T-cell receptors (TCRs) provide insights into immune recognition of cancer. We explore whether interpretable rule-based classifiers derived from SCEPTR embeddings of TCR sequences can differentiate cancer repertoires from healthy controls. Using the Set Covering Machine algorithm, we developed models with hyperplane and similarity based rules across alpha and beta chains. Despite strong performance on training data, models failed to generalize to external datasets. Unexpectedly, alpha-chain models often outperformed beta-chain models, and single rules sometimes achieved high training accuracy, suggesting overfitting. Our findings highlight challenges in detecting cancer-specific TCR signatures and indicate current embeddings may capture technical patterns rather than biological signal. We propose future directions including improved rule generation strategies and validation with functionally annotated repertoires.
