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The Landscape of Precision Cancer Combination Therapy: A Single-Cell Perspective

By Saba Ahmadi, Pattara Sukprasert, Rahulsimhan Vegesna, Sanju Sinha, Fiorella Schischlik, Natalie Artzi, Samir Khuller, Alejandro A. Schäffer, Eytan Ruppin

Posted 29 Jan 2020
bioRxiv DOI: 10.1101/2020.01.28.923532

The availability of single-cell transcriptomics data opens new opportunities for rational design of combination cancer treatments in a systematic manner. Mining such data, we employed combinatorial optimization techniques to explore the landscape of optimal combination therapies in solid tumors, including brain, head and neck, melanoma, lung, breast and colon cancers. 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. In most cancer types, personalized combinations composed of at most four targets are sufficient to kill at least 80% of the tumor cells while killing at most 10% of the non-tumor cells in each patient. The number of distinct targets needed to do that for all patients in 8 of the 9 cohorts we studied is at most 11, while one larger melanoma cohort requires over 30 distinct targets. Further requiring that the target genes be lowly expressed across many different healthy tissues uncovers qualitatively similar trends. However, as one requires either more stringent killing thresholds or more stringent sparing of non-cancerous tissues beyond these baseline values, the number of targets needed rises rapidly. Emerging promising targets include the gene PTPRZ1, which is frequently found in the optimal combinations for brain and head and neck cancers, and EGFR, a recurring target in multiple tumor types. In sum, this is the first systematic single-cell based characterization of the landscape of combinatorial receptor-mediated cancer treatments, identifying promising targets for future development.

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