Marina Bagnoli1, Silvana Canevari2, Daniela Califano3, Simona Losito4, Massimo Di Maio5, Francesco Raspagliesi6, Maria Luisa Carcangiu7, Giuseppe Toffoli8, Erika Cecchin8, Roberto Sorio9, Vincenzo Canzonieri10, Daniela Russo3, Giosué Scognamiglio4, Gennaro Chiappetta3, Gustavo Baldassarre11, Domenica Lorusso6, Giovanni Scambia12, Gian Franco Zannoni13, Antonella Savarese14, Mariantonia Carosi15, Paolo Scollo16, Enrico Breda17, Viviana Murgia18, Francesco Perrone5, Sandro Pignata19, Loris De Cecco2, Delia Mezzanzanica20. 1. Molecular Therapies Unit, Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy. 2. Functional Genomics, Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy. 3. Functional Genomic Unit, Istituto Nazionale per lo Studio e la Cura dei Tumori "Fondazione G Pascale", IRCCS, Naples, Italy. 4. Surgical Pathology Unit, Istituto Nazionale per lo Studio e la Cura dei Tumori "Fondazione G Pascale", IRCCS, Naples, Italy. 5. Clinical Trials Unit, Istituto Nazionale per lo Studio e la Cura dei Tumori "Fondazione G Pascale", IRCCS, Naples, Italy. 6. Unit of Gynaecological Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy. 7. Anatomic Pathology 1 Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy. 8. Experimental and Clinical Pharmacology Unit, Centro di Riferimento Oncologico, Istituto Ricovero e Cura Carattere Scientifico (CRO-IRCCS), Aviano, Italy. 9. Medical Oncology C, Centro di Riferimento Oncologico, Istituto Ricovero e Cura Carattere Scientifico (CRO-IRCCS), Aviano, Italy. 10. Unit of Pathology, Centro di Riferimento Oncologico, Istituto Ricovero e Cura Carattere Scientifico (CRO-IRCCS), Aviano, Italy. 11. Division of Experimental Oncology 2, Centro di Riferimento Oncologico, Istituto Ricovero e Cura Carattere Scientifico (CRO-IRCCS), Aviano, Italy. 12. Department of Obstetrics and Gynecology, Gynecologic Oncology Unit, Catholic University of the Sacred Heart, Rome, Italy. 13. Department of Human Pathology, Division of Gynecologic Pathology, Catholic University of the Sacred Heart, Rome, Italy. 14. Division of Medical Oncology 1, Regina Elena Cancer Institute, Rome, Italy. 15. Division of Pathology, Regina Elena Cancer Institute, Rome, Italy. 16. Department of Obstetrics and Gynecology, Azienda Ospedaliera Cannizzaro, Catania, Italy. 17. Medical Oncology Unit Ospedale S Giovanni Calibita Fatebenefratelli, Rome, Italy. 18. Medical Oncology Unit Ospedale S Chiara, Trento, Italy. 19. Department of Urogynaecological Oncology, Istituto Nazionale per lo Studio e la Cura dei Tumori "Fondazione G Pascale", IRCCS, Naples, Italy. 20. Molecular Therapies Unit, Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy. Electronic address: delia.mezzanzanica@istitutotumori.mi.it.
Abstract
BACKGROUND: Risk of relapse or progression remains high in the treatment of most patients with epithelial ovarian cancer, and development of a molecular predictor could be a valuable tool for stratification of patients by risk. We aimed to develop a microRNA (miRNA)-based molecular classifier that can predict risk of progression or relapse in patients with epithelial ovarian cancer. METHODS: We analysed miRNA expression profiles in three cohorts of samples collected at diagnosis. We used 179 samples from a Multicenter Italian Trial in Ovarian cancer trial (cohort OC179) to develop the model and 263 samples from two cancer centres (cohort OC263) and 452 samples from The Cancer Genome Atlas epithelial ovarian cancer series (cohort OC452) to validate the model. The primary clinical endpoint was progression-free survival, and we adapted a semi-supervised prediction method to the miRNA expression profile of OC179 to identify miRNAs that predict risk of progression. We assessed the independent prognostic role of the model using multivariable analysis with a Cox regression model. FINDINGS: We identified 35 miRNAs that predicted risk of progression or relapse and used them to create a prognostic model, the 35-miRNA-based predictor of Risk of Ovarian Cancer Relapse or progression (MiROvaR). MiROvaR was able to classify patients in OC179 into a high-risk group (89 patients; median progression-free survival 18 months [95% CI 15-22]) and a low-risk group (90 patients; median progression-free survival 38 months [24-not estimable]; hazard ratio [HR] 1·85 [1·29-2·64], p=0·00082). MiROvaR was a significant predictor of progression in the two validation sets (OC263 HR 3·16, 95% CI 2·33-4·29, p<0·0001; OC452 HR 1·39, 95% CI 1·11-1·74, p=0·0047) and maintained its independent prognostic effect when adjusted for relevant clinical covariates using multivariable analyses (OC179: adjusted HR 1·48, 95% CI 1·03-2·13, p=0·036; OC263: adjusted HR 3·09 [2·24-4·28], p<0·0001; and OC452: HR 1·41 [1·11-1·79], p=0·0047). INTERPRETATION: MiROvaR is a potential predictor of epithelial ovarian cancer progression and has prognostic value independent of relevant clinical covariates. MiROvaR warrants further investigation for the development of a clinical-grade prognostic assay. FUNDING: AIRC and CARIPLO Foundation.
BACKGROUND: Risk of relapse or progression remains high in the treatment of most patients with epithelial ovarian cancer, and development of a molecular predictor could be a valuable tool for stratification of patients by risk. We aimed to develop a microRNA (miRNA)-based molecular classifier that can predict risk of progression or relapse in patients with epithelial ovarian cancer. METHODS: We analysed miRNA expression profiles in three cohorts of samples collected at diagnosis. We used 179 samples from a Multicenter Italian Trial in Ovarian cancer trial (cohort OC179) to develop the model and 263 samples from two cancer centres (cohort OC263) and 452 samples from The Cancer Genome Atlas epithelial ovarian cancer series (cohort OC452) to validate the model. The primary clinical endpoint was progression-free survival, and we adapted a semi-supervised prediction method to the miRNA expression profile of OC179 to identify miRNAs that predict risk of progression. We assessed the independent prognostic role of the model using multivariable analysis with a Cox regression model. FINDINGS: We identified 35 miRNAs that predicted risk of progression or relapse and used them to create a prognostic model, the 35-miRNA-based predictor of Risk of Ovarian Cancer Relapse or progression (MiROvaR). MiROvaR was able to classify patients in OC179 into a high-risk group (89 patients; median progression-free survival 18 months [95% CI 15-22]) and a low-risk group (90 patients; median progression-free survival 38 months [24-not estimable]; hazard ratio [HR] 1·85 [1·29-2·64], p=0·00082). MiROvaR was a significant predictor of progression in the two validation sets (OC263 HR 3·16, 95% CI 2·33-4·29, p<0·0001; OC452 HR 1·39, 95% CI 1·11-1·74, p=0·0047) and maintained its independent prognostic effect when adjusted for relevant clinical covariates using multivariable analyses (OC179: adjusted HR 1·48, 95% CI 1·03-2·13, p=0·036; OC263: adjusted HR 3·09 [2·24-4·28], p<0·0001; and OC452: HR 1·41 [1·11-1·79], p=0·0047). INTERPRETATION: MiROvaR is a potential predictor of epithelial ovarian cancer progression and has prognostic value independent of relevant clinical covariates. MiROvaR warrants further investigation for the development of a clinical-grade prognostic assay. FUNDING: AIRC and CARIPLO Foundation.
Authors: Kun Zhou; Monique A Spillman; Kian Behbakht; Julia M Komatsu; Juan E Abrahante; Douglas Hicks; Brent Schotl; Evan Odean; Kenneth L Jones; Michael W Graner; Lynne T Bemis Journal: Anal Biochem Date: 2017-08-10 Impact factor: 3.365
Authors: Sanjana Balachandra; Samuel B Kusin; Rebecca Lee; James-Michael Blackwell; Jasmin A Tiro; Lindsay G Cowell; Cheng-Ming Chiang; Shwu-Yuan Wu; Sanskriti Varma; Erika L Rivera; Helen G Mayo; Lianghao Ding; Baran D Sumer; Jayanthi S Lea; Aditya Bagrodia; Linda M Farkas; Richard Wang; Carole Fakhry; Kristina R Dahlstrom; Erich M Sturgis; Andrew T Day Journal: Cancer Date: 2020-12-03 Impact factor: 6.860
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