What question did this study set out to answer?

The aim is to enhance precision oncology by interpreting variants with unknown significance in breast cancer.

April 5, 2026

Abstract 6878: AI-driven structural variant annotation expands therapeutic stratification in breast cancer.

Key Points

The aim is to enhance precision oncology by interpreting variants with unknown significance in breast cancer.
Developed VAMOS, a machine learning framework integrating multi-omics data.
Analyzed over 14,000 mutations across 1,000+ breast tumors.
Used CRISPR dependency and drug response datasets for validation.
Identified 395 variant clusters associated with ESR1 and EZH2 regulatory activity.
36% of rare variants co-localized with known oncogenic hotspots.
Expanded actionable variant sets by over 30% for better patient stratification.

Abstract

Abstract Interpreting variants of unknown significance (VUS) remains a critical barrier to precision oncology, particularly in breast cancer, where the majority of somatic mutations are rare and lack functional annotation. We developed VAMOS (Variant Annotation through Multi-Omics and Structural Biology), a machine learning framework that integrates genomic, transcriptomic, and protein structural data to predict the regulatory impact of coding variants on cancer-driving pathways. Applied to 14,000 mutations across 1,000+ breast tumors, VAMOS identified 395 variant clusters in 346 proteins associated with dysregulated ESR1 and EZH2 activity, which are two key regulators of endocrine response and epigenetic reprogramming. Spatially resolved clustering revealed that 36% of rare variants co-localize with known oncogenic hotspots, enabling functional reclassification of clinically ambiguous mutations. These predictions were validated using CRISPR dependency and drug response datasets, revealing subtype-specific vulnerabilities. For example, distinct PIK3CA and TP53 clusters were differentially associated with response to mTOR, AKT, and DNA repair inhibitors. This structure-informed approach expands the set of potentially actionable variants by over 30%, offering new biomarkers for patient stratification and rational therapeutic targeting. By linking variant positions in 3D protein space to transcriptional phenotypes and drug sensitivity, VAMOS provides a scalable framework to bridge molecular profiling and clinical decision-making. These findings support the integration of AI-driven structural genomics into translational oncology pipelines to improve precision treatment strategies. Citation Format: Kriti Shukla, Yue Wang, Philip M. Spanheimer, Elizabeth Brunk. AI-driven structural variant annotation expands therapeutic stratification in breast cancer abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 6878.

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