A prognostic miRNA/mRNA signature from theA prognostic miRNA/mRNA signature from the
integrated analysis ofintegrated anal...
Ferrara, ItalyFerrara, Italy -- Marco Galasso, Carlotta Zerbinati, Marco Manfrini, MaurizioMarco Galasso, Carlotta Zerbina...
Breast Cancer andBreast Cancer and
microRNAsmicroRNAs
Iorio, M. V. et al. Cancer Res 2005
Clustering
of
six
solid cancers
by
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 th...
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 ass...
TCGA
Invasive
Ductal
Carcinoma
Cohort
(n=466)
TCGA mRNA
TCGA miRNA
N Stage Intrinsic SubtypeDisease Stage EROther Classes
Hazard Ratios
. . . .
DNA methylation Somatic ...
Matrix of
Hazard
Ratios
in
Breast
Cancer
subclasses
The prognostic performance of 37-gene
miRNA/mRNA integrated predictor in IDC
(TCGA cohort)
The Receiver
Operating
Characte...
Variables included in the initial model:
TP53 Mut, PIK3CA/AKT/PTEN Mut, PAM50 subtypes, Disease Stage, T stage, Estrogen R...
Cohort
Clinical
End
point
RNA
profile
Integrated
miRNA/
mRNA
10-miRNA
GGI
97-gene
IGS
186-Gene
95-gene
Naoi
76-gene
Rotter...
miRNAs and mRNAs
Interact
to produce proteins.
Proteins are the effectors.
This could explain why the
prognostic value of ...
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Stefano Volinia, miRNA Signature - Breast Cancer, fged_seattle_2013

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

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Stefano Volinia, miRNA Signature - Breast Cancer, fged_seattle_2013

  1. 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. 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. 3. Breast Cancer andBreast Cancer and microRNAsmicroRNAs Iorio, M. V. et al. Cancer Res 2005
  4. 4. Clustering of six solid cancers by miRNA expression (Volinia et al, 2006) Average Fold change: Cancer/Normal
  5. 5. Stage 0 Breast Cancer: DCISStage 0 Breast Cancer: DCIS
  6. 6. Progression from Normal Breast to Invasive Ductal Carcinoma: microRNAs
  7. 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. 8. Progression from Normal Breast to Invasive Ductal Carcinoma: mRNAs
  9. 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. 10. TCGA Invasive Ductal Carcinoma Cohort (n=466)
  11. 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. 12. Matrix of Hazard Ratios in Breast Cancer subclasses
  13. 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. 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. 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. 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
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