Evidence map›Paper›PMID 35477343›Full record

ArticleClinical proteomics2022

Use of MS-GUIDE for identification of protein biomarkers for risk stratification of patients with prostate cancer.

Sandra Goetze, Peter Schüffler, Alcibiade Athanasiou, Anika Koetemann, Cedric Poyet, Christian Daniel Fankhauser, Peter J Wild, Ralph Schiess, Bernd Wollscheid

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In one paragraph

Article in Clinical proteomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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

  1. Pooled it
  2. Article
  3. Biological biomarkers of oral cancer.Periodontology 2000 · 2024
    Review
  4. Article
  5. Review
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

9 authors.

Sandra Goetze *Department of Health Sciences and Technology, Institute of Translational Medicine, Swiss Federal Institute of Technology, ETH Zurich, 8093, Zurich, Switzerland.ORCID http://orcid.org/0000-0001-6880-8020
Peter Schüffler *Institute of General and Surgical Pathology, Technical University of Munich, 81675, Munich, Germany.
Alcibiade AthanasiouProteomedix AG, 8952, Schlieren, Switzerland.
Anika KoetemannDepartment of Health Sciences and Technology, Institute of Translational Medicine, Swiss Federal Institute of Technology, ETH Zurich, 8093, Zurich, Switzerland.
Cedric PoyetClinic of Urology, University Hospital Zurich, University of Zurich, 8091, Zurich, Switzerland.
Christian Daniel FankhauserClinic of Urology, University Hospital Zurich, University of Zurich, 8091, Zurich, Switzerland.
Peter J WildDepartment of Pathology and Molecular Pathology, University Hospital Zurich, University of Zurich, 8091, Zurich, Switzerland. Peter.Wild@kgu.de.
Ralph SchiessProteomedix AG, 8952, Schlieren, Switzerland. schiess@proteomedix.com.
Bernd WollscheidDepartment of Health Sciences and Technology, Institute of Translational Medicine, Swiss Federal Institute of Technology, ETH Zurich, 8093, Zurich, Switzerland. wbernd@ethz.ch.ORCID http://orcid.org/0000-0002-3923-1610

Funding

innosuisse - schweizerische agentur für innovationsförderung KTI 16771.3personalized health and related technologies strategic focus area of eth PHRT 0-21411-18schweizerischer nationalfonds zur förderung der wissenschaftlichen forschung 31003A_160259
6 · The paper itself

Abstract

backgroundNon-invasive liquid biopsies could complement current pathological nomograms for risk stratification of prostate cancer patients. Development and testing of potential liquid biopsy markers is time, resource, and cost-intensive. For most protein targets, no antibodies or ELISAs for efficient clinical cohort pre-evaluation are currently available. We reasoned that mass spectrometry-based prescreening would enable the cost-effective and rational preselection of candidates for subsequent clinical-grade ELISA development.

methodsUsing Mass Spectrometry-GUided Immunoassay DEvelopment (MS-GUIDE), we screened 48 literature-derived biomarker candidates for their potential utility in risk stratification scoring of prostate cancer patients. Parallel reaction monitoring was used to evaluate these 48 potential protein markers in a highly multiplexed fashion in a medium-sized patient cohort of 78 patients with ground-truth prostatectomy and clinical follow-up information. Clinical-grade ELISAs were then developed for two of these candidate proteins and used for significance testing in a larger, independent patient cohort of 263 patients.

resultsMachine learning-based analysis of the parallel reaction monitoring data of the liquid biopsies prequalified fibronectin and vitronectin as candidate biomarkers. We evaluated their predictive value for prostate cancer biochemical recurrence scoring in an independent validation cohort of 263 prostate cancer patients using clinical-grade ELISAs. The results of our prostate cancer risk stratification test were statistically significantly 10% better than results of the current gold standards PSA alone, PSA plus prostatectomy biopsy Gleason score, or the National Comprehensive Cancer Network score in prediction of recurrence.

conclusionUsing MS-GUIDE we identified fibronectin and vitronectin as candidate biomarkers for prostate cancer risk stratification.

Indexed as

ELISAMachine learningParallel reaction monitoringProstate cancerRisk stratification

Identifiers

PMID35477343
PMCPMC9044739

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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.