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

• Published in: Nature communications 2022-03, Vol.13 (1), p.1613-17, Article 1613
• Main Authors: Ahmadi, Saba, Sukprasert, Pattara, Vegesna, Rahulsimham, Sinha, Sanju, Schischlik, Fiorella, Artzi, Natalie, Khuller, Samir, Schäffer, Alejandro A., Ruppin, Eytan
• Format: Article
• Language: English
• Subjects: 38/39; 631/114/794; 631/67/1059; 631/67/69; Antibodies; Cancer; Cell surface; Combination therapy; Combinatorial analysis; Computational neuroscience; Head & neck cancer; Head and Neck Neoplasms - drug therapy; Head and Neck Neoplasms - genetics; Heterogeneity; Humanities and Social Sciences; Humans; multidisciplinary; Nanoparticles; Optimization; Optimization techniques; Receptor-Like Protein Tyrosine Phosphatases, Class 5; Receptors; Science; Science (multidisciplinary); Therapy; Transcriptomics; Tumor cells; Tumor Microenvironment; Tumors
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-022-29154-2

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. Intra-tumor heterogeneity is often associated with resistance to targeted therapy, requiring the design of combinatorial therapies. Here, based on tumor single-cell transcriptomic datasets, the authors develop a computational approach to identify optimal combinatorial treatments targeting membrane receptors for cancer therapy.