Variant calling from RNA-Seq data reveals allele-specific differential expression of pathogenic cancer variants
Abstract Background Genetic variants play a pivotal role in the initiation and progression of many diseases, including cancer. Detecting these variants is the first step in understanding their contribution to disease mechanisms. RNA sequencing (RNA-Seq) has become a crucial assay in cancer research,...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
|
| Series: | Communications Medicine |
| Online Access: | https://doi.org/10.1038/s43856-025-00901-y |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850242585400967168 |
|---|---|
| author | Audrey Bollas Jeffrey Gaither Kathleen M. Schieffer Peter White Elaine R. Mardis |
| author_facet | Audrey Bollas Jeffrey Gaither Kathleen M. Schieffer Peter White Elaine R. Mardis |
| author_sort | Audrey Bollas |
| collection | DOAJ |
| description | Abstract Background Genetic variants play a pivotal role in the initiation and progression of many diseases, including cancer. Detecting these variants is the first step in understanding their contribution to disease mechanisms. RNA sequencing (RNA-Seq) has become a crucial assay in cancer research, offering insights beyond those provided by DNA sequencing. This study introduces VarRNA, a novel method that utilizes RNA-Seq data to classify single nucleotide variants and insertions/deletions from tumor transcriptomes. Methods VarRNA distinguishes transcriptome variants as germline, somatic, or artifact using a combination of two XGBoost machine learning models. These models were trained and validated using a cohort of pediatric cancer samples with paired tumor and normal DNA exome sequencing data serving as ground truth. We performed additional validation on RNA-Seq data from two distinct cancer datasets, demonstrating that VarRNA outperforms existing RNA variant calling methods. Results VarRNA identifies 50% of the variants detected by exome sequencing and detects unique RNA variants absent in paired tumor and normal DNA exome data. Some variants classified by VarRNA exhibit variant allele frequencies distinct from the corresponding DNA exome data. Strikingly, this phenomenon is prevalent in cancer-driving genes, where VarRNA analysis of the RNA-Seq data reveals the variant allele expression as much higher than expected based on the exome sequencing data. Conclusions These findings highlight the potential of RNA-Seq not only to uncover clinically relevant genetic variants but also to offer a deeper understanding of disease-specific expression dynamics that influence cancer pathogenesis, with implications for prognosis and therapeutic strategies. |
| format | Article |
| id | doaj-art-f3a3d64e05e641beb15ba9e50e1aa805 |
| institution | OA Journals |
| issn | 2730-664X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Medicine |
| spelling | doaj-art-f3a3d64e05e641beb15ba9e50e1aa8052025-08-20T02:00:14ZengNature PortfolioCommunications Medicine2730-664X2025-05-015111210.1038/s43856-025-00901-yVariant calling from RNA-Seq data reveals allele-specific differential expression of pathogenic cancer variantsAudrey Bollas0Jeffrey Gaither1Kathleen M. Schieffer2Peter White3Elaine R. Mardis4The Office of Data Sciences, The Abigail Wexner Research Institute, Nationwide Children’s HospitalThe Office of Data Sciences, The Abigail Wexner Research Institute, Nationwide Children’s HospitalThe Steve and Cindy Rasmussen Institute for Genomic Medicine, The Abigail Wexner Research Institute, Nationwide Children’s HospitalThe Office of Data Sciences, The Abigail Wexner Research Institute, Nationwide Children’s HospitalThe Steve and Cindy Rasmussen Institute for Genomic Medicine, The Abigail Wexner Research Institute, Nationwide Children’s HospitalAbstract Background Genetic variants play a pivotal role in the initiation and progression of many diseases, including cancer. Detecting these variants is the first step in understanding their contribution to disease mechanisms. RNA sequencing (RNA-Seq) has become a crucial assay in cancer research, offering insights beyond those provided by DNA sequencing. This study introduces VarRNA, a novel method that utilizes RNA-Seq data to classify single nucleotide variants and insertions/deletions from tumor transcriptomes. Methods VarRNA distinguishes transcriptome variants as germline, somatic, or artifact using a combination of two XGBoost machine learning models. These models were trained and validated using a cohort of pediatric cancer samples with paired tumor and normal DNA exome sequencing data serving as ground truth. We performed additional validation on RNA-Seq data from two distinct cancer datasets, demonstrating that VarRNA outperforms existing RNA variant calling methods. Results VarRNA identifies 50% of the variants detected by exome sequencing and detects unique RNA variants absent in paired tumor and normal DNA exome data. Some variants classified by VarRNA exhibit variant allele frequencies distinct from the corresponding DNA exome data. Strikingly, this phenomenon is prevalent in cancer-driving genes, where VarRNA analysis of the RNA-Seq data reveals the variant allele expression as much higher than expected based on the exome sequencing data. Conclusions These findings highlight the potential of RNA-Seq not only to uncover clinically relevant genetic variants but also to offer a deeper understanding of disease-specific expression dynamics that influence cancer pathogenesis, with implications for prognosis and therapeutic strategies.https://doi.org/10.1038/s43856-025-00901-y |
| spellingShingle | Audrey Bollas Jeffrey Gaither Kathleen M. Schieffer Peter White Elaine R. Mardis Variant calling from RNA-Seq data reveals allele-specific differential expression of pathogenic cancer variants Communications Medicine |
| title | Variant calling from RNA-Seq data reveals allele-specific differential expression of pathogenic cancer variants |
| title_full | Variant calling from RNA-Seq data reveals allele-specific differential expression of pathogenic cancer variants |
| title_fullStr | Variant calling from RNA-Seq data reveals allele-specific differential expression of pathogenic cancer variants |
| title_full_unstemmed | Variant calling from RNA-Seq data reveals allele-specific differential expression of pathogenic cancer variants |
| title_short | Variant calling from RNA-Seq data reveals allele-specific differential expression of pathogenic cancer variants |
| title_sort | variant calling from rna seq data reveals allele specific differential expression of pathogenic cancer variants |
| url | https://doi.org/10.1038/s43856-025-00901-y |
| work_keys_str_mv | AT audreybollas variantcallingfromrnaseqdatarevealsallelespecificdifferentialexpressionofpathogeniccancervariants AT jeffreygaither variantcallingfromrnaseqdatarevealsallelespecificdifferentialexpressionofpathogeniccancervariants AT kathleenmschieffer variantcallingfromrnaseqdatarevealsallelespecificdifferentialexpressionofpathogeniccancervariants AT peterwhite variantcallingfromrnaseqdatarevealsallelespecificdifferentialexpressionofpathogeniccancervariants AT elainermardis variantcallingfromrnaseqdatarevealsallelespecificdifferentialexpressionofpathogeniccancervariants |