Integrative Pathways to
Maximally Predict Cancer
Outcomes
Josh Stuart
Center for Biomolecular Science and Engineering,
UC Santa Cruz
Disclosure 1
Five3 Genomics LLC, Scientific Advisory Board
Disclosure 2: Postdoc Positions Available
https://genome-cancer.ucsc.edu
Jing Zhu and UCSC cancer group
UCSC Cancer Genomics Browser
genome-cancer.ucsc.edu
TCGA
The Cancer Genome Atlas
https://tcga1.cse.ucsc.edu
I-SPY TRIAL
SU2C Dream Team
Breast Cancer Clinical Trial
Stand Up to Cancer
https://instinct1.cse.ucsc.edu
https://genome-cancersu2c.cse.ucsc.edu
UCSC Cancer
Genomics
Browser
Open Source:
First public deposit Oct. 2007
Public access portal:
All major published studies
Spin-off Browsers
Immuno Browser
Stem Cell Browser
UCSC Cancer Genomics Browser
UCSC Cancer Genome Heatmaps
Breast Cancer Amplified / Deleted Regions
Grade
ER
Survival
Samples
TCGA Brain Cancer Amplified / Deleted Regions
Clinical
Data
Tumor
Samples
Normal
Genome Position
Jing Zhu and UCSC cancer group
UCSC Cancer Genomics Browser
TCGA ovarian
Jing Zhu and UCSC
cancerdata
group
UCSC Cancer Genomics Browser
Click to sort according
to clinical information
TCGA ovarian
Jing Zhu and UCSC
cancerdata
group
Perform Robust Statistics across the Entire Genome,
Identifying Genomic Regions to Study Further
Breast Cancer Cell Lines, Amplified / Deleted Genomic Regions
Low
Genomic
Data
High
Genome-wide
statistics
UCSC Genome Browser
Quickly identify
genes significantly
amplified in samples
with low TP53
expression, a critical
tumor suppressor
Clinical Data
TCGA Breast
Copy number
variations
Summary Views
Heatmap View - Amplified / Deleted Regions
Box Plot Summary View
Proportional Summary View
Similarities Shared by Multiple Cancers
Pancreatic Copy Number (Jones 2008)
Glioblastoma Multiforme Copy Number (TCGA 2009)
Melanoma Copy Number (Lin 2008)
CDKN2A/B Homozygous Deletion
Jing Zhu and UCSC cancer group
Statistical Comparison of Two Subsets of
Samples
t test
stats
Perform
Statistics
Select
features
Define
subgroups
Jing Zhu and UCSC cancer group
Identify genetic markers dynamically
Copy Number Data
Gene Expression Data
Co-amplified region
ERBB2 locus
Jing Zhu and UCSC cancer group
Signatures now Implemented in the Browser
C. Enter Signature
(math formula)
A. select
dataset
B. Signature
Signature Demo
E. Real-time Signature Score
correlates with pathCR, ER, Her2, or
prediction score in publication
#ChemoTFAC30+ E2F3+ MELK+ RRM2+ BTG3- CTNND2- GAMT- METRN- ERBB4- ZNF552- CA12- KDM4B- NKAIN1- SCUBE2- KIAA1467- MAPT- FLJ10916-
C. Signature Score
D. Click to sort
A. Enter Signature
B. Name Signature, Press “Validate & Add” Button
Jing Zhu and UCSC cancer group
Pull up the signature genes as a gene set
C. update
#ChemoTFAC30+ E2F3+ MELK+ RRM2+ BTG3- CTNND2- GAMT-
A. enter gene set
ChemoTFAC30
...
B. save user-defined geneset
Jing Zhu and UCSC cancer group
Compare Multiple Signatures
#Oncotype_DX#Pro
#ChemoTFAC30+ E2F3+ MELK+ RRM2+ BTG3- CTNND2- GAMT- METRN- ERBB4- ZNF552- CA12- KDM4B- NKAIN1- SCUBE2- KIAA1467- MAPT- FLJ10916- BECN1- RAMP1- GFRA1- IGFBP4- FG
TFAC30 OncotypeDX-like
Pathways-based organization, GBM data
somatic
mutations
germline
mutations
Normal
Tumor
Copy number
variation
stats
Jing Zhu and cancer group
Analysis like investigating a plane crash
Patient Sample 1
Patient Sample 2
Patient Sample 3
Patient Sample N
…
Pathways as genetic unit
p53
altered
in
87%
cases
RAS
altered in
88%
cases
RB
altered
in
78%
cases
3The
Cancer Genome Atlas, Nature, 2008
Main Approach: Detailed models of
gene expression and interaction
MDM2
TP53
Main Approach: Detailed models of
expression and interaction
Two Parts:
MDM2
TP53
1. Gene Level Model
(central dogma)
2. Interaction Model
(regulation)
Patient-specific alterations for known
pathways
Collect publicly available pathways (NCI,
WikiPathways, KEGG, …)
Convert to graphical model
Infer pathway “levels” for each “concept in each
patient sample

concepts: gene activities, apoptosis, DNA repair, small
molecules, complexes, etc.
Train classifiers w/ inferred pathway levels

E.g. 3-year disease-free survival
Pathway Recognition Algorithm Using
Data Integration on Genomic Models
(PARADIGM)
Gene
Charlie Vaske, Steve Benz
PARADIGM
Gene Model to Integrate Data
3-state discrete
variables
relative to non-cancer,
is this sample:
up,
same,
down?
Charlie Vaske, Steve Benz
PARADIGM Gene-level Model
relative to non-cancer,
is this sample:
up,
same,
down?
3-state discrete
variables
CNA \
Exp
Down
Same
Up
Down
Same
Up
0.90
0.05
0.01
0.09
0.90
0.09
0.01
0.05
0.90
PARADIGM Gene Model to Integrate Data
Charlie Vaske, Steve Benz
PARADIGM Gene Model to Integrate Data
Charlie Vaske, Steve Benz
PARDIGM Gene Model to Integrate Data
Charlie Vaske, Steve Benz
Interactions Matter
Apoptosis
Apoptosis
 Given
information
about the
expression of
TP53 alone
 Reasoning
predicts
apoptosis is in
tact in these
cells.
Charlie Vaske, Steve Benz
Interactions Matter
 Given the
interaction and data
about MDM2.
 apoptosis inference
reversed
Quantitative Output
Log likelihood Ratio:
log
log odds of state
and data
prior log odds
P Data | Apoptosis  active,
P Data, Apoptosis  active | 
P Apoptosis  active | 
log
log
P Data | Apoptosis  active, = P Data, Apoptosis  active |  P Apoptosis  active | 
Charlie Vaske, Steve Benz


PARADIGM Interaction Components
Transcriptio
n Factor
Kinase
Target Gene
Transcription
al Regulation
Phosphorylated
gene
Post-translational
Modification
Charlie Vaske, Steve Benz
PARADIGM Interaction Components
Subunit A Subunit BSubunit C
Protein
Complex
Gene A
Gene B
Gene C
Gene family, proteins
with interchangeable
function
Charlie Vaske, Steve Benz
PARADIGM Interaction Components
Subunit A Subunit BSubunit C
Noisy AND
function
Protein
Complex
Gene A
Gene B
Gene C
Noisy OR
function
Gene family, proteins
with interchangeable
function
Charlie Vaske, Steve Benz
Pathway Interpretation of Omics Data
PARADIGM Pathway Analysis
CNA
Expression
p53 pathway
FoxM1 network
Angiopoietin receptor Tie2-mediated
signaling
~100
Pathways
316 TCGA Ovarian
Samples
Tumor Samples
Concepts
Per-sample
integrated
pathway
levels
Sample 1
Sample 2
Sample 3
TCGA Ovarian Cancer
Inferred Pathway Activities
Pathway Concepts (867)
Patient Samples (247)
Ovarian: FOXM1 pathway altered
in majority of serous ovarian tumors
Patient Samples (247)
Pathway Concepts (867)
FOXM1 Transcription Network
Patient Samples
Expression
Copy Number
FOXM1 Amplification Leads to Secondary
Responses Predicted by its Pathway Interactions
Ovarian: CircleMap of FOXM1
Transcription Factor and Targets
FOXM1
Expression vs. Fallopian Normal
Copy Number Variation
Each spoke of the circle
represents a single sample
Ovarian: FOXM1 central to
DNA repair and cell proliferation
Ovarian: IPLs statify by survival time
Charlie Vaske, Steve Benz
Ovarian: IPLs improve the accuracy of platinum
response predictors
SuperPathway based Predictors
Merge constituent pathway
models into a single
superimposed pathway
(SuperPathway)
Single interpretation for
every concept
Score concepts according
to whether they are
predictive of an outcome
Identifies Pathway
Signatures of subtypes
Pathway Signatures: Differential
Subnetworks from a “SuperPathway”
Pathway Activities
Pathway Activities
Pathway Signatures: Differential
Subnetworks from a “SuperPathway”
Pathway Activities
Pathway Activities
Pathway Signatures: Differential
Subnetworks from a “SuperPathway”
SuperPathway Activities
SuperPathway Activities
Pathway
Signature
Test Approach in Breast Cancer Cell Lines
Basal cells more aggressive, stem cell like
What pathways underlie this subclass of cancer?
~50 breast cancer cell lines of various subtypes
Copy number and gene expression for all cells
Do the pathway fingerprints of basal breast cancer
give clues about drug treatment?
Basal Pathway Markers
Basal Pathway Markers: Bird’s eye view
Each edge is supported by at least one publication
965 nodes
941 edges
Blue down
Red up
– Infer activities in a global
“super pathway”
– Associate activities with
drug sensitivity to find
activity markers
– Search for focused
subnetworks with
interconnected markers
– Activity nodes linked to
therapeutic inhibitors
Dominant features:
FOXA1 / FOXA2 Network
• Controls transcription of ERregulated genes (1,2)
• Expression associated with
good prognosis (1) and
luminal breast cancers (2)
CHROMATIN
REMODELING
Inhibitor
Up/ON
Down/OF
F
1. Eur J Cancer. 2008 Jul;44(11):1541-51
2. Clin Cancer Res. 2007 Aug 1;13(15 Pt 1):4415-21.
Note PARADIGM
can easily
incorporate drugs
into the network
-allows inference
-drug’s effect
Dominant features: MYB/MYC/MAX Network
1. MYB is required for
proliferation of ER-breast
tumor cell lines
2. MYB influences
angiogenesis,
proliferation and
apoptosis
3. MYC is known oncogene
MYC/MAX
COMPLEX
1.
2.
Expert Opin Biol Ther. 2008 Jun;8(6):713-7.
Expert Opin Ther Targets. 2003 Apr;7(2):235-48.
Dominant features: ERK1/2 Network
• RAF/MEK/ERK
pathway frequently
deregulated in
breast cancers
• Influences several
downstream
processes,
including: cell
cycle, adhesion,
invasion,
macrophage
activation
Networks can predict response to treatment:
FOXM1/PLK/DNA Network
• DNA damage network
is upregulated in basal
breast cancers
• Basal breast cancers
are sensitive to PLK
inhibitors
GSK-PLKi
Luminal
Claudinlow
Basal
Up
Down
Networks can predict response to treatment:
HDAC Network
• HDAC Network is downregulated in basal breast
cancer cell lines
• Basal/CL breast cancers
are resistant to HDAC
inhibitors
HDAC inhibitor
VORINOSTAT
Network can suggest new biology
• Integrins are
upregulated in basal
breast cancers
• beta2 integrins
associated with
macrophage binding
and stimulation
• alphaMbeta2 integrin =
macrophage 1 antigen
= Mac-1 antigen
LIGANDPHAGOCYTOSIS
BINDINGTRIGGERED BY
IMMUNE
RESPONSE
αM/β2
INTEGRIN
ACTIVATION
OF CASPASE
ACTIVITY
SIMVISTATIN
(ZOCOR)
Summary
Top down merits: Pathway interpretation of
integrated cancer data powerful for pinpointing
coherent themes in tumor samples.
Pathways provide robust features for sub-typing and
predictor building.
Local models reveal known mechanisms underlying
breast cancer subtypes and possibly drug response.
Open Problems
(Toward Smoother Flying)
Dynamically build discriminative pathways for outcome
prediction (e.g. survival, treatment response)?
Reconstruct “event histories” of cancer cell transformations?
Simulate networks to predict points of attack?
Combine top-down with bottom-up approaches:
 E.g.
Dynamically build discriminative pathways for outcome prediction
(e.g. survival, treatment response
(Finding biologists who will help interpret nets!)
Patient-specific Prediction
of Therapeutic Targets
Mechanism Proxy
X
X
 Pathways provide a proxy for
mechanism
 Explore knock-out effects in silico
 Idea: Rank genes according to their
KO benefit



Given data for a patient
Each gene’s KO changes the patients
molecular signature
Measure if signature becomes more
like those associated with good
prognosis
Pathway Models as Predictors
(analogy to protein homology detection)
• Protein homology
• Set of sequences with shared property (bind same DNA
motif, homologous, etc)
• PFAM database stores HMMs; each recognize a different
protein domain.
• Annotate new sequences by running all HMMs in DB
• Patient clinical outcomes
•
•
•
Set of samples with shared property (drug response,
survival, tissue of origin)
Train pathway models to “recognize” each clinicallyrelevant class
Analysis of new patient using all pathway models.
UCSC Cancer
Integration Group
Steve Benz
David Haussler
Jing Zhu
James Durbin
Chris Szeto
Larry Meyer
Charlie Vaske
Sam Ng
Zack Sanborn
Amie Radenbaugh
Mia Grifford
Ted Golstein
Cancer Engineering Team
Jing Zhu, Director
jzhu@soe.ucsc.edu
Brian Craft
craft@soe.ucsc.edu
Kyle Ellrott
kellrott@soe.ucsc.edu
Kord Kober
kord@soe.ucsc.edu
Acknowledgments
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UCSC Cancer Genomics
Steve Benz
Sam Ng
Ted Goldstein
Charles Vaske
Zack Sanborn
Jingchun Zhu
Larry Meyer
Christopher Szeto
James Durbin
Tracy Ballinger
Daniel Zerbino
Mia Grifford
Fan Hsu
Sofie Salama
• UCSC Genome Browser Staff
• Cluster Admins
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Collaborators
The Cancer Genome Atlas
Stand Up To Cancer
Christopher Benz, Buck Institute
Laura Esserman, UCSF
Joe Gray, LBL
Laura Heiser, LBL
Eric Collisson, UCSF
Ting Wang, Washington University
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Funding Agencies
NCI/NIH
NHGRI
American Association for Cancer Research (AACR)
UCSF Comprehensive Cancer Center
California Institute for Quantitative Biosciences (QB3)