Name
Abstract | ||
|---|---|---|
Motivation: RNA-sequencing (RNA-seq) of tumor tissue is typically only used to measure gene expression. Here, we present a statistical approach that leverages existing RNA-seq data to also detect somatic copy number alterations (SCNAs), a pervasive phenomenon in human cancers, without a need to sequence the corresponding DNA. Results: We present an analysis of 4942 participant samples from 28 cancers in The Cancer Genome Atlas (TCGA), demonstrating robust detection of SCNAs from RNA-seq. Using genotype imputation and haplotype information, our RNA-based method had a median sensitivity of 85% to detect SCNAs defined by DNA analysis, at high specificity (similar to 95%). As an example of translational potential, we successfully replicated SCNA features associated with breast cancer subtypes. Our results credential haplotype-based inference based on RNA-seq to detect SCNAs in clinical and population-based settings. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1093/bioinformatics/btab861 | BIOINFORMATICS |
DocType | Volume | Issue |
Journal | 38 | 6 |
ISSN | Citations | PageRank |
1367-4803 | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zuhal Ozcan | 1 | 0 | 0.34 |
| Francis A San Lucas | 2 | 0 | 0.34 |
| Justin W Wong | 3 | 0 | 0.34 |
| Kyle Chang | 4 | 0 | 0.34 |
| Konrad H Stopsack | 5 | 0 | 0.34 |
| Jerry Fowler | 6 | 1 | 1.09 |
| Yasminka A Jakubek | 7 | 0 | 0.68 |
| Paul Scheet | 8 | 0 | 0.34 |