title: Prediction of Optimal Cytoreductive Surgery of Serous Ovarian Cancer With Gene Expression Data.
abstract: OBJECTIVE
  Cytoreductive surgery is the cornerstone of ovarian cancer (OVCA) treatment. Detractors of initial maximal surgical effort argue that aggressive tumor biology will dictate survival, not the surgical effort. We investigated the role of biology in achieving optimal cytoreduction in serous OVCA using microarray gene expression analysis.
  
  
  METHODS
  For the initial model, we used a gene expression signature from a microarray expression analysis of 124 women with serous OVCA, defining optimal cytoreduction as removal of all disease greater than 1 cm (with 64 women having optimal and 60 suboptimal cytoreduction). We then applied this model to 2 independent data sets: the Australian Ovarian Cancer Study (AOCS; 190 samples) and The Cancer Genome Atlas (TCGA; 468 samples). We performed a second analysis, defining optimal cytoreduction as removal of all disease to microscopic residual, using data from AOCS to create the gene signature and validating results in TCGA data set.
  
  
  RESULTS
  Of the 12,718 genes included in the initial analysis, 58 predicted accuracy of cytoreductive surgery 69% of the time (P = 0.005). The performance of this classifier, measured by the area under the receiver operating characteristic curve, was 73%. When applied to TCGA and AOCS, accuracy was 56% (P = 0.16) and 62% (P = 0.01), respectively, with performance at 57% and 65%, respectively. In the second analysis, 220 genes predicted accuracy of cytoreductive surgery in the AOCS set 74% of the time, with performance of 73%. When these results were validated in TCGA set, accuracy was 57% (P = 0.31) and performance was at 62%.
  
  
  CONCLUSIONS
  Gene expression data, used as a proxy of tumor biology, do not predict accurately nor consistently the ability to perform optimal cytoreductive surgery. Other factors, including surgical effort, may also explain part of the model. Additional studies integrating more biological and clinical data may improve the prediction model.
authors.affiliation: *Gynecologic Oncology Program, Department of Women's Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL; and †Gynecologic Oncology, Department of Obstetrics and Gynecology, University of Iowa Hospitals and Clinics, Iowa City, IA.
authors.forename: Reem
authors.initials: R
authors.lastname: Abdallah
authors.forename: Hye Sook
authors.initials: HS
authors.lastname: Chon
authors.forename: Nadim
authors.initials: N
authors.lastname: Bou Zgheib
authors.forename: Douglas C
authors.initials: DC
authors.lastname: Marchion
authors.forename: Robert M
authors.initials: RM
authors.lastname: Wenham
authors.forename: Johnathan M
authors.initials: JM
authors.lastname: Lancaster
authors.forename: Jesus
authors.initials: J
authors.lastname: Gonzalez-Bosquet
mesh_terms: D014408:Biomarkers, Tumor / Q000235:genetics*; D018284:Cystadenocarcinoma, Serous / Q000235:genetics* / Q000473:pathology / Q000601:surgery; D065426:Cytoreduction Surgical Procedures / Q000592:standards*; D005260:Female; D005500:Follow-Up Studies; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D006801:Humans; D008875:Middle Aged; D060787:Neoplasm Grading; D009367:Neoplasm Staging; D018365:Neoplasm, Residual / Q000235:genetics* / Q000473:pathology / Q000601:surgery; D020411:Oligonucleotide Array Sequence Analysis; D010051:Ovarian Neoplasms / Q000235:genetics* / Q000473:pathology / Q000601:surgery; D011379:Prognosis; D012372:ROC Curve
chemical_list: D014408:Biomarkers, Tumor