TY - JOUR
T1 - The landscape of receptor-mediated precision cancer combination therapy via a single-cell perspective
AU - Ahmadi, Saba
AU - Sukprasert, Pattara
AU - Vegesna, Rahulsimham
AU - Sinha, Sanju
AU - Schischlik, Fiorella
AU - Artzi, Natalie
AU - Khuller, Samir
AU - Schäffer, Alejandro A.
AU - Ruppin, Eytan
N1 - Funding Information:
This research is supported in part by the Intramural Research program of the National Institutes of Health, National Cancer Institute. This research is supported in part by the University of Maryland Year of Data Science Program. This research is supported in part by start-up funds from Northwestern University (S.A., P.S., S.K) and a research award from Amazon to support the research of S.K. This work utilized the computational resources of the NIH HPC Biowulf cluster ( http://hpc.nih.gov ). Thanks to E. Michael Gertz for technical assistance with SCIP, Gurobi, and Biowulf. Thanks to Allon Wagner, Keren Yizhak, and Sushant Patkar for assistance in identifying and retrieving suitable single-cell RNA-seq data sets. Thanks to Leandro Hermida for technical advice.
Funding Information:
Open Access funding provided by the National Institutes of Health (NIH).
Publisher Copyright:
© 2022, This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.
PY - 2022/12
Y1 - 2022/12
N2 - Mining a large cohort of single-cell transcriptomics data, here we employ combinatorial optimization techniques to chart the landscape of optimal combination therapies in cancer. We assume that each individual therapy can target any one of 1269 genes encoding cell surface receptors, which may be targets of CAR-T, conjugated antibodies or coated nanoparticle therapies. We find that in most cancer types, personalized combinations composed of at most four targets are then sufficient for killing at least 80% of tumor cells while sparing at least 90% of nontumor cells in the tumor microenvironment. However, as more stringent and selective killing is required, the number of targets needed rises rapidly. Emerging individual targets include PTPRZ1 for brain and head and neck cancers and EGFR in multiple tumor types. In sum, this study provides a computational estimate of the identity and number of targets needed in combination to target cancers selectively and precisely.
AB - Mining a large cohort of single-cell transcriptomics data, here we employ combinatorial optimization techniques to chart the landscape of optimal combination therapies in cancer. We assume that each individual therapy can target any one of 1269 genes encoding cell surface receptors, which may be targets of CAR-T, conjugated antibodies or coated nanoparticle therapies. We find that in most cancer types, personalized combinations composed of at most four targets are then sufficient for killing at least 80% of tumor cells while sparing at least 90% of nontumor cells in the tumor microenvironment. However, as more stringent and selective killing is required, the number of targets needed rises rapidly. Emerging individual targets include PTPRZ1 for brain and head and neck cancers and EGFR in multiple tumor types. In sum, this study provides a computational estimate of the identity and number of targets needed in combination to target cancers selectively and precisely.
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U2 - 10.1038/s41467-022-29154-2
DO - 10.1038/s41467-022-29154-2
M3 - Article
C2 - 35338126
AN - SCOPUS:85127086773
SN - 2041-1723
VL - 13
JO - Nature Communications
JF - Nature Communications
IS - 1
M1 - 1613
ER -
