Psb tutorial cancer_pathways

Translational Genomics Research Institute | www.tgen.org
Cancer Pathway Analysis and
Personalized Medicine
Jeﬀ
Kiefer

Research
Associate
Inves4gator

Transla4onal
Genomics
Research

Ins4tute

Big Cancer Data Resources and Secondary Data Tools

Pathway Analysis - Resources, Methods, and Tools

Personalized Medicine - ‘Interpretation bottleneck’

Drug to Genomic Event Matching
Outline

Cancer Genome Data Repositories
https://www.ebi.ac.uk/arrayexpress/
http://www.ncbi.nlm.nih.gov/geo/
http://cancergenome.nih.gov/
https://icgc.org/

Cancer Genome Data Repositories and Data
Portals
https://genome-cancer.ucsc.edu/
http://www.cbioportal.org/public-portal/
http://cancergenome.broadinstitute.orgTumorPortal
https://dcc.icgc.org/
http://genomeportal.stanford.edu/pan-tcga
http://cancer.sanger.ac.uk/cancergenome/projects/cosmic/

http://www.cbioportal.org/public-portal/

Pathways Analysis
Pathway analysis encompasses a number of diﬀerent approaches
and methods applied to large-scale -omic data sets.

The goal is to discover meaningful biological knowledge from
large data sets often in the form of a gene list.

Pathway is a term that describes a step-wise signal transduction
pathway. However, the term ‘pathway’ is also loosely used to
encompass genes sets derived from signatures or other biological
processes such as the gene ontology.

(2012). PLOS Computational Biology, 8(2), e1002375. doi:10.1371/journal.pcbi.1002375.t001
Pathways Analysis
Good general review outlining techniques, resources, and issues
in pathway analysis

Pathways Analysis
Threshold-Based = Enrichment analysis performed on a gene list
derived from statistical test.

Non-threshold Based = All data is used. First popularized by gene
set enrichment analysis (GSEA).

‘de-novo’ Based = Pathways or gene sets derived from primary
data.

Pathway Resources
http://www.reactome.org/
http://www.genome.jp/kegg/pathway.html
http://www.broadinstitute.org/gsea/msigdb/index.jsp
Commercial Resources
http://www.pathwaycommons.org/about/#main-container

Threshold-based Pathway Enrichment Tools
https://toppgene.cchmc.org/
http://amp.pharm.mssm.edu/Enrichr
http://www.ici.upmc.fr/cluego/

ToppGene extensive pathway
gene sets available for
enrichment analysis

Easy to use web interface
Add list of gene identiﬁers to

perform enrichment analysis on.

Results sorted based on signiﬁcance.

Results
Gene Set/Pathway
Categories

Different Result Outputs

http://www.ici.upmc.fr/cluego/
ClueGO integrates Gene Ontology (GO) terms as well as pathways and creates a functionally organized GO/
pathway term network.
COL9A1
COL28A1
COL14A1
COL9A3
COL20A1
COL12A1
COL9A2
Collagen
biosynthesis and
modifying
enzymes
Collagen formation
forebrain
development
SEMA3A
SYPL2
FGF9
CNTNAP2
SLC6A4
NDNF
SLC5A3
HEPH
SLC14A1
Transport of
glucose and other
sugars, bile salts
and organic acids,
metal ions and
amine compounds
RHBG SLC6A20
TBX5
RAC3
negative regulation of cell
differentiation
negative regulation of Wnt
signaling pathway
BICC1
PRICKLE1
DKK1
SFRP2
EFEMP1
regulation of cell development
COL1A1
EPHA3
SLIT2
FES APCDD1SULF1
PPP2R3A
regulation of canonical Wnt
signaling pathway
regulation of Wnt
signaling pathway
DDR2
LTF
regulation of cell
differentiation
SP7
MT3
BAX
S100A9
S100A8
NDUFA13
regulation of cysteine-type
endopeptidase activity involved
in apoptotic process
BBC3
regulation of intrinsic apoptotic
signaling pathway
IGFBP3
MEGF10
SLN
CACNG4
CCL4
CACNB2
ENPP1
KCNH2
regulation of ion transport
positive
regulation of ion
transport
CCL3
CTLA4
SCN4B
GADD45G
TRIB3
intrinsic apoptotic signaling
pathway
p53 signaling
pathway
BAI1
SEPT4
CD82
SFN
TLR4
osteoblast
differentiation
TLR3
Rheumatoid
arthritis
IL8
LOC100509457
CXCL5 ANGPT1
Toll-like receptor signaling
pathway
CTSK
RUNX2
Cytoscape App

Non-Threshold Pathway Enrichment Tools
http://www.broadinstitute.org/gsea/index.jsp

GSEA
Can be accessed through a number of
resources and methods
Java Desktop

R-GSEA

Gene Pattern

GSEA Use Case
Anaplastic Thyroid Cancer vs Non-Tumor Thyroid

GSEA Use Case

GSEA Visualization with Enrichment Map

GSEA Visualization with Enrichment Map
(2010) PLoS ONE, 5(11), 1–12. doi:10.1371/journal.pone.0013984.t001
http://www.baderlab.org/Software/EnrichmentMap
Cytoscape App

EDDY computes the discrepancy between probability distributions of
candidate networks structures based on likelihood of each network
across classes of samples.

Methodology that can exploit complex interactions between
two conditions, such as tumor v normal that might be missed
in traditional approaches based on diﬀerential gene
expression

Investigate differential dependencies between conditions
–  Evaluation of Differential DependencY
–  Computes the differential dependency statistics (JS) and its statistical
significance (p-value, via permutation) between conditions, based on
the likelihoods of genetic networks (a probabilistic distribution)
Likelihood
… Possible (or probable)
dependency structures JS
A
B C
Gene set of interest
A
B
C
A
B
C
Class 1
Class 2
MSigDB,
…
Gene set
DB
Class 2 specific dependency
Class 1 specific dependency
Common dependency
EDDY computes the discrepancy between probability distributions of
candidate networks structures based on likelihood of each network
across classes of samples.

Likelihood
… Possible (or probable)
dependency structures
A
B
C
A
B
C
Class 1
Class 2
A
B C
A
B C A
B C
Class 1
Specific
dependency
Class 2
Specific
Dependency
A
B C
Common dependency

•  GSEA appears under-powered, and also select disproportionate
amount.
•  GSCA appears to be overly sensitive – high false positive
(#): Overlap with EDDY gene sets
The number of identified subtype-specific gene sets
methods
GSEA and
ts the area
C curves in
simulation
es superior
his is partly
rom models
Comparison of EDDY with other methods in application
to TCGA GBM gene expression data
Table 2 lists the number of statistically significant gene
sets identified with the three different methods for each
subtype. EDDY and GSEA produced different results,
as EDDY identified 10 $ 22 gene sets for each subtype,
whereas GSEA identified 245 gene sets for mesenchymal
but just a few for other subtypes. Moreover, there is only
and EDDY in identifying differential gene sets from the interaction-focused simulation
and EDDY
v ¼ 30
0.5965
0.6075
0.6704
0.7064
Table 2. The number of statistically significant gene sets for each
subtype
Method Classical Mesenchymal Neural Proneural
EDDY 13 10 22 22
GSEA 1 (0) 245 (1) 6 (0) 3 (0)
GSCA 1590 (11) 1432 (7) 1681 (21) 1563 (17)
The number of common cases with EDDY is indicated in the
parentheses.
byguestonFebruary6,2014http://nar.oxfordjournals.org/Downloadedfrom

G2 pathway and p53 pathway gene sets to have differential dependencies that are related to the enrichment of
p53 mutations in the proneural subtype. Heat maps show that genes in pathway are not differentially expressed
so would not be identified by GSEA technique.
Two Pathways Identified with EDDY Enriched in Proneural
Glioblastoma Phenotype

PARADIGM
March 20, 2014 Vol507 Nature
MEMo
https://www.genome.gov/Multimedia/Slides/TCGA1/TCGA1_Ciriello.pdf
Both methods employ multiple genomic

data types to identiﬁed altered pathways
Employed in TCGA studies

Personalized Medicine
‘Interpretation Bottleneck’
Drug Target Annotation

Personalized Medicine Pipeline
Good, B. M., Ainscough, B. J., McMichael, J. F., Su, A. I., & Griﬃth, O. L. (2014). Organizing knowledge
to enable personalization of medicine in cancer, 1–9. doi:10.1186/s13059-014-0438-7

Drug Target Matching for Personalized Medicine

Framework for Clinical Mapping Genomic
Aberration to Drugs

Drug Target Resources
A number of resources available for drug mapping to gene
targets.
Issues with available sources
•Diﬀerent annotations schemes and data structures leads to misleading
results for end user.

•Contextual information around the drug and target is often not annotated.
•Not all annotations are therapeutically actionable or appropriate.

Drug to Target Annotation
Information for linking drugs to genes should be based on
primary literature.

Curated information should be annotated with controlled
vocabulary and arrayed in a structured format.

Rules need to capture explicit drug-target response
information but also be ﬂexible enough to capture inferred
information that may not always be explicitly stated. Important
for further research.

Example annotation workﬂow for
capturing drug to target information.

Visualization of Drug Target Network

CNV
OtherEXPDRUG
SNV
Aberration Type Color Key
=no_direct
=no_inferred=yes_inferred
=yes_direct
Edge Interaction Key Aberration Type Color Key
=DRUG
=BIOMARKER
=MODIFIER
Patient Speciﬁc Drug Target Network
Patient Genomic Information

Impact Areas for Text Mining
•Identify and extract interaction information for network and pathway
reconstruction.

•Aid in identifying and extracting genomic events linked to drug response to
better enable personalized medicine.

Psb tutorial cancer_pathways

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