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Genomic predictive model for recurrence and metastasis development in head and neck squamous cell carcinoma patients.
- Source :
-
Scientific reports [Sci Rep] 2017 Oct 24; Vol. 7 (1), pp. 13897. Date of Electronic Publication: 2017 Oct 24. - Publication Year :
- 2017
-
Abstract
- The head and neck squamous cell carcinoma (HNSCC) population consists mainly of high-risk for recurrence and locally advanced stage patients. Increased knowledge of the HNSCC genomic profile can improve early diagnosis and treatment outcomes. The development of models to identify consistent genomic patterns that distinguish HNSCC patients that will recur and/or develop metastasis after treatment is of utmost importance to decrease mortality and improve survival rates. In this study, we used array comparative genomic hybridization data from HNSCC patients to implement a robust model to predict HNSCC recurrence/metastasis. This predictive model showed a good accuracy (>80%) and was validated in an independent population from TCGA data portal. This predictive genomic model comprises chromosomal regions from 5p, 6p, 8p, 9p, 11q, 12q, 15q and 17p, where several upstream and downstream members of signaling pathways that lead to an increase in cell proliferation and invasion are mapped. The introduction of genomic predictive models in clinical practice might contribute to a more individualized clinical management of the HNSCC patients, reducing recurrences and improving patients' quality of life. The power of this genomic model to predict the recurrence and metastases development should be evaluated in other HNSCC populations.
- Subjects :
- Adult
Chromosomes, Human genetics
Cohort Studies
Comparative Genomic Hybridization
Female
Humans
Male
Middle Aged
Neoplasm Metastasis
Prognosis
Recurrence
Squamous Cell Carcinoma of Head and Neck diagnostic imaging
Genomics
Models, Statistical
Squamous Cell Carcinoma of Head and Neck genetics
Squamous Cell Carcinoma of Head and Neck pathology
Subjects
Details
- Language :
- English
- ISSN :
- 2045-2322
- Volume :
- 7
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- Scientific reports
- Publication Type :
- Academic Journal
- Accession number :
- 29066758
- Full Text :
- https://doi.org/10.1038/s41598-017-14377-x