stefano volinia, mirna signature - breast cancer, fged_seattle_2013
DESCRIPTION
A prognostic miRNA/mRNA signature from the integrated analysis of patients with invasive breast cancerTRANSCRIPT
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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
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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
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Breast Cancer and Breast Cancer and microRNAsmicroRNAs
Iorio, M. V. et al. Cancer Res 2005
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Clustering of six
solid cancersby
miRNA expression(Volinia et al, 2006)
Average Fold change:
Cancer/Normal
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Stage 0 Breast Cancer: DCISStage 0 Breast Cancer: DCIS
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Progression from Normal Breast to Invasive Ductal Carcinoma:
microRNAs
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• 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
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Progression from Normal Breast to Invasive Ductal Carcinoma:
mRNAs
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Bet: we can use the molecular information for the stratification of patients.
• To identify molecular mechanisms.• To assess individual risk.• To administer appropriate therapy.
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TCGA InvasiveDuctalCarcinomaCohort(n=466)
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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)
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Matrix of Hazard Ratios
inBreast Cancer
subclasses
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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).
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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
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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.
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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