When it comes to treating cancer, understanding a patient’s unique genetic profile is essential. Traditionally, DNA testing has been used to detect mutations that might predict how well a patient will respond to a particular therapy. However, DNA testing alone doesn’t always give the full picture. Most cancer drugs actually target proteins, and DNA mutations don’t always result in meaningful changes to proteins. This is where RNA sequencing can make a difference.

Researchers from the U.S. Food and Drug Administration explored how targeted RNA sequencing can detect mutations that may not be picked up by DNA testing. RNA acts as the messenger between DNA and protein, so studying RNA gives us a better sense of which mutations are actually expressed and may affect the behavior of cancer cells.

In their study, the team used RNA sequencing to analyze a reference set of cancer samples. They found that RNA sequencing identified clinically important mutations that DNA sequencing missed. Some of these variants were highly relevant to disease progression or treatment response. Others were only visible in RNA because DNA-level changes didn’t tell the whole story.

Interestingly, some mutations were detected by both methods, while others appeared only in one. This shows that combining DNA and RNA testing gives a more complete and accurate view. Mutations missed by RNA were often not expressed, suggesting they may have less impact on cancer biology and may not be as useful in guiding treatment.

Using RNA-seq results to verify and prioritize DNA variants

a The numbers of different types of calls reported by various panels without controlling the FPR. The “true set” was established in our previous study using the same reference samples. Variants not included in the “true set” were categorized as uncharacterized calls. The blue line represents verified true variants that are targeted and sequenced by the specific panels (AGLR or ROCR), meaning they fall within the regions targeted by the probes in the panels. b Comparison of expression levels between two groups of known positive variants: those detected and those missed by targeted RNA-seq across different panels. The number of reads (Y axis) was set to zero if a call was absent in the expression results. A Wilcoxon Rank Sum Test was applied, resulting in a significant p-value of 2.2e-16 for all panels. c Known positive variants missed by targeted RNA-seq data. Not-expressed: not detectably expressed. For example, it may be fairly expressed but the bait performance is poor. Low-VAF: calls were expressed but had a VAF < 2%, Low-DP: expressed calls with VAF ≥ 2% but had a DP < 20, Low-ADP: expressed calls with VAF ≥ 2% and DP ≥ 20, but ADP < 2 (=1). d Comparison of average recall values across panels, considering conditions with non-stringent cutoff versus where the FPR was reduced to 50 per million bases.

By integrating RNA sequencing into clinical biomarker panels, doctors can better interpret genetic test results and select treatments that are more likely to work for each individual patient. This approach could improve how we diagnose cancer, assess prognosis, and predict drug responses, all leading to better patient outcomes.