The document discusses the future of personalized predictive medicine, highlighting advancements such as full DNA genotyping and the use of databases to improve disease diagnosis and treatment selection, moving away from trial-and-error methods. It emphasizes the need for more efficient genetic analysis technologies, particularly for identifying low-effect genetic variants associated with common diseases, and describes Manteia's SNP typing technology as a scalable and cost-effective solution. The potential market for personalized genome cards and treatment guidelines is substantial, with projected revenues from both disease risk profiling and response predictions.

2. The basis for personalized predictive medicine

3. Tomorrow:
Patient DNA is fully genotyped one time only
A database is consulted in order to
oDevelop a molecular diagnosis of specific disease
oPredict responses to each of the available treatmentsDiagnosis and treatment
Today’s medical practice is for the most part:
Imprecise in diagnosis
Selecting treatment by trial-and-error

4. Tomorrow: the Personal Genome Card is available.
The database is consulted whenever necessary
oGenetic susceptibility to specific diseases is assessed
Preventive measures are taken in consultation with a family physician, including:
oLife style changes
oRoutine screenings for those at elevated risk, allowing for early diagnosis and better prognosis
oPersonalized preventive treatment
Today’s medical practice is for the most part reactive to diseasePrevention

5. 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)

6. Drug Responses are Multigenic
Pharmacokinetics Pharmacodynamics
individual
metabolism
individual
action
Molecular sub-types
Drug Individual responses
individual
pathways
Individual response to medicines is likely a
consequence of many low-effect genetic variants

7. sporadic
Combinations of many low-effect
gene variants
(eg: AD, Migraine, NID- Diabetes, Psoriasis)
Most disease is the result of combinations of low- effect genetic variantsCommon Diseases are Multigenicfamilial
Moderate-penetrance
gene variants
(eg: BRCA1,2)
Single high- penetrance
gene variants
(eg: CF, Huntington Disease)

8. ABCDEFG
ABCDEFG ABCDEFG
ABCDEFG ABCDEFG
ABCDEFG ABCDEFG ABCDEFG ABCDEFG
Over Generations
A combination of many subtle
genetic variants may tip the balance
in favor of disease
ABCDEFG ABCDEFG
Combinations of low-effect variants

9. 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

10. 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

11. Technology Overview

12. 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

13. 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

14. Patient 1
Patient n
Isolate
Genomic DNA
Cut DNA with
Restriction Endonuclease Enzyme
1
2
3
4
5
1
2
3
4
5

15. Type IIs
recognition site
n
Genomicfragment
n
Ligation
Type IIs
digest
Short genomic
fragment
n
Linker 1

16. Restriction site
Type IIs
recognition sites
n
Genomicfragment
n
Ligation
n
Type IIs
digest
Short genomic
fragmentsPAS2

17. n
n
Ligation
Linearized
Colony Template
Linker 2
5
4
3
2
1
DNA fragment sizes
normalized
Each restriction endonuclease=> ~1.5 million fragments

18. 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

19. n
Colony vectors
Short primers
n
n
Functionalization
Chemically functionalized surface

20. PAS Array
Density = f([template],[primer],t)
ss DNA Colony Vector(107/cm2)
ss Oligonucleotide
Primers (4x104/μm2)
Glass surface
1
2
5’ endscovalently attached
3’ endsfree in solution

21. 100 nm
Arch formation
DNA:DNA
Hybrid
DNA replication
Add nucleotides + polymerase
(25b complementarity)

22. Replicated
Colony Vectors
Attached
terminus
Free
terminus
Attached
terminus
2
1
1
2
Denaturation
Attached
terminus
Attached
terminus

23. 1-2 μm
DNA
Colonies
(1000-2000 copies in each)
1
2
100 μm

24. Sequencing primers
Added to the array
DNA:DNA
Hybrids
C
A
C
T
G
C
T
G
A
Sequencing primer
Anonymous
Fragment of genomic
DNA (Variable region)
Colony Vector (Constant region)
Colony Vector (Constant region)

25. Cycle 3
C
A
C
T
G
C
T
G
A
G
T
0
1
2
3
4
Signal
A
G
T
C
C
A
C
T
G
C
T
G
A
A
0
1
2
3
4
Signal
A
G
T
C
Cycle 1
Wash
Add
C
A
C
T
G
C
T
G
A
Cycle 2
G
0
1
2
3
4
Signal
A
G
T
C

26. Manteia Sequencer Prototype

27. Signal intensity dataDNA colonies image processing
Raw image
10 mm
Processed image
10 mm

28. Expected sequence: GGCTGTATAGAutomated colony sequencing results

29. From Sequence Fragments to SNPS

30. 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 600bp
Frequency >= 1% : 1 SNP every 290bp
Frequency >= 0.1%: 1 SNP every 200 bp

31. The same stretches of DNA are sequenced in each patient
patient #1
patient #47
patient #125
patient #571
....
....
Sequenced fragments
acgtaggtgcaggtcagt
acgtaggtgcaggtcagt
acgtaggtgcaggtcagt
acgtaggtgcaggtcagt
acgtaggtgcaggtcagt
acgtaggtgcaggtcagt
acgtaggtgcaggtcagt
acgtaggtgcaggtcagt
acgtaggtgcaggtcagt
…
tagcgtAtcgtaggtagat
tagcgtAtcgtaggtagat
tagcgtAtcgtaggtagat
tagcgtAtcgtaggtagat
tagcgtGtcgtaggtagat
tagcgtAtcgtaggtagat
tagcgtAtcgtaggtagat
tagcgtAtcgtaggtagat
tagcgtGtcgtaggtagat
tagcgtAtcgtaggtagat
…
SNP
Making SNP identification possible
Each restriction endonuclease: => 1.5 million fragments
=> 25 million bases sequenced
=> 1% of the genome scanned
=> 100,000 SNPs scored

32. 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”

33. 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)

34. 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.

35. 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 analysisCollaborations

36. Collaborations
Clinical
Studies
Association Studies
Gene Variants
Disease
Causation
Progression
Drug Targets
Response to Therapy
Drug Discovery
Predictive Tests
Marketing
Manteia
Technology
Individual
Patterns

37. 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

38. Treatment Guidelines
Single Disease Clinical Populations
Association Studies
Patterns ofGene Variants
Manteia
Technology
Therapy 1
Responders
Non
Responders
Therapy 2
Responders
Non
Responders
Therapy 3
Responders
Non
Responders
Pharmacokinetics
Pharmacodynamics
Disease subgrouping
Genotypes
Personal
Genotype
Card
Treatment
Guideline
PRODUCT

39. 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

40. 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

41. Risk Profiles
Association Studies
Patterns ofGene Variants
Manteia
Technology
Genotypes
Personal
Genotype
Card
Risk Profile
PRODUCT
Single Disease Clinical Populations
Disease
Subgroups
Matched Populations

42. 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

43. 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