title: Stratification of endometrioid endometrial cancer patients into risk levels using somatic mutations.
abstract: Patients with endometrioid endometrial cancer are stratified as high risk and low risk for extrauterine disease by surgical staging. Since patients with low-grade, minimally invasive disease do not benefit from comprehensive staging, pre-surgery stratification into a risk category may prevent unnecessary surgical staging in low risk patients. Our objective was to develop a predictive model to identify risk levels using somatic mutations that could be used preoperatively.
  
  
  
  We classified endometrioid endometrial cancer patients in The Cancer Genome Atlas (TCGA) dataset into high risk and low risk categories: high risk patients presented with stage II, III or IV disease or stage I with high-intermediate risk features, whereas low risk patients consisted of the remaining stage I patients with either no myometrial invasion or low-intermediate risk features. Three strategies were used to build the prediction model: 1) mutational status for each gene; 2) number of somatic mutations for each gene; and 3) variant allele frequencies for each somatic mutation for each gene.
  
  
  
  Each prediction strategy had a good performance, with an area under the curve (or AUC) between 61% and 80%. Analysis of variant allele frequency produced a superior prediction model for risk levels of endometrial cancer as compared to the other two strategies, with an AUC=91%. Lasso and Ridge methods identified 53 mutations that together had the highest predictability for high risk endometrioid endometrial cancer.
  
  
  
  This prediction model will assist future retrospective and prospective studies to categorize endometrial cancer patients into high risk and low risk in the preoperative setting.
authors.affiliation: Department of Obstetrics and Gynecology, University of Iowa, Iowa City, IA, United States.
authors.forename: Donghai
authors.initials: D
authors.lastname: Dai
authors.affiliation: Department of Obstetrics and Gynecology, University of Iowa, Iowa City, IA, United States.
authors.forename: Kristina W
authors.initials: KW
authors.lastname: Thiel
authors.affiliation: Department of Obstetrics and Gynecology, University of Iowa, Iowa City, IA, United States.
authors.forename: Erin A
authors.initials: EA
authors.lastname: Salinas
authors.affiliation: Department of Obstetrics and Gynecology, University of Iowa, Iowa City, IA, United States; Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA, United States.
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Goodheart
authors.affiliation: Department of Obstetrics and Gynecology, University of Iowa, Iowa City, IA, United States; Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA, United States.
authors.forename: Kimberly K
authors.initials: KK
authors.lastname: Leslie
authors.affiliation: Department of Obstetrics and Gynecology, University of Iowa, Iowa City, IA, United States. Electronic address: jesus-gonzalezbosquet@uiowa.edu.
authors.forename: Jesus
authors.initials: J
authors.lastname: Gonzalez Bosquet
mesh_terms: D000368:Aged; D018269:Carcinoma, Endometrioid / Q000235:genetics* / Q000473:pathology; D016889:Endometrial Neoplasms / Q000235:genetics* / Q000473:pathology; D059472:Exome; D005260:Female; D005787:Gene Frequency; D005820:Genetic Testing; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D008957:Models, Genetic; D009154:Mutation; D009367:Neoplasm Staging; D057285:Precision Medicine; D012372:ROC Curve; D012189:Retrospective Studies; D012306:Risk
keywords: Endometrial cancer; Individualized treatment; Prediction model; Risk levels; Somatic mutations