The Biomarker Toolkit - an evidence-based guideline to predict cancer biomarker success and guide development.

Biomarkers Breast cancer Clinical utility Colorectal cancer Translational research

Journal

BMC medicine
ISSN: 1741-7015
Titre abrégé: BMC Med
Pays: England
ID NLM: 101190723

Informations de publication

Date de publication:
04 10 2023
Historique:
received: 01 03 2023
accepted: 08 09 2023
medline: 2 11 2023
pubmed: 5 10 2023
entrez: 4 10 2023
Statut: epublish

Résumé

An increased number of resources are allocated on cancer biomarker discovery, but very few of these biomarkers are clinically adopted. To bridge the gap between Biomarker discovery and clinical use, we aim to generate the Biomarker Toolkit, a tool designed to identify clinically promising biomarkers and promote successful biomarker translation. All features associated with a clinically useful biomarker were identified using mixed-methodology, including systematic literature search, semi-structured interviews, and an online two-stage Delphi-Survey. Validation of the checklist was achieved by independent systematic literature searches using keywords/subheadings related to clinically and non-clinically utilised breast and colorectal cancer biomarkers. Composite aggregated scores were generated for each selected publication based on the presence/absence of an attribute listed in the Biomarker Toolkit checklist. Systematic literature search identified 129 attributes associated with a clinically useful biomarker. These were grouped in four main categories including: rationale, clinical utility, analytical validity, and clinical validity. This checklist was subsequently developed using semi-structured interviews with biomarker experts (n=34); and 88.23% agreement was achieved regarding the identified attributes, via the Delphi survey (consensus level:75%, n=51). Quantitative validation was completed using clinically and non-clinically implemented breast and colorectal cancer biomarkers. Cox-regression analysis suggested that total score is a significant driver of biomarker success in both cancer types (BC: p>0.0001, 95.0% CI: 0.869-0.935, CRC: p>0.0001, 95.0% CI: 0.918-0.954). This novel study generated a validated checklist with literature-reported attributes linked with successful biomarker implementation. Ultimately, the application of this toolkit can be used to detect biomarkers with the highest clinical potential and shape how biomarker studies are designed/performed.

Sections du résumé

BACKGROUND
An increased number of resources are allocated on cancer biomarker discovery, but very few of these biomarkers are clinically adopted. To bridge the gap between Biomarker discovery and clinical use, we aim to generate the Biomarker Toolkit, a tool designed to identify clinically promising biomarkers and promote successful biomarker translation.
METHODS
All features associated with a clinically useful biomarker were identified using mixed-methodology, including systematic literature search, semi-structured interviews, and an online two-stage Delphi-Survey. Validation of the checklist was achieved by independent systematic literature searches using keywords/subheadings related to clinically and non-clinically utilised breast and colorectal cancer biomarkers. Composite aggregated scores were generated for each selected publication based on the presence/absence of an attribute listed in the Biomarker Toolkit checklist.
RESULTS
Systematic literature search identified 129 attributes associated with a clinically useful biomarker. These were grouped in four main categories including: rationale, clinical utility, analytical validity, and clinical validity. This checklist was subsequently developed using semi-structured interviews with biomarker experts (n=34); and 88.23% agreement was achieved regarding the identified attributes, via the Delphi survey (consensus level:75%, n=51). Quantitative validation was completed using clinically and non-clinically implemented breast and colorectal cancer biomarkers. Cox-regression analysis suggested that total score is a significant driver of biomarker success in both cancer types (BC: p>0.0001, 95.0% CI: 0.869-0.935, CRC: p>0.0001, 95.0% CI: 0.918-0.954).
CONCLUSIONS
This novel study generated a validated checklist with literature-reported attributes linked with successful biomarker implementation. Ultimately, the application of this toolkit can be used to detect biomarkers with the highest clinical potential and shape how biomarker studies are designed/performed.

Identifiants

pubmed: 37794461
doi: 10.1186/s12916-023-03075-3
pii: 10.1186/s12916-023-03075-3
pmc: PMC10552368
doi:

Substances chimiques

Biomarkers, Tumor 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

383

Informations de copyright

© 2023. BioMed Central Ltd., part of Springer Nature.

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Auteurs

Katerina-Vanessa Savva (KV)

Division of Surgery, Department of Surgery and Cancer, Imperial College London, London, UK. k.savva17@imperial.ac.uk.

Michal Kawka (M)

Division of Surgery, Department of Surgery and Cancer, Imperial College London, London, UK.

Bhamini Vadhwana (B)

Division of Surgery, Department of Surgery and Cancer, Imperial College London, London, UK.

Rahul Penumaka (R)

Division of Surgery, Department of Surgery and Cancer, Imperial College London, London, UK.

Imogen Patton (I)

Division of Surgery, Department of Surgery and Cancer, Imperial College London, London, UK.

Komal Khan (K)

Division of Surgery, Department of Surgery and Cancer, Imperial College London, London, UK.

Claire Perrott (C)

Division of Surgery, Department of Surgery and Cancer, Imperial College London, London, UK.

Saranya Das (S)

Division of Surgery, Department of Surgery and Cancer, Imperial College London, London, UK.

Maxime Giot (M)

Division of Surgery, Department of Surgery and Cancer, Imperial College London, London, UK.

Stella Mavroveli (S)

Division of Surgery, Department of Surgery and Cancer, Imperial College London, London, UK.

George B Hanna (GB)

Division of Surgery, Department of Surgery and Cancer, Imperial College London, London, UK.

Melody Zhifang Ni (MZ)

Division of Surgery, Department of Surgery and Cancer, Imperial College London, London, UK.

Christopher J Peters (CJ)

Division of Surgery, Department of Surgery and Cancer, Imperial College London, London, UK.

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Classifications MeSH