Abstract
Immune checkpoint blockade (ICB) therapy holds promise for bringing long-lasting clinical gains for the treatment of cancer. However, studies show that only a fraction of patients respond to the treatment. In this regard, it is valu- able to develop gene expression signatures based on RNA sequencing (RNAseq) data and machine learning meth- ods to predict a patient’s response to the ICB therapy, which contributes to more personalized treatment strategy and better management of cancer patients. However, due to the limited sample size of ICB trials with RNAseq data available and the vast number of candidate gene expression features, it is challenging to develop well-performed gene expression signatures. In this study, we used several published melanoma datasets and investigated approaches that can improve the construction of gene expression-based prediction models. We found that merging datasets from multiple studies and incorporating prior biological knowledge yielded prediction models with higher predic- tive accuracies. Our finding suggests that these two strategies are of high value to identify ICB response biomarkers in future studies.
Document Type
Article
Publication Date
2024
Digital Object Identifier (DOI)
https://doi.org/10.1186/s13104-024-06760-5
Funding Information
This work is supported by the Biostatistics and Bioinformatics Shared Resource Facility of the University of Kentucky Markey Cancer Center [P30CA177558]. The Van Allen et al. dataset downloaded from dbGaP was supported by the National Human Genome Research Institute (NHGRI) Large Scale Sequencing Program, Grant U54 HG003067 to the Broad Institute (PI, Lander).
Repository Citation
Zeng, Tiantian; Zhang, Jason Z.; Stromberg, Arnold; Chen, Jin; and Wang, Chi, "Strategies for improving the performance of prediction models for response to immune checkpoint blockade therapy in cancer" (2024). Markey Cancer Center Faculty Publications. 260.
https://uknowledge.uky.edu/markey_facpub/260
Notes/Citation Information
© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativeco mmons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.