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Related Experiment Video

Updated: Aug 5, 2025

A Blood-based Test for the Detection of ROS1 and RET Fusion Transcripts from Circulating Ribonucleic Acid Using Digital Polymerase Chain Reaction
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Improving RNA Fusion Call Confidence and Reliability in Molecular Diagnostic Testing.

Mariusz Shrestha1, Sasha Blay2, Sydney Liang3

  • 1Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada.

The Journal of Molecular Diagnostics : JMD
|March 23, 2023
PubMed
Summary

Next-generation sequencing (NGS) reliably detects RNA fusions, but poor sample quality can cause false negatives. A novel proxy quality control (pQC) metric using 15 genes improves RNA quality assessment and fusion call confidence in clinical settings.

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Area of Science:

  • Molecular Biology
  • Genomics
  • Cancer Research

Background:

  • Next-generation sequencing (NGS) offers superior RNA fusion detection compared to FISH and RT-PCR.
  • Poor RNA quality in formalin-fixed, paraffin-embedded (FFPE) tissues can compromise NGS accuracy, leading to false negatives.
  • Current quality control metrics may not adequately reflect sample integrity for reliable fusion calling.

Purpose of the Study:

  • To develop a robust quality control (QC) metric for NGS to improve confidence in RNA fusion detection.
  • To establish a proxy quality control (pQC) gene set that reflects internal sample quality.
  • To minimize false-negative calls in clinical NGS assays.

Main Methods:

  • Evaluated gene expression across 361 patient tumor samples to identify 15 robust genes for a pQC metric.
  • Assessed normalized expression of the 15 pQC genes using NGS data.
  • Tested a revised library preparation method to improve the pass rate of low-quality samples.

Main Results:

  • A pQC metric using 11 out of 15 genes demonstrated a 4.71% fail rate, deemed acceptable for clinical stringency.
  • A revised library preparation method, increasing cDNA input, enabled 75% of previously failed samples to pass pQC.
  • The pQC metric serves as a surrogate for housekeeping genes, enhancing fusion call reliability.

Conclusions:

  • The developed pQC metric provides a reliable assessment of RNA quality for NGS.
  • Implementing the pQC tool enhances confidence in RNA fusion detection, reducing false negatives.
  • Optimized library preparation protocols can improve the success rate of analyzing low-quality FFPE samples with NGS.