A prognostic miRNA/mRNA signature from the integrated analysis of patients with invasive breast cancer

1. A prognostic miRNA/mRNA signature from theA 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

2. Ferrara, ItalyFerrara, Italy -- Marco Galasso, Carlotta Zerbinati, Marco Manfrini, MaurizioMarco 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 StateOhio 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

3. Breast Cancer andBreast Cancer and
microRNAsmicroRNAs
Iorio, M. V. et al. Cancer Res 2005

4. Clustering
of
six
solid cancers
by
miRNA expression
(Volinia et al, 2006)
Average
Fold change:
Cancer/Normal

5. Stage 0 Breast Cancer: DCISStage 0 Breast Cancer: DCIS

6. Progression from Normal Breast to
Invasive Ductal Carcinoma:
microRNAs

7. • 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

8. Progression from Normal Breast to
Invasive Ductal Carcinoma:
mRNAs

9. Bet: we can use the molecular
information for the stratification of
patients.
• To identify molecular mechanisms.
• To assess individual risk.
• To administer appropriate therapy.

10. TCGA
Invasive
Ductal
Carcinoma
Cohort
(n=466)

11. 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)

12. Matrix of
Hazard
Ratios
in
Breast
Cancer
subclasses

13. 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).

14. 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

15. Cohort
Clinical
End
point
RNA
profile
Integrated
miRNA/
mRNA
10-miRNA
GGI
97-gene
IGS
186-Gene
95-gene
Naoi
76-gene
Rotterdam
NKI
MammaPrint
70-gene
Oncotype
DX
TCGA IDC
(n=466)
OS
mRNA/
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 IDC
Early stages
I and II
(n=348)
OS
mRNA/
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)
DRFS
mRNA/
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 mRNA
0.75
(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 mRNA
0.65
(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 mRNA
0.62
(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 mRNA
0.59
(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 mRNA
0.64
(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 mRNA
0.68
(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 mRNA
0.69
(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.

16. miRNAs and mRNAs
Interact
to 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