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Development of Novel Immunotherapeutics 165 Figure 10 The diversity of biomarkers and their usefulness. that started to relapse or progress after standard therapy, while the process is not clinically evident yet (e.g., PSA- or CA-125-positive patients with no overt clinical disease). The reason is that cancer vaccines are more applicable to indications associated with limited disease burden or minimal residual disease; therefore, identifying patients in that earlier stage is of paramount importance. In addition, since the length of clinical trials is a limiting factor in the development of therapies for oncologic disorders, in a setting of limited disease, biomarkers that correlate and anticipate clinical progression are critical in providing timely data set that would support a rationale for a decisional process. Finally, patient stratification approaches based on biomarkers and applied to minimum residual disease post-standard therapy may result in identification and expedited evaluation of efficacy of investigational agents. There are quite several markers in this expanding category such as VEGF, CA-125, and PSA. If validated through clinical experimentation, such biomarkers—alone or as clusters—may become important diagnostics complementary to more conventional approaches. With the emerging maturation of various databases on biomarker expression in human normal tissues as well as diseased tissues or tumor archives resulting from worldwide omics efforts, there is an exciting possibility for target discovery based on biomarker database mining. Careful computer-based analysis of proteomics, transcriptomics, and genomics data may reveal entire signal transduction pathways with associated membrane receptors amenable to biomolecule targeting and downstream enzymes that can be targeted via conventional small molecule technologies. A category of biomarkers of paramount importance for the development of new molecular targeted therapies is that of markers that predict responsiveness to an investigational drug. Since molecular pathogenesis and thus tumor susceptibility to drugs is quite heterogeneous, these biomarkers have a severalfold impact: First, they would outline a subpopulation of patients that would benefit the most from drug exposure; second, that would minimize unnecessary exposure of patients that are less likely to benefit from the drug. Overall, this would lead to more rapid, focused development of the drug by increasing therapeutic
166 Bot and Obrocea index, efficacy-to-noise ratio, diminishing the size of the clinical trials, and improving the odds that a specific investigational agent studied will gain market approval by the regulatory authorities. An obvious subcategory of biomarkers considered is that of target molecules themselves. If the target molecule is not expressed within the tumor tissue, irrespective of the MOA, the likelihood of clinical response is diminished should there be no significant “off target” effects. Well-known examples for such biomarkers in support of antibody-based treatment are Her-2/Neu expression or CD20 expression by tumor cells defining patients eligible for transtuzumab or rituximab treatment, respectively; but the paradigm spans other categories of molecular targeted therapies such as small molecules (bcr/abl in the case of imatinib, and EGFR polymorphism for gefitinib). It is not difficult to imagine that by not having a biomarker-based approach to stratify patients what would have been the fate of trastuzumab (Herceptin 1 ) since the treatment is relevant to only *35%ofbreastcarcinoma patients that display upregulation of Her-2/Neu. Due to the indirect and complex MOA, the challenges faced by active immunotherapy in light of this are considerably more significant. It would be ideal to have appropriate reagents and methodologies to determine and quantify the targets in clinical setting since the target molecules in many cases are MHC-peptide complexes expressed by the tumor cells. Unfortunately, this field is not mature yet; nevertheless, there is exciting new research on a new generation of antibody-like molecules that directly recognize MHC-peptide complexes (5). In the absence of measuring the target molecules, the next best approach—used by several groups developing antigenbased cancer vaccines—is to measure by immunohistochemistry the target antigen and MHC class I expression. Due to the fact that antigen processing and presentation is heterogeneous and subject to a variety of immune escape phenomena, this is obviously only a surrogate for target (MHC-peptide) molecule expression. A difficult aspect related to using such biomarkers is the correlation of the pharmacological activity with their biomarkers’ level of expression; in addition, the magnitude of the activity may depend to a high extent on the nature and potency of the investigational drug. Other approaches, for example, based on whole tumor lysates, allogeneic tumor cells or in general, not directed to a specific but a collection of antigens may not benefit from a target-related biomarker strategy. This increases considerably the risk throughout development. If we just consider the fact that a significant percentage of tumors show clear immune escape phenomena via MHC class I and/or TAP defects (between 20 and 50%), a lack of patient stratification based on target molecule expression may have drastic consequences in terms of reduction in response rate (even assuming an excellent pharmacological effect of the investigational drug). Conversely, use of such biomarker-based approaches to direct the testing of investigational agents in select populations—when afforded by the immunotherapeutic strategy—may have a negative impact on the number of patients treated in a specific disease setting. However this may ultimately result in an enhanced opportunity from medical and commercial standpoint due to a likely increased efficacy in clinic.
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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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Factoring in Antigen Processing in
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Preclinical Models and Clinical Sit
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