biosystemix, ltd. data-driven solutions for clinical prediction and functional discovery chi,...

Biosystemix, Ltd.

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

2 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

3 Biosystemix, Ltd.

Biosystemix value to customers:Succesful personalized medicine programs ultimately depend on

understanding the data and deriving meaningful predictions

Biosystemixsolutions

Clinical data

Experimentalplatforms

Personalized medicine

predictors

Therapeutic markers &

targets

Signaling pathways &

networks

Genomics & proteomics

Biomedical data

Discoveries and models

Integrative data mining and predictive modeling

You must pass through here

4 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.

5 Biosystemix, Ltd.

An advance in personalized medicine /

predictive medicine

6 Biosystemix, Ltd.

A personalized medicine scientific case study: Predicting clinical drug response in MS (multiple sclerosis)

Nonlinear & combinatorial

predictive models

Gene A expression

Good responder to

interferon

Gene B expression

Clinical RNA expression

profiling data

Computational modeling

Personalized medicine outcome

Gene C expression

Poor responder to

interferon

7 Biosystemix, Ltd.

Predicting drug response before IFN treatment in MS:

Two genes work better than one

10 samples are misclassified by Caspase 10 alone

Blue: poor responseRed: good response

1d IBIS models

2d IBIS models

15 samples are misclassified by FLIP alone

Only 5 samples are misclassified by FLIP and Caspase 10 together

Poor response predictive

region

Good response predictive

region

8 Biosystemix, Ltd.

Predicting drug response before IFN treatment in MS:

Three genes work better than two

• 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 responseRed: good response

3d IBIS model

2d IBIS models

9 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 Francisco2School of Computing, Queen’s University, Kingston, Ontario, Canada. 3Department of Neuroimmunology, Hospital Vall d’Hebron, Barcelona, Spain4Department of Neurology, Clinica Universitaria de Navarra, University of Navarra, Spain, 5Biosystemix Ltd., Sydenham, Ontario, Canada

10 Biosystemix, Ltd.

What have we found?

• Combinatorial 3d models predicting IFN 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


Gene function and pathway discovery through gene network reverse engineering


IFN gamma receptor heterodimers activate Jak2

SOS1 and Grb2 complex activates RAS/MAPK pathway

leading to FOS activation

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

Predicting the molecular mechanisms underlying differential drug response: Data-driven, computational reverse engineering reconstructs

signaling pathways directly from clinical MS gene expression data

Red lines: Gene interactions Red lines: Gene interactions in good respondersin good responders

Green lines: Gene interactions Green lines: Gene interactions in poor respondersin poor responders


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:.

– 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.


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.


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


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


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

Acknowledgements


Caspase 10

Caspase 10

FL

IPF

LIP Caspase 2

Caspase 2

Poor response predictive regionGood response

predictive region

Good response predictive region

Good response predictive region

Linking genes and pathways to predict therapeutic outcome in a complex disease


Supplementary Slides


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 (IFN) treatment

Study Design:

– RNA isolated from peripheral blood mononuclear cells after IFN 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


• High-quality molecular and physiological profiling– Study design to capture key components of medical outcomes– Study design to assist better post-hoc discovery of outcome-

predictive 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

Scientific challenges in personalized medicine


The need for data-driven models for tuning therapies to individual need

Complexpredictive

models

Clinical assay intensity C

Gene expression A

Therapeutic compound X

Compound cocktail Y

Drug dose Z

Protein abundance B

Diagnostic and prognostic profiling

Computational modeling

Personalized medicine therapy


Effective inference and modeling for personalized medicine must deal with biological complexity

• Interaction networks

• Nonlinearity

• Combinatorics

1 2 3 4 5 6 7 8 9 100

5

10

15

20

25

30

35

k

Lo

g1

0 (

C(N

,k))

N = 10

N = 1000

N = 100

N = 10,000

k=4

“Curse of dimensionality”

e.g. 400 million million combinations from 10,000 genes

single input

single output

multiple inputs

single output

multigenic regulation

single input

multiple outputs

pleiotropicregulation

multiple inputs

multiple outputs

genetic network

single input

single output

multiple inputs

single output

multigenic regulation

single input

multiple outputs

pleiotropicregulation

multiple inputs

multiple outputs

genetic network


Data mining

Personalized medicine: The ultimate application of systems biology

Genetic variationGenetic variation

characterizationcharacterization

Predictive modeling

Clinical Clinical testingtesting

Laboratory Laboratory validationvalidation

Target & marker Target & marker discoverydiscovery

Clinical assayClinical assaydatadata

RNA, RNA, protein, metabolite protein, metabolite

profilingprofiling

Personalized Personalized medicinemedicine

Drugs, diagnostics Drugs, diagnostics & predictive models& predictive models

Systems Biology

Computational analysis and Bioinformatics

Biomedical validation


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

Recipes for success

biosystemix, ltd. data-driven solutions for clinical prediction and functional discovery chi,...

Documents

d models

d predictive models

d ibis models

biosystemix value

clinical drug response

predicting drug response

interferon slide

effects slide