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The Network is a
Biomarker in Cancer
Signatures
Sol Efroni
The Mina & Everard Goodman Faculty of
Life Sciences
Bar Ilan University
4th Conference in Quantitative Biology
QUB, September 2013
Sol Efroni
My Lab
Rep-Seq
Cancer
Genomics
• Vainas et al,
Autoimmunity 2011
• Mascanfroni et al,
Nature Immunology 2013
Benichou et al,
Immunology 2012
4th Conference in Quantitative Biology
Systems
Immunology
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
Introduction • Biomarkers • Regulation • Implementation
The problem with Multi-gene mRNA
markers
Robustness
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
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
Introduction • Biomarkers • Regulation • Implementation
Phenotype comparison
Tumor
4th Conference in Quantitative Biology
Normal
QUB, September 2013
Sol Efroni
Introduction • Biomarkers • Regulation • Implementation
Phenotype comparison
Tumor
4th Conference in Quantitative Biology
Normal
QUB, September 2013
Sol Efroni
Introduction • Biomarkers • Regulation • Implementation
A single number to
represent the
Pathway within this
sample
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QUB, September 2013
Sol Efroni
Introduction • Biomarkers • Regulation • Implementation
Regulation as metric
Example
4th Conference in Quantitative Biology
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Sol Efroni
Gene 1
Gene 1
is a perfect
biomarker
1
The pathway is enriched with the interesting genes Gene 1
and Gene 2 and and is therefore important
2
Gene 2
Gene 2
is a perfect
biomarker
Gene 1
is a poor
biomarker
Gene 1
Network
1
Perfect corr
1
?
2
-1
Gene 2
Perfect anti corr
The two
gene
network
is a perfect
biomarker
Gene 2
is a poor
biomarker
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
4th Conference in Quantitative Biology
QUB, September 2013
Sol Efroni
Introduction • Biomarkers • Regulation • Implementation
Greenblum et al. BMC Bioinformatics 2011
Efroni et al IET Systems Biology 2013
Efroni et al PLoS ONE 2009
Pathways as biomarkers
and
pathways as targets in
GBM
and in
Ovarian cancer
4th Conference in Quantitative Biology
QUB, September 2013
Sol Efroni
Introduction • Biomarkers • Regulation • Implementation ovarian
Rotem Ben-Hamo
4th Conference in Quantitative Biology
Dr. Helit Cohen
QUB, September 2013
Sol Efroni
Methodology
Introduction • Biomarkers • Regulation • Implementation ovarian
Data
Collection
Whole
Genome/Exome
Sequencing
microRNA
Methylation
Gene Expression
Clinical Data
Copy Number
Variation
Ovarian Cancer – 511 patients, 348 whole exomes
4th Conference in Quantitative Biology
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Sol Efroni
Introduction • Biomarkers • Regulation • Implementation ovarian
Ovarian cancer gene
signature
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Sol Efroni
Introduction • Biomarkers • Regulation • Implementation ovarian
Survival Analysis: Gene based
Ben-Hamo R, Efroni S. BMC Systems Biology (2012)
4th Conference in Quantitative Biology
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Sol Efroni
Introduction • Biomarkers • Regulation • Implementation ovarian
A pathway view highlights a single pathway
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Sol Efroni
Introduction • Biomarkers • Regulation • Implementation ovarian
Ovarian Cancer: PDGF Signaling Pathway
provides a robust signature
TCGA 511 patients
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Duke1 Dataset 119 patients
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Duke2 Dataset 42 patients
Sol Efroni
Introduction • Biomarkers • Regulation • Implementation GBM
GBM
Again, a collection of sources for clinical and molecular data
4th Conference in Quantitative Biology
QUB, September 2013
Sol Efroni
Introduction • Biomarkers • Regulation • Implementation GBM
•
•
•
•
•
•
•
Pathway 1
Pathway 2
Pathway 3
Pathway 4
Pathway 5
Pathway 6
…
• Pathway 579
4th Conference in Quantitative Biology
QUB, September 2013
Sol Efroni
Introduction • Biomarkers • Regulation • Implementation GBM
The p38 pathway is most
significant
“p38 signaling mediated by mapkap kinases”
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Preliminary Results
Introduction • Biomarkers • Regulation • Implementation GBM
The p38 pathway is robust across multiple datasets
Ben-Hamo R, Efroni S. Genome Medicine (2011)
Ben-Hamo R, Efroni S. Systems Biomedicine (2013)
4th Conference in Quantitative Biology
QUB, September 2013
Sol Efroni
Introduction • Biomarkers • Regulation • Implementation GBM
Another type
of regulation
has been
suggested:
microRNA
Control over
Pathways
Inui M et al. Nature Reviews
Molecular Cell Biology
(2010)
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
Introduction • Biomarkers • Regulation • Implementation GBM
P38 signaling pathway
P38 signaling pathway
P38/MAPKP Pathway AND hsa-miR-9
R2 = -0.67
hsa-miR-9
R2 = -0.21
hsa-miR-9
4th Conference in Quantitative Biology
QUB, September 2013
Sol Efroni
Preliminary Results - Computational
Introduction • Biomarkers • Regulation • Implementation GBM
P38 signaling pathway
P38 signaling pathway
P38/MAPKP Pathway AND hsa-miR-9
R2 = -0.67
R2 = -0.21
hsa-miR-9
hsa-miR-9
4th Conference in Quantitative Biology
QUB, September 2013
Sol Efroni
Preliminary Results - Experimental
Introduction • Biomarkers • Regulation • Implementation GBM
microRNAs regulation of pathways - GBM
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.
4th Conference in Quantitative Biology
QUB, September 2013
Sol Efroni
Introduction • Biomarkers • Regulation • Implementation GBM
microRNAs regulation of pathways - GBM
Expression levels of P38 pathway genes in HeLa cell lines after miR-9
transfection
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QUB, September 2013
Sol Efroni
Introduction • Biomarkers • Regulation • Implementation GBM
miR-9 down regulation of the p38 network improves prognosis
?
Can we see the same effect with drug response?
4th Conference in Quantitative Biology
QUB, September 2013
Sol Efroni
Introduction • Biomarkers • Regulation • Implementation GBM
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 pathway
And
• drugs that do not target genes in the pathway
4th Conference in Quantitative Biology
QUB, September 2013
Sol Efroni
Introduction • Biomarkers • Regulation • Implementation GBM
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
4th Conference in Quantitative Biology
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Sol Efroni
Group1
Group2
• Low survival
• High survival
• 169 patients
• 63 patients
• Average overall survival time – 433 days
• Average overall survival – 896 days
• Median survival time – 310 days
• Median survival time – 691 days
• All patients did not received p38 targeted drugs
• 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
Measure
the
Network
Target
the
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
4th Conference in Quantitative Biology
QUB, September 2013
Sol Efroni
Acknowledgements
Helit Cohen
Rotem Ben Hamo
Alona Zilberberg
Jennifer Benichou
Rivka Cashman
Moriah Cohen
Miri Gordin
Dror Hibsh
Ido Sloma
Hagit Philip
Renana Kozol
SYSTEMS BIOMEDICINE JOURNAL
Mechanisms