FFPERescuer: deep unsupervised domain adaptation for the reconstruction of gene expression profiles derived from formalin-fixed paraffin-embedded samples

Authors

He, l., Song, K., Li, Y., Dong, Y., Wong, C. Y. N., Qi, L., Zhang, X., Lenos, K., Back, T. d., Elbers, C., Xu, C., Leung, R. M. H., Deng, R., Zhang, Y., Qiao, S., Gao, F., Chen, Y., Ng, S. S.-M., Zhou, S., Vermeulen, L., Wang, X.

Abstract

Formalin-fixed paraffin-embedded (FFPE) tumor tissues often suffer from RNA degradation, posing a long-standing challenge for reliable transcriptomic profiling. Here, we propose FFPERescuer, a deep learning framework employing unsupervised domain adaptation, to rectify distorted gene expression data. FFPERescuer comprises a partial encoder that maps a small subset of genes to high-level representations and a decoder to reconstruct full gene expression profiles. On simulated data with varying noise levels, FFPERescuer faithfully recovered gene expression profiles, achieving high Pearson correlation coefficients (PCCs > 0.85) with the ground truth. In FF-FFPE-matched cohorts, FFPERescuer significantly enhanced expression profile concordance, with average PCCs increased by 23% (P < 0.05). Applying to cancer subtyping, FFPERescuer improved classification accuracy from 67% to 92%, recapitulated subtype-specific biological properties lost in the FFPE-derived data, and enhanced survival associations. Our studies provide a powerful framework for reliable transcriptomic profiling from FFPE-archived tumor samples that are widely available in the clinic.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 09 Sep 2026.

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