XXV Riunione MITO Napoli 25 Giugno 2014 MITO2 miRNA microarray profile identifies a strong predictor of disease relapse in ovarian cancer XXV Riunione MITO Napoli 25 Giugno 2014 MITO2 miRNA microarray profile identifies a strong predictor of disease relapse in ovarian cancer Unit of Molecular Therapies Department of Experimental Oncology and Molecular Medicine

2 2 MITO2 miRNA profiling Aim: development of a prognostic model to predict disease early relapse Overall design - training set: 179 cases from MITO2 trial; OC179 - validation set1: 263 cases from INT-CRO series; OC263 - validation set2: 452 cases from TCGA data; OC452 Overall 894 EOC cases were analyzed: “the largest miRNA EOC data set so far available” Three different platforms used for profiling; Re-annotation of all detected miRNAs on miRBase unique miRNA shared among all studies

2 2 Patients’ clinical-pathological characteristics

n events median 0.95CI PFS time (months) n events median 0.95CI n events median 0.95CI PFS time (months) n events median 0.95CI Surv time (months) n events median 0.95 CI nyr 63-NA time (months) n events median 0.95CI Surv time (months) 2 2 Patients’ clinical-pathological characteristics OC179 OC263 OC452 OS PFS

miRNA-based classifier End point: disease recurrance Application of an algorithm that on the basis of time-to-progression and relative expression of the 385 miRNAs, classified MITO2 patients as high or low risk to relapse. After a 10-fold cross validation a model was developed containing 35 unique miRNAs whose expression, although with different relevance, significantly contributed to define the risk of relapse of MITO2 cohort. miRNAs (19) whose expression associated to poor prognosis miRNAs (16) whose expression associated to good prognosis 2 2

2 2 OC179 – MITO2 patients’ stratification according to n° events median 0.95Cl HR OD SOD PFS time (months) Log-rank P<1.001 n° events median 0.95Cl HR high risk low risk nyr Log-rank P<1.001 the molecular classifierResidual disease

TTR time (months) 2 2 Stratification for risk prediction and RD improved prognosis of a patients’ subgroup n° events median 0.95Cl miRLr-OD nyr miRLr-SOD nyr miRHr-OD miRHr-SOD High risk-OD includes 9 stage I-II patients defined NED at primary surgery Stratification for risk prediction and RD improved prognosis of a patients’ subgroup and identified a subset of patients with favorable clinical prognostic marker and high risk of relapse

2 2 Molecular classifier validation on independent datasets neventsmedian0.95CI miR=L miR=H HR 3.16 neventsmedian0.95CI miR=L miR=H HR 1.39

TTR neventsmedian0.95CI miRH_OD miRH_SOD miRL_OD miRL_SOD miRH_ OD miRH_ SOD miRL_ OD miRL_ SOD TTR time (months) High risk-OD includes 8 stage I-II patients, 7 defined NED at primary surgery High risk-OD includes 14 stage I-II patients, 11 defined NED at primary surgery n° events median 0.95Cl miRLr-OD miRLr-SOD miRHr-OD miRHr-SOD OC452 OC263 n° events median 0.95Cl miRLr-OD miRLr-SOD miRHr-OD miRHr-SOD Stratification for risk prediction and RD improved prognosis of a patients’ subgroup and identified a subset of patients with favorable clinical prognostic marker but high risk of relapse

PFS time (months) miR_treat 2 2 No interaction with treatment arm miR-Low risk miR-high risk

2 2 The miRNA molecular classifier is an independent prognostic marker Covariates: Stage: III-IV vs. I-II Residual disease: >1cm vs. <1cm 35 miRNA model: above threshold cut-off vs. below threshold cut-off The miRNA molecular classifier is an independent prognostic marker also in HGSOC subgroup

Punti di forza:  prima meta-analisi di miRNAs su EOC  EOC dataset al momento più numeroso (n=894; OC179 da MITO2; OC263 da INT-CRO; OC452 da TCGA)  meta-analisi su diverse piattaforme  analisi di sottotipi miRNA integrabili con espressione genica (RNAseq) per sviluppo di modelli predittivi di risposta  Emendamento per RNAseq su MITO2 ed emendamento su MITO7 per validazione custom panel miRNA/Genes 2 2 MITO2 miRNA profiling: conclusions P= OC179 MITO2

Punti critici:  annotazioni diverse tra piattaforme con conseguente riduzione dei miRNA comuni alle piattaforme  continuo aggiornamento miRBASE  necessità di recuperare nuove casistiche indipendenti di validazione: MITO7, MITO16?? 2 2 MITO2 miRNA profiling: conclusions Ringraziamenti:  Sandro Pignata, Danela Califano, Simona Losito, Gennaro Chiappetta, Massimo Di Maio, Franco Perrone  Loris De Cecco, Marina Bagnoli, Silvana Canevari, Francesco Raspagliesi, Ketta Lorusso, Maria Luisa Carcangiu  Giuseppe Toffoli, Erika Cecchin  tutto il gruppo MITO

MITO2 miRNA profiling: future plans miRNA expression patterns on OC179 identified 4 robust and stable patients’ subtypes with different prognosis Consensus matrix miRNA subtype classifier maintained prognostic relevance in OC263 and TCGA case materials 2 2 Strenght: possible integration with gene expression profile to predict response to therapy P= OC179 MITO2