title: Predicting high-risk human papillomavirus infection, progression of cervical intraepithelial neoplasia, and prognosis of cervical cancer with a panel of 13 biomarkers tested in multivariate modeling.
abstract: Comprehensive multivariate models were used to disclose whether any of our previously analyzed 13 markers would be independent predictors of intermediate end point markers in cervical carcinogenesis. The expression of the following biomarkers, E-cadherin, extracellular signal-regulated kinase 1, 67-kd laminin receptor (LR67), matrix metalloproteinase 2, tissue inhibitor of metalloproteinase 2, nuclear factor-kappaB, nm23-H1, p16, proliferating cell nuclear antigen, survivin, human telomerase reverse transcriptase, topoisomerase 2alpha, and vascular endothelial growth factor (VEGF) C in 150 cervical cancer (CC) and 152 cervical intraepithelial neoplasia (CIN) lesions were determined immunohistochemically. Multivariate models were constructed to test predictive power of the markers for 3 outcomes: (1) high-grade CIN, (2) high-risk human papillomavirus (HR-HPV), and (3) CC survival. Performance indicators were calculated and compared by the areas under receiver operating characteristic (ROC) curve. Three marker panels were identified consisting of 5 independent predictors of CIN2 (E-cadherin, extracellular signal-regulated kinase 1, LR67, topoisomerase 2alpha, and VEGF-C), 3 predictors of HR-HPV (survivin, p16, and human telomerase reverse transcriptase), and 2 predictors of CC survival (nm23-H1 and tissue inhibitor of metalloproteinase 2). In predicting CIN2, the best balance between sensitivity (SE) and specificity (SP) was obtained by combining the 2 most powerful predictors in panel 1 (VEGF-C and LR67), giving the area under ROC curve, 0.897 (95% confidence interval [CI], 0.847-0.947); odds ratio, 86.27 (95% CI, 19.71-377.47); SE, 86.0%; SP, 93.3%; positive predictive value (PPV), 99.1%; and negative predictive value (NPV), 43.1%. In a hypothetical screening setting (10,000 women; CIN2 prevalence, 1%), this marker combination should theoretically detect CIN2 with 86.0% SE, 100% SP, 99.1% PPV, and 99.6% NPV, area under ROC curve of 0.930 (95% CI, 0.909-0.951), and odds ratio, 29998.0 (95% CI, 7,879.0-37,338.0). Combining 2 markers (LR67 and VEGF-C) enables accurate detection of high-grade CIN in a clinical setting. However, testing the performance of this marker combination in a screening setting necessitates their analysis in cytological samples.
authors.affiliation: Unità Citoistopatologia, Centro Nazionale di Epidemiologia, Sorveglianza e Promozione della Salute, Istituto Superiore di Sanità, Rome, Italy.
authors.forename: Margherita
authors.initials: M
authors.lastname: Branca
authors.forename: Marco
authors.initials: M
authors.lastname: Ciotti
authors.forename: Colomba
authors.initials: C
authors.lastname: Giorgi
authors.forename: Donatella
authors.initials: D
authors.lastname: Santini
authors.forename: Luigi
authors.initials: L
authors.lastname: Di Bonito
authors.forename: Silvano
authors.initials: S
authors.lastname: Costa
authors.forename: Arrigo
authors.initials: A
authors.lastname: Benedetto
authors.forename: Donatella
authors.initials: D
authors.lastname: Bonifacio
authors.forename: Paola
authors.initials: P
authors.lastname: Di Bonito
authors.forename: Pierluigi
authors.initials: P
authors.lastname: Paba
authors.forename: Luisa
authors.initials: L
authors.lastname: Accardi
authors.forename: Stina
authors.initials: S
authors.lastname: Syrjänen
authors.forename: Cartesio
authors.initials: C
authors.lastname: Favalli
authors.forename: Kari
authors.initials: K
authors.lastname: Syrjänen
mesh_terms: D014408:Biomarkers, Tumor / Q000378:metabolism*; D018450:Disease Progression; D005260:Female; D006801:Humans; D008962:Models, Theoretical; D015999:Multivariate Analysis; D030361:Papillomavirus Infections / Q000150:complications / Q000175:diagnosis* / Q000378:metabolism*; D011237:Predictive Value of Tests; D011379:Prognosis; D017474:Receptors, Laminin / Q000378:metabolism; D012269:Ribosomal Proteins; D012680:Sensitivity and Specificity; D002583:Uterine Cervical Neoplasms / Q000175:diagnosis* / Q000378:metabolism* / Q000821:virology; D042582:Vascular Endothelial Growth Factor C / Q000378:metabolism; D002578:Uterine Cervical Dysplasia / Q000175:diagnosis* / Q000378:metabolism* / Q000821:virology
chemical_list: D014408:Biomarkers, Tumor; C510624:RPSA protein, human; D017474:Receptors, Laminin; D012269:Ribosomal Proteins; D042582:Vascular Endothelial Growth Factor C