Abstract A031: Unsupervised graph-based visualization of variational autoencoder latent spaces reveals hidden multiple myeloma subtypes

Abstract Latent space representations learned through variational autoencoders (VAEs) offer a powerful, unsupervised means of capturing nonlinear structure in high-dimensional oncology data. The latent embedding spaces often encode information that differs from traditional bioinformatics methods such as t-SNE or UMAP. However, a persistent challenge remains: how to meaningfully visualize and interpret these latent variables. Common dimensionality reduction techniques like UMAP and t-SNE, while effective, can obscure graph-theoretic relationships that may underlie important biological patterns. We present a novel approach for intuitive latent space interpretation using NetFlow, a method that visualizes the organizational structure of samples as a graph derived from their latent embeddings. NetFlow constructs a topological representation based on the metric structure of the latent space, drawing on concepts from network analysis, optimal mass transport, topological data analysis, and lineage tracing. The result is an interpretable graph in which nodes represent individual subjects and edges reflect local and global similarity among the samples. We applied this method to multiple myeloma (MM), a hematologic malignancy marked by malignant plasma cell proliferation and inevitable relapse. To uncover hidden disease subtypes, we trained a VAE on multimodal data from 659 patients in the MMRF CoMMpass dataset (IA19), integrating transcriptomic, genomic, and clinical features. Direct clustering of latent space vectors failed to yield subgroups with significant differences in progression-free survival (PFS). In contrast, NetFlow generated a latent space graph that, when clustered using Louvain community detection, identified three distinct subtypes: one high-risk and two low-risk groups. The high-risk group exhibited a median PFS of 1.5 years shorter than the low-risk groups (p<0.001) and was enriched for known poor prognostic markers including gain 1q21 (59%), MAF translocations (17%), and t(4;14) (66%). Although the two low-risk groups had similar PFS outcomes, they differed in their molecular profiles, suggesting they may benefit from different therapeutic strategies. These preliminary results demonstrate that variational autoencoders and NetFlow graph analysis can reveal latent substructures missed by traditional clustering, thereby advancing latent space explainability and enabling improved subtype discovery in MM. Our framework offers a generalizable pipeline for interpreting deep generative models in cancer genomics. Citation Format: Anish K. Simhal, Rena Elkin, Ross S. Firestone, Jung Hun Oh, Joseph O. Deasy. Unsupervised graph-based visualization of variational autoencoder latent spaces reveals hidden multiple myeloma subtypes [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr A031.

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