טכניון מכון טכנולוגי לישראל
הטכניון מכון טכנולוגי לישראל - בית הספר ללימודי מוסמכים  
Ph.D Thesis
Ph.D StudentGrossman Iris
SubjectGenetic Analysis of Multiple Sclerosis Patients According
to their Response Pattern to Glatiramer Acetate
(Copaxone) Treatment as a Means to
Develop Pharmacogenetic...
DepartmentDepartment of Medicine
Supervisors Professor Ariel Miller
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Full Thesis textFull thesis text - English Version


Abstract

The goal of pharmacogenetics is to identify “genetic fingerprints” that may predict a patient’s response to pharmaceutical treatment in order to replace the “trial and error” strategy, which governs much of our clinical decision-making regarding treatment allocation in current medical practice, with individually tailored therapy. This thesis presents pharmacogenetic research guidelines, which implement high-throughput technology to establish correlation between drug-responsiveness and genetic polymorphisms. Specific considerations of pharmacogenetic study-designs are discussed in details, including the impact of placebo treatment on the subsequent power of results, and a proposal for candidate genes prioritization strategy.


The genetic mapping of drug-response traits is often characterized by a poor signal-to-noise ratio that is placebo-related, and which distinguishes pharmacogenetic association studies from classical case-control studies for disease susceptibility. Hence, the statistical power of candidate-genes association studies under different pharmacogenetic scenarios was evaluated via simulation analysis, with special emphasis on the placebo effect.

    Results show that
  1. placebo "response" strongly affects statistical power of association studies;
  2. the power of a pharmacogenetic association study depends primarily on the penetrance of the response genotype;
  3. power is dramatically increased when adding markers, and
  4. an optimal study design includes a similar number of placebo- and drug-treated patients.

Autoimmune diseases seem to have strong genetic attributes, and are affected to some extent by shared susceptibility loci. The latter potentially amount to hundreds of candidate genes, creating the need for a prioritization strategy in genetic association studies. To form such a strategy, autoimmune-related candidate genes were genotyped for single nucleotide polymorphisms (SNPs) in three distinct Israeli ethnic populations: Ashkenazi Jews, Sephardic Jews and Arabs. Four quantitative criteria reflecting population stratification were analyzed: allele frequencies; haplotype frequencies; the Fst statistic for homozygotes distribution and linkage disequilibrium extents. Results demonstrate a correlation between the biological role of autoimmune-related candidate genes and their inter-population diversity profiles as classified by the different analyses. We suggest a research strategy by which candidate-genes association studies should focus first on likely conserved gene categories, to increase the likelihood of attaining significant results and promote the development of gene-based therapies.


Finally, we describe in details a model pharmacogenetic study, investigating genetic markers that can predict response to Glatiramer Acetate (GA), one of the leading treatments approved for relapsing multiple sclerosis. Patients' DNA samples were obtained from two fractional cohorts of GA clinical trials and genotyped for SNPs in candidate genes, presumed to be involved in the drug’s mode-of-action. Statistical analysis included SNP-by-SNP and haplotype analyses of drug-by-genotype effects in drug- and placebo-treated groups. Six and two genes showed repeatedly significant associations with GA-response under different response-definitions and various statistical analyses in each cohort, respectively. The associations detected could indicate that GA-response is under the genetic control of a limited number of genes, each potentially having a rather pronounced effect. One gene showed identical association (same SNP and allele) independently in both cohorts. Our results indicate that genomic-based personalized-treatment for multiple sclerosis may be attainable, warranting larger prospective studies examining additional markers and genes.