Innovations in biomarker stratification for precision oncology

Abstract

Biomarker 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.

Metadata

Item Type:	Article
Authors/Creators:	Naeemaee, R. Harris, K. Cross, N. Griffin, J. https://orcid.org/0000-0003-3969-7637 Quayle, L.A. https://orcid.org/0000-0002-6394-4186
Copyright, Publisher and Additional Information:	© The Author(s) 2026. 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/.
Keywords:	Cancer prognosis; Clinical validation; Data-driven methods; Machine learning; Predictive biomarkers; Prognostic modelling
Dates:	Submitted: 10 February 2026 Accepted: 1 April 2026 Published (online): 28 April 2026 Published: 28 April 2026
Institution:	The University of Sheffield
Academic Units:	The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Medicine and Population Health
Date Deposited:	29 Jun 2026 14:52
Last Modified:	29 Jun 2026 14:52
Status:	Published
Publisher:	Springer Science and Business Media LLC
Refereed:	Yes
Identification Number:	10.1007/s10238-026-02150-2
Related URLs:	PubMed URL
Sustainable Development Goals:
Open Archives Initiative ID (OAI ID):	oai:eprints.whiterose.ac.uk:242637

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Innovations in biomarker stratification for precision oncology

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