What Does RNA Analysis Actually Add to Hereditary Cancer Testing?

Not All RNA Analysis Is Created Equal

Genetic testing has transformed how we understand cancer risk. For nearly three decades, DNA-based analysis has been the standard for hereditary cancer testing. But as the science advances, one question is gaining traction in clinical circles: What does RNA analysis actually add?

The short answer: When used selectively and appropriately, RNA analysis can clarify ambiguous DNA results and provide more definitive answers for patients. The goal is not to apply RNA analysis indiscriminately, but to deploy it when it offers clinical value.

Limitations of DNA Testing in Variant Interpretation

While DNA testing reveals inherited changes in a person’s genes, it doesn’t always tell the full story of how those changes affect gene function. Some variants fall into a gray zone known as a “variant of uncertain significance” (VUS).

VUS findings leave clinicians in limbo. Over 36% of variants in ClinVar remain in this uncertain category.¹ For providers, this can mean unclear medical guidance or delayed intervention. This is where RNA can play a role.

RNA analysis captures how genes are expressed. It helps determine whether a DNA variant actually impacts normal gene splicing, which is key to helping understand variant classification.¹

The Clinical Impact of RNA Genetic Testing

RNA analysis continues to demonstrate the clinical relevance around complex splicing events, such as “leaky” splicing, where both normal and aberrant transcripts coexist.  A 2020 study explored this topic using BRCA2 exon 3, a specific section of a cancer-related gene often affected by splicing variants.² This study showed that even partial splicing defects, where up to 60% of transcripts remained functional, can carry clinical significance—suggesting that the presence of sufficient normally spliced transcripts may help preserve gene function and reduce the patient’s cancer risk, despite the variant.

Yet this shift is only meaningful when RNA analysis is used wisely. Blanket testing of all cases may detect additional transcript changes, but without allele-specific RNA quantification, it can produce false positives, misinterpretations, or simply add cost and confusion.^2,3,4 Myriad Oncology™ takes a targeted approach by applying RNA analysis only when DNA findings lead to a VUS and allele-specific RNA data that could clarify whether a variant is pathogenic or functionally benign.

The VUS Bottleneck: How to Resolve Uncertain Genetic Test Results

It is clear that the industry has a VUS problem. And solving it doesn’t mean doing more testing, it means doing smarter testing.

Myriad Oncology invests in proprietary tools like Pheno^®, which uses personal and family history algorithms to classify variants with >99.5% predictive accuracy.⁵ To minimize VUS rates without over-relying on RNA, Myriad also sequences regions just outside the coding portions of genes—known as intron-exon boundaries¹—which can still affect how genes function.

When RNA is needed, Myriad’s allele-specific quantification determines whether a splice defect is complete or partial (“leaky”). This prevents misclassification, a risk when labs rely on standard RNA-seq without confirming whether aberrant transcripts coexist with normal ones.^6,7

Myriad’s BRCA1 and BRCA2 VUS rates are just 0.30% and 0.70%, respectively.¹ That means more patients receive clear, actionable results the first time.

RNA Analysis in a Rapidly Evolving Field

RNA analysis is no longer just academic. Emerging applications like spatial and single-cell RNA sequencing are mapping tumor heterogeneity and immune responses with extraordinary accuracy.

But in hereditary cancer risk assessment, the power of RNA isn’t in testing more. It’s in knowing when it matters. Myriad’s approach is selective, patient-informed, and clinically driven, leading a shift from indiscriminate RNA use to focused, impactful implementation.

What a VUS Result Means—And What Happens Next

When patients receive a VUS result, it can stall decision-making and strain the patient-provider relationship. Clinical approaches to these findings, however, vary significantly across labs.

Myriad Oncology proactively reevaluates VUS over time and leverages RNA analysis when it may provide clinically relevant information. Every VUS is reviewed with both algorithmic analysis and human expertise, ensuring that reclassifications are accurate and meaningful.

This strategy of combining deep DNA analysis with expert-driven, allele-specific RNA confirmation has resulted in some of the industry’s lowest VUS rates, especially with high-impact genes like BRCA1 and BRCA2.

This means fewer inconclusive results, more confident decisions, and clearer care pathways from the start. The chart below highlights the impact of this approach: Over four years, 16 of 30 genes showed a decrease in VUS rates, with 18 of these genes now under a single percent. Even among newly added genes, early data suggests strong classification performance with 13 of 16 genes showing VUS rates under 1% after just 18 months of testing.⁸ Myriad’s consistent improvements underscore the commitment to reducing uncertainty for patients and providers alike.

Table 1: VUS reporting rate in 2019 and 2023 by gene. Source: RNA Whitepaper

Learn more about partnering with Myriad Oncology to support confident, personalized decisions for every patient at each step with exactly what you need.

References:

1. Mundt E, McGreevy K, Nix P, et al: Myriad’s Multidisciplinary Approach and Consistent Investment in Variant Classification for Clinical Decision Making. Myriad Genetics. June 2025. White paper.
2. Tubeuf H, Caputo S, Sullivan T, et al. Calibration of pathogenicity due to variant-induced leaky splicing defects by using BRCA2 exon 3 as a model system. Cancer Res. 2020;80(17):3593–3605.
3. Ziemann M, Eren Y, El-Osta A: Gene length bias in RNA sequencing analysis: when being long is not advantageous. Genome Biol 20(1):240, 2019.
4. Wu DC, Yao J, Ho KS, et al: Limitations of alignment-free tools in total RNA-seq quantification. F1000Res 7:1321, 2018.
5. Esterling E, Wijayatunge R, Brown K, et al: Impact of a Cancer Gene Variant Reclassification Program Over a 20-Year Period. JCO Precis Oncol 4:PO.20.00020, 2020.
6. Tardaguila M, de la Fuente L, Marti C, et al: SQANTI: extensive characterization of long-read transcript sequences for quality control in full-length transcriptome identification and quantification. Genome Biol 21(1):304, 2020.
7. Tang AD, Soulette CM, van Baren MJ, et al: Full-length transcript characterization of SF3B1 mutation in chronic lymphocytic leukemia reveals downregulation of retained introns. Nat Commun 12(1):2026, 2021.
8. Table 1.