Title | Discriminatory Power of Combinatorial Antigen Recognition in Cancer T Cell Therapies. |
Publication Type | Journal Article |
Year of Publication | 2020 |
Authors | Dannenfelser, R, Allen, GM, VanderSluis, B, Koegel, AK, Levinson, S, Stark, SR, Yao, V, Tadych, A, Troyanskaya, OG, Lim, WA |
Journal | Cell Syst |
Volume | 11 |
Issue | 3 |
Pagination | 215-228.e5 |
Date Published | 2020 Sep 23 |
ISSN | 2405-4720 |
Abstract | <p>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).</p> |
DOI | 10.1016/j.cels.2020.08.002 |
Alternate Journal | Cell Syst |
PubMed ID | 32916097 |
PubMed Central ID | PMC7814417 |
Grant List | R01 CA196277 / CA / NCI NIH HHS / United States R01 GM071966 / GM / NIGMS NIH HHS / United States T32 HG003284 / HG / NHGRI NIH HHS / United States U54 CA244438 / CA / NCI NIH HHS / United States |