tranSMART Community Meeting 5-7 Nov 13 - Session 5: Advancing tranSMART Analytical Capabilities with Knowledge Content Sirimon Ocharoen, Thomson Reuters To effectively analyze data in tranSMART, biological analysis/knowledge-based approach is needed. Through a case study, we will demonstrate how system biology content can be integrated in tranSMART to enable functional analysis and biological interpretation. We will also share our experience and user feedbacks from various projects.

1. ADVANCING TRANSMART ANALYTICAL
CAPABILITIES WITH KNOWLEDGE CONTENT
tranSMART Community Meeting
Sirimon O’Charoen
sirimon.ocharoen@thomsonreuters.com
November 6th, 2013

2. 4 EXAMPLES OF tranSMART USE CASES
• Use case 1: Leveraging public datasets
• Use case 2: Finding information on variant and
mutation
• Use case 3: Biological interpretation
• Use case 4: Implementing classification model
• Feedbacks from tranSMART users
2

3. USE CASE 1
Leveraging Public Datasets
3

4. Up-regulated in
an asthma study
Where else is
IL-33 gene
significantly
expressed?
4

5. What are
other genes
significantly
expressed in
Ulcerative
Colitis?
5

6. REG1A
6

7. SLC6A14
7

8. MICROARRAY REPOSITORY:
PROCESSING PROCEDURE
A. Search for Datasets in public
databases & Data loading
(A)
(B)
B. Quality Control (QC) testing of Raw
Assays (filtering out of unsuitable
defective Assays)
C. GCRMA Processing of QC-approved
Assays
D. Assays Annotation:
i.
Assignment of experimental
meta-data values to the
Assays
ii.
Assignment of experimental
Assays Groups and their
Comparisons
E. Statistical analysis of defined
Comparisons:
i.
Differential expression testing
ii.
Calculation of Fold Changes
iii. Functional Descriptors
calculation
(D)
(C)
(E)
Optional: cutoffs
8

9. MICROARRAY REPOSITORY:
QC PROCEDURE
• Datasets undergo rigorous
quality control during processing
• An assay is removed from the
dataset if it’s identified as an
outlier by the majority of qc
metrics
• Users are able to see which
tests the datasets passed/failed
9

10. MICROARRAY REPOSITORY:
ANNOTATION PROCEDURE
Additional manual annotation of datasets increases granularity & numbers of
groups and comparisons
METACORE
10

11. USE CASE 2
Finding Information on Variant and Mutation
11

12. How does 17p13 deletion correlated to thalidomide
response in chronic lymphocytic leukemia (CLL) patients?
WBC Reduction at Day 7
12
No abberation vs. 17p13 deletion

13. What are other diseases or drugs that 17p13
deletion is associated to?
13

14. GENE VARIANT RECORD
14

15. GENE VARIANT ASSOCIATIONS
15

16. GENE VARIANT API
Significance of genotype-phenotype relationships across the translational pipeline
IDENTIFY ACTIONABLE
GENE VARIANTS
DISEASE RECORD
A. Establish variant significance
B. Characterize the variant
C. Asses the utility of the
variant:
RESPONSE RECORD
O
OH
O
O
VARIANT DISEASE
• Understanding Disease
Mechanism
• Treatment & Response
Disease Profiling
Diagnosis
Prognosis
Screening, Risk
VARIANT DRUG
DISEASE
Predicting Efficacy / Toxicity
Monitoring Efficacy / Toxicity
Selection for Therapy
Resistance
MANUALLY CURATED CONTENT FROM A RANGE OF SOURCES
DISCOVERY
HTP Studies
Candidate Studies
VALIDATION
Preclinical in vitro &
animal studies
Clinical studies in
patient segments
APPLICATION
FDA approvals
Clinical guidelines
16

17. GENE VARIANT API: PROCEDURE
SOURCES
SOURCE
SELECTION
VS.
REJECTION
SELECTION
CRITERIA BY
THE ANALYST
conference abstracts, patents, peer reviewed journal
articles, clinical trial registries, clinical guidelines, and
authority approval documents (ex. FDA)
•Retrospective selection or prospective screening for
frontfile. Items are screened by a text-mining tool to identify
and remove items that have no relevance to the Gene
Variant API.
•All articles not removed are sent to manual selection by
trained annotators who follow the policy.
•A clear study design, and valuable results are required by
the analyst. The item must satisfy requirements of
evidence-based medicine in order to be taken into
consideration.
•Statistics and / or statement by the author of the variant
effect on health are required. If both components are
absent, the item is rejected.
17

18. USE CASE 3
Biological Interpretation
18

19. INVASIVE BREAST CANCER STUDY
Find predictors for
treatment response
19

20. MARKER SELECTION WORKFLOW
RCB 0/I
RCB III
20

21. ENRICHMENT ANALYSIS
Significant signaling
pathways enriched
with differentially
expressed genes in
Responders vs.
Non-responders
Estrogen/Progesteron signaling
Cell Cycle regulation
DNA damage repair
Epigenetic regulation of gene
expression
NFIB nuclear factor IB type- a potential biomarker in breast neoplasms
STK24 –induction of apoptosis
ESR1- Estrogen receptor 1
PGR – Progesteron receptor
CDKN2A Cyclin-dependent kinase inhibitor 2A
Geminin – DNA replication inhibitor
MCM2/5- a regulatory subunit inhibiting the helicase complex
CDC45L- Cell cycle control protein
MSH6 and MSH2 - MutS homologues, proteins involved in DNA repair
RAD50 – DNA repair protein (homologues recombination-dependent repair)
DNMT1 – DNA methylation enzyme
EZH2- Histone methylation enzyme
HDAC2- Histone acetylation enzyme
21

22. PATHWAY MAPS
Brca1 and Brca2 in breast cancer
Cell cycle: Start of DNA replication in early S phase
22

23. PATHWAY MAPS
PR action in breast cancer -stimulation of cell
growth and proliferation
Epigenetic alterations in ovarian cancer
23

24. USE CASE 4
Implementing Classification Model
24

25. PATIENT STRATIFICATION MODEL
25

26. IMPLEMENTING MODEL IN tranSMART
Associated clinical phenotype
Sample
Molecular Subtype
Model
Biomarkers
Stratification rule
Mechanism
Drug target
Pathways
Standard tranSMART
Additional functionality in tranSMART
MetaCore
Cytoscape
Subnetworks
Applicable for both One Mind and
Orion projects

27. SYSTEMS BIOLOGY TOOLS
Network/Pathways based Approach–
•
•
METABASE
The most comprehensive data available
Drug Targets
Drug Repositioning
•
Biomarker Identification
•
Biological Mechanism
Reconstruction
SYSTEMS BIOLOGY TOOL LIBRARY
State of the art methods
Drug Combinations
Prognostic Biomarkers
•
OMICs data +
other data types
including clinical
response
•
•
Statistical Approach
Predictive Biomarkers

28. EXAMPLES OF NETWORK APPROACHES
Subnetworks
(Chuang et al., 2012)
Systems Biology
(Zhang et al., 2011)
Probabilistic Inference
Pathway Activity
(Su et al., 2009)
(Lee et al., 2007)
Pathway Based
(Kim et al., 2012)
RRFE
(Johannes et al., 2010)

29. FEEDBACKS FROM
tranSMART USERS
29

30. STATISTICAL ANALYSIS QUESTIONS
• How to control Type 1 error rate?
– Testing set vs. Validation set
• How to perform longitudinal analysis?
– Regression models
• How to identify covariance variables? Which
variable has the highest correlation with the
outcome?
– Multivariate analysis
• Other analysis methods/workflows
30

31. SCIENTIFIC QUESTIONS
• How to set QC framework around uploaded data?
Community developed QC standard?
• How to do across study analysis (easier)?
• How to do across species analysis?
• How to the community report these (and bugs)?
31

32. FEATURE WISH LIST
• Multiple improvement to R advance workflows
• Sending results (gene list, patient subsets) from an
advanced workflow back to summary statistics
• Saving a workflow (history/output)
• Using gene expression data to create subsets
• Viewing specific subject records
• Adding data types (i.e. date, longitudinal measurement)
• Improving exported tables
and many more ….
32