Zero-Cost Virtual RNA: Approximating Immunotherapy Signatures via Cross-Modal WSI Retrieval

Sigrid Vila-Bagaria, Mar Teixidó, Miquel Piñol, Felip Vilardell, Robert Montal, Veronica Vilaplana

Keywords: Computational Pathology, Cross-Modal Retrieval, Gastric Cancer.

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Abstract: Identifying the "Inflamed" immunophenotype in Gastric Adenocarcinoma predicts immunotherapy response but requires an expensive 10-gene RNA signature. While deep learning on standard H&E slides offers a scalable alternative, conventional binary classifiers oversimplify continuous RNA data and introduce label noise. To resolve this, we propose VITA (VIrtual Transcriptomic Approximation). By aligning H&E and RNA into a joint latent space during training, VITA requires only standard H&E at inference to retrieve morphologically similar historical cases and approximate the continuous RNA signature. Achieving 0.72 classification accuracy and a 0.66 Spearman correlation, VITA provides a cost-effective "virtual transcriptomics" pre-screening tool that preserves the continuous phenotypic spectrum without requiring genomic sequencing.

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Originality Policy: Yes

Single-blind & Not Under Review Elsewhere: Yes

LLM Policy: Yes

Submission Number: 18