Enhanced Prediction of CAR T-Cell Cytotoxicity with Quantum-Kernel Methods

• URL: http://arxiv.org/abs/2507.22710v1
• Date: Wed, 30 Jul 2025 14:21:32 GMT
• Title: Enhanced Prediction of CAR T-Cell Cytotoxicity with Quantum-Kernel Methods
• Authors: Filippo Utro, Meltem Tolunay, Kahn Rhrissorrakrai, Tanvi P. Gujarati, Jie Shi, Sara Capponi, Mirko Amico, Nate Earnest-Noble, Laxmi Parida,
• Abstract summary: Chimeric antigen receptor (CAR) T-cells are T-cells engineered to recognize and kill specific tumor cells.<n>It is possible to modify CAR T-cell phenotype through integrating novel signaling components into the co-stimulatory domains.<n>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.
• Score: 2.1356338340947096
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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