Manteia non confidential-presentation 2003-09
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Transcript of Manteia non confidential-presentation 2003-09
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The basis for personalized predictive medicine
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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
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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
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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)
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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
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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)
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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
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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
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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
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Technology Overview
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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
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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
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Patient 1
Patient n
Isolate
Genomic DNA
Cut DNA with
Restriction Endonuclease Enzyme
1
23
4
5
1
23
4
5
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Type IIs
recognition site
n
Genomic
fragment
n
Ligation Type IIs
digest
Short genomic
fragment
n
Linker 1
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Restriction site
Type IIs
recognition sites
n
Genomic
fragment
n
Ligation
n
Type IIs
digest
Short genomic
fragments
PAS2
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n
n
Ligation
Linearized
Colony Template
Linker 2
5
4
3
2
1
DNA fragment sizes
normalized
Each restriction endonuclease
=> ~1.5 million fragments
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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
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n
Colony vectors
Short primers
n
nFunctionalization
Chemically functionalized surface
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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
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100 nm
Arch formation
DNA:DNAHybrid
DNA replication
Add nucleotides + polymerase
(25b complementarity)
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ReplicatedColony Vectors
Attachedterminus
Freeterminus
Attachedterminus
2
11
2
Denaturation
Attachedterminus
Attachedterminus
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1-2 μm
DNAColonies
(1000-2000 copies in each)
1
2
100 μm
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Sequencing primersAdded to the array
DNA:DNAHybrids
CACTGCTGA
Sequencing primer
AnonymousFragment of genomicDNA (Variable region)
Colony Vector (Constant region)
Colony Vector (Constant region)
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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
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Manteia Sequencer Prototype
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Signal intensity data
DNA colonies image processing
Raw image
10 mm
Processed image
10 mm
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Expected sequence: GGCTGTATAG
Automated colony sequencing results
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From Sequence Fragments to SNPS
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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
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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
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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”
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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)
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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.
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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
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Collaborations
ClinicalStudies
Association Studies
Gene VariantsDisease
Causation
Progression
Drug Targets
Response
to Therapy
Drug Discovery
Predictive Tests
Marketing
Manteia
Technology
Individual Patterns
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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
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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
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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
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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
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Risk Profiles
Association Studies
Patterns of
Gene Variants
Manteia
Technology
GenotypesPersonal
Genotype Card
Risk Profile
PRODUCT
Single Disease
Clinical Populations
Disease
Subgroups
Matched
Populations
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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
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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