The basis for personalized predictive medicine

Tomorrow:

Patient DNA is fully genotyped one time only

A database is consulted in order to

o Develop a molecular diagnosis of specific disease

o Predict responses to each of the available treatments

Diagnosis and treatment Today’s medical practice is for the most part:

Imprecise in diagnosis

Selecting treatment by trial-and-error

Tomorrow: the Personal Genome Card is available.

The database is consulted whenever necessary

o Genetic susceptibility to specific diseases is assessed

Preventive measures are taken in consultation with a family

physician, including:

o Life style changes

o Routine screenings for those at elevated risk, allowing for early

diagnosis and better prognosis

o Personalized preventive treatment

Today’s medical practice is for the most part

reactive to disease

Prevention

Bridging the gap

Need to decipher the genetic basis of common complex diseases and responses to treatment

Today’s technologies are not up to the task:

Too complex (e.g., procedures are SNP specific)

Too expensive (> 0.1€ per SNP)

Drug Responses are Multigenic

Pharmacokinetics Pharmacodynamicsindividualmetabolism

individual action

Molecular sub-types

Drug Individual responses

individualpathways

Individual response to medicines is likely a

consequence of many low-effect genetic variants

sporadicCombinations of many low-effectgene variants(eg: AD, Migraine, NID-Diabetes, Psoriasis)

Most disease is the result of combinations of low-

effect genetic variants

Common Diseases are Multigenic

familial Moderate-penetrance gene variants(eg: BRCA1,2)

Single high-penetrance gene variants(eg: CF, Huntington Disease)

ABCDEFG

ABCDEFGABCDEFG

ABCDEFGABCDEFG

ABCDEFGABCDEFGABCDEFGABCDEFGOver Generations

A combination of many subtle

genetic variants may tip the balance

in favor of disease

ABCDEFGABCDEFG

Combinations of low-effect variants

Finding low effect variants will require high density genotyping of large populations

“…a density of SNPs of one every 10,000 – 30,000 bp can rapidly

narrow the search for susceptibility genes*.” Roses. Nature, 405

(2000) pp862. (SVP, Genetics Research, GSK)

“…roughly 500,000 SNPs will be required for whole-genome

association studies in samples drawn from large outbred

populations.” (pp139). “…efficient technologies are needed for

genotyping hundreds of thousands of SNPs in thousands of

individuals” (pp143). Kruglyak. Nature Genetics, 22 (1999). (Fred

Hutchinson Cancer Research Center & HHMI)

*100,000 – 300,000 SNPs

Multigenic Diseases: Gene Hunting

Genome-wide / hypothesis-free approach

Using very high density markers

At least 300,000 SNPs/genome

Large numbers of subjects

At least 2,000 per disease/treatment

Totaling at least 600 million SNPs typed/disease

Today cost/SNP = 10-20¢

Tractable when cost falls below 1¢/SNP

Technology Overview

SNPtyping with Manteia technology

No SNP map needed

Not SNP-specific

“One” tube per patient

Readily scalable

Detection method: sequencing genome fragments

Below 0.1¢ per SNP

Manteia Technology: PAS( Parallel Amplification and Sequencing )

Four basic steps

1: Isolate genomic DNA from blood or cheek-swab

2: Cut up the DNA and collect the fragments

3: Amplify all the fragments in parallel on a solid surface

4: Sequence all the fragments in parallel

Patient 1

Patient n

Isolate

Genomic DNA

Cut DNA with

Restriction Endonuclease Enzyme

1

23

4

5

1

23

4

5

Type IIs

recognition site

n

Genomic

fragment

n

Ligation Type IIs

digest

Short genomic

fragment

n

Linker 1

Restriction site

Type IIs

recognition sites

n

Genomic

fragment

n

Ligation

n

Type IIs

digest

Short genomic

fragments

PAS2

n

n

Ligation

Linearized

Colony Template

Linker 2

5

4

3

2

1

DNA fragment sizes

normalized

Each restriction endonuclease

=> ~1.5 million fragments

5

4

3

2

1

Clone DNA fragments

Into “DNA Colony Vectors”

5

4

3

2

1

DNA fragment sizes

normalized

n

Variable region

Constant region Constant region

n

Colony vectors

Short primers

n

nFunctionalization

Chemically functionalized surface

PAS ArrayDensity = f([template],[primer],t)

ss DNA Colony Vector(107/cm2)

ss OligonucleotidePrimers (4x104/μm2)

Glass surface

1

2

5’ ends

covalently attached

3’ endsfree in solution

100 nm

Arch formation

DNA:DNAHybrid

DNA replication

Add nucleotides + polymerase

(25b complementarity)

ReplicatedColony Vectors

Attachedterminus

Freeterminus

Attachedterminus

2

11

2

Denaturation

Attachedterminus

Attachedterminus

1-2 μm

DNAColonies

(1000-2000 copies in each)

1

2

100 μm

Sequencing primersAdded to the array

DNA:DNAHybrids

CACTGCTGA

Sequencing primer

AnonymousFragment of genomicDNA (Variable region)

Colony Vector (Constant region)

Colony Vector (Constant region)

Cycle 3

CACTGCTGA

G

T

0

1

2

3

4

Sig

nal

A G T C

CACTGCTGA

A

0

1

2

3

4

Sig

nal

A G T C

Cycle 1

Wash

Add

CACTGCTGA

Cycle 2

G

0

1

2

3

4

Sig

nal

A G T C

Manteia Sequencer Prototype

Signal intensity data

DNA colonies image processing

Raw image

10 mm

Processed image

10 mm

Expected sequence: GGCTGTATAG

Automated colony sequencing results

From Sequence Fragments to SNPS

Genetic variability in the human population:

Between 2 individuals: 1 SNP every 1331 bp (SNP consortium, Nature 409,928)

In the population (Krugliak, Nature Genetics 27,234 ):Frequency >= 10% : 1 SNP every 600bpFrequency >= 1% : 1 SNP every 290bpFrequency >= 0.1%: 1 SNP every 200 bp

The same stretches of DNA are sequenced in each patient

patient #1

patient #47

patient #125

patient #571

....

....

Sequenced fragments

acgtaggtgcaggtcagtacgtaggtgcaggtcagtacgtaggtgcaggtcagtacgtaggtgcaggtcagtacgtaggtgcaggtcagtacgtaggtgcaggtcagtacgtaggtgcaggtcagtacgtaggtgcaggtcagtacgtaggtgcaggtcagt…

tagcgtAtcgtaggtagattagcgtAtcgtaggtagattagcgtAtcgtaggtagattagcgtAtcgtaggtagattagcgtGtcgtaggtagattagcgtAtcgtaggtagattagcgtAtcgtaggtagattagcgtAtcgtaggtagattagcgtGtcgtaggtagattagcgtAtcgtaggtagat…

SNPMaking SNP identification possible

Each restriction endonuclease:

=> 1.5 million fragments

=> 25 million bases sequenced

=> 1% of the genome scanned

=> 100,000 SNPs scored

Mega-SNP data analysis: “genetic” approach

Classical frequent SNP problem: - number SNP >> population- distance between SNP > linkage range- moderate population (50~300)

=> How to differentiate real linkage signal from false positives/negatives

Manteia’s Mega-SNP approach:- distance between SNP < linkage range- moderately frequent SNPs- large population (1,000~10,000)

=> SNP clusters of high statistical signifcance

1 Mbp

Linkage

Signal

1 Mbp

Linkage

Signal

2~4 LD range “running average”

SNPtyping with Manteia technology

No dependent on SNP maps

Not SNP-specific

“One” tube per patient

Readily scalable

Detection method: sequencing genome fragments

Tracktable biostatistics and bioinformatics

Below 0.1¢ per SNP (Q1-2006)

Business Model

Identify Gene Variant Associations

Alone or in partnerships

Retain rights to these associations for application to:

Therapeutic response prediction

Disease risk assessments

License out rights for application to:

Drug discovery

Develop and market a Personal Genome Card in conjunction with access to a database of clinical and genetic associations.

Collaborations with biopharmaceutical companies

Clinical partnerships

Clinical trials assessment & recruitment

Drug revival

Development of marketed Companion Tests

Discovery partnerships

Target discovery in diseased populations

Transcriptome analysis

Collaborations

Collaborations

ClinicalStudies

Association Studies

Gene VariantsDisease

Causation

Progression

Drug Targets

Response

to Therapy

Drug Discovery

Predictive Tests

Marketing

Manteia

Technology

Individual Patterns

Personal Genome Card

Internal Programs:

Personal Treatment Guidelines

In conjunction with Personal Genome Cards

Predict patient responses to therapy

Efficacy and side-effects

Personal Risk Profiles

In conjunction with Personal Genome Cards

Predict lifetime risk of sporadic cases of common diseases.

Permit appropriate interventions and monitoring for those at risk.

Business Model

Treatment Guidelines

Single Disease

Clinical Populations

Association Studies

Patterns of

Gene Variants

Manteia

Technology

Therapy 1

Responders Non

Responders

Therapy 2

Responders Non

Responders

Therapy 3

Responders Non

Responders

Pharmacokinetics

Pharmacodynamics

Disease

subgrouping

GenotypesPersonal

Genotype Card

Treatment

Guideline

PRODUCT

Disease Selection

Serious diseases

High incidence

Several treatments available

Each treatments works for only a fraction of patients

Treatments are expensive

Treatments have serious side effects

Delaying effective treatments leads to poorer prognosis

All frequent diseases where sub-optimal treatment has a high cost

Personal Treatment Guidelines

Market example: Breast Cancer

200,000 new diagnoses each year in US; 300,000 in EU.

$2,500 per comprehensive Treatment Guideline

Potential US+EU market: $1.25B/year

Maximal penetration @ 30% = $375MM/year

Net income @ 20% = $75MM/year

Personal Genome Card

Risk Profiles

Association Studies

Patterns of

Gene Variants

Manteia

Technology

GenotypesPersonal

Genotype Card

Risk Profile

PRODUCT

Single Disease

Clinical Populations

Disease

Subgroups

Matched

Populations

Disease Selection

High incidence

Prevention is possible

Preventive treatment is available

Early diagnosis leads to much better prognosis

Where there is either no available screen

Where screening is expensive or unpleasant

Personal Risk Profiles

Market example: Colorectal cancer

4,000,000 turn 50 each year in the US

8,000,000 target population US+EU

$500 Risk Profile for colorectal cancers

Potential US+EU market: $4B per year

Maximal penetration @ 10% = $400MM/year

Net income @ 10% = $40MM/year

Personal Genome Card