Using low-coverage whole genome sequencing technique to analyze the chromosomal copy number alterations in the exfoliative cells of cervical cancer.
- Author:
Tong REN
1
;
Jing SUO
;
Shikai LIU
;
Shu WANG
;
Shan SHU
;
Yang XIANG
;
Jing He LANG
Author Information
- Publication Type:Original Article
- Keywords: Uterine Cervical Neoplasms; Mass Screening; High-Throughput Nucleotide Sequencing; DNA Copy Number Variation
- MeSH: Diagnosis; DNA Copy Number Variations; Female; Genome*; High-Throughput Nucleotide Sequencing; Humans; Mass Screening; Population Characteristics; Retrospective Studies; Sensitivity and Specificity; Uterine Cervical Neoplasms*
- From:Journal of Gynecologic Oncology 2018;29(5):e78-
- CountryRepublic of Korea
- Language:English
- Abstract: OBJECTIVES: We analyzed the chromosomal-arm-level copy number alterations (CNAs) in the cervical exfoliative cell and tissue samples by using the low-coverage whole genomic sequencing technique. METHODS: In this study, we retrospectively collected 55 archived exfoliated cervical cell suspension samples and the corresponding formalin-fixed and paraffin-embedded tissue section samples including 27 invasive cervical cancer and 28 control cases. We also collected 19 samples of the cervical exfoliative cells randomly from women to verify the new algorithm model. We analyzed the CNAs in cervical exfoliated cell and tissue samples by using the low-coverage next generation of sequencing. RESULTS: In the model-building study, multiple chromosomal-arm-level CNAs were detected in both cervical exfoliated cell and tissue samples of all cervical cancer cases. By analyzing the consistency of CNAs between exfoliated cells and cervical tissue samples, as well as the heterogeneity in individual patient, we also established a C-score algorithm model according to the chromosomal-arm-level changes of 1q, 2q, 3p, 7q. The C-score model was then validated by the pathological diagnosis of all 74 exfoliated cell samples (including 55 cases in model-building group and 19 cases in verification group). In our result, a cutoff value of C-score > 6 showed 100% sensitivity and 100% specificity in the diagnosis of cervical cancer. CONCLUSION: In this study, we found that CNAs of cervical exfoliated cell samples could robustly distinguish invasive cervical cancer from cancer-free tissues. And we have also developed a C-score algorithm model to process the sequencing data in a more standardized and automated way.
