Evidence map›Paper›PMID 37794461›Full record

ArticleBMC medicine2023

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

Katerina-Vanessa Savva, Michal Kawka, Bhamini Vadhwana, Rahul Penumaka, Imogen Patton, Komal Khan, Claire Perrott, Saranya Das, Maxime Giot, Stella Mavroveli and 3 more

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In one paragraph

Article in BMC medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Katerina-Vanessa SavvaDivision of Surgery, Department of Surgery and Cancer, Imperial College London, London, UK. k.savva17@imperial.ac.uk.
Michal KawkaDivision of Surgery, Department of Surgery and Cancer, Imperial College London, London, UK.
Bhamini VadhwanaDivision of Surgery, Department of Surgery and Cancer, Imperial College London, London, UK.
Rahul PenumakaDivision of Surgery, Department of Surgery and Cancer, Imperial College London, London, UK.
Imogen PattonDivision of Surgery, Department of Surgery and Cancer, Imperial College London, London, UK.
Komal KhanDivision of Surgery, Department of Surgery and Cancer, Imperial College London, London, UK.
Claire PerrottDivision of Surgery, Department of Surgery and Cancer, Imperial College London, London, UK.
Saranya DasDivision of Surgery, Department of Surgery and Cancer, Imperial College London, London, UK.
Maxime GiotDivision of Surgery, Department of Surgery and Cancer, Imperial College London, London, UK.
Stella MavroveliDivision of Surgery, Department of Surgery and Cancer, Imperial College London, London, UK.
George B HannaDivision of Surgery, Department of Surgery and Cancer, Imperial College London, London, UK.
Melody Zhifang NiDivision of Surgery, Department of Surgery and Cancer, Imperial College London, London, UK.
Christopher J PetersDivision of Surgery, Department of Surgery and Cancer, Imperial College London, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAn 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.

methodsAll 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.

resultsSystematic 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).

conclusionsThis 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.

Indexed as

Biomedical ResearchColorectal NeoplasmsBiomarkers, TumorChecklistHumansBiomarkers, TumorBiomarkersBreast cancerClinical utilityColorectal cancerTranslational research

Identifiers

PMID37794461
PMCPMC10552368

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.