Discriminatory Power of Combinatorial Antigen Recognition in Cancer T Cell Therapies

Overview

Journal Cell Syst

Publisher Cell Press

Specialties Biology
Cell Biology
Molecular Biology

Date 2020 Sep 11

PMID 32916097

Citations 41

Authors

Ruth Dannenfelser

Gregory M Allen

Benjamin VanderSluis

Ashley K Koegel

Sarah Levinson

Sierra R Stark

Vicky Yao

Alicja Tadych

Olga G Troyanskaya

Wendell A Lim

Affiliations

Soon will be listed here.

Abstract

Precise discrimination of tumor from normal tissues remains a major roadblock for therapeutic efficacy of chimeric antigen receptor (CAR) T cells. Here, we perform a comprehensive in silico screen to identify multi-antigen signatures that improve tumor discrimination by CAR T cells engineered to integrate multiple antigen inputs via Boolean logic, e.g., AND and NOT. We screen >2.5 million dual antigens and ∼60 million triple antigens across 33 tumor types and 34 normal tissues. We find that dual antigens significantly outperform the best single clinically investigated CAR targets and confirm key predictions experimentally. Further, we identify antigen triplets that are predicted to show close to ideal tumor-versus-normal tissue discrimination for several tumor types. This work demonstrates the potential of 2- to 3-antigen Boolean logic gates for improving tumor discrimination by CAR T cell therapies. Our predictions are available on an interactive web server resource (antigen.princeton.edu).

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