title: Integrative Protein-Based Prognostic Model for Early-Stage Endometrioid Endometrial Cancer.
abstract: OBJECTIVE
  Endometrioid endometrial carcinoma (EEC) is the major histologic type of endometrial cancer, the most prevalent gynecologic malignancy in the United States. EEC recurrence or metastasis is associated with a poor prognosis. Early-stage EEC is generally curable, but a subset has high risk of recurrence or metastasis. Prognosis estimation for early-stage EEC mainly relies on clinicopathologic characteristics, but is unreliable. We aimed to identify patients with high-risk early-stage EEC who are most likely to benefit from more extensive surgery and adjuvant therapy by building a prognostic model that integrates clinical variables and protein markers.
  
  
  METHODS
  We used two large, independent early-stage EEC datasets as training (n = 183) and validation cohorts (n = 333), and generated the levels of 186 proteins and phosphoproteins using reverse-phase protein arrays. By applying an initial filtering and the elastic net to the training samples, we developed a prognostic model for overall survival containing two clinical variables and 18 protein markers and optimized the risk group classification.
  
  
  RESULTS
  The Kaplan-Meier survival analyses in the validation cohort confirmed an improved discriminating power of our prognostic model for patients with early-stage EEC over key clinical variables (log-rank test, P = 0.565 for disease stage, 0.567 for tumor grade, and 1.3 × 10(-4) for the integrative model). Compared with clinical variables (stage, grade, and patient age), only the risk groups defined by the integrative model were consistently significant in both univariate and multivariate analyses across both cohorts.
  
  
  CONCLUSIONS
  Our prognostic model is potentially of high clinical value for stratifying patients with early-stage EEC and improving their treatment strategies.
authors.affiliation: Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas. Department of Applied Mathematics, Kumoh National Institute of Technology, Gumi-si, South Korea.
authors.forename: Ji-Yeon
authors.initials: JY
authors.lastname: Yang
authors.affiliation: Centre for Cancer Biomarkers, Department of Clinical Science, The University of Bergen, Bergen, Norway. Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway.
authors.forename: Henrica M J
authors.initials: HM
authors.lastname: Werner
authors.affiliation: Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
authors.forename: Jie
authors.initials: J
authors.lastname: Li
authors.affiliation: Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas.
authors.forename: Shannon N
authors.initials: SN
authors.lastname: Westin
authors.affiliation: Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
authors.forename: Yiling
authors.initials: Y
authors.lastname: Lu
authors.affiliation: Centre for Cancer Biomarkers, Department of Clinical Science, The University of Bergen, Bergen, Norway. Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway.
authors.forename: Mari K
authors.initials: MK
authors.lastname: Halle
authors.affiliation: Centre for Cancer Biomarkers, Department of Clinical Science, The University of Bergen, Bergen, Norway. Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway.
authors.forename: Jone
authors.initials: J
authors.lastname: Trovik
authors.affiliation: Centre for Cancer Biomarkers, Department of Clinical Science, The University of Bergen, Bergen, Norway. Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway.
authors.forename: Helga B
authors.initials: HB
authors.lastname: Salvesen
authors.affiliation: Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas. hliang1@mdanderson.org gmills@mdanderson.org.
authors.forename: Gordon B
authors.initials: GB
authors.lastname: Mills
authors.affiliation: Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas. Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas. hliang1@mdanderson.org gmills@mdanderson.org.
authors.forename: Han
authors.initials: H
authors.lastname: Liang
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D014408:Biomarkers, Tumor / Q000378:metabolism; D018269:Carcinoma, Endometrioid / Q000378:metabolism* / Q000473:pathology*; D018572:Disease-Free Survival; D016889:Endometrial Neoplasms / Q000378:metabolism* / Q000473:pathology*; D005260:Female; D006801:Humans; D053208:Kaplan-Meier Estimate; D008875:Middle Aged; D009364:Neoplasm Recurrence, Local / Q000378:metabolism / Q000473:pathology; D009367:Neoplasm Staging / Q000379:methods; D011379:Prognosis; D011506:Proteins / Q000378:metabolism*; D055815:Young Adult
chemical_list: D014408:Biomarkers, Tumor; D011506:Proteins