Clinical factors associated with comprehensive genomic variation profiling of cervical cancer.

Abstract

62 Background: To better understand the mechanism contributing to cervical carcinogenesis, we perform a comprehensive analysis of genomic alterations in cervical cancer (CC). Methods: We conducted whole-exome sequencing of 43 CCs and established strict integrated workflow of genomic analysis. Correlation analysis was performed to measure the relationships between gene mutations and clinical characteristics of CC patients. Results: Genomic analysis revealed 28 genes frequently mutated in CC including BRD4 (4/43), AXL (4/43), MED12 (4/43), which are undetermined genomic features in CC, and identified recurrent genetic mutations in PIK3CA (16/43), KMT2D (11/43), KMT2C (6/43), FBXW7 (5/43), FAT1 (5/43), ERBB2 (4/43), EP300 (3/43) and NFE2L2 (3/43). Intriguingly, CC patients harbored high frequent mutations in BRCA2 (7/43), but not in BRCA1. The relationship between BRCA2 mutations and clinical factors is yet to be determined. The identified mutations predominately resulted in the dysregulation of the PI3K/AKT/mTOR pathway. Interestingly, we found that AXL mutations were positively correlated with FIGO stage ( P = 0.028), regional lymph node metastasis ( P = 0.011) and distant metastasis ( P = 0.022). KMT2C mutations were positively correlated with FIGO stage ( P = 0.004) and distant metastasis ( P = 0.009). SF3B1 mutations were positively correlated with regional lymph node metastasis ( P = 0.027) and distant metastasis ( P< 0.001). NFE2L2, ERBB2, RAC1, MED12, MET, BAP1, PTPRD and HNF1Amutations were positively correlated with distant metastasis ( P = 0.045, P = 0.004, P = 0.022, P = 0.001, P< 0.001, P< 0.001, P = 0.014, P< 0.001, respectively). POLD1 mutations were positively associated with regional lymph node metastasis ( P = 0.029). However, NOTCH1 mutations were negatively associated with regional lymph node metastasis ( P = 0.047). STK11 mutations were negatively associated with FIGO stage ( P = 0.013). Conclusions: Our results highlight the application of deep sequencing for understanding the molecular mechanisms and uncovering potential diagnostic and therapeutic targets of CC.