Lanza, Ben and Parashar, D. (2021) Do support vector machines play a role in stratifying patient population based on cancer biomarkers? Archives of Proteomics and Bioinformatics, 2 (1). pp. 20-38. ISSN 2767-391X.

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Abstract

Biomarkers are known to be the key driver behind targeted cancer therapies by either stratifying the patients into risk categories or identifying patient subgroups most likely to benefit. However, the ability of a biomarker to stratify patients relies heavily on the type of clinical endpoint data being collected. Of particular interest is the scenario when the biomarker involved is a continuous one where the challenge is often to identify cut-offs or thresholds that would stratify the population according to the level of clinical outcome or treatment benefit. On the other hand, there are well-established Machine Learning (ML) methods such as the Support Vector Machines (SVM) that classify data, both linear as well as non-linear, into subgroups in an optimal way. SVMs have proven to be immensely useful in data-centric engineering and recently researchers have also sought its applications in healthcare. Despite their wide applicability, SVMs are not yet in the mainstream of toolkits to be utilised in observational clinical studies or in clinical trials. This research investigates the very role of SVMs in stratifying the patient population based on a continuous biomarker across a variety of datasets. Based on the mathematical framework underlying SVMs, we formulate and fit algorithms in the context of biomarker stratified cancer datasets to evaluate their merits. The analysis reveals their superior performance for certain data-types when compared to other ML methods suggesting that SVMs may have the potential to provide a robust yet simplistic solution to stratify real cancer patients based on continuous biomarkers, and hence accelerate the identification of subgroups for improved clinical outcomes or guide targeted cancer therapies.

Item Type:	Journal Article
Subjects:	Q Science > QR Microbiology R Medicine > RC Internal medicine R Medicine > RZ Other systems of medicine
Divisions:	Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School > Health Sciences > Statistics and Epidemiology Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School
Library of Congress Subject Headings (LCSH):	Tumor markers, Cancer -- Treatment, Biochemical markers -- Diagnostic use -- Data processing, Machine learning , Support vector machines
Journal or Publication Title:	Archives of Proteomics and Bioinformatics
Publisher:	Scientific Archives
ISSN:	2767-391X
Official Date:	2021
Dates:	Date Event 2021 Published 27 July 2021 Accepted
Volume:	2
Number:	1
Page Range:	pp. 20-38
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Open Access (Creative Commons)
Date of first compliant deposit:	18 October 2021
Date of first compliant Open Access:	18 October 2021
RIOXX Funder/Project Grant:	Project/Grant ID RIOXX Funder Name Funder ID MR/R015910/1 Medical Research Council http://dx.doi.org/10.13039/501100000265
Related URLs:	Publisher
Open Access Version:	bioRxiv

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