IMPACT: a web server for exploring immunotherapeutic predictive and cancer prognostic biomarkers
Liu, Y.; Zhang, Y.; Xie, W.; Zhao, J.; Dong, Y.; Xu, C.; Wang, Y.; Li, M.; Wang, G.; Zhu, X.; Wang, W.; Lin, K.; Lu, H.; Han, Y.; Li, L.; Duan, J.; Cai, S.; Wang, J.; Wang, Z.
Show abstract
Immune checkpoint inhibitors (ICIs) are a breakthrough in oncology treatment, and studies of screening predictive biomarkers of ICIs are emerging. We developed a web server named IMPACT (http://impact.brbiotech.com/) to thoroughly explore immunotherapeutic predictive or prognostic biomarkers. IMPACT contains a large dataset of 6,276 patients treated with ICIs and integrates 11 well-designed function modules, enabling an in-depth solution for biomarkers exploration. Compared with the existing tools, IMPACT was implemented with one exclusive module for interaction analysis and several optimized conventional functions for discovering novel biomarkers. Specifically, the interaction analysis of biomarker-treatment effect is essential to determine whether a biomarker is predictive and/or prognostic for ICIs. Moreover, several optimized functions allow complicated biomarker exploration, including customized selections of variant types in more detail, automatically screening meaningful co-mutations among multiple genes, and selecting cut-off values for gene expression biomarkers. In summary, IMPACT is a comprehensive analysis resource to facilitate biomarker research of ICIs.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Definition of a new blood cell count (BCT) score for early survival prediction for non-small cell lung cancer patients treated with atezolizumab: Integrated analysis of 4 multicenter clinical trials 95%
- Profiling tumor immune microenvironment of non-small cell lung cancer using multiplex immunofluorescence 95%
- Dissected subgroups predict the risk of recurrence of stage II colorectal cancer and select rational treatment 94%
Similar papers in this journal
- Mime: A flexible machine-learning framework to construct and visualize models for clinical characteristics prediction and feature selection 95%
- Finding new cancer epigenetic and genetic biomarkers from cell-free DNA by combining SALP-seq and machine learning:esophageal cancer as an example 94%
- Comparative Analysis of Pathology Foundation Models for Automated Detection of Tertiary Lymphoid Structures in H&E-Stained Digital Pathology Images 90%
Similar papers in this journal
- Risk assessment of cancer patients based on HLA-I alleles, neobinders and expression of cytokines 93%
- A Comprehensive Targeted Panel of 295 Genes: Unveiling Key Disease Initiating and Transformative Biomarkers in MultipleMyeloma 92%
- Development of an absolute assignment predictor for triple-negative breast cancer subtyping using machine learning approaches 90%
Similar papers in this journal
- Genomic Insights Guiding Personalized First-Line Immunotherapy Response in Lung and Bladder Tumors 93%
- Three Conserved Immune Dysfunction and Exclusion Subtypes in Bladder and Pan-cancers: Prognostic and Immunotherapeutic Significance 93%
- A Community Challenge to Predict Clinical Outcomes After Immune Checkpoint Blockade in Non-Small Cell Lung Cancer 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.