2015 bioinformatics personal_genomics_wim_vancriekinge

1. FBW 15-12-2015 Wim Van Criekinge

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3. Who does practical exam 5th of january (8u30 – 11u30)?

4. Hapmap

5. Lab for Bioinformatics and computational genomics 107 106 105 104 103 102 101 1108109 Full genome bp G E N E T I C Whole-genome sequencing Enrichment seq (Exome) PCR Enrichment Targeted Panels Instrument and Assay providers CLIA Lab service providers

6. Personalized Medicine • The use of diagnostic tests (aka biomarkers) to identify in advance which patients are likely to respond well to a therapy • The benefits of this approach are to – avoid adverse drug reactions – improve efficacy – adjust the dose to suit the patient – differentiate a product in a competitive market – meet future legal or regulatory requirements • Potential uses of biomarkers – Risk assessment – Initial/early detection – Prognosis – Prediction/therapy selection – Response assessment – Monitoring for recurrence

7. Biomarker First used in 1971 … An objective and « predictive » measure … at the molecular level … of normal and pathogenic processes and responses to therapeutic interventions Characteristic that is objectively measured and evaluated as an indicator of normal biologic or pathogenic processes or pharmacologic response to a drug A biomarker is valid if: – It can be measured in a test system with well established performance characteristics – Evidence for its clinical significance has been established

8. Rationale 1: Why now ? Regulatory path becoming more clear There is more at stake than efficient drug development. FDA « critical path initiative » Pharmacogenomics guideline Biomarkers are the foundation of « evidence based medicine » - who should be treated, how and with what. Without Biomarkers advances in targeted therapy will be limited and treatment remain largely emperical. It is imperative that Biomarker development be accelarated along with therapeutics

9. Why now ? First and maturing second generation molecular profiling methodologies allow to stratify clinical trial participants to include those most likely to benefit from the drug candidate—and exclude those who likely will not—pharmacogenomics- based Clinical trials should attain more specific results with smaller numbers of patients. Smaller numbers mean fewer costs (factor 2-10) An additional benefit for trial participants and internal review boards (IRBs) is that stratification, given the correct biomarker, may reduce or eliminate adverse events.

10. Molecular Profiling The study of specific patterns (fingerprints) of proteins, DNA, and/or mRNA and how these patterns correlate with an individual's physical characteristics or symptoms of disease.

11. Generic Health advice • Exercise (Hypertrophic Cardiomyopathy) • Drink your milk (MCM6 Lactose intolarance) • Eat your green beans (glucose-6-phosphate dehydrogenase Deficiency) • & your grains (HLA-DQ2 – Celiac disease) • & your iron (HFE - Hemochromatosis) • Get more rest (HLA-DR2 - Narcolepsy)

12. Generic Health advice (UNLESS) • Exercise (Hypertrophic Cardiomyopathy) • Drink your milk (MCM6 Lactose intolarance) • Eat your green beans (glucose-6-phosphate dehydrogenase Deficiency) • & your grains (HLA-DQ2 – Celiac disease) • & your iron (HFE - Hemochromatosis) • Get more rest (HLA-DR2 - Narcolepsy)

13. Generic Health advice (UNLESS) • Exercise (Hypertrophic Cardiomyopathy) • Drink your milk (MCM6 Lactose intolerance) • Eat your green beans (glucose-6-phosphate dehydrogenase Deficiency) • & your grains (HLA-DQ2 – Celiac disease) • & your iron (HFE - Hemochromatosis) • Get more rest (HLA-DR2 - Narcolepsy)

14. Generic Health advice (UNLESS) • Exercise (Hypertrophic Cardiomyopathy) • Drink your milk (MCM6 Lactose intolerance) • Eat your green beans (glucose-6-phosphate dehydrogenase Deficiency) • & your grains (HLA-DQ2 – Celiac disease) • & your iron (HFE - Hemochromatosis) • Get more rest (HLA-DR2 - Narcolepsy)

15. EGFR based therapy in mCRC

16. Overview Personalized Medicine, Biomarkers … … Molecular Profiling First Generation Molecular Profiling Next Generation Molecular Profiling Next Generation Epigenetic Profiling Concluding Remarks

17. Before molecular profiling …

20. First Generation Molecular Profiling • Flow cytometry correlates surface markers, cell size and other parameters • Circulating tumor cell assays (CTC’s) quantitate the number of tumor cells in the peripheral blood. • Exosomes are 30-90 nm vesicles secreted by a wide range of mammalian cell types. • Immunohistochemistry (IHC) measures protein expression, usually on the cell surface.

21. First Generation Molecular Profiling • Gene sequencing for mutation detection • Microarray for m-RNA message detection • RT-PCR for gene expression • FISH analysis for gene copy number • Comparative Genome Hybridization (CGH) for gene copy number

22. Basics of the “old” technology • Clone the DNA. • Generate a ladder of labeled (colored) molecules that are different by 1 nucleotide. • Separate mixture on some matrix. • Detect fluorochrome by laser. • Interpret peaks as string of DNA. • Strings are 500 to 1,000 letters long • 1 machine generates 57,000 nucleotides/run • Assemble all strings into a genome.

23. Genetic Variation Among People 0.1% difference among people GATTTAGATCGCGATAGAG GATTTAGATCTCGATAGAG Single nucleotide polymorphisms (SNPs)

24. The genome fits as an e-mail attachment

25. Lab for Bioinformatics and computational genomics

33. Lab for Bioinformatics and computational genomics The Technical Feasibility Argument The Quality Argument The Price Argument The Logistics Argument

35. Lab for Bioinformatics and computational genomics Recreational genomics

36. Lab for Bioinformatics and computational genomics Recreational genomics • Experimental designs are outdated by technological advances • Genetic background (reference genome) as a concept will need to be updated • Traits dependent on multiple loci are “complicated”: educate and provide tools to deal with it

38. Lab for Bioinformatics and computational genomics Recreational genomics • Eye color … why not the ear wax/asparagus or unibrown example • … metabolize nutrients (newborns ?) • … metabolize drugs in case you need it urgently ?

40. Lab for Bioinformatics and computational genomics Recreational genomics “several 23andMe users have reported taking the FDA’s advice of reviewing their genetic results with their physicians, only to find the doctors unprepared, unwilling, or downright hostile to helping interpret the data”

47. Lab for Bioinformatics and computational genomics my genome is too important (for me) to leave it (only) to doctors

48. Lab for Bioinformatics and computational genomics NXTGNT biohackerspace …

49. Lab for Bioinformatics and computational genomics PGMv2: Personal Genomics Manifesto

50. Lab for Bioinformatics and computational genomics Everyone should have the power and legitimacy to be able to discover, develop and find new things about their own genome data. Intelligent exploration, experimentation and trial to push the boundaries of knowledge are a basic human right. PGMv2: Personal Genomics Manifesto

51. Lab for Bioinformatics and computational genomics Personal genome data access should be affordable to all irrespective of nationality, gender, social background or any other circumstance. Not having access to a personal genetic test is in itself a new kind of discrimination. PGMv2: Personal Genomics Manifesto

52. Lab for Bioinformatics and computational genomics Whether one wants to share genome data or keep it private should be a matter of personal choice. Whatever attitude a person has towards personal genome privacy, it should be utterly respected. Corporate interest can never compromise any human right. Laws must fully protect individual human rights of equality for every person, irrespective of predicted risks from genetic data. PGMv2: Personal Genomics Manifesto

53. Lab for Bioinformatics and computational genomics Stating that genetic tests merely provide non- clinical information misses the point of what personal genomics is all about. Most genomic information is uninterpretable and may well be meaningless. But those are not reasons to deny it to people. Genetic test results are not unrelated to someone’s health, one’s ability to respond to certain drugs and one’s ethnic ancestry. PGMv2: Personal Genomics Manifesto

54. Lab for Bioinformatics and computational genomics Education in risks and opportunities for personal genetic testing should be the primary aim of policy makers. Restricting access to interested people makes no sense and it is virtually impossible to ensure. Access to personal genomics data and tools for its interpretation should become accessible to everyone. PGMv2: Personal Genomics Manifesto

56. Genomeslikemine

58. mRNA Expression Microarray

60. Translational Medicine: An inconvenient truth • 1% of genome codes for proteins, however more than 90% is transcribed • Less than 10% of protein experimentally measured can be “explained” from the genome • 1 genome ? Structural variation • > 200 Epigenomes ?? • Space/time continuum …

61. Translational Medicine: An inconvenient truth • 1% of genome codes for proteins, however more than 90% is transcribed • Less than 10% of protein experimentally measured can be “explained” from the genome • 1 genome ? Structural variation • > 200 Epigenomes … • “space/time” continuum

62. Epigenetic (meta)information = stem cells Cellular programming

63. Cellular reprogramming Tumor Epigenetically altered, self- renewing cancer stem cells Tumor Development and Growth

64. Gene-specific Epigenetic reprogramming Cellular reprogramming

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