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  • Poster presentation
  • Open Access

Computational discovery and experimental validation of novel drug targets in immuno-oncology

  • Ofer Levy1,
  • Arthur Machelnkin1,
  • Galit Rotman1,
  • Amir Toporik1,
  • Gady Cojocaro1,
  • Liat Dassa1,
  • Ilan Vaknin1,
  • Spencer Liang1,
  • John Hunter1,
  • Eyal Neria1 and
  • Zurit Levin1
Journal for ImmunoTherapy of Cancer20153(Suppl 2):P184

https://doi.org/10.1186/2051-1426-3-S2-P184

Published: 4 November 2015

Keywords

Experimental DataPredictive ModelDrug TargetTherapeutic PotentialExperimental Validation

The past few years have witnessed a renaissance in the field of immuno-oncology largely due to the clinical success in targeting the immune checkpoints CTLA-4 and PD-1. Towards identification of novel immune checkpoint drug targets we developed a dedicated predictive discovery platform.

The B7/CD28 discovery platform is a predictive model based on genomic and protein features along with expression patterns of known B7/CD28 proteins. The platform has been tested and validated extensively and has demonstrated its validity by identifying non-novel immune checkpoints such as TIGIT and VISTA, which were not used in the design stage.

The B7/CD28 predictive platform was employed to identify several novel immune checkpoint candidates which are currently in different validation stages. In this poster, we will describe our discovery approach as well as our validation path. In addition, we will present experimental data demonstrating the immuno-modulatory function and expression patterns of several of our novel immune checkpoints. These experimental results serve as an additional confirmation to the accuracy of our B7/CD28 predictive discovery platform and shed light on the therapeutic potential of the novel immune checkpoints identified using this unique discovery approach.

Authors’ Affiliations

(1)
Compugen, Tel Aviv, Israel

Copyright

© Levy et al. 2015

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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