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Diagnostic Approaches to Maximize Therapeutic Effect 199 Figure 2 Theranostic approach. drugs, let alone novel drugs developed with these criteria in mind. Ultimately this may lead to individualized genetic profiles to select the safest and most effective drug for each individual. The same insight will allow us to achieve the most desirable goal, to prevent disease to begin with. We are uncertain as to the overall direction of theranostics over the next 10 years. Although new analytical systems introduced during the last decade have offered incremental improvements over previously available technology, there are still many daunting scientific challenges, besides the ethical issues that need to be resolved. Genetic information and individual or ethnic group stratification raises privacy questions and ethical dilemmas about disease susceptibility, prognosis, and treatment options. Obviously, information of this type must be carefully safeguarded to ensure privacy. Many legal and economic issues will need to be resolved. The vision of theranostics is leading us to a more individualized approach to drug therapy, while revealing limits inherent to the treatment of disease in broad patient populations. Whether or not these new technologies and approaches find their way into everyday clinical use during the next 10 years, they will no doubt prove valuable tools in clinical research directed at optimizing drug therapy. REFERENCES 1. Lesko, LJ. Personalized medicine: elusive dream or imminent reality? Clin Pharmacol Ther 2007; 81(6):807–816. 2. Papadopoulos N, Kinzler KW, Vogelstein B. The role of companion diagnostics in the development and use of mutation-targeted cancer therapies. Nat Biotechnol 2006; 24(8):985–995.
200 Chiang et al. 3. Willard HF, Angrist M, Ginsburg GS. Genomic medicine: genetic variation and its impact on the future of health care. Philos Trans R Soc Lond B Biol Sci 2005; 360(1460):1543–1550. 4. Boyer CM, Borowitz MJ, McCarty KS Jr, et al. Heterogeneity of antigen expression in benign and malignant breast and ovarian epithelial cells. Int J Cancer 1989; 43(1): 55–60. 5. Dunn GP, Bruce AT, Ikeda H, et al. Cancer immunoediting: from immunosurveillance to tumor escape. Nat Immunol 2002; 3(11):991–998. 6. Stevanovic S. Identification of tumour-associated T-cell epitopes for vaccine development. Nat Rev Cancer 2002; 2(7):514–520. 7. Davis ID, Jefford M, Parente P, et al. Rational approaches to human cancer immunotherapy. J Leukoc Biol 2003; 73(1):3–29. 8. Ward PL and Schreiber H. Tumor antigens recognized by T cells. Biological approaches to cancer treatment. Biomodulation. pp. 72–97 (Mitchell, Ed.). New York: McGraw Hill Inc. 1993. 9. York IA, Goldberg AL, Mo XY, et al. Proteolysis and class I major histocompatibility complex antigen presentation. Immunol Rev 1999; 172:49–66. 10. Lankat-Buttgereit B, Tampé R. The transporter associated with antigen processing: function and implications in human diseases. Physiol Rev 2002; 82(1):187–204. 11. Bouvier M. Accessory proteins and the assembly of human class I MHC molecules: a molecular and structural perspective. Mol Immunol 2003; 39(12):697–706. 12. Momburg F, Tan P. Tapasin-the keystone of the loading complex optimizing peptide binding by MHC class I molecules in the endoplasmic reticulum. Mol Immunol 2002; 39(3–4):217–233. 13. Hicklin DJ, Wang Z, Arienti F, et al. beta2-Microglobulin mutations, HLA class I antigen loss, and tumor progression in melanoma. J Clin Invest 1998; 101(12): 2720–2729. 14. Boon T, Cerottini JC, Van den Eynde B, et al. Tumor antigens recognized by T lymphocytes. Annu Rev Immunol 1994; 12:337–365. 15. Rosenbery SA. Development of cancer immunotherapies based on identification of the genes encoding cancer regression antigens. J Natl Cancer Inst 1996; 88(22): 1635–1644. 16. Benitez R, Godelaine D, Lopez-Nevot MA, et al. Mutations of the beta2-microglobulin gene result in a lack of HLA class I molecules on melanoma cells of two patients immunized with MAGE peptides. Tissue Antigens 1998; 52(6):520–529. 17. Seliger B, Maeurer MJ, Ferrone S. Antigen-processing machinery breakdown and tumor growth. Immunol Today 2000; 21(9):455–464. 18. Ahmad M, Rees RC, Ali SA. Escape from immunotherapy: possible mechanisms that influence tumor regression/progression. Cancer Immunol Immunother 2004; 53(10): 844–854. 19. van Duinen SG, Ruiter DJ, Broecker EB, et al. Level of HLA antigens in locoregional metastases and clinical course of the disease in patients with melanoma. Cancer Res 1988; 48(4):1019–1025. 20. Restifo NP, Marincola FM, Kawakami Y, et al. Loss of functional beta 2-microglobulin in metastatic melanomas from five patients receiving immunotherapy. J Natl Cancer Inst 1996; 88(2):100–108. 21. Jäger E, Ringhoffer M, Altmannsberger M, et al. Immunoselection in vivo: independent loss of MHC class I and melanocyte differentiation antigen expression in metastatic melanoma. Int J Cancer 1997; 71(2):142–147.
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Translational Medicine Series 6 Can
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TRANSLATIONAL MEDICINE SERIES 1. Pr
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Informa Healthcare USA, Inc. 52 Van
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Preface Throughout the last 20 year
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Preface vii Table 1 A Diverse Techn
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Preface . . . . v Contributors . .
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Contributors Pedro Alves Ludwig Ins
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1 Factoring in Antigen Processing i
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Preclinical Models and Clinical Sit
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56 Srinivasan disease agents. Garda
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62 Srinivasan (69). In another stud
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104 Luptrawan et al. antigens (84,8
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7 Multimodality Immunization Approa
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Multimodality Immunization Approach
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