A prognostic miRNA/mRNA signature from the A prognostic miRNA/mRNA signature from the integrated analysis ofintegrated analysis of

patients with invasive breast cancerpatients with invasive breast cancer

FGED, June 20th 2013 Stefano Volinia, University of Ferrara – Ohio State Univ

Ferrara, Italy Ferrara, Italy - - Marco Galasso, Carlotta Zerbinati, Marco Manfrini, Maurizio Marco Galasso, Carlotta Zerbinati, Marco Manfrini, Maurizio Previati, Maria Elena Sana, Riccardo Zanella, Marco Catozzi, Christina Scheiner.Previati, Maria Elena Sana, Riccardo Zanella, Marco Catozzi, Christina Scheiner.

Ohio State Ohio State - - Gianpiero Di Leva, Cecilia Fernandez, Jeff Palatini, Sarah Warner, Gianpiero Di Leva, Cecilia Fernandez, Jeff Palatini, Sarah Warner, Arianna Bottoni, Alessandro Cannella, Hansjuerg Alder, & Prof. Carlo Croce.Arianna Bottoni, Alessandro Cannella, Hansjuerg Alder, & Prof. Carlo Croce.

Ferrara

Columbus

Breast Cancer and Breast Cancer and microRNAsmicroRNAs

Iorio, M. V. et al. Cancer Res 2005

Clustering of six

solid cancersby

miRNA expression(Volinia et al, 2006)

Average Fold change:

Cancer/Normal

Stage 0 Breast Cancer: DCISStage 0 Breast Cancer: DCIS

Progression from Normal Breast to Invasive Ductal Carcinoma:

microRNAs

• miR-210 Is Induced by Hypoxia and correlated with prognosis in BC- Camps et al, Clin Cancer Res 2008

• HIF1 regulates the expression of mir-210 in a variety of tumor types through a hypoxia-responsive element – Huang et al, Molecular Cell 2009

• miR-210 is overexpressed in primary tumors with distant metastasis – Volinia et al, PNAS 2013

• Circulating biomarker for early cancer detection

miR-210 in cancer

Progression from Normal Breast to Invasive Ductal Carcinoma:

mRNAs

Bet: we can use the molecular information for the stratification of patients.

• To identify molecular mechanisms.• To assess individual risk.• To administer appropriate therapy.

TCGA InvasiveDuctalCarcinomaCohort(n=466)

TCGA mRNA

TCGA miRNA

N Stage Intrinsic SubtypeDisease Stage EROther Classes

Hazard Ratios

. . . .

DNA methylation Somatic Mutations

Prognostic gene set

TCGA IDC cohort integrated RNA profile

(n=466)

UK cohort (n=207)

Bos cohort (n=195)

TNBC cohort (n=383)

Hatzis cohort (n=508)

Kao cohort (n=327)

Wang cohort (n=286)

TRANSBIG cohort (n=198)

NKI cohort (n=295)

Matrix of Hazard Ratios

inBreast Cancer

subclasses

The prognostic performance of 37-gene miRNA/mRNA integrated predictor in IDC

(TCGA cohort)

The Receiver Operating Characteristic (ROC) curve plots the true-positive vs. false-positive predictions, thus higher AUC indicates better model performance

(with AUC=0.5 indicating random performance).

Variables included in the initial model:TP53 Mut, PIK3CA/AKT/PTEN Mut, PAM50 subtypes, Disease Stage, T stage, Estrogen Receptor, N stage.Stratified by age groups (143 patients <=55 years, 195 patients >55 years).Method = Backward Stepwise (Wald)

Multivariate Cox proportional hazards model for OS in IDC

CohortClinical

Endpoint

RNAprofile

IntegratedmiRNA/mRNA

10-miRNAGGI

97-geneIGS

186-Gene95-gene

Naoi76-gene

Rotterdam

NKIMammaPrint

70-gene

Oncotype DX

TCGA IDC(n=466)

OSmRNA/miRNA

0.74(p<0.001)

n.s.§0.62

(p=0.034)0.61

(p=0.032)0.61

(p=0.043)n.s.§ n.s.§ n.s.§

TCGA IDCEarly stages I and II (n=348)

OSmRNA/miRNA

0.77(p<0.001)

n.s.§ n.s.§ n.s.§ n.s.§ n.s.§0.66

(p=0.028)n.s.§

UK (n=207)

DRFSmRNA/miRNA

0.65(p=0.004)

0.76(p<0.001)

0.66(p=0.001)

0.70(p<0.001)

0.72(p<0.001)

0.66(p=0.003)

0.73(p<0.001)

0.68(p<0.001)

NKI(n=295)

OS mRNA0.75

(p<0.001)na#

0.73(p<0.001)

0.75(p<0.001)

0.74(p<0.001)

0.67(p<0.001)

0.76(p<0.001)

0.76(p<0.001)

Hatzis(n=508)

DRFS mRNA0.65

(p<0.001)na#

0.66(p<0.001)

0.65(p<0.001)

0.64(p<0.001)

0.62(p=0.001)

0.62(p<0.001)

0.63(p<0.001)

Kao(n=327)

OS mRNA0.62

(p=0.006)na#

0.58(p=0.051)

0.66(p<0.001)

0.66(p<0.001)

0.58(p=0.038)

0.64(p=0.005)

0.65(p<0.001)

Wang(n=286)

DRFS mRNA0.59

(p=0.025)na#

0.59(p=0.017)

0.60(p=0.006)

0.71(p<0.001)

0.65(p<0.001)

0.57(p=0.051)

0.62(p<0.001)

TRANSBIG(n=198)

OS mRNA0.64

(p=0.015)na#

0.70(p=0.002)

0.63(p=0.018)

n.s.§0.64

(p=0.023)n.s.§

0.65(p<0.001)

Bos (n=195)

DRFS mRNA0.68

(p=0.011)na#

0.67(p=0.031)

0.68(p=0.016)

n.s.§ n.s.§0.69

(p=0.016)0.74

(p=0.003)TNBC(n=383)

DRFS mRNA0.69

(p<0.001)na#

0.65(p<0.001)

0.68(p<0.001)

0.69(p<0.001)

0.65(p<0.001)

0.68(p<0.001)

0.66(p<0.001)

The Prognostic Values of 8 RNA Signatures in 9 Breast Cancer Cohorts

§ n.s. , p>0.05. The permutation p value was computed for testing the null hypothesis (AUC=0.5) using 1000 permutations. # na, no assessment was possible, since the miRNA signature could not be applied to an mRNA only profile.

miRNAs and mRNAsInteractto produce proteins.

Proteins are the effectors.

This could explain why the prognostic value of a hybrib miRNA/mRNA signature is higher than that of each individual component alone (mRNA or miRNA)

Figure courtesy by Meister et al, 2007