Abstract 2688: The landscape of precision cancer combination therapy: a single-cell perspective

Abstract 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 explore the landscape of optimal combination therapy targets in solid tumors (including brain, head and neck, melanoma, lung, breast and colon cancers). To this end, we developed MadHitter (https://github.com/ruppinlab/madhitter), which analyzes tumor single-cell transcriptomics data using combinatorial algorithms to identify precision combination of treatment targets that are predicted to maximize the killing of cancer cells while minimizing the killing of noncancerous ones. We started with a predefined set of 533 proteins that are lowly abundant across healthy tissues and thus may have low off-tumor targeting and toxicity if targeted via chimeric antigen receptors (CAR) T cell therapy. In most cancer types analyzed, we find that combinations composed of a single-digit number of 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. We identify targets that are shared across datasets of the same cancer type (e.g., TNR) or across different cancer types (the SOX family, MAGE family, and KRT16). Next, we expanded our search for a set of 1269 known cell surface receptors, which may be precisely targeted by antibody or nanoparticles delivering a toxin into cells via receptor mediated endocytosis. The resulting optimal target sets are encouragingly also in the single-digit size. The gene PTPRZ1, a tyrosine phosphatase receptor, is frequently found in the optimal combinations for brain and head and neck cancers, and EGFR is a high-coverage target in multiple tumor types. Notably, requiring more stringent levels of cancer killing, the number of targets that need to be combined rises sharply in both search spaces described above. In sum, this analysis provides the first systematic characterization of potential combinatorial targets in solid tumors, uncovering promising future targets for both CAR therapy and conjugated toxin delivering antibodies and nanoparticles. Citation Format: Saba Ahmadi, Pattara Sukprasert, Rahulsimham Vegesna, Sanju Sinha, Natalie Artzi, Samir Khuller, Alejandro A. Schäffer, Eytan Ruppin. The landscape of precision cancer combination therapy: a single-cell perspective [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2688.