Abstract
Purpose
To investigate chromosomal instability (CIN) as a biomarker for glioma risk stratifications, with cost-effective, low-coverage whole-genome sequencing assay (WGS).
Methods
Thirty-five formalin-fixed paraffin-embedded glioma samples were collected from Huashan Hospital. DNA was sent for WGS by Illumina X10 at low (median) genome coverage of 1.86x (range: 1.03–3.17×), followed by copy number analyses, using a customized bioinformatics workflow—Ultrasensitive Copy number Aberration Detector.
Results
Among the 35 glioma patients, 12 were grade IV, 10 grade III, 11 grade II, and 2 Grade I cases, with high chromosomal instability (CIN +) in 24 (68.6%) of the glioma patients. The other 11 (31.4%) had lower chromosomal instability (CIN−). CIN significantly correlates with overall survival (P = 0.00029). Patients with CIN + /7p11.2 + (12 grade IV and 3 grade III) had the worst survival ratio (hazard ratio:16.2, 95% CI:6.3–41.6) with a median overall survival of 24 months. Ten (66.7%) patients died during the first two follow-up years. In the CIN + patients without 7p11.2 + (6 grade III, 3 grade II), 3 (33.3%) patients died during follow-up, and the estimated overall survival was around 65 months. No deaths were reported in the 11 CIN- patients (2 grade I, 8 grade II, 1 grade III) during the 80-month follow-up period. In this study, chromosomal instability served as a prognosis factor for gliomas independent of tumor grades.
Conclusion
It is feasible to use cost-effective, low-coverage WGS for risk stratification of glioma. Elevated chromosomal instability is associated with poor prognosis.
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Data availability
All data generated or analysed during this study are included in this article. Further enquiries can be directed to the corresponding author.
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Acknowledgements
This study was supported by Application of improved finite element mechanics technology in cranioplasty (No. 201940126), Application of machine learning of computed tomography in prognosis of craniocerebral trauma (CKY2020-28), and The value of modified machine learning model in the prognosis of emergency elderly traumatic brain injury (CKY2021-26).
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Conception and design of the research: J-JQ Acquisition of data: FX, Z-LS Analysis and interpretation of the data: JJQ Statistical analysis: Z-LS Obtaining financing: X-ZC Writing of the manuscript: J-JQ, FX Critical revision of the manuscript for intellectual content: Xian-zhen Chen, Zhao-li Shen All authors read and approved the final draft.
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Qin, JJ., Xue, F., Shen, ZL. et al. Low-coverage and cost-effective whole-genome sequencing assay for glioma risk stratification. J Cancer Res Clin Oncol 149, 8359–8367 (2023). https://doi.org/10.1007/s00432-023-04716-z
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DOI: https://doi.org/10.1007/s00432-023-04716-z