cs.LGApr 7, 2026

MO-RiskVAE: A Multi-Omics Variational Autoencoder for Survival Risk Modeling in Multiple MyelomaMO-RiskVAE

Authors: Zixuan Chen, Heng Zhang, YuPeng Qin, WenPeng Xing, Qiang Wang, Da Wang, Changting Lin, Meng Han

Organizations: Binjiang Institute of Zhejiang University · Zhejiang University · School of Medicine, Zhejiang University

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Abstract

Multimodal variational autoencoders (VAEs) have emerged as a powerful framework for survival risk modeling in multiple myeloma by integrating heterogeneous omics and clinical data. However, when trained under survival supervision, standard latent regularization strategies often fail to preserve prognostically relevant variation, leading to unstable or overly constrained representations. Despite numerous proposed variants, it remains unclear which aspects of latent design fundamentally govern performance in this setting. In this work, we conduct a controlled investigation of latent modeling choices for multimodal survival prediction within a unified extension of the MyeVAE framework. By systematically isolating regularization scale, posterior geometry, and latent space structure under identical architectures and optimization protocols, we show that survival-driven training is primarily sensitive to the magnitude and structure of latent regularization rather than the specific divergence formulation. In particular, moderate relaxation of KL regularization consistently improves survival discrimination, while alternative divergence mechanisms such as MMD and HSIC provide limited benefit without appropriate scaling. We further demonstrate that structuring the latent space can improve alignment between learned representations and survival risk gradients. A hybrid continuous--discrete formulation based on Gumbel--Softmax enhances global risk ordering in the continuous latent subspace, even though stable discrete subtype discovery does not emerge under survival supervision. Guided by these findings, we instantiate a robust multimodal survival model, termed MO-RiskVAE, which consistently improves risk stratification over the original MyeVAE without introducing additional supervision or complex training heuristics.

Figures & tables

Figure 1: Hybrid latent space formulation with Gumbel–Softmax.
ModalityDescriptionSamplesFeatures
ClinicalAge, sex, ISS stage1,1433
RNA-seqGene expression (log 2 TPM)767High
CNV (WGS)Copy number states ( −2-2 to +2+2 )877High
SBS signatures (WES)Mutational signature activities94710
IgH translocationCytogenetic subtype labels7838
IntegratedAll modalities combined628Composite
Table 1: Summary of data modalities used in this study.
Figure 2: Effect of KL regularization weight β\beta on validation C-index.
ModelBest C-index
KL ( β=1.0\beta=1.0 )0.7138
β\beta -KL ( β=0.33\beta=0.33 )0.7419
MMD0.7121
MMD+HSIC0.7140
Table 2: Best validation C-index (single-run) under identical training settings.
Figure 3: Kaplan–Meier survival curves on the validation set (median risk split).
Figure 4: t-SNE visualization of continuous latent representations colored by predicted survival risk.
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