4.7 Article

Detection of Structural Variations and Fusion Genes in Breast Cancer Samples Using Third-Generation Sequencing

期刊

出版社

FRONTIERS MEDIA SA
DOI: 10.3389/fcell.2022.854640

关键词

long-read sequencing; breast cancer; structural variation; fusion gene; sequencing panel

资金

  1. National Key Research and Development Program of China [2021YFE0203200]
  2. National Natural Science Foundation of China [92059105, 82002979]
  3. Beijing Municipal Natural Science Foundation [7202212]
  4. Peking University People's Hospital [RDX 2021-05, RDY 2020-16]
  5. Peking University Health Science Center [BMU2021PYB013]

向作者/读者索取更多资源

In this study, a 28-gene panel for long-read sequencing was developed and used to identify structural variations in breast cancer patients. The results showed that recurring somatic SVs were found in selected genes, with the majority occurring in the non-exonic region. Hotspot regions for SVs were also discovered, expanding our previous understanding.
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.

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