Set covering machine on t-cell receptor LLM representations for lung cancer prediction

Vegas Morales, Neus

Set covering machine on t-cell receptor LLM representations for lung cancer prediction

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Vegas Morales, N. Set covering machine on t-cell receptor LLM representations for lung cancer prediction. 2025. handle: http://hdl.handle.net/10230/71765

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Abstract

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.

Description

Treball fi de màster de: Erasmus Mundus joint Master in Artificial Intelligence (EMAI)
Supervisors: Benny Chain and John Shawe-Taylor
Tutor: Massimo Mecella