Department of Experimental Oncology and Molecular Medicine

Presentazione sul tema: "Department of Experimental Oncology and Molecular Medicine"— Transcript della presentazione:

1 Department of Experimental Oncology and Molecular Medicine
Unit of Molecular Therapies XXIV Riunione MITO Pisa 4 Dicembre 2014 Development of a molecular predictor of disease recurrance by MITO2 miRNA profiling

2 2 MITO2 miRNA profiling Case material: 179 cases (of 226 profiled); hereafter OC179 Platform: Agilent SurePrint human miRNA arrays (mirBASE17.0) Main aims: Identification of groups of pts diverging from specific baselines Identification of miRNA-related subgroups of patients development of a prognostic model Overall design - training set: OC179 from MITO2 - validation set1: INT-CRO series (microarray data); hereafter OC263 - validation set2: TCGA (microarray data ); hereafter OC452

3 Case materials profiled for miRNA expression
2 Case materials profiled for miRNA expression

4 Case materials analyzed for miRNA expression
2 Case materials analyzed for miRNA expression Total number of cases profiled at INT-Milan: - 263 cases on Illumina Platform from INT and CRO - 179 cases on Agilent Platform from MITO2 In-silico Case Material analyzed - 452 cases on Agilent Platform from TCGA consortium Total: 894 cases “the greatest data set available for EOC miRNA profile” Data merging miRNA re-annotation: Illumina data probes detected corresponding to 581 miRNA Agilent data –Milan 921 probes detected Agilent data –TCGA probes detected Re-annotation on miRBase unique miRNA shared among all studies Batch effect Adjustment: the empirical Bayes (EB) method [Johnson, 2007]

5 Case materials analyzed for miRNA expression
2 Case materials analyzed for miRNA expression Training set: OC179 from MITO2 Median PFS months Validation set1: OC263 from INT-CRO Median PFS 16 months Validation set2: OC452 from TCGA Median PFS months

6 2 Identification of groups of patients diverging from specific miRNA baselines A) Data deconvolution: definition of a baseline group of patient with similar clinical characteristics. Definition of each patient “molecular distance” from baseline B) Dimensional shape recognition by topology networking Colored by deviation from baseline: Blue similar to baseline Red different from baseline

7 Approach for baseline definition on OC179 MITO2 dataset
Stage III-IV patients with no residual disease (NED) and long PFS (no relapse) ID GF (Milano) Age (years) FIGO Grading Histo Residual PFS Status PFS Time (months) AP91 74 IV G3 serous none 73 AQ29 53 III 94 AR59 63 67 AQ67 58 Undif 100 AQ98 75 90 AP33 51 AR32 64 83 AR52 57

8 2 Baseline definition Patients NED and with long PFS (no relapse) A B
Time (Months) Survival function P=0.0016 A B C OC179 MITO2 dataset Patients closer to baseline (A) have similar good prognosis

9 EOC subtypes validation in independent datasets
2 EOC subtypes validation in independent datasets OC263 –INT-CRO P=1.87E-04

10 2 Identification of OC Subtypes driven by miRNA expression patterns on OC179 MITO2 dataset Consensus matrix OC179 MITO2 dataset Silhouette Plot P=

11 EOC subtypes validation in independent datasets
2 OC263 –INT-CRO P=3.98E-14

12 2 Patients prognosis is correctly identified by both miRNA-driven sub classification

13 miRNAs differentially expressed between arm A vs. Arm C patients
2 miRNAs differentially expressed between arm A vs. Arm C patients Time (Months) Survival function P=0.0016 A B C OC179 MITO2 dataset

14 How to develop a clinically useful classifier?
2 E’ stato utilizzato un algoritmo che, sulla base dei dati di PFS della casistica MITO2 (OC179) e della relativa espressione dei 385 miRNAs rilevati, ha costruito un modello in grado di stratificare le pazienti ad alto e basso rischio di ricaduta. Il modello contiene 35 miRNAs che dopo cross-validazione (10-fold) mantengono il loro impatto prognostico anche se con rilevanza diversa. miRNAs la cui espressione è associata a prognosi sfavorevole (score superiore al cut-off di algoritmo) miRNAs la cui espressione è associata a prognosi favorevole (score inferiore al cut-off di algoritmo)

15 2 OC179 – MITO2 patients’ stratification according to the molecular classifier P=6.83E-4 high risk low risk Sample size  Median PFS (months) HR= % CI = to

16 Molecular classifier validation on independent datasets
2 Molecular classifier validation on independent datasets

17 2 35 miRNA molecular classifier performance in defining patients’ prognosis

18 2 35 miRNA molecular classifier performance in defining patients’ prognosis

19 miRNAs identified with different strategies
2 miRNAs identified with different strategies

20 The miRNA molecular classifier is an independent prognostic marker
2 The miRNA molecular classifier is an independent prognostic marker Covariates: 35 miRNA model: above threshold cut-off vs. below threshold cut-off Stage: III-IV vs. I-II Grade: vs. 1,2 Histology: serous vs. others Residual disease: >1cm vs. <1cm

21 MITO2 miRNA profiling: conclusions
Punti di forza: prima meta-analysis di miRNAs su EOC EOC dataset al momento più numeroso (n=894; OC179 da MITO2; OC263 da INT-CRO; OC452 da TCGA) meta-analysis su diverse piattaforme gli approcci “baseline” e subtyping individuano un gruppo di tumori (cluster4/Cl4) con prognosi molto sfavorevole questo cluster si ritrova nei validation sets Punti critici: annotazioni diverse tra piattaforme riduzione dei miRNA comuni continuo aggiornamento miRBASE Take home message: analisi dei casi del clusterC/Cl4 individuazione di miRNA per validazione funzionale/biologica ClusterC/Cl4 quale gruppi di pazienti vanno utilizzati per la costruzione di un corretto modello prognostico?????