Download Array CGH

Manifestation of Novel Social Challenges of the European Union
in the Teaching Material of
Medical Biotechnology Master’s Programmes
at the University of Pécs and at the University of Debrecen
Identification number: TÁMOP-4.1.2-08/1/A-2009-0011
Manifestation of Novel Social Challenges of the European Union
in the Teaching Material of
Medical Biotechnology Master’s Programmes
at the University of Pécs and at the University of Debrecen
Identification number: TÁMOP-4.1.2-08/1/A-2009-0011
Beáta Scholtz
Molecular Therapies- Lecture 2
FUNCTIONAL GENOMICS 2
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FUNCTIONAL GENOMICS 1
The aim of this chapter is to describe the main goals, tools and
techniques of functional genomics. We will discuss its contribution to
the advancement of modern medicine through specific examples.
1.1 DEFINITIONS
1.2 ABOUT DISEASES
1.3 APPROACHES TO UNDERSTANDING DISEASE MECHANISMS
1.3.1 Gene expression is regulated in several basic ways
1.3.2 Microarrays: functional genomics in cancer research
1.3.3 Genetic Alterations and Disease
1.3.4 Genomic microarrays
1.3.4.1 Array based comparative genome hybridization (aCGH)
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Microarrays:
Functional genomics to improve cancer therapy
• Identify who is at risk (Prognosis)
• Identify who will and won’t respond to each agent
• Identify alternatives for patients with
chemo-resistant disease
• Better utilization of existing and new drugs
• Strategies for unique combinations of drugs
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Holly Dressman, IGSP, Genomes 101 2007
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Holly Dressman, IGSP, Genomes 101 2007
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more than
50 genes
Holly Dressman, IGSP, Genomes 101 2007
14 cell lines
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Potti et al. Nat Med 2006
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Genomic
signatures for
other chemo
agents - the
same rationale
Gene lists for
NCI-60 cell lines
Potti et al. Nat Med 2006
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Data for real patients (ovarian cancer)
Pre-existing gene expression data from GEO database
Probability score assigned by Potti et al.
Sensitivity data from the same study
Potti et al. Nat Med 2006
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Correlation
between oncogenic
pathway activation
and resistance
to chemo drugs:
Combination therapy
with pathway inhibitors?
src: SU6656
PI3K: LY-294002
Potti et al. Nat Med 2006
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Why study DNA in tumors?
•It is the fundamental repository of information.
• If the same DNA aberration occurs repeatedly in
tumors, how can one ignore it?
• There are powerful, general methods of assessing certain
types of aberrations.
• DNA is relatively robust and can be assayed
specimens that have been treated in multiple ways,
including archival tissue from hospital laboratories.
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A Variety of Genetic Alterations Underlie Developmental
Abnormalities and Disease
“Point”mutation – change of one or a few bases -- leads to altered
protein or change in expression level.
Loss of gene copy reduces expression level. (tumor suppressor loss)
Gain of gene copies increases expression level. (oncogene activation)
(De)Methylation of gene promoters (increase)decrease expression
level. ((oncogene) tumor suppressor)
Breaking and abnormal rejoining of DNA makes novel genes.
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Mapping of genetic aberration
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Genomic microarrays
Description:
A microarray technology that detects chromosomal
abnormalities
Uses:
Clinical lab: complementary to fluorescence
in situ hybridization (FISH)
Research lab: discover genetic basis of diseases
Significance:
Many disorders are likely to be caused by microdeletions
and other chromosomal abnormalities that cannot be
detected by FISH. SNP arrays may offer even more resolution,
and additional information (both genotype and copy number).
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Different arrays for different purposes
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Array CGH
Array based comparative genome hybridization (CGH)
Measures amount of DNA, not RNA
Comparison between two samples
‘Test’ sample
‘Reference’ sample
High resolution
1-3 Mb (whole genome array CGH), or 10-25 kb (oligo aCGH) vs 5-10
Mb (karyotyping)
Speed : 3-4 days (array CGH) vs 2-4 weeks (karyotyping)
Simple DNA prep for array CGH instead of metaphase synchronization
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Array CGH
Detecting genomic rearrangements found in cancer (tumor genome vs
normal genome)
Study of genomic copy number variation
Segregating variants found in the population
Pathogenic variants associated with some disease
Compare ‘affected’ vs ‘control’ individuals
Use of known probes linked to genetic markers allows better
understanding of disorders
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Array CGH Maps DNA Copy Number
Alterations to Positions in the Genome
position on sequence
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Array CGH Analysis of a Tumor Genome
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Tumor copy number profiles are a reflection of two processes
• Selection for alterations in gene expression that favor
tumor development. Selective advantage to maintain set
of aberrations.
• Mechanisms of genetic instability promoting changes in
the genome. (initiating oncogenetic event in murine
models and methotrexate resistance in MMR deficient
and proficient cell lines)
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Benefits of aCGH in cancer research
• Based on the results better tests can be performed that measure the
DNA copy number of oncogenes and TSGs.
• Monitor cancer progression and distinguish between mild and
metastatic cancerous lesions using FISH (Florescence in situ
hybridization) probes on regions of recurrent copy number
aberrations in several tumor types.
• It can be used to reveal more regional copy number markers that
can be used for cancer prediction.
• Identifying and understanding the genes that are involved in cancer
will help to design therapeutic drugs that target the dysfunction
genes and/or avoid therapies that cause tumor resistance.
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Alterations in Cancer Cell Line Genome:
Alignment of Chromosomal and Microarray Based CGH
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Amplifications: Activated oncogenic genes
Deletions: Inactivated (tumor suppressor) genes
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The power of SNP arrays: Copy number silent LOH discovery
Tan DSP et al. Laboratory Investigation 2007.
87:737
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SNP repositories
dbSNP at NCBI
http://www.ncbi.nlm.nih.gov/SNP
Human SNP database (Whitehead Institute)
http://www.broad.mit.edu/tools/data/genvar.html
The SNP Consortium (TSC)
http://snp.cshl.org
J Pevsner: Bioinformatics and functional genomics
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Top-bottom approach
to identify novel
therapeutic targets
Tan DSP et al. Pathobiology 2008. 75:63
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Bottom-up approach
to identify novel
therapeutic targets
Tan DSP et al. Pathobiology 2008. 75:63
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aCGH analysis of multiple myeloma
55 MM cell lines, 73 patient samples
Carrasco DR et al. Cancer Cell 2006. 9:313
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aCGH analysis of multiple myeloma:
Prognostic classification
Carrasco DR et al. Cancer Cell 2006. 9:313
hyperdiploid: k1, k2
nonhyperdiploid: k3,k4
Conclusion:
ch11 gain : better outcome
ch1q gain: worse
ch13 loss: worse
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Combined gene expression and aCGH analysis
of multiple myeloma
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Carrasco DR et al. Cancer Cell 2006. 9:313
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aCGH analysis of squamous cell lung cancer
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A: All samples
B: High CNAs
PIK3CA
3q26.2-q27.3
Boelens MC et al. Lung Cancer 2009. 66:372
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aCGH analysis of squamous cell lung cancer:
Correlation of PIK3CA expression levels and
gene amplification
Novel therapeutic target?
Boelens MC et al. Lung Cancer 2009. 66:372
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The PIK3/Akt/mTOR signalling pathway
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Profiles of PI3K inhibitors in clinical trial
Ihle N T , Powis G Mol Cancer Ther 2009;8:1-9
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