Cooperative Modular Representation Learning for Lung Adenocarcinoma Survival Prediction from Transcriptomic and Clinical Data

Abstract

Accurate prognosis in lung adenocarcinoma (LUAD) requires integration of high-dimensional transcriptomic profiles with compact but clinically stable patient covariates. Naïve fusion strategies allow the high-variance RNA-seq modality to dominate learned representations, suppressing clinical signal. We present Cooperative Modular Representation Learning (CMRL), an uncertainty-gated multimodal framework that dynamically regulates inter-modality information flow based on sample-level epistemic uncertainty estimated via Evidential Deep Learning (EDL). Each modality encoder produces a latent embedding and a scalar uncertainty score; an adaptive communication gate controls how much each module updates its representation from messages sent by the other module. A Variational Information Bottleneck (VIB) on the transcriptomic encoder further suppresses noise in the high-dimensional genomic latent space. CMRL is evaluated via 5-fold stratified cross-validation on 490 TCGA-LUAD patients with matched RNA-seq (504 features) and clinical data. It achieves a concordance index (C-index) of 0.732 ± 0.024, AUROC of 0.772 ± 0.019, and AUPRC of 0.773 ± 0.056 for 3-year survival prediction, outperforming a concatenation-fusion baseline (C-index 0.656), RNA-only (0.711), and clinical-only (0.670) variants, as well as several published LUAD survival models including CustOmics (0.625) and a whole-slide imaging method (0.675). An ablation study confirms that the uncertainty gate and evidential heads each contribute independently to the gain. Calibration analysis yields an Expected Calibration Error of 0.122, and uncertainty-stratified evaluation shows that low-uncertainty patients achieve AUROC 0.795 versus 0.681 for high-uncertainty patients, providing interpretable evidence that the gate mechanism is functioning as intended.