Academic Journal
Strategies for improving the performance of prediction models for response to immune checkpoint blockade therapy in cancer
| Τίτλος: | Strategies for improving the performance of prediction models for response to immune checkpoint blockade therapy in cancer |
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| Συγγραφείς: | Tiantian Zeng, Jason Z. Zhang, Arnold Stromberg, Jin Chen, Chi Wang |
| Πηγή: | BMC Research Notes, Vol 17, Iss 1, Pp 1-9 (2024) |
| Στοιχεία εκδότη: | BMC, 2024. |
| Έτος έκδοσης: | 2024 |
| Συλλογή: | LCC:Medicine LCC:Biology (General) LCC:Science (General) |
| Θεματικοί όροι: | Immune checkpoint blockade (ICB) therapy, RNA sequencing, Predictive model, Machine learning, Medicine, Biology (General), QH301-705.5, Science (General), Q1-390 |
| Περιγραφή: | 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. |
| Τύπος εγγράφου: | article |
| Περιγραφή αρχείου: | electronic resource |
| Γλώσσα: | English |
| ISSN: | 1756-0500 |
| Relation: | https://doaj.org/toc/1756-0500 |
| DOI: | 10.1186/s13104-024-06760-5 |
| Σύνδεσμος πρόσβασης: | https://doaj.org/article/ab91d270b49f449bb79c0cd6b5f9a5c6 |
| Αριθμός Καταχώρησης: | edsdoj.b91d270b49f449bb79c0cd6b5f9a5c6 |
| Βάση Δεδομένων: | Directory of Open Access Journals |
| ISSN: | 17560500 |
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| DOI: | 10.1186/s13104-024-06760-5 |