4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
The Network is a Biomarker in Cancer
Signatures
Sol EfroniThe Mina & Everard Goodman
Faculty of Life SciencesBar Ilan University
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
My Lab
Rep-Seq
Benichou et al,Immunology 2012
• Vainas et al,Autoimmunity 2011
• Mascanfroni et al,Nature Immunology 2013
Cancer Genomics
Systems Immunology
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
Introduction • Biomarkers • Regulation • Implementation
Complex Biomarkers
Clinically motivated multi gene markers
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
The problem with Multi-gene mRNA markers
Robustness
Introduction • Biomarkers • Regulation • Implementation
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
Introduction • Biomarkers • Regulation • Implementation
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
Introduction • Biomarkers • Regulation • Implementation
TCGA
“...a comprehensive and coordinated effort to accelerate our understanding of the molecular basis of cancer through the application of genome analysis technologies, including large-scale genome sequencing”
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
Introduction • Biomarkers • Regulation • Implementation
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
Phenotype comparison
Tumor Normal
Introduction • Biomarkers • Regulation • Implementation
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
Tumor Normal
Introduction • Biomarkers • Regulation • Implementation
Phenotype comparison
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
Introduction • Biomarkers • Regulation • Implementation
? ?? ?
? ?
?A single number to represent thePathway within thissample
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
Example
Regulation as metric
Introduction • Biomarkers • Regulation • Implementation
Gene 1
Gene 2
Gene 1is a perfectbiomarker
Gene 2is a perfectbiomarker
1
2
The pathway is enriched with the interesting genes Gene 1 and Gene 2 and and is therefore important
Gene 1
Gene 2
1
2
?1
-1
Network
Gene 1is a poorbiomarker
Gene 2is a poorbiomarker
The two gene network is a perfectbiomarker
Perfect corr
Perfect anti corr
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
Introduction • Biomarkers • Regulation • Implementation
Efroni et al IET Systems Biology 2013
Greenblum et al. BMC Bioinformatics 2011
Efroni et al PLoS ONE 2009
Introduction • Biomarkers • Regulation • Implementation
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
Pathways as biomarkersand
pathways as targets inGBM
and in Ovarian cancer
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
Rotem Ben-Hamo Dr. Helit Cohen
Introduction • Biomarkers • Regulation • Implementation ovarian
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
Methodology
Gene Expression
Clinical Data
Copy Number Variation
Methylation
microRNA
Whole Genome/Exome
Sequencing
Ovarian Cancer – 511 patients, 348 whole exomes
Introduction • Biomarkers • Regulation • Implementation ovarian
Data Collection
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
Ovarian cancer gene signature
Introduction • Biomarkers • Regulation • Implementation ovarian
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
Ben-Hamo R, Efroni S. BMC Systems Biology (2012)
Survival Analysis: Gene based
Introduction • Biomarkers • Regulation • Implementation ovarian
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
A pathway view highlights a single pathway
Introduction • Biomarkers • Regulation • Implementation ovarian
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
Duke1 Dataset 119 patients Duke2 Dataset 42 patientsTCGA 511 patients
Ovarian Cancer: PDGF Signaling Pathwayprovides a robust signature
Introduction • Biomarkers • Regulation • Implementation ovarian
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
Again, a collection of sources for clinical and molecular data
GBM
Introduction • Biomarkers • Regulation • Implementation GBM
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
• Pathway 1• Pathway 2• Pathway 3• Pathway 4• Pathway 5• Pathway 6• …
• Pathway 579
Introduction • Biomarkers • Regulation • Implementation GBM
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
The p38 pathway is most significant
“p38 signaling mediated by mapkap kinases”
Introduction • Biomarkers • Regulation • Implementation GBM
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
Preliminary Results
Ben-Hamo R, Efroni S. Genome Medicine (2011)Ben-Hamo R, Efroni S. Systems Biomedicine (2013)
The p38 pathway is robust across multiple datasets
Introduction • Biomarkers • Regulation • Implementation GBM
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
Another type of regulation has been suggested:microRNA Control over Pathways
Inui M et al. Nature Reviews Molecular Cell Biology (2010)
Introduction • Biomarkers • Regulation • Implementation GBM
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
Introduction • Biomarkers • Regulation • Implementation GBM
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
hsa-miR-9 hsa-miR-9
R2 = -0.21R2 = -0.67
P38 s
ign
alin
g p
ath
way
P38 s
ign
alin
g p
ath
way
P38/MAPKP Pathway AND hsa-miR-9
Introduction • Biomarkers • Regulation • Implementation GBM
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
Preliminary Results - Computational
hsa-miR-9 hsa-miR-9
R2 = -0.21R2 = -0.67
P38 s
ign
alin
g p
ath
way
P38 s
ign
alin
g p
ath
way
P38/MAPKP Pathway AND hsa-miR-9
Introduction • Biomarkers • Regulation • Implementation GBM
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
Preliminary Results - Experimental
Expression levels of P38 pathway genes in HeLa Vs. HMEC cell lines
• HeLa: cervical cancer cell line.• HMEC: (Human mammary epithelial cell) primary cell line.
microRNAs regulation of pathways - GBM
Introduction • Biomarkers • Regulation • Implementation GBM
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
Expression levels of P38 pathway genes in HeLa cell lines after miR-9 transfection
microRNAs regulation of pathways - GBM
Introduction • Biomarkers • Regulation • Implementation GBM
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
miR-9 down regulation of the p38 network improves prognosis
?
Can we see the same effect with drug response?
Introduction • Biomarkers • Regulation • Implementation GBM
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
Drug response• Patients are treated using a wide spectrum of 69
different drugs• Drugs are classified into two groups:• drugs that target genes in the p38 pathwayAnd• drugs that do not target genes in the pathway
Introduction • Biomarkers • Regulation • Implementation GBM
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
Out of the 69 drugs given to the patients 6 drugs target genes that are part of the p38 network
Drug Name Target Pathway
Accutane RARA map kinase inactivation of smrt co-repressor
CCNU STMN4 Signaling mediated by p38-gamma and p38-delta pathway
Celebrex COX2 Signaling mediated by p38-alpha and p38-beta pathway
Cis Retinoic Acid RARA map kinase inactivation of smrt co-repressor
Sorafenib RAF1 p38 signaling mediated by MAPKAP kinases
Tamoxifen ESR1 Signaling mediated by p38-alpha and p38-beta pathway
Introduction • Biomarkers • Regulation • Implementation GBM
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
Group1
• Low survival
• 169 patients
• Average overall survival time – 433 days
• Median survival time – 310 days
• All patients did not received p38 targeted
drugs
Group2
• High survival
• 63 patients
• Average overall survival – 896 days
• Median survival time – 691 days
• All patients received p38 targeted drugs
Ben-Hamo R, Efroni S. Genome Medicine (2011)Ben-Hamo R, Efroni S. Systems Biomedicine (2013) (CAMDA first prize)
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
Summary
Measurethe
Network
Targetthe
Network
4th Conference in Quantitative Biology QUB, September 2013 Sol Efroni
Summary
Core biology hides in functional wiring of the network
By selecting for robust signatures we achieve significant markers for prognosis
By following the outline of these signatures we discover biology that may lead to treatment
Acknowledgements
Helit CohenRotem Ben Hamo
Alona Zilberberg
Jennifer BenichouRivka CashmanMoriah CohenMiri GordinDror HibshIdo SlomaHagit PhilipRenana Kozol
SYSTEMS BIOMEDICINE JOURNAL
Mechanisms
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