Transcript Predictive modeling

Data-Driven Solutions for Clinical Prediction
and Functional Discovery
CHI, Molecular Medicine Tri-Conference
Emerging Company Profile
April 19, 2005
Roland Somogyi, Ph.D.
Larry D. Greller, Ph.D.
Biosystemix, Ltd.
[email protected]
[email protected]
www.biosystemix.com
(613)-376-3126
Biosystemix, Ltd.
Personalized medicine:
The future of therapeutic discovery, practice and business
• Diseases are complex
– Genes and pathways lead to the same symptoms in different ways
in different individuals
– We must target these specific causes, not the symptoms
• Some drugs are only effective in specific individuals
– Drug targets can be specific for genetic variants of disease
– Individual pathway activity fingerprints may determine efficacy
• Some drugs cause adverse effects in a very small
subpopulation
– Toxicity due to genetic variants of drug metabolism
– Physiological and pathway background patterns may lead to
unanticipated side effects
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Biosystemix, Ltd.
Biosystemix value to customers:
Succesful personalized medicine programs ultimately depend on
understanding the data and deriving meaningful predictions
You must pass
through here
Biomedical
data
Experimental
platforms
Genomics &
proteomics
Clinical
data
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Integrative data mining
and predictive modeling
Biosystemix
solutions
Discoveries
and models
Personalized
medicine
predictors
Therapeutic
markers &
targets
Signaling
pathways &
networks
Biosystemix, Ltd.
Biosystemix focuses on the opportunity for
therapeutic solutions, services and products
• The predictive models which integrate the knowledge of markers,
patterns and pathways associated with disease and therapeutic
outcome, will become vital
– to personalized therapeutic practice,
– target and drug discovery, validation and approval, and
– an economic engine for the biomedical industry.
• Biosystemix provides key technologies and experience in
– extracting complex patterns of key markers from genomic and
clinical data,
– integrating predictive molecular profiles, functional knowledge and
clinical outcomes into comprehensive predictive models, and
– generating personalized medicine marker, target and model IP in a
large variety of disease and biomedical application areas.
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Biosystemix, Ltd.
An advance in
personalized medicine /
predictive medicine
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Biosystemix, Ltd.
A personalized medicine scientific case study:
Predicting clinical drug response in MS (multiple sclerosis)
Clinical
RNA expression
profiling data
Gene A
expression
Gene B
expression
Gene C
expression
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Computational
modeling
Nonlinear &
combinatorial
predictive
models
Personalized
medicine
outcome
Good responder
to
interferon b
Poor responder
to
interferon b
Biosystemix, Ltd.
Predicting drug response before IFNb treatment in MS:
Two genes work better than one
15 samples are
misclassified by
FLIP alone
1d IBIS
models
10 samples are
misclassified by
Caspase 10 alone
2d IBIS
models
Good
response
predictive
region
Blue: poor response
Red: good response
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Poor
response
predictive
region
Only 5 samples are misclassified
by FLIP and Caspase 10 together
Biosystemix, Ltd.
Predicting drug response before IFNb treatment in MS:
Three genes work better than two
3d IBIS
model
2d IBIS
models
•
The yellow, orange and blue
arrows point to samples that are
incorrectly classified in the 2d
models and correctly classified
in the 3d models
•
Note: 3d models pass stringent
statistical cross-validation criteria
•
A & B:
Views of 3d model predicting
good and poor drug responders
from the expression of 3 genes
•
B, C & D:
All 3 possible 2d predictive
models involving the same genes
Blue: poor response
Red: good response
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Biosystemix, Ltd.
Reference
S. Baranzini1, P. Mousavi2, J. Rio3, S. Caillier1, A. Stillman1, P. Villoslada4, M. Wyatt1, M.
Comabella3, L. Greller5, R. Somogyi5, X. Montalban3, J. Oksenberg1
Classification and prediction of response to IFNß using gene expression
profiling the supervised computational methods. (2004) PLoS Biol 3(1): e2
1Department
of Neurology, School of Medicine, University of California at San Francisco
2School of Computing, Queen’s University, Kingston, Ontario, Canada.
3Department of Neuroimmunology, Hospital Vall d’Hebron, Barcelona, Spain
4Department of Neurology, Clinica Universitaria de Navarra, University of Navarra, Spain,
5Biosystemix Ltd., Sydenham, Ontario, Canada
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Biosystemix, Ltd.
What have we found?
• Combinatorial 3d models predicting IFNb response outcome in MS
achieve high accuracy and statistical validation scores.
• These predictive models provide valuable diagnostic/prognostic
answers in complex diseases for which no markers exist
– Next step is in-depth clinical validation
• Single genes and pairs do not achieve high predictive accuracy
• Finding the nonlinear and combinatorial patterns at the root of these
models requires advanced data mining
– Conventional statistics not effective here
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Biosystemix, Ltd.
Gene function and pathway discovery through
gene network reverse engineering
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Biosystemix, Ltd.
Predicting the molecular mechanisms underlying differential drug response:
Data-driven, computational reverse engineering reconstructs
signaling pathways directly from clinical MS gene expression data
Literature quote:
“…interferon-inducible stat2: stat1 heterodimer
preferentially binds in vitro to a consensus
element found in the promoters of a subset of
interferon-stimulated genes”
Jak2 phosphorylates only Stat1
resulting in Stat1 homodimer
formation and GAS (cis element)
activation of Interferon gamma
induced genes
 Red lines: Gene interactions
in good responders
 Green lines: Gene interactions
in poor responders
IFN gamma receptor
heterodimers activate Jak2
SOS1 and Grb2 complex
activates RAS/MAPK pathway
leading to FOS activation
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Biosystemix, Ltd.
What made it possible?
•
Setting the stage with thorough experimental design
– Careful clinical study design and patient recruitment
– Sufficient number of high quality, clinical blood and RNA sample
•
A solid foundation of precisions measurements
– Quantitiave, gene expression RT-PCR assays
• Reverse transcription – polymerase chain reaction
• Combines stringent hybridization with amplification
– Only the best assays should be used for clinical applications
•
Providing the edge with advanced computational analysis
– Nonlinear and combinatorial methods for pattern recognition
– Higher-dimensional predictive modeling and statistical validation
•
In the words of by Kaminski and Achiron, highlighting the Baranzini study in
PLoS Med 2(2): e33:.
–
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However, the importance of Baranzini and colleagues’ study lies not in its mechanistic insights, but
in its clinical relevance. The careful design of the experiment, the use of reproducible real-time
PCR instead of microarrays, the meticulous analysis, and the previous observations support the
notion that PBMCs express clinically relevant gene expression signatures in MS and probably in
other organ-confined diseases.
Biosystemix, Ltd.
Data-driven predictive models provide
opportunities for better medical practice
•
Step 1: Diagnosis of the disease
– Specific form of a disease is not apparent in superficial symptoms
– Higher-dimensional diagnostic models based on in-depth patient profiling
• Molecular and physiological fingerprints distinguish forms of a disease.
•
Step 2: Prognosis of the outcome
– Complex prognostic models based on in-depth profiling data can enable
reliable choices for timing of therapeutic interference
•
Step 3: Therapeutic choice
– Therapeutic decision models based on detailed patient state information will
significantly increase the probability of successsful treatment
•
Step 4: Therapeutic discovery
– Data from personalized medicine studies will be used in the
data-driven discovery of new disease mechanisms and pathways for
individually-targeted intervention.
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Biosystemix, Ltd.
Biosystemix currently provides its expertise and services
to partners in predictive medicine and genomics
• Immunogenomics
– “S2K”, Genome Canada / Genome Quebec-funded multi-center
consortium
• Infectious diseases
– HIV
– SARS
– HTLV
• Transplant rejection
– Immune Tolerance Network, NIH/NIAID-funded multi-center
consortium
• Autoimmune diseases
– Allergy
– Diabetes
– UCSF, Department of Neurology
• Predicting drug response in multiple sclerosis
• Cancer
– Queens University, Ontario Cancer Institute
• Predicting good and poor outcomes in Follicular Lymphoma
• Toxicogenomics
– University of Michigan
• Inference of pathways involved in toxicity from gene expression data
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Biosystemix, Ltd.
Biosystemix sees growing opportunities in
personalized medicine
•
Growing market for diagnostic and prognostic products
– Marker sets, assay kits and hardware for more effective diagnostic/prognostic profiling
•
Information products
– Computational models linking complex diagnostic/prognostic patterns to outcomes
– Web-based, personalized medicine tools for use by physicians and patients
•
Product linkage
– A drug may only be effectively applied if linked to a prognostic test
• Patent and regulatory approval for product sets that are only effective in combination
• May be required in the future by regulatory agencies for specifically-targeted drugs
– Opportunity for extracting value from generic drugs
• Novel combinations of generic drugs to match individual patient need
• Combinations and predictive models generating these combinations constitute valuable IP
•
Creating new markets
– Providing new tools and therapies where they are currently non-existent or unreliable
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Biosystemix, Ltd.
Acknowledgements
Larry D. Greller, Ph.D.
Biosystemix CSO, Co-Founding Director
Parvin Mousavi, Ph.D.
Assist. Prof. Queen’s University School of Computing
Sergio Baranzini, Ph.D.
Assist. Prof. Neurology University of California San Francisco
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Biosystemix, Ltd.
Linking genes and pathways to predict
therapeutic outcome in a complex disease
Poor response predictive region
Good response
predictive region
FLIP
Good response
predictive region
Good response predictive region
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Biosystemix, Ltd.
Supplementary
Slides
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Biosystemix, Ltd.
A collaborative, predictive medicine study in MS
Investigational Groups:
– Sergio Baranzini, Jorge Oksenberg: UCSF
– Xavier Montalban: Hospital Vall d’Hebron (Barcelona, Spain)
– Parvin Mousavi, Larry Greller, Roland Somogyi: Biosystemix, Ltd.
Multiple Sclerosis:
– Autoimmune, neuroinflammatory CNS disease
– Primary therapy: interferon-beta (IFNb) treatment
Study Design:
– RNA isolated from peripheral blood mononuclear cells after IFNb
treatment at 6 time points (0, 3, 6, 9, 12, 18 and 24 months)
– 70 genes measured by kRTPCR
– 52 patients
– 33 good responders
– 19 bad responders
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Biosystemix, Ltd.
Scientific challenges in personalized medicine
• High-quality molecular and physiological profiling
– Study design to capture key components of medical outcomes
– Study design to assist better post-hoc discovery of outcomepredictive profiles
– Adequate samples for statistical support
• Data management and integration
– Making different assay types commensurable
– Standards for data integration
• Data-driven computational discovery and modeling
– Complex outcome-predictive patterns
– Predictive models for clinical decision support
– Mechanistic discovery for novel intervention strategies
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Biosystemix, Ltd.
The need for data-driven models for
tuning therapies to individual need
Diagnostic and
prognostic profiling
Gene
expression A
Computational
modeling
Protein
abundance B
Complex
predictive
models
Clinical assay
intensity C
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Personalized
medicine
therapy
Therapeutic
compound X
Compound
cocktail Y
Drug
dose Z
Biosystemix, Ltd.
Effective inference and modeling for personalized medicine
must deal with biological complexity
• Interaction networks
multigenic
regulation
single input
pleiotropic
regulation
multiple inputs
genetic
network
single input
multiple inputs
• Nonlinearity
• Combinatorics
single output
multiple
outputs
single output
multiple
outputs
“Curse of dimensionality”
Log10 (C(N,k))
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N = 10,000
30
25
N = 1000
k=4
20
15
N = 100
10
5
0
N = 10
1
2
3
4
5
6
7
8
9 10
k
e.g. 400 million million combinations from 10,000 genes
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Biosystemix, Ltd.
Personalized medicine:
The ultimate application of systems biology
Biomedical validation
Systems Biology
RNA,
protein, metabolite
profiling
Genetic variation
characterization
Computational analysis
and Bioinformatics
Data Predictive
mining modeling
Clinical assay
data
Drugs, diagnostics
& predictive models
Target & marker
discovery
Laboratory
validation
Clinical
testing
Personalized
medicine
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Biosystemix, Ltd.
Recipes for success
1. More than a vision
• It will be difficult …
• Personalized medicine and integrative biology is technologically challenging
• …but it’s tractable.
• Many technological components are there – they now need to work together
2. The devil is in the details
• Thorough and integrative scientific study design
• High quality assay technology and execution
• Advanced computational data mining and predictive modeling
3. It all depends on people and technologies working together
• Integration of biomedical, physical, and math/statistical/computational sciences
• Acceptance of new technologies by regulatory bodies and medical practioners
• Support of R&D and commercialization by businesses community
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Biosystemix, Ltd.