Enhanced Prediction of CAR T-Cell Cytotoxicity with Quantum-Kernel Methods
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908473002819584 |
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| author | Utro, Filippo Tolunay, Meltem Rhrissorrakrai, Kahn Gujarati, Tanvi P. Shi, Jie Capponi, Sara Amico, Mirko Earnest-Noble, Nate Parida, Laxmi |
| author_facet | Utro, Filippo Tolunay, Meltem Rhrissorrakrai, Kahn Gujarati, Tanvi P. Shi, Jie Capponi, Sara Amico, Mirko Earnest-Noble, Nate Parida, Laxmi |
| contents | Chimeric antigen receptor (CAR) T-cells are T-cells engineered to recognize and kill specific tumor cells. Through their extracellular domains, CAR T-cells bind tumor cell antigens which triggers CAR T activation and proliferation. These processes are regulated by co-stimulatory domains present in the intracellular region of the CAR T-cell. Through integrating novel signaling components into the co-stimulatory domains, it is possible to modify CAR T-cell phenotype. Identifying and experimentally testing new CAR constructs based on libraries of co-stimulatory domains is nontrivial given the vast combinatorial space defined by such libraries. This leads to a highly data constrained, poorly explored combinatorial problem, where the experiments undersample all possible combinations. We propose a quantum approach using a Projected Quantum Kernel (PQK) to address this challenge. PQK operates by embedding classical data into a high dimensional Hilbert space and employs a kernel method to measure sample similarity. Using 61 qubits on a gate-based quantum computer, we demonstrate the largest PQK application to date and an enhancement in the classification performance over purely classical machine learning methods for CAR T cytotoxicity prediction. Importantly, we show improved learning for specific signaling domains and domain positions, particularly where there was lower information highlighting the potential for quantum computing in data-constrained problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_22710 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhanced Prediction of CAR T-Cell Cytotoxicity with Quantum-Kernel Methods Utro, Filippo Tolunay, Meltem Rhrissorrakrai, Kahn Gujarati, Tanvi P. Shi, Jie Capponi, Sara Amico, Mirko Earnest-Noble, Nate Parida, Laxmi Machine Learning Quantitative Methods Quantum Physics Chimeric antigen receptor (CAR) T-cells are T-cells engineered to recognize and kill specific tumor cells. Through their extracellular domains, CAR T-cells bind tumor cell antigens which triggers CAR T activation and proliferation. These processes are regulated by co-stimulatory domains present in the intracellular region of the CAR T-cell. Through integrating novel signaling components into the co-stimulatory domains, it is possible to modify CAR T-cell phenotype. Identifying and experimentally testing new CAR constructs based on libraries of co-stimulatory domains is nontrivial given the vast combinatorial space defined by such libraries. This leads to a highly data constrained, poorly explored combinatorial problem, where the experiments undersample all possible combinations. We propose a quantum approach using a Projected Quantum Kernel (PQK) to address this challenge. PQK operates by embedding classical data into a high dimensional Hilbert space and employs a kernel method to measure sample similarity. Using 61 qubits on a gate-based quantum computer, we demonstrate the largest PQK application to date and an enhancement in the classification performance over purely classical machine learning methods for CAR T cytotoxicity prediction. Importantly, we show improved learning for specific signaling domains and domain positions, particularly where there was lower information highlighting the potential for quantum computing in data-constrained problems. |
| title | Enhanced Prediction of CAR T-Cell Cytotoxicity with Quantum-Kernel Methods |
| topic | Machine Learning Quantitative Methods Quantum Physics |
| url | https://arxiv.org/abs/2507.22710 |