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

TL;DR

This paper introduces a quantum kernel method to improve the prediction of CAR T-cell cytotoxicity, demonstrating enhanced classification performance over classical methods in a data-constrained setting.

Contribution

It presents the largest application of a Projected Quantum Kernel to date, showing its effectiveness in predicting CAR T-cell behavior and highlighting quantum computing's potential in biological data analysis.

Findings

Quantum kernel method outperforms classical models in CAR T prediction

Enhanced learning for specific signaling domains and positions

Largest PQK application with 61 qubits on a gate-based quantum computer

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.