AI Article Synopsis
- - The study challenges traditional methods of evaluating potential clinical biomarkers like tumor mutational burden (TMB), which typically use a single cutoff to categorize patients, and highlights the limitations of this approach when relationships are non-monotonic.
- - Researchers propose a two-cutoff method and the application of neural networks to accurately represent complex relationships between TMB and patient outcomes.
- - Findings indicate that while TMB often shows a simple relationship with survival, there are exceptions, emphasizing the need for more flexible models in biomarker analysis.
Article Abstract
Unlabelled: Potential clinical biomarkers are often assessed with Cox regressions or their ability to differentiate two groups of patients based on a single cutoff. However, both of these approaches assume a monotonic relationship between the potential biomarker and survival. Tumor mutational burden (TMB) is currently being studied as a predictive biomarker for immunotherapy, and a single cutoff is often used to divide patients. In this study, we introduce a two-cutoff approach that allows splitting of patients when a non-monotonic relationship is present and explore the use of neural networks to model more complex relationships of TMB to outcome data. Using real-world data, we find that while in most cases the true relationship between TMB and survival appears monotonic, that is not always the case and researchers should be made aware of this possibility.
Significance: When a non-monotonic relationship to survival is present it is not possible to divide patients by a single value of a predictor. Neural networks allow for complex transformations and can be used to correctly split patients when a non-monotonic relationship is present.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11229404 | PMC |
| http://dx.doi.org/10.1158/2767-9764.CRC-24-0061 | DOI Listing |
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