1. 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

2. FUNCTIONAL GENOMICS 2 Beáta Scholtz Molecular Therapies- Lecture 2 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

3. TÁMOP-4.1.2-08/1/A-2009-0011 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) 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. FUNCTIONAL GENOMICS 1

4. 4 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 TÁMOP-4.1.2-08/1/A-2009-011

5. 5 Holly Dressman, IGSP, Genomes 101 2007 TÁMOP-4.1.2-08/1/A-2009-011

6. 6 Holly Dressman, IGSP, Genomes 101 2007 TÁMOP-4.1.2-08/1/A-2009-011

7. 7 14 cell lines more than 50 genes Holly Dressman, IGSP, Genomes 101 2007 TÁMOP-4.1.2-08/1/A-2009-011

8. 8 Potti et al. Nat Med 2006 TÁMOP-4.1.2-08/1/A-2009-011

9. 9 Genomic signatures for other chemo agents - the same rationale Potti et al. Nat Med 2006 Gene lists for NCI-60 cell lines TÁMOP-4.1.2-08/1/A-2009-011

10. 10 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 TÁMOP-4.1.2-08/1/A-2009-011

11. 11 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 TÁMOP-4.1.2-08/1/A-2009-011

12. 12 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. Why study DNA in tumors? TÁMOP-4.1.2-08/1/A-2009-011

13. 13 “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. A Variety of Genetic Alterations Underlie Developmental Abnormalities and Disease TÁMOP-4.1.2-08/1/A-2009-011

14. 14 Mapping of genetic aberration TÁMOP-4.1.2-08/1/A-2009-011

15. 15 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). TÁMOP-4.1.2-08/1/A-2009-011

16. 16 Different arrays for different purposes TÁMOP-4.1.2-08/1/A-2009-011

17. 17 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 TÁMOP-4.1.2-08/1/A-2009-011

18. 18 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 TÁMOP-4.1.2-08/1/A-2009-011

19. 19 Array CGH Maps DNA Copy Number Alterations to Positions in the Genome position on sequence TÁMOP-4.1.2-08/1/A-2009-011

20. 20 Array CGH Analysis of a Tumor Genome TÁMOP-4.1.2-08/1/A-2009-011

21. 21 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) Tumor copy number profiles are a reflection of two processes TÁMOP-4.1.2-08/1/A-2009-011

22. 22 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. Benefits of aCGH in cancer research TÁMOP-4.1.2-08/1/A-2009-011

23. 23 Alterations in Cancer Cell Line Genome: Alignment of Chromosomal and Microarray Based CGH Amplifications: Activated oncogenic genes Deletions: Inactivated (tumor suppressor) genes TÁMOP-4.1.2-08/1/A-2009-011

24. 24 The power of SNP arrays: Copy number silent LOH discovery Tan DSP et al. Laboratory Investigation 2007. 87:737 TÁMOP-4.1.2-08/1/A-2009-011

25. 25 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 TÁMOP-4.1.2-08/1/A-2009-011

26. 26 TÁMOP-4.1.2-08/1/A-2009-011

27. 27 Top-bottom approach to identify novel therapeutic targets Tan DSP et al. Pathobiology 2008. 75:63 TÁMOP-4.1.2-08/1/A-2009-011

28. 28 Bottom-up approach to identify novel therapeutic targets Tan DSP et al. Pathobiology 2008. 75:63 TÁMOP-4.1.2-08/1/A-2009-011

29. 29 aCGH analysis of multiple myeloma Carrasco DR et al. Cancer Cell 2006. 9:313 55 MM cell lines, 73 patient samples TÁMOP-4.1.2-08/1/A-2009-011

30. 30 nonhyperdiploid: k3,k4 hyperdiploid: k1, k2 Carrasco DR et al. Cancer Cell 2006. 9:313 Conclusion: ch11 gain : better outcome ch1q gain: worse ch13 loss: worse aCGH analysis of multiple myeloma: Prognostic classification TÁMOP-4.1.2-08/1/A-2009-011

31. 31 Carrasco DR et al. Cancer Cell 2006. 9:313 Combined gene expression and aCGH analysis of multiple myeloma TÁMOP-4.1.2-08/1/A-2009-011

32. 32 aCGH analysis of squamous cell lung cancer Boelens MC et al. Lung Cancer 2009. 66:372 PIK3CA 3q26.2-q27.3 A: All samples B: High CNAs TÁMOP-4.1.2-08/1/A-2009-011

33. 33 aCGH analysis of squamous cell lung cancer: Correlation of PIK3CA expression levels and gene amplification Boelens MC et al. Lung Cancer 2009. 66:372 Novel therapeutic target? TÁMOP-4.1.2-08/1/A-2009-011

34. 34 The PIK3/Akt/mTOR signalling pathway TÁMOP-4.1.2-08/1/A-2009-011

35. 35 Profiles of PI3K inhibitors in clinical trial Ihle N T, Powis G Mol Cancer Ther 2009;8:1-9 TÁMOP-4.1.2-08/1/A-2009-011