Abstract
Alternative splicing enhanced with deep machine learning: a novel method to detect ovarian cancer
Gynecologic oncology, Vol.162, pp.S86-S87
08/2021
DOI: 10.1016/S0090-8258(21)00805-2
Abstract
There is a continued effort to diagnose ovarian cancer in early stages. Alternative splicing analysis may provide a unique signature to discriminate between malignant and benign tissues. RNA-sequencing has enabled genome-wide assessment of alternative splicing. Furthermore, deep-learning prediction models of alternative splicing based on exon-specific sequences features enhances the value of this analysis. The aim of this study is to create a model that would discriminate high grade serous ovarian cancer (HGSC) from normal tube using deep-learning (or artificial intelligence, AI) and alternative splicing analysis.
This is a case-control study of patients with confirmed HGSC and patients without personal history of HGSC undergoing salpingectomy for benign conditions. RNA-sequencing was performed on all tissue samples. Resulting RNA sequences were introduced in DARTS (Deep-learning Augmented RNA-Seq) software suite. DARTS created a model of differential alternative splicing aimed to discriminate between HGSC and normal fallopian tube. Performance of prediction models were measured by area under the curve (AUC).
112 HGSC and 12 benign samples were successfully sequenced. Prior to deep machine-learning, we found 1,726 differentially spliced single exon transcripts between HGSC and benign samples (p<0.005). These transcripts discriminated benign from malignant groups with an AUC of 88% (95% CI: 0.77-1.0). After deep machine-learning, the performance improved to 91% (95% CI: 78-1.0). 95% CIs of both overlapped.
Individual exon expression used to assess alternative splicing was analyzed with AI methods and created a model that identified HGSC with an AUC of 91%. This method could be applied to single-cell RNA-seq technologies to create diagnostic tools for detecting early HGSC.
Details
- Title: Subtitle
- Alternative splicing enhanced with deep machine learning: a novel method to detect ovarian cancer
- Creators
- Silvana Pedra Nobre - University of Iowa Hospitals and ClinicsNicholas Cardillo - University of Iowa Hospitals and ClinicsEric Devor - University of IowaMegan McDonald - University of IowaMichael Goodheart - University of IowaDavid Bender - University of IowaJesus Gonzalez Bosquet - University of Iowa
- Resource Type
- Abstract
- Publication Details
- Gynecologic oncology, Vol.162, pp.S86-S87
- Publisher
- Elsevier Inc
- DOI
- 10.1016/S0090-8258(21)00805-2
- ISSN
- 0090-8258
- eISSN
- 1095-6859
- Language
- English
- Date published
- 08/2021
- Academic Unit
- Obstetrics and Gynecology
- Record Identifier
- 9984317105002771
Metrics
21 Record Views