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Immunotherapy
Article
Data sources: UnpayWall
Immunotherapy
Article . 2012 . Peer-reviewed
Data sources: Crossref
Immunotherapy
Article . 2012
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Combination Strategies to Enhance Antitumor ADCC

Authors: Holbrook E, Kohrt; Roch, Houot; Aurélien, Marabelle; Hearn Jay, Cho; Keren, Osman; Matthew, Goldstein; Ronald, Levy; +1 Authors

Combination Strategies to Enhance Antitumor ADCC

Abstract

The clinical efficacy of monoclonal antibodies as cancer therapeutics is largely dependent upon their ability to target the tumor and induce a functional antitumor immune response. This two-step process of ADCC utilizes the response of innate immune cells to provide antitumor cytotoxicity triggered by the interaction of the Fc portion of the antibody with the Fc receptor on the immune cell. Immunotherapeutics that target NK cells, γδ T cells, macrophages and dendritic cells can, by augmenting the function of the immune response, enhance the antitumor activity of the antibodies. Advantages of such combination strategies include: the application to multiple existing antibodies (even across multiple diseases), the feasibility (from a regulatory perspective) of combining with previously approved agents and the assurance (to physicians and trial participants) that one of the ingredients - the antitumor antibody - has proven efficacy on its own. Here we discuss current strategies, including biologic rationale and clinical results, which enhance ADCC in the following ways: strategies that increase total target-monoclonal antibody-effector binding, strategies that trigger effector cell 'activating' signals and strategies that block effector cell 'inhibitory' signals.

Keywords

Antibody-Dependent Cell Cytotoxicity, Antibodies, Monoclonal, Antineoplastic Agents, Cancer Vaccines, Combined Modality Therapy, Antigens, Neoplasm, Neoplasms, Antineoplastic Combined Chemotherapy Protocols, Animals, Humans, Immunologic Factors, Immunotherapy

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    selected citations
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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    85
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
85
Top 10%
Top 10%
Top 10%
bronze
Related to Research communities
Cancer Research