Detection of Structural Variations and Fusion Genes in Breast Cancer Samples Using Third-Generation Sequencing
2022
Taobo Hu | Jingjing Li | Jingjing Li | Mengping Long | Jinbo Wu | Zhen Zhang | Fei Xie | Jin Zhao | Houpu Yang | Qianqian Song | Sheng Lian | Jiandong Shi | Xueyu Guo | Daoli Yuan | Dandan Lang | Guoliang Yu | Baosheng Liang | Xiaohua Zhou | Toyotaka Ishibashi | Xiaodan Fan | Weichuan Yu | Depeng Wang | Yang Wang | I-Feng Peng | Shu Wang
Background: Structural variations (SVs) are common genetic alterations in the human genome that could cause different phenotypes and diseases, including cancer. However, the detection of structural variations using the second-generation sequencing was limited by its short read length, which restrained our understanding of structural variations.Methods: In this study, we developed a 28-gene panel for long-read sequencing and employed it to Oxford Nanopore Technologies and Pacific Biosciences platforms. We analyzed structural variations in the 28 breast cancer-related genes through long-read genomic and transcriptomic sequencing of tumor, para-tumor, and blood samples in 19 breast cancer patients.Results: Our results showed that some somatic SVs were recurring among the selected genes, though the majority of them occurred in the non-exonic region. We found evidence supporting the existence of hotspot regions for SVs, which extended our previous understanding that they exist only for single nucleotide variations.Conclusion: In conclusion, we employed long-read genomic and transcriptomic sequencing to identify SVs from breast cancer patients and proved that this approach holds great potential in clinical application.
Afficher plus [+] Moins [-]Informations bibliographiques
Cette notice bibliographique a été fournie par Directory of Open Access Journals
Découvrez la collection de ce fournisseur de données dans AGRIS