دورية أكاديمية
A deep-learning-based genomic status estimating framework for homologous recombination deficiency detection from low-pass whole genome sequencing
| العنوان: | A deep-learning-based genomic status estimating framework for homologous recombination deficiency detection from low-pass whole genome sequencing |
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| المؤلفون: | Yang Liu, Xiang Bi, Yang Leng, Dan Chen, Juan Wang, Youjia Ma, Min-Zhe Zhang, Bo-Wei Han, Yalun Li |
| المصدر: | Heliyon, Vol 10, Iss 4, Pp e26121- (2024) |
| بيانات النشر: | Elsevier, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Science (General) LCC:Social sciences (General) |
| مصطلحات موضوعية: | Homologous recombination deficiency, Deep learning, Genome sequencing, Science (General), Q1-390, Social sciences (General), H1-99 |
| الوصف: | Genome-wide sequencing allows for prediction of clinical treatment responses and outcomes by estimating genomic status. Here, we developed Genomic Status scan (GSscan), a long short-term memory (LSTM)-based deep-learning framework, which utilizes low-pass whole genome sequencing (WGS) data to capture genomic instability-related features. In this study, GSscan directly surveys homologous recombination deficiency (HRD) status independent of other existing biomarkers. In breast cancer, GSscan achieved an AUC of 0.980 in simulated low-pass WGS data, and obtained a higher HRD risk score in clinical BRCA-deficient breast cancer samples (p = 1.3 × 10−4, compared with BRCA-intact samples). In ovarian cancer, GSscan obtained higher HRD risk scores in BRCA-deficient samples in both simulated data and clinical samples (p = 2.3 × 10−5 and p = 0.039, respectively, compared with BRCA-intact samples). Moreover, HRD-positive patients predicted by GSscan showed longer progression-free intervals in TCGA datasets (p = 0.0011) treated with platinum-based adjuvant chemotherapy, outperforming existing low-pass WGS-based methods. Furthermore, GSscan can accurately predict HRD status using only 1 ng of input DNA and a minimum sequencing coverage of 0.02 × , providing a reliable, accessible, and cost-effective approach. In summary, GSscan effectively and accurately detected HRD status, and provide a broadly applicable framework for disease diagnosis and selecting appropriate disease treatment. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2405-8440 |
| Relation: | http://www.sciencedirect.com/science/article/pii/S2405844024021522; https://doaj.org/toc/2405-8440 |
| DOI: | 10.1016/j.heliyon.2024.e26121 |
| URL الوصول: | https://doaj.org/article/0e18dbe0ee8648e2ad9299638646b583 |
| رقم الأكسشن: | edsdoj.0e18dbe0ee8648e2ad9299638646b583 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 24058440 |
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| DOI: | 10.1016/j.heliyon.2024.e26121 |