Sign in
Genetic & Genomic MedicinemedRxiv
Preprint

Optimizing mixed sample analysis as a step to comprehensive desease screening: a pilot study

Lucia Krasničanová, Natália ForgáčováiD, Tatiana Sedláčková, Jaroslav BudišiD, Juraj GazdaricaiD, Vanda RepiskáiD, Tomáš SzemesiD

Abstract

BackgroundLynch Syndrome (LS) is an autosomal dominant hereditary syndrome associated with a diverse range of cancer types. Despite being one of the most prevalent hereditary cancer syndromes, the detection of LS remains challenging due to the absence of well-defined diagnostic criteria which would be able to select all patients who should undergo testing for LS and the limitations of existing screening methods. The implementation of an efficient screening program capable of accurately detecting the majority of LS cases remains a topic of continuous discussion in the scientific literature, with recent studies emphasizing the significance of a universal screening program. MethodsOur study aimed to develop and optimize a cost-effective universal screening method for detecting mutation in the mismatch repair (MMR) genes through mixed sample analysis. We tested five approaches in terms of the use of biological material and the analysis of mixed samples. ResultsEach approach successfully detected a specific Lynch-associated pathogenic variant in mixed in the pooled samples with frequency 5.00%, with the lowest allelic fraction recorded at 3.04%. Approach 2, which involved isolating DNA from each patient individually, demonstrated the highest average allelic fraction (7.04%). However, considering financial and time requirements, approach 1, where DNA was isolated only after mixing aliquots of whole blood, proved to be the most favorable. ConclusionThe findings of our study present a promising opportunity to improve LS detection. The identification of LS not only has the potential to prevent cancer-related morbidity and mortality but also facilitates continued progress in understanding the primary prevention of cancer.

02 /Specification & Classification

Click any tag to browse matching papers

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISSingh '22Walker '23Barrett '25Miller '18Pope '20Gudgeon '19
69% semantic matchGenetic & Genomic Medicine
Vinit Singh, Catherine Mezzacappa, Peter Gershkovich+6 more·Journal of Medical Genetics·Sep 17, 2022· 10 citations