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It examines how these approaches address heterogeneity, confounding, and overfitting, and assesses strengths and limitations for clinically actionable risk stratification. Evidence synthesis supports more robust, reproducible prognostic modelling and outlines future directions to improve biomarker-based decision-making 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26:235  \n[https://doi.org/10.1007/s10238-026-02150-2](https://doi.org/10.1007/s10238-026-02150-2)  \nREVIEW  \nInnovations in biomarker stratification for precision oncology  \nRonak Naeemaee1 · Keith Harris1 · Neil Cross2 · Jon Griffin3,4 · Lewis A. Quayle2,3  \nReceived: 10 February 2026 / Accepted: 1 April 2026 © The Author(s) 2026  \nAbstract  \nBiomarker stratification underpins precision oncology, yet survival analysis often relies on arbitrary thresholds that undermine reproducibility and clinical relevance, particularly for continuous biomarkers. This review focuses on methodological approaches for stratifying continuous biomarkers within survival analysis frameworks, examining conventional strategies alongside data-driven and machine learning methods in the context of threshold selection and clinical interpretability. We evaluate the extent to which these approaches address key challenges including heterogeneity, confounding, and overfitting, and critically appraise their strengths and limitations for clinically actionable risk stratification. By synthesising current evidence, we highlight opportunities for more robust and reproducible prognostic modelling and outline future directions to improve the reliability of biomarker-driven decision-making in oncology.  \nKeywords Predictive biomarkers · Cancer prognosis · Prognostic modelling · Machine learning · Data-driven methods · Clinical validation  \nIntroduction  \nPrecision oncology is built on the principle that treatments should be tailored to the biological and clinical characteristics of individual patients. Central to this approach is the identification of reliable biomarkers that can stratify patients into clinically meaningful groups, enabling improved outcome prediction and more effective treatment selection. In oncology, survival analysis provides the statistical foundation for evaluating how biomarkers relate to outcomes such as overall survival, disease-specific survival, progressionfree survival, or distant metastasis–free survival [1] . By accounting for censored data and modelling risk over time, survival analysis has become a cornerstone of biomarker  \n􀀍 Lewis A. Quayle [l.quayle@shu.ac.uk](l.quayle@shu.ac.uk)  \n1 School of Computing and Digital Technologies, Sheffield Hallam University, Sheffield, UK  \n2 School of Biosciences and Chemistry, Sheffield Hallam University, Sheffield, UK  \n3 Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield, UK  \n4 Department of Histopathology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK  \nresearch [2] . The Kaplan-Meier estimator is widely used to compare survival between categorical groups (e.g., high versus low expression/abundance), while the Cox proportional hazards model can incorporate continuous covariates directly - though both approaches often hinge on careful threshold selection for continuous biomarkers.  \nDespite its central role in precision oncology, survival analysis faces well-recognised challenges when applied to biomarker stratification. Traditional methods are well suited to discrete variables such as tumour stage or mutation status, where natural categories exist. However, an increasing number of biomarkers - particularly those generated by high-throughput technologies such as next-generation sequencing or mass spectrometry-are continuous in nature. Unlike discrete biomarkers, continuous variables lack obvious thresholds for defining clinically relevant subgroups. To overcome this, researchers often rely on arbitrary cut points, such as splitting a cohort at the median value or dividing it into tertiles or quartiles. Although simple and easy to apply, these strategies risk oversimplifying complex biological relationships and ultimately limiting clinical translation [3, 4] .  \nThe use of arbitrary cut points has several important limitations. These include obscuring non-linear associations bet","cbCaihplFlCeRQuK","https://ap.wps.com/l/cbCaihplFlCeRQuK","pdf",1086145,"English","# Introduction\n## Stratification in precision oncology and the role of survival analysis\n## Challenges of thresholding continuous biomarkers\n## Limitations of arbitrary cut points\n## Need for robust, reproducible biomarker stratification approaches","[{\"question\":\"Why is biomarker stratification important in precision oncology?\",\"answer\":\"It enables patients to be grouped into clinically meaningful categories, improving outcome prediction and informing more effective treatment selection.\"},{\"question\":\"What challenges arise when using survival analysis for continuous biomarkers?\",\"answer\":\"Continuous biomarkers lack natural clinical thresholds, so analyses often use arbitrary cut points, which can obscure non-linear relationships, reduce statistical power, and increase false findings.\"},{\"question\":\"How do researchers improve threshold selection and clinical interpretability?\",\"answer\":\"The review highlights approaches that derive thresholds from large well-annotated cohorts and test them in independent validation datasets, alongside data-driven and machine learning methods that better capture complexity.\"}]","Innovations in biomarker stratification for precision oncology | PDF",1790103196]