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Cell Signaling

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Genetic and epigenetic (in)activation of oncogenes and tumor suppressor genes ... Giuseppe Fedele Silvano Bosari. Michelangelo Fiorentino Valentina Vaira ... – PowerPoint PPT presentation

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Title: Cell Signaling


1
High-throughput oncogene mutation profiling
Massimo Loda ESMO Milano, 2008
2
Somatic mutations
  • Genetic and epigenetic (in)activation of
    oncogenes and tumor suppressor genes

3
Critical evaluation of mutations
  • Where does the mutation occur? Heterogeneity,
    cancer stem cells
  • Genetic and epigenetic assessment
  • Passenger mutation, oncogene-induced senescence,
    oncogene addiction (context is important)
  • Lineage-survival genes (MITF, AR)
  • Metabolic oncogenes

4
Biological/clinical Heterogeneity
5
Techniques to detect genomic and proteome
alterations
  • High throughput sequencing (point mutations)
  • High density SNPs/aCGH (amplifications,
    deletions)
  • Gene expression profiling (signatures)
  • miRNA profiling
  • Immunohistochemistry, ISH, FISH
  • Mass Spectrometry

6
From Cancer Genome Discovery to Targeted
Therapeutics?
  • BCR-ABL ? Gleevec
  • KIT ? Gleevec
  • PDGFR-alpha ? Gleevec
  • EGFR ? Tarceva
  • ERBB2 ? Herceptin
  • mdm2 ? nutlin
  • PIK3CA ? PI3K inhibitors
  • mTOR ? rapamycin

7
High throughput sequencing
8
Sequencing methodologies
  • Sanger sequencing
  • 1 M bp/24 hrs low sensitivity low cost
  • Single molecule sequencing by synthesis (Solexa)
  • 100 M bp/3 days high sensitivity 3K/100 M bp
  • Massively parallel sequencing by pyrosequencing
    (454)
  • 100 M bp/8 hrs high sensitivity 10K/100M bp

9
Genome sequencing in microfabricated high-density
picoliter reactorsMargulies et al., 2005.
454 picolitre-scale sequencing
Sanger DNA sequencing electrophoresis
  • 100X throughput improvement 20M-40M bp / 4hr run
  • Need 10-12 times oversampling to get same
    accuracy as Sanger

10
454 pyrosequencing (massively parallel
sequencing)
  • 100 Mbp/8hrs, 10K
  • Accuracy gt99.99

11
Flowgram
TTCTGCGAA
Cont.
12
Solexa sequencing
13
(No Transcript)
14
  • Other sequencing technologies resources
  • 454 www.454.com
  • Solexa www.solexa.com
  • Helicos tSMS www.helicosbio.com
  • ABI seq by ligation on a solid surface
    www.appliedbiosystems.com
  • Agilent nanopore sequencing

15
High-Throughput Genotyping to Identify Oncogene
Mutations
Each allele yields a different mass extension
product
Run on MALDI-TOF mass spec.
Removes excess dNTPs
C
G
C
G
C
G
Genotype GG CG
CC
16
Sample with no mutation
wildtype
mutant
Sample with mutation
wildtype
mutant
17
OncoMap Proof-of-Principle
  • OncoMap pilot study
  • 238 assays
  • 17 oncogenes
  • 60 multiplexed assays per sample
  • 1000 tumors, 17 lineages
  • 65/sample
  • ABL1
  • AKT2
  • BRAF
  • CDK4
  • EGFR
  • ERBB2
  • FGFR1
  • FGFR3
  • FLT3
  • HRAS
  • JAK2
  • KIT
  • KRAS
  • NRAS
  • PDGFRa
  • PIK3CA
  • RET

R. Thomas, A. Baker et al., Nature Genetics, 2007
18
gt 70 cancer enrichment --gt WGA --gt multiplex PCR
--gt mass spec
19
OncoMap results
  • Mutations events are rare and generally do not
    hit the same codon
  • Of 17 oncogenes analyzed, 14 were mutated at
    least once and 30 of samples carried at least
    one mutation
  • Driver or passenger mutation?

20
Hybrid capture of all exons
  • high-density microarrays can capture any desired
    fraction of the human genome, gt 200,000
    protein-coding exons. Up to 98 of intended exons
    can be recovered.

Hodges et al Nat Genet. 2007 Dec39(12)1522-7
21
Polymorphisms, passenger mutations or driver
mutations?
22
Integration of genomic and expression analyses
23
Oncogene mutation vs pathway activation
How do we identify activated pathways in order to
target them therapeutically and monitor response?
24
What can we monitor?
From Engelman Liu and Cantley 2006
25
Mutations in PI3k-Akt pathway in human cancer
PTEN Loss of heterozygosity Glioblastoma 54
(98/180) Prostate 35 (88/250) Breast 23
(37/164) Melanoma 37 (53/143) Gastric 47
(14/30) PTEN Mutations Glioblastoma 28
(122/432) Prostate 12 (26/218) Breast 0
(0/164) Melanoma 8 (15/185) Gastric 0 (0/30)
AKT Amplifications Ovarian 12 (18/147)
Pancreatic 20 (7/35) Breast 3 (3/106)
Gastric 20 (1/5) Head and neck 30 (12/40)
PIK3R1 (p85a) Mutations Ovarian 4 (3/80)
Colon 2 (1/60)
p110a Mutations Breast 26 (176/684) Colon 26
(88/337) Glioma 8 (14/182) Hepatocellular 36
(26/73) Ovarian 10 (35/365) Lung 2 (4/253)
Gastric 7 (24/338) p110a Amplifications Head
and neck 42 (54/128) Thyroid 9 (12/128)
Lung Squamous cell 66 (46/70) Adenocarcinoma
5 (4/86) Breast 9 (8/92) Gastric 36 (20/55)
Esophageal adenocarcinoma 6 (5/87) Cervical
69 (11/16)
From Engelman, Luo, Cantley, 2006
26
PTEN -/- pAKT pS6 PPRAS 40
10
How to define the shades of gray?
80
PTEN / pAKT - pS6- PPRAS40-
10
27
High Throughput
Gene Expression
aCGH
SNP
Validation is low throughput and rate limiting
TMA
FISH/ISH
IHC
Integration of all of these is virtually
nonexistent
28
Integration of IHC and gene expression data
29
IHC
Tumor
Normal
Only Tumor
30
Mutation detection current status
  • Next generation sequencing is here and works
    (single molecule sequencing)
  • Hybrid capture of all exons is feasible
  • Currently 100 discovery 150 validation
    tumor/normal pairs would currently cost 3M
    dollars
  • Best done in collaboration/consortium

31
How will genomic tests reach patients?
  • Genomic facilities in individual cancer centers
  • Pharmaceutical companies
  • Start up companies
  • Diagnostic companies
  • Combination of the above

32
Objectives towards the development of a new
molecular classification of tumors and targeted
therapy
  • How will sarcomas be treated and studied over the
    next ten years?
  • What kind of science should be pursued?
  • What should the balance/interaction be between
    clinical work and basic research?
  • What new technologies or concepts can best be
    developed and applied?

33
Goals
  • Achieve a more detailed classification of tumors
    by adding detailed molecular annotation to
    existing pathologic and clinical data, that will
    guide therapeutic options in real time
  • Predict inhibitors in the pipeline and
    concentrate on developing tools to a) select
    appropriate patients (for clinical trial and in
    routine diagnostics) and b) monitor response to
    therapy

34
Acknowledgments
  • Center for Cancer Genome Discovery - CCGD (Dana
    Farber Cancer Institute)
  • Laura MacConaill
  • Levi Garraway
  • Center for Molecular Oncologic Pathology - CMOP
    (Dana Farber Cancer Institute), Univ. of Milan
  • Giuseppe Fedele Silvano Bosari
  • Michelangelo Fiorentino Valentina Vaira
  • Elisa Benedettini Giorgia Zadra
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