# RNA Panel Sequencing Is an Effective Tool to Help Classify Splice Variants for Clinical Oncogenetic Diagnosis

## TL;DR

RNA sequencing helps classify uncertain genetic variants in cancer by analyzing their effect on mRNA splicing and transcription.

## Contribution

RNA panel sequencing is shown to effectively classify splice variants in clinical oncogenetics.

## Key findings

- 21 out of 53 variants were classified as likely neutral, and 10 as pathogenic or likely pathogenic.
- RNA sequencing was useful for characterizing large duplications and detecting deep intronic variants.
- Four specific variants demonstrated the effectiveness of RNA panel sequencing in clinical diagnosis.

## Abstract

Routine gene panel analysis identifies pathogenic variants in clinically relevant genes. However, variants of unknown significance (VUSs) are commonly observed, many of which potentially have an impact on mRNA transcription and splicing. Several software programs attempt to predict the impact of variants on splicing and thus make it possible to select the variants for which it is important to study the effect on the transcripts. Transcript analysis is also necessary to show the tandem character of large duplications, and it can be useful for the search for deep intronic variants that are difficult to identify in a DNA panel. We analyzed 53 variants of unknown significance by targeted sequencing of 48 genes using RNA extracted from patient blood samples. RT-PCR and Sanger sequencing of patient mRNA or minigene monoallelic analysis was also carried out when necessary. For the 53 VUSs, 21 could be classified as likely neutral and 10 as pathogenic or likely pathogenic. Data are comprehensively presented for four variants: PTEN c.206+6T>G, MLH1 c.791-489_791-20del, BRCA2 c.68-8_68-7delinsAA, and MSH2 c.(1076+1_1077-1)_(1276+1_1277-1)dup. These four examples illustrate the usefulness of blood RNA panel sequencing in clinical oncogenetics to help classify VUSs with predicted splice effects. It could also be useful for characterizing large duplications and for detecting deep intronic variants with an impact on expressed transcripts.