Graph-Based Multi-Omics Integration Improves Subtype Recovery and Survival Prediction Over Classical Integration Strategies in TCGA-BRCA

TL;DR

This study demonstrates that graph-based multi-omics data integration enhances breast cancer subtype recovery and survival prediction compared to classical single-omic or early concatenation methods, though larger cohorts are needed for conclusive benefits.

Contribution

The paper introduces a graph-based multi-omics fusion approach that outperforms traditional integration strategies in breast cancer subtype classification and survival prediction.

Findings

SNF produces a stable two-cluster partition with high biological validity.

Multi-omics fusion outperforms single-omic baselines in subtype recovery.

Survival prediction improves with multi-omics integration, but statistical significance depends on cohort size.

Abstract

Background. Breast cancer comprises at least five molecular subtypes with distinct prognoses, yet PAM50 classification relies on transcriptomics alone. Whether integrating DNA methylation and copy number data improves subtype recovery and survival prediction over single-omic baselines remains an open question. Methods. We applied Similarity Network Fusion (SNF) to n = 644 TCGA-BRCA patients with matched RNA-seq, 450k DNA methylation, and GISTIC2 copy number profiles. Per-modality patient similarity networks were iteratively fused (K = 20, T = 20, u = 0.5) and partitioned by spectral clustering; k = 2 was pre-specified on eigengap and silhouette criteria. SNF was benchmarked against RNA-only, CNV-only, methylation-only, and early concatenation baselines using PAM50 NMI for subtype recovery and out-of-fold concordance index (OOF C-index) from a Ridge Cox model with N = 1,000 bootstrap CIs for pairwise comparisons. Results. SNF produced a stable two-cluster partition (stability ARI = 1.00, silhouette = 0.228), with NMI = 0.495 versus PAM50, exceeding RNA-only (0.428) and early concatenation (0.175). IHC receptor data confirmed cluster biology independently (ER+: 92.8% vs 15.6%; triple-negative: 1.0% vs 45.4%; both p < 10^-100). SNF achieved an OOF C-index of 0.681 (95% CI 0.610-0.760), significantly outperforming CNV-only (Delta = +0.122, CI 0.020-0.211); the advantage over RNA-only (Delta = +0.049, CI -0.036-0.144) did not exclude zero. Conclusion. Graph-based multi-omics fusion recovers breast cancer subtype biology more faithfully than feature concatenation and outperforms the weakest unimodal baselines in survival prediction. The improvement over RNA-seq alone is positive in direction but not yet statistically conclusive at this cohort size, pointing to the trade-off between integration complexity and the sample sizes needed to quantify its marginal benefit.