The landscape of receptor-mediated precision cancer combination therapy via a single-cell perspective

Saba Ahmadi, Pattara Sukprasert, Rahulsimham Vegesna, Sanju Sinha, Fiorella Schischlik, Natalie Artzi, Samir Khuller, Alejandro A. Schäffer () and Eytan Ruppin ()
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Saba Ahmadi: University of Maryland
Pattara Sukprasert: University of Maryland
Rahulsimham Vegesna: Cancer Data Science Laboratory, National Cancer Institute
Sanju Sinha: Cancer Data Science Laboratory, National Cancer Institute
Fiorella Schischlik: Cancer Data Science Laboratory, National Cancer Institute
Natalie Artzi: Brigham and Women’s Hospital, Harvard Medical School
Samir Khuller: University of Maryland
Alejandro A. Schäffer: Cancer Data Science Laboratory, National Cancer Institute
Eytan Ruppin: Cancer Data Science Laboratory, National Cancer Institute

Nature Communications, 2022, vol. 13, issue 1, 1-17

Abstract: Abstract 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.

Date: 2022
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DOI: 10.1038/s41467-022-29154-2

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