Back

Highly selective Cooperative Primers for multiplex detection of rare mutations

Kent, J. O.; Christensen, M. C.

2021-07-23 molecular biology
10.1101/2021.07.23.453558 bioRxiv
Show abstract

We describe a real time PCR-based technique capable of detecting and quantifying rare somatic mutations in circulating tumor DNA reference materials. Our approach utilizes previously described Cooperative Primers, structurally modified to exhibit high allele-specificity and one-copy target sensitivity. Cooperative Primers are bi-functional molecules, consisting of a high affinity probe fragment that guarantees sensitivity, and a covalently attached lower affinity primer providing specificity. Additional optimization of Cooperative Primer structure generated molecules capable of reliable detection of allele changes as small as a single nucleotide. These highly selective Cooperative Primers maintain excellent discrimination properties in rare mutant allele scenarios, in both monoplex and multiplex assays. With synthetic DNA samples, Cooperative Primers can detect as little as 100 copies of mutant template amongst 1 000 000 copies of wild-type template (minor allele fraction of 0.01 %). Multiplex Cooperative Primer assay was validated with cell-free DNA reference materials and consistently detected the lowest minor allele fraction available (0.1 %) for EGFR L858R, G719S and V769-D770insASV mutations, while simultaneously providing qualitative and quantitative assessment of cell-free DNA with integrated {beta}-Actin assay. Easy to design, rapid and inexpensive, Cooperative Primer - based real time PCR assays are a promising tool for evaluation of cancer therapy response, occurrence of resistance mutations and relapse monitoring.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.