Frontiers in bioinformatics · 2026
STRIVE: interpretable multimodal subtype discovery in Women's cancers using an attention-weighted joint variational autoencoder.
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Zhao Yamei, Chen Chao, Xu Xiaoming, Ge Xiaoxu, Ding Kefeng, Zheng Shu, Wang Jian, Sun Lifeng
Аннотация
BACKGROUND: Breast, endometrial, and cervical cancers share a common challenge: molecular heterogeneity that single-modality profiling cannot fully resolve. Gene expression and DNA methylation capture distinct and partially non-overlapping regulatory programs, yet most subtyping frameworks treat them independently or merge them without accounting for modality-specific noise and scale.
OBJECTIVE: To develop an interpretable multimodal framework that integrates transcriptomic and epigenomic signals for principled molecular subtype discovery and molecular patient stratification with exploratory assessment of outcome associations across women's cancers.
METHODS: We developed STRIVE (Stratification via Integrated Variational Embeddings), an attention-based joint variational autoencoder that learns modality-aware embeddings, fuses them into a shared latent space, and supports post hoc feature attribution. Subtypes were defined by K-means clustering on latent embeddings, cluster-defining signatures were derived using standardized mean differences (q < 0.05), and within-subtype survival prediction was assessed via permutation feature importance across multiple classifiers.
RESULTS: In TCGA-BRCA (n = 866), STRIVE identified six molecular subtypes with coherent multimodal signatures, with DNA-methylation features prominently represented in specific cluster signatures. Within-subtype survival-status prediction showed variable performance across classifiers and subtypes. Replication in TCGA-UCEC (n = 454) and TCGA-CESC (n = 296) recapitulated key signature patterns, with DNA-methylation features prominently represented in specific cluster signatures and more consistent predictive performance in UCEC than CESC.
CONCLUSION: STRIVE demonstrates that attention-weighted multimodal integration surfaces molecularly coherent subtypes in which cluster-defining signatures frequently included epigenomic features, a structure that single-modality approaches would obscure. These findings position STRIVE as a framework for multimodal molecular stratification and future evaluation of subtype-specific clinical associations.
OBJECTIVE: To develop an interpretable multimodal framework that integrates transcriptomic and epigenomic signals for principled molecular subtype discovery and molecular patient stratification with exploratory assessment of outcome associations across women's cancers.
METHODS: We developed STRIVE (Stratification via Integrated Variational Embeddings), an attention-based joint variational autoencoder that learns modality-aware embeddings, fuses them into a shared latent space, and supports post hoc feature attribution. Subtypes were defined by K-means clustering on latent embeddings, cluster-defining signatures were derived using standardized mean differences (q < 0.05), and within-subtype survival prediction was assessed via permutation feature importance across multiple classifiers.
RESULTS: In TCGA-BRCA (n = 866), STRIVE identified six molecular subtypes with coherent multimodal signatures, with DNA-methylation features prominently represented in specific cluster signatures. Within-subtype survival-status prediction showed variable performance across classifiers and subtypes. Replication in TCGA-UCEC (n = 454) and TCGA-CESC (n = 296) recapitulated key signature patterns, with DNA-methylation features prominently represented in specific cluster signatures and more consistent predictive performance in UCEC than CESC.
CONCLUSION: STRIVE demonstrates that attention-weighted multimodal integration surfaces molecularly coherent subtypes in which cluster-defining signatures frequently included epigenomic features, a structure that single-modality approaches would obscure. These findings position STRIVE as a framework for multimodal molecular stratification and future evaluation of subtype-specific clinical associations.