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Personalizing Medicines

Personalizing Medicines. Tumor Genomics E.g., Trastuzumab (Herceptin). Germline DNA. Tamoxifen: Used to treat estrogen-positive breast cancer. Tamoxifen: Is a Pro-drug. Tamoxifen is a Pro-Drug. Jin Y et al: J Natl Cancer Inst 97:30, 2005. CP1229323-3. CYP2D6 Polymorphisms *4 Allele.

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Personalizing Medicines

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  1. Personalizing Medicines Tumor Genomics E.g., Trastuzumab (Herceptin) Germline DNA

  2. Tamoxifen: Used to treat estrogen-positive breast cancer

  3. Tamoxifen: Is a Pro-drug Tamoxifen is a Pro-Drug Jin Y et al: J Natl Cancer Inst 97:30, 2005 CP1229323-3

  4. CYP2D6 Polymorphisms *4 Allele Endoxifen Tamoxifen Tamoxifen Tamoxifen Tamoxifen CYP3A4 CYP2D6 Endoxifen Endoxifen Endoxifen Active Drug CYP2D6 Has Reduced Function Alleles CYP2D6 Polymorphisms *10 Allele In Japanese Inactive Prodrug

  5. Individuals with CYP2D6*4 Have Lower Levels of Endoxifen P<0.001, r2=0.24 CYP2D6 *4/*4 Polymorphisms Plasma Endoxifen (nM) CYP2D6*4 (most common genetic variant associated with the CYP2D6 poor metabolizer state) Jin Y et al: J Natl Cancer Inst 97:30, 2005

  6. Individuals with CYP2D6*4 Have Have Poorer Relapse Free Survival CYP2D6 WT/WT % With Relapse Free Survival CYP2D6 *4/WT CYP2D6 *4/*4 P=0.020 CYP2D6 *4/*4 Polymorphisms Years after randomization Goetz et al J Clin Oncol. 2005;23(36):9312-8.

  7. Women with CYP2D6 PolymorphismsHave Poorer Response to Tamoxifen N = 1325 CYP2D6 Polymorphisms *4, and other Schroth, W. et al. JAMA 2009;302:1429-1436.

  8. Japanese Women with CYP2D6 Polymorphism,Have Poorer Response to Tamoxifen Cancer Science, May 2008, page 995-99

  9. Japanese Women with CYP2D6 Polymorphism,Have Poorer Response to Tamoxifen Plasma Endoxifen (nM) Wt/Wt, noinhibitor Venlafaxine Sertraline Paroxetine *4/*4, noinhibitor Jin Y et al: J Natl Cancer Inst 97:30, 2005

  10. NIH Pharmacogenomics Research Network EUROPE Research Groups Collaborating Sites Network Resources CANADA UW-EXOME TAIWAN PHONT NWAP WU-NGS P-STAR PG-POP GAP-J JAPAN PHAT BCM-HGSC PHRAT PPII PAAR PGRN-CGM XGEN PARC PNAT PAPI ALASKA PMT Hawaii Cancer Pharmacogenomics PAT PAAR4KIDS PGBD PEAR

  11. GAP-J Partners Riken- Center for Genomic Medicine: NIH Pharmacogenomics Research Network:

  12. Goal of GAP-J To identify genetic predictors of therapeutic and adverse drug response using genomewide approaches. Personalized Medicines Picture from CGM Website

  13. Genomewide Markers Strategies in Genomic Studies Disease Genetic Studies Identify Mutant/ Polymorphic Genes Biological/ Pharmacologic Mechanism Patients Controls Diabetics

  14. P=10-15 Genomewide Association of Seven Common Diseases Bipolar Coronary Artery Disease Crohn’s Hypertension P value -log10 Rheumatoid Arthritis Type 1 Diabetes Type 2 Diabetes Wellcome Trust, Nature, 2007 Chromosome Number

  15. January of 2009, > 200 Genomewide Association Studies Identifying Disease Risk Alleles Fewer than 20 Pharmacogenomic Studies 60 40 Number of Studies Drug Response/ Toxicity 20 0 Cardiovascular/ Diabetes/ Obesity Aging/ Psychiatric/ Other Drug Response/ Toxicity Inflammatory Disease Physiologic/ Biochem Trait Cancer http://www.genome.gov/gwastudies/

  16. Genomewide Markers Strategies in Genomic Studies Pharmacogenomic Studies Identify Mutant/ Polymorphic Genes Biological/ Pharmacologic Mechanism Patients Receive Drug No Adverse Reaction Receive Drug Adverse Reaction

  17. 4 cycles – 8 wk AC q 2 wk 60 mg/m2 600 mg/m2 Stratification Pre-Post Menopausal ER/PgR 6 cycles – 12 wk 4 cycles – 8 wk Paclitaxel q 2 wk 175 mg/m2 6 cycles – 12 wk AC = doxorubicin/cyclophosphamide Target Accrual 4646 pts Pharmacogenetic analysis of predictors of paclitaxel and AC toxicity CALGB 40101 – 2 X 2 Factorial DesignAdjuvant Therapy for Women with Breast Cancer with 0-3 Positive Nodes

  18. Genes ABCB1 ABCC1 ABCC2 ABCG2 CYP1B1 CYP2C8 CYP3A4 CYP3A5 MAPT TP53 SLCO1B3 Endpoints Pharmacokinetics Toxicities Outcomes (DFS, OS) Findings Small Populations Inconsistent results Paclitaxel Candidate Gene Studies are Inconclusive

  19. Riken Center for Genomic Medicine and PGRN Collaboration for Genome Wide Association Study of CALGB 40101 • Genome wide analysis of CALGB 40101 samples using Illumina 610-Quad platform • Doxorubicin/Cyclophosphamide Arm (n=919) • Paclitaxel Arm (n=1040)

  20. 1040 Subjects Study Design Illumina 610-Quad (≈592K SNPs) QC (Subject CR >99%, SNP CR >95%; Chr 1-23, MAF > 0.5%, IBD) 1029 Subjects/554,450 SNPs ‘Genetic Caucasian’ PC1,PC2,PC3 all within 2 SDs of mean values from all patients self-declaring as ‘White’ Principal Components Analysis 859 Genetically Western Europeans 170 Remaining Subjects Ordinal Regression Logistic Regression Regression, Admix Mapping Time to Event Analysis Replication Study

  21. Principal Component Analysis Identifies 859 Genetically Similar Subjects of Western European Descent

  22. Neuropathy Grading Criteria Activities of Daily Living (ADL) Instrumental ADL: Preparing meals, shopping for groceries or clothes, using the telephone, managing money, etc. Self care ADL: Bathing, dressing and undressing, feeding self, using the toilet, taking medications, and not bedridden.

  23. Paclitaxel Sensory Peripheral Neuropathy in ‘Genetic Europeans’ Omitted 4 Grade 3 59 Grade 0 280 Grade 2 147 Grade 1 369 Maximum grade sensory peripheral neuropathy during treatment or follow-up: 859 subjects

  24. Analysis Strategy • Ordinal Logistic Regression • Highest grade sensory peripheral neuropathy • Additive genetic model • Covariate: Log cumulative dose/BSA at first instance of maximum grade toxicity. If no toxicity, equal to cumulative dose at end of treatment. • Time to Event Analysis • “Event” - The first occurrence of a grade 2 or higher sensory peripheral neuropathy. • “Time” - Cumulative dose/BSA at event. If no toxicity, equal to cumulative dose at end of treatment. • Additive genetic model

  25. Time to Event Analysis EPHA5 PITPNA FGD4 GRIP1

  26. Ordinal Regression FZD3

  27. Time to Event Analysis EPHA5 GRIP1 Probability of NOT having an Event Probability of NOT having an Event

  28. Ephrin Signaling is Important in Axon Guidance

  29. EphrinB2 Levels Increase in the Dorsal Root Ganglion in Response to Injury Kobayashi et al. Spine 32:1592-1598, 2007

  30. Time to Event Analysis FGD4 NDRG1 Probability of NOT having an Event Probability of NOT having an Event Probability of NOT having an Event

  31. FGD4 is Implicated in Congenital Neuropathic Disease Am J Hum Genet 81:158-164, 2007

  32. Demyelination in Patients with FGD4 Mutations Sural nerve biopsy M298R E543fs Stendel et al. Am J Human Genet 81:158-164, 2007

  33. Ordinal Regression FZD3

  34. The Wnt Receptor FZD3 is Critical for Neurite Outgrowth Endo et al. Mol Cell Biol 28:2368-2379, 2008

  35. AJHG 2007 AJHG 2009 Gene Ontology Analysis is Being Applied to GWA Studies

  36. Exploratory Gene Association Networks (EGAN) • Developed by Jesse Paquette at the Biostatistics and Computational Biology Core (BCB) at the Helen Diller Cancer Center • Tool for visualizing gene-gene networks and pathway enrichment using several publicly available databases • http://akt.ucsf.edu/EGAN/ • Paquette and Tokuuasu Bioinformatics 26:285-6, 2010

  37. EGAN: CALGB 40101 Neuropathy GWA Data • Used top 2500 candidate SNPs (based on P-value) • Illumina’sannotation used for gene assignments • SNP with smallest P-value used for each gene • Taking into account genes with multiple hits and SNPs without gene annotation: • 1154 SNPs were analyzed • “Background” Gene Space • Genes represented on the Illumina 610quad considered as all “potential” hits (i.e. the denominator in calculations of pathway “enrichment”)

  38. SNP Hits are Enriched in Neuronal Development and Cell Adhesion Pathways

  39. Visualization of Protein-Protein Interactions

  40. Replication Strategies BioBank Japan – no dosing data Remaining 40101 samples Scottish Ovarian Cancer Trial: 454 women receiving 175 mg/m2paclitaxel plus carboplatin SWOG 0221: Phase III trial of AC + G vs. Q 2 wks AC followed by paclitaxel weekly or Q 2 wks

  41. SNPs for Replication (*Number of Shared SNPs)

  42. Additional Phenotypes Ovarian function Breast cancer outcomes

  43. Pharmacogenomics: Focus on breast cancer Kathy Giacomini Deanna Kroetz Joan Venticinque

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