Cancer Pharmacogenomics: Setting a Research Agenda to Accelerate Translation

Slide 1 of 32: Cancer Pharmacogenomics Development, Science, and Translation

Richard M. Weinshilboum, M.D.
Chair, Division of Clinical Pharmacology
Professor of Molecular Pharmacology and Experimental Therapeutics
Mayo Clinic College of Medicine

July 21, 2009


Slide 2 of 32: Cancer Pharmacogenomics

  • Introduction
  • Present
  • Promise
  • Conclusions

Slide 3 of 32: Pharmacogenetics – Pharmacogenomics: Introduction

Critical component of “personalized” or “individualized” medicine


Slide 4 of 32: Pharmacogenetics – Pharmacogenomics: Clinical Goals

  • Avoid adverse drug reactions
  • Maximize drug efficacy
  • Select responsive patients

Slide 5 of 32: Pharmacogenetics – Pharmacogenomics: Scientific Goals

  • Link variation in genotype to variation in phenotype
  • Determine mechanisms responsible for that link
  • Translate the link into enhanced understanding, treatment and prevention of disease

Slide 6 of 32: Cancer Pharmacogenomics

  • Introduction
  • Present
  • Promise
  • Conclusions

Slide 7 of 32: Pharmacogenetics – Pharmacogenomics: FDA Hearings

Pharmacogenetics and Drug Labeling

  • Thiopurines – TPMT*
  • Irinotecan – UGT1A1*
  • Warfarin – CYP2C9 and VKORC1*
  • Tamoxifen – CYP2D6*

*germline polymorphisms


Slide 8 of 32: Childhood ALL Survival: St. Jude Experience

[Image] showing graph that displays probability of overall survival as a percentage and years after diagnosis of ALL for several different studies.

Pui and Evans, NEJM. 2006;354:166-78. Copyright © 2006 Massachusetts Medical Society. All rights reserved.


Slide 9 of 32: TPMT Genetic Polymorphism Clinical Consequences

  • Low TPMT
    • Increased thiopurine toxicity
    • Increased risk for secondary neoplasm
  • High TPMT
    • Decreased therapeutic effect

Slide 10 of 32: Selected Human TPMT Alleles

[Image] showing TPMT*1, *3A, *3B, and *3C alleles along with common polymorphisms.

Reprinted by permission from Macmillan Publishers Ltd. Weinshilboum, R and Wang, L. Nature Rev. Drug Discovery. 2004; 3: 739-748. Copyright 2004.


Slide 11 of 32: Pharmacogenetics – Pharmacogenomics: FDA Hearings

Pharmacogenetics and Drug Labeling

  • Thiopurines – TPMT*
  • Irinotecan – UGT1A1*
  • Warfarin – CYP2C9 and VKORC1*
  • Tamoxifen – CYP2D6*

*germline polymorphisms


Slide 12 of 32: Tamoxifen Biotransformation

[Image] showing pathway for tamoxifen biotransformation to endoxifen.

Jin et al., J. Natl. Cancer Inst. 2005; 97:20-39. Reprinted by permission of the Oxford University Press.


Slide 13 of 32: CYP2D6 Pharmacogenetics

[Image] showing a graph of the number of subjects with various CYP2D6 genotypes (ultrametabolizer, extensive metabolizer, and poor metabolizer) and theirdebrisoquinone to 4-hydroxyderisoquine metabolic ratios. The cutoff for poor metabolizers is around ratio of 10.

Reprinted by permission from Macmillan Publishers Ltd. Bertilsson, L. et al. Clin. Pharmacol. Ther. 1992; 51: 388-397. Copyright 1992.


Slide 14 of 32: Tamoxifen Pharmacogenetics

[Image] showing two graphs that display the percent of breast cancer patients with relapse-free survival and disease-free survival by metabolizer status based on years after randomization.

Goetz et al., Breast Cancer Res. Treat. 2007; 101:113-121. Reprinted by permission of Springer Science+Business Media.


Slide 15 of 32: Tamoxifen Pharmacogenetics

[Image] showing three Kaplan-Meier probabilities of relapse-free time (RFT) for CYP2D6-metabolizer phenotypes predicted from genotypes: (A) patients not treated with tamoxifen, (B) patients treated with adjuvant tamoxifen, and (C) carriers of one or two impaired CYP2D6 alleles predictive for “decreased” enzyme activity or extensive metabolizer.

Schroth et al, Journal of Clinical Oncology. 2007; 25:5187-5193. Reprinted with permission. © 2010 American Society of Clinical Oncology. All rights reserved.


Slide 16 of 32: Pharmacogenomics: Evolution

  • One gene, one or a few SNPs
  • One gene, intragene haplotypes
  • PK and PD pathways and haplotypes
  • Genome-wide association studies

Slide 17 of 32: Cancer Pharmacogenomics

  • Introduction
  • Present
  • Promise
  • Conclusions

Slide 18 of 32: Pharmacogenomic Genome-Wide Model System

“Human Variation Panel” Cell Lines

  • 96 CA, 96 AA, 96 HCA
  • Illumina genome-wide SNPs
  • Affymetrix 6.0 genome-wide SNPs
  • Affymetrix U133 2.0 Plus expression data
  • Affymetrix exon array data

From unpublished work by Liewei Wang, M.D., Ph.D.


Slide 19 of 32: Cytidine Analogues

[Image] showing chemical structures of cytarabine (Ara-C) and gemcitabine.


Slide 20 of 32: Gemcitabine Pathway

[Image] showing gemcitabine metabolism pathway.


Slide 21 of 32: Gemcitabine – AraC IC50 Expression Association

[Image] showing association between expression array data and IC50 values for gemcitabine and Ara-C.

Reprinted with permission from Li et al. Cancer Res. 2008; 68:7050-7058.


Slide 22 of 32: “Human Variation Panel” Strategy

  • “Biased” – pathway-based
  • “Unbiased” – genome-wide
  • Functional validation
  • NT5C3, a “pathway” gene,  and FKBP5, a “non-pathway” gene encoding a 51 kDa immunophilin, were selected for functional study based on p values and QRT-PCR verification.

Slide 23 of 32: The Therapeutic Revolution

[Image] of Goodman and Gilman’s “The Pharmacologic Basis of Therapeutics.”


Slide 24 of 32: Functional Characterization of FKBP5: Gemcitabine

[Image] showing significantly decreased levels of FKBP5 protein in SU86 pancreatic cancer and MDA-MB-231 breast cancer cells after treatment with FKBP5-specific siRNA.

Reprinted with permission from Li et al. Cancer Res. 2008; 68:7050-7058.


Slide 25 of 32: FKBP5 Functional Characterization: Caspace-3/7 Activity

[Image] showing caspase activity in SU86 and MOA-MB-231 cells after FKBP5 siRNA treatment.

Reprinted with permission from Li et al. Cancer Res. 2008; 68:7050-7058.


Slide 26 of 32: Cancer Pharmacogenomics

  • Introduction
  • Present
  • Promise
  • Conclusions

Slide 27 of 32: Pharmacogenomics Genomic Era

Developments

  • Next Gen DNA Sequencing
  • 1000 Genomes Project
  • ENCODE
  • RNA-seq
  • DTC Genomics

Slide 28 of 32: Pharmacogenomics

Clinical Goals

  • Avoid adverse drug reactions
  • Maximize drug efficacy
  • Select responsive patients

Slide 29 of 32: Cancer Pharmacogenomics

Challenges

  • Germline and/or somatic genome
  • Clinical trials and/or population studies
  • Translational and/or mechanistic studies
  • Funding to incorporate rapidly changing, expensive technologies
  • Collaboration and replication

Slide 30 of 32: Acknowledgements

  • Mayo PGRN – GM61388
  • Indiana PGRN – GM061373
  • Mayo Breast Cancer SPORE – CA166201
  • Mayo Pancreatic Cancer SPORE – CA102701
  • K22 CA130828 and R01 CA138461
  • Breast Cancer Intergroup of North America – NCIC-CTG, NCCTG, ECOG, SWOG, CALGB
  • RIKEN Yokohama Institute Center for Genomic Medicine (CGM)

Slide 31 of 32: Pharmacogenetics Research Network

[Image] of United States with dots indicating the primary investigator sites and co-investigator sites of the NIH-funded Pharmacogenetics Research Network.


Slide 32 of 32: Mayo Pharmacogenomics Laboratories - 2009

[Image] of staff at Mayo Pharmacogenomics Laboratories

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