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Journal of Cancer
Article . 2017 . Peer-reviewed
Data sources: Crossref
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Journal of Cancer
Article
License: CC BY NC
Data sources: UnpayWall
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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PubMed Central
Conference object . 2017
Data sources: PubMed Central
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Strategies for Bispecific Single Chain Antibody in Cancer Immunotherapy

Authors: Zhou, Shu-juan; Wei, Jia; Su, Shu; Chen, Fang-jun; Qiu, Yu-dong; Liu, Bao-rui;

Strategies for Bispecific Single Chain Antibody in Cancer Immunotherapy

Abstract

Genetic engineering has resulted in more than 50 recombinant bispecific antibody formats over the past two decades. Bispecific scFv antibodies represent a successful and promising immunotherapy platform that retargets cytotoxic T cells to tumor cells, with one scFv directed to tumor-associated antigens and the other to T cells. Based on this antibody construct, strategies for both specific tumor targeting and T cell activation are reviewed here. Three distinct types of tumor antigens are considered to optimize specificity and safety in bispecific scFv based treatment: cancer-testis antigens, neo-antigens and virus-associated antigens. In terms of T cell activation, although CD3 has been widely applied in bispecific scFvs being developed, CD28 and CD137 among co-stimulatory signals are also ideal candidates to be evaluated. Besides, LIGHT and HIV-Tat101 have drawn much attention as their potential roles in modulating antitumor responses.

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  • BIP!
    Impact byBIP!
    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).
    13
    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).
    Average
    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!
13
Top 10%
Average
Top 10%
Green
gold
Related to Research communities
Cancer Research