E Calura1, L Paracchini2, R Fruscio3, A DiFeo4, A Ravaggi5, J Peronne4, P Martini1, G Sales1, L Beltrame2, E Bignotti5, G Tognon6, R Milani7, L Clivio2, T Dell'Anna7, G Cattoretti8, D Katsaros9, E Sartori5, C Mangioni10, L Ardighieri11, M D'Incalci12, S Marchini2, C Romualdi1. 1. Department of Biology, University of Padova, Padova. 2. Department of Oncology, IRCCS 'Mario Negri' Institute for Pharmacological Research. 3. Clinic of Obstetrics and Gynaecology, University of Milano-Bicocca, San Gerardo Hospital, Monza MaNGO Group, Milano, Italy. 4. Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, USA. 5. Division of Gynaecologic Oncology, 'Angelo Nocivelli' Institute of Molecular Medicine. 6. Department of Obstetrics and Gynaecology, Spedali Civili of Brescia, University of Brescia, Brescia. 7. Clinic of Obstetrics and Gynaecology, University of Milano-Bicocca, San Gerardo Hospital, Monza. 8. Anatomo-pathology Unit, University of Milan-Bicocca, San Gerardo Hospital, Monza. 9. MaNGO Group, Milano, Italy Department of Surgical Science and Gynecology, Azienda Ospedaliero Universitaria, Città della Salute, presidio S.Anna, University of Torino, Torino. 10. MaNGO Group, Milano, Italy A.O. della Provincia di Lecco - P.O.A Manzoni, Lecco. 11. Department of Molecular and Translational Medicine, 'Angelo Nocivelli' Institute for Molecular Medicine Department of Pathology, Spedali Civili of Brescia, University of Brescia, Brescia, Italy. 12. Department of Oncology, IRCCS 'Mario Negri' Institute for Pharmacological Research MaNGO Group, Milano, Italy maurizio.dincalci@marionegri.it.
Abstract
BACKGROUND: Clinical and pathological parameters of patients with epithelial ovarian cancer (EOC) do not thoroughly predict patients' outcome. Despite the good outcome of stage I EOC compared with that of stages III and IV, the risk assessment and treatments are almost the same. However, only 20% of stage I EOC cases relapse and die, meaning that only a proportion of patients need intensive treatment and closer follow-up. Thus, the identification of cell mechanisms that could improve outcome prediction and rationalize therapeutic options is an urgent need in the clinical practice. PATIENTS AND METHODS: We have gathered together 203 patients with stage I EOC diagnosis, from whom snap-frozen tumor biopsies were available at the time of primary surgery before any treatment. Patients, with a median follow-up of 7 years, were stratified into a training set and a validation set. RESULTS AND CONCLUSIONS: Integrated analysis of miRNA and gene expression profiles allowed to identify a prognostic cell pathway, composed of 16 miRNAs and 10 genes, wiring the cell cycle, 'Activins/Inhibins' and 'Hedgehog' signaling pathways. Once validated by an independent technique, all the elements of the circuit resulted associated with overall survival (OS) and progression-free survival (PFS), in both univariate and multivariate models. For each patient, the circuit expressions have been translated into an activation state index (integrated signature classifier, ISC), used to stratify patients into classes of risk. This prediction reaches the 89.7% of sensitivity and 96.6% of specificity for the detection of PFS events. The prognostic value was then confirmed in the external independent validation set in which the PFS events are predicted with 75% sensitivity and 94.7% specificity. Moreover, the ISC shows higher classification performance than conventional clinical classifiers. Thus, the identified circuit enhances the understanding of the molecular mechanisms lagging behind stage I EOC and the ISC improves our capabilities to assess, at the time of diagnosis, the patient risk of relapse.
BACKGROUND: Clinical and pathological parameters of patients with epithelial ovarian cancer (EOC) do not thoroughly predict patients' outcome. Despite the good outcome of stage I EOC compared with that of stages III and IV, the risk assessment and treatments are almost the same. However, only 20% of stage I EOC cases relapse and die, meaning that only a proportion of patients need intensive treatment and closer follow-up. Thus, the identification of cell mechanisms that could improve outcome prediction and rationalize therapeutic options is an urgent need in the clinical practice. PATIENTS AND METHODS: We have gathered together 203 patients with stage I EOC diagnosis, from whom snap-frozen tumor biopsies were available at the time of primary surgery before any treatment. Patients, with a median follow-up of 7 years, were stratified into a training set and a validation set. RESULTS AND CONCLUSIONS: Integrated analysis of miRNA and gene expression profiles allowed to identify a prognostic cell pathway, composed of 16 miRNAs and 10 genes, wiring the cell cycle, 'Activins/Inhibins' and 'Hedgehog' signaling pathways. Once validated by an independent technique, all the elements of the circuit resulted associated with overall survival (OS) and progression-free survival (PFS), in both univariate and multivariate models. For each patient, the circuit expressions have been translated into an activation state index (integrated signature classifier, ISC), used to stratify patients into classes of risk. This prediction reaches the 89.7% of sensitivity and 96.6% of specificity for the detection of PFS events. The prognostic value was then confirmed in the external independent validation set in which the PFS events are predicted with 75% sensitivity and 94.7% specificity. Moreover, the ISC shows higher classification performance than conventional clinical classifiers. Thus, the identified circuit enhances the understanding of the molecular mechanisms lagging behind stage I EOC and the ISC improves our capabilities to assess, at the time of diagnosis, the patient risk of relapse.