Wenfang Yang

A Novel Germline Mutation of BRCA1 and Integrated Analysis With Somatic Mutation in a Chinese Multi-Cancer Family

Abstract The presence of mutations in the BRCA1 gene (MIM: 113705) is widely recognized as a significant genetic predisposition for ovarian cancer. This study investigated the genomic mutations in a Chinese family with a history of ovarian, breast, and rectal adenocarcinoma. A novel germline mutation (Phe1695Val) in BRCA1 was identified through whole-exome sequencing. Subsequently, we performed whole-genome sequencing to identify somatic mutations and analyze mutational signatures in individuals carrying the novel germline mutation. Our findings revealed a correlation between somatic mutational signatures and the BRCA1 germline mutation in the proband with ovarian cancer, while no such association was observed in the tumor tissue from the patient with breast cancer. Furthermore, distinct somatic driver mutations were identified, a truncated mutation in the TP53 gene in the ovarian tumor tissue, and a hotspot mutation in the PIK3CA gene in the breast cancer. According to our findings, the BRCA1 F1695V mutation is linked to ovarian cancer susceptibility in the family and causes specific somatic mutational profiles.

Competing risk nomogram predicting cancer‐specific mortality for endometrial cancer patients treated with hysterectomy

AbstractBackgroundThe incidence of endometrial cancer has tended to increase in recent years. However, competing risk nomogram combining comprehensive factors for endometrial cancer patients treated with hysterectomy is still scarce. Therefore, we aimed to build a competing risk nomogram predicting cancer‐specific mortality for endometrial cancer patients treated with hysterectomy.MethodsPatients diagnosed with endometrial cancer between 2010 and 2012 were abstracted from the Surveillance, Epidemiology, and End Results (SEER) database. Competing risk model was performed to select prognostic variables to build the competing risk nomogram to predict the cumulative 3‐ and 5‐year incidences of endometrial cancer‐specific mortality. Harrell's C‐index, receiver operating characteristic (ROC) curve, and calibration plot were used in the internal validation. And decision curve analysis was applied to evaluate clinical utility.ResultsA total of 10,447 patients were selected for analysis. The competing risk nomogram identified eight prognostic variables, including age at diagnosis, race, marital status at diagnosis, grade, histology, tumor size, FIGO stage, and number of regional nodes positive. The C‐index of the competing risk nomogram was 0.857 (95% confidence interval [CI]: 0.854–0.859), and the calibration plots were adequately fitted. When the threshold probabilities were between 1% and 57% for 3‐year prediction and between 2% and 67% for 5‐year prediction, the competing risk nomogram was of good clinical utility.ConclusionsA competing risk nomogram for endometrial cancer patients treated with hysterectomy was successfully built and internally validated. It was an accurately predicted and clinical useful tool, which could play an important role in consulting and health care management of endometrial cancer patients.

Calculating the overall survival probability in patients with cervical cancer: a nomogram and decision curve analysis-based study

Abstract Background Cervical cancer has long been a common malignance troubling women. However, there are few studies developing nomogram with comprehensive factors for the prognosis of cervical cancer. Hence, we aimed to build a nomogram to calculate the overall survival (OS) probability in patients with cervical cancer. Methods Data of 9876 female patients in SEER database and diagnosed as cervical cancer during 2010–2015, was retrospectively analyzed. Univariate and multivariate Cox proportional hazard regression model were applied to select predicted factors and a nomogram was developed to visualize the prediction model. The nomogram was compared with the FIGO stage prediction model. Harrell’s C-index, receiver operating curve, calibration plot and decision curve analysis were used to assess the discrimination, accuracy, calibration and clinical utility of the prediction models. Result Eleven independent prognostic variables, including age at diagnosis, race, marital status at diagnosis, grade, histology, tumor size, FIGO stage, primary site surgery, regional lymph node surgery, radiotherapy and chemotherapy, were used to build the nomogram. The C-index of the nomogram was 0.826 (95% CI: 0.818 to 0.834), which was better than that of the FIGO stage prediction model (C-index: 0.785, 95% CI: 0.776 to 0.793). Calibration plot of the nomogram was well fitted in 3-year overall OS prediction, but overfitting in 5-year OS prediction. The net benefit of the nomogram was higher than the FIGO prediction model. Conclusion A clinical useful nomogram for calculating the overall survival probability in cervical cancer patients was developed. It performed better than the FIGO stage prediction model and could help clinicians to choose optimal treatments and precisely predict prognosis in clinical care and research.