkpoint inhibitors
Jiamin Chen1, Daniel Rebibo2, Jianquan Cao1,3
1Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR.
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.
Insights
Predicting immune checkpoint inhibitor (ICI) efficacy is challenging due to varied responses. This resource centralizes information on ICI efficacy predictors and datasets, aiding researchers in tracking and comparing findings.
Area of Science:
- Immunology
- Oncology
- Bioinformatics
Background:
- Immune checkpoint inhibitors (ICIs) offer durable cancer responses, but efficacy varies widely.
- Predicting patient response to ICIs is crucial for treatment stratification.
- Existing research on ICI efficacy predictors is fragmented, hindering comparison and accessibility.
Purpose of the Study:
- To develop a centralized knowledgebase and web portal for ICI efficacy information.
- To facilitate access to and comparison of data on ICI efficacy predictors and datasets.
- To support researchers in navigating the growing body of literature on ICI efficacy.
Main Methods:
- Systematic recording of publications on ICI efficacy, proposed predictors, and validation datasets.
- Manual curation of all recorded information for accuracy.
- Development of a web portal with search, filter, and sort functionalities.
Main Results:
- A comprehensive knowledgebase of ICI efficacy research has been established.
- The web portal provides summarized method details and predictor evaluation results.
- Centralized access to information on ICI efficacy, predictors, and datasets is now available.
Conclusions:
- The developed knowledgebase and portal offer a valuable resource for the research community.
- This tool aids in understanding and comparing the efficacy of immune checkpoint inhibitors.
- It supports the advancement of personalized cancer therapy through improved prediction of treatment response.
