Academic Journal
Strategies for improving the performance of prediction models for response to immune checkpoint blockade therapy in cancer
| Title: | Strategies for improving the performance of prediction models for response to immune checkpoint blockade therapy in cancer |
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| Authors: | Tiantian Zeng, Jason Z. Zhang, Arnold Stromberg, Jin Chen, Chi Wang |
| Source: | BMC Research Notes, Vol 17, Iss 1, Pp 1-9 (2024) |
| Publisher Information: | BMC, 2024. |
| Publication Year: | 2024 |
| Collection: | LCC:Medicine LCC:Biology (General) LCC:Science (General) |
| Subject Terms: | Immune checkpoint blockade (ICB) therapy, RNA sequencing, Predictive model, Machine learning, Medicine, Biology (General), QH301-705.5, Science (General), Q1-390 |
| Description: | 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 valuable to develop gene expression signatures based on RNA sequencing (RNAseq) data and machine learning methods 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 predictive accuracies. Our finding suggests that these two strategies are of high value to identify ICB response biomarkers in future studies. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 1756-0500 |
| Relation: | https://doaj.org/toc/1756-0500 |
| DOI: | 10.1186/s13104-024-06760-5 |
| Access URL: | https://doaj.org/article/ab91d270b49f449bb79c0cd6b5f9a5c6 |
| Accession Number: | edsdoj.b91d270b49f449bb79c0cd6b5f9a5c6 |
| Database: | Directory of Open Access Journals |
| ISSN: | 17560500 |
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| DOI: | 10.1186/s13104-024-06760-5 |