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Author Spotlight: Shear Assay Protocol for the Determination of Single-Cell Material Properties
Published on: May 19, 2023
The landscape of receptor-mediated precision cancer combination therapy via a single-cell perspective
Saba Ahmadi1,2,3, Pattara Sukprasert1,2, Rahulsimham Vegesna4
1Department of Computer Science, University of Maryland, College Park, MD, 20742, USA.
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.
Insights
Personalized cancer therapies using at most four targets can effectively kill tumor cells while sparing healthy cells. This computational approach identifies optimal gene targets for combination therapies.
Area of Science:
- Oncology
- Computational Biology
- Immunotherapy
Background:
- Cancer treatment faces challenges in selectively targeting tumor cells while sparing normal tissues.
- Single-cell transcriptomics offers a high-resolution view of cellular heterogeneity in tumors.
- Developing effective combination therapies requires precise identification of targetable genes.
Purpose of the Study:
- To computationally determine optimal gene target combinations for cancer therapy.
- To assess the feasibility of achieving high tumor cell killing with minimal off-target effects.
- To identify specific gene targets for various cancer types.
Main Methods:
- Utilized combinatorial optimization techniques on a large dataset of single-cell transcriptomics data.
- Modeled therapies targeting 1269 cell surface receptor genes for CAR-T, antibody, and nanoparticle applications.
- Evaluated target combinations based on tumor cell killing and non-tumor cell sparing percentages.
Main Results:
- Personalized combinations of up to four targets can achieve >80% tumor cell killing and >90% non-tumor cell sparing in most cancers.
- Higher selectivity and killing efficiency necessitate a rapid increase in the number of required targets.
- Identified PTPRZ1 (brain, head and neck cancers) and EGFR (multiple tumor types) as emerging individual targets.
Conclusions:
- Computational methods can precisely estimate the number and identity of targets for effective and selective cancer combination therapies.
- Four-target combinations show promise for personalized cancer treatment, balancing efficacy and safety.
- This approach provides a framework for designing next-generation targeted cancer therapies.
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