Evidence map›Paper›PMID 41105202›Full record

ArticleVirchows Archiv : an international journal of pathology2025

Influence of pre-analytical factors on the success rates of smMIP and MLPA analyses in epithelial ovarian cancer.

L Lanjouw, M J E Mourits, G H de Bock, F J G Scherpen, S Z Commandeur-Jan, J Bart, A Ter Elst

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Article in Virchows Archiv : an international journal of pathology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

L LanjouwDepartment of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands. l.lanjouw@umcg.nl.ORCID http://orcid.org/0000-0002-2172-728X
M J E MouritsDepartment of Obstetrics and Gynecology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
G H de BockDepartment of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
F J G ScherpenDepartment of Pathology and Medical Biology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
S Z Commandeur-JanDepartment of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
J BartDepartment of Pathology and Medical Biology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
A Ter ElstDepartment of Pathology and Medical Biology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mutation analyses are increasingly performed on tumor specimens to determine treatment eligibility and screen for genetic cancer predisposition. Sufficient tumor cell percentages and DNA concentrations are required to perform reliable mutation analyses, but even when these requirements are met, results do not always meet reliability standards. This study investigated potential factors associated with successful mutation analyses using single-molecule molecular inversion probe (smMIP)-based next generation sequencing (NGS) technology for the detection of BRCA1/2 pathogenic variants (PVs) in epithelial ovarian cancer. Data of a consecutive series of smMIP analyses performed between 2018 and 2022 in clinical practice were collected from hospital databases, including data of Multiplex Ligation-dependent Probe Amplification (MLPA) analyses if performed alongside the smMIP analysis. Data included analysis outcome (successful/not successful), storage time, histotype, FIGO stage, center of block preparation, material type, tumor cell percentage, and receival of chemotherapy. Successful analyses were defined as analyses providing test results deemed adequate for clinical decision-making, based on the judgement of a clinical scientist in molecular pathology. A minimum threshold of 100 × coverage for at least 97% of the regions of interest was applied. In total, 88.3% (401/454) of smMIP analyses and 78.4% (356/453) of MLPA analyses were successful. A time interval of ≤ 48 days between tumor specimen collection and analysis was significantly associated with successful smMIP and MLPA analyses (OR 2.05, 95% CI 1.07-3.93, p = 0.030; OR 1.88, 95% CI 1.19-2.98, p = 0.007; respectively). Based on these observations, we recommend performing the analyses directly after specimen collection to enhance efficiency and reduce costs.

Indexed as

BRCA1/2Genetic testingNext-generation sequencingOvarian cancerPathogenic variants

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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.