The spatially informed mFISHseq assay resolves biomarker discordance and predicts treatment response in breast cancer

Background: Breast cancer (BCa) is a heterogeneous disease requiring precise diagnostics to guide effective treatment. Current assays fail to adequately address the complex biology of BCa subtypes/risk groups and accurately predict responses to treatments like antibody-drug conjugates (ADCs). To address these limitations, we developed and validated a novel diagnostic, prognostic, and predictive tool, mFISHseq. Methods: Our approach, mFISHseq, integrates multiplexed RNA fluorescent in situ hybridization with RNA-sequencing, guided by laser capture microdissection. This technique ensures tumor purity, allows unbiased profiling of whole transcriptome data, and explicitly quantifies intratumoral heterogeneity. Results: In a retrospective cohort study involving 1,082 FFPE breast tumors, mFISHseq demonstrated high analytical validity with 93% accuracy compared to immunohistochemistry across training and test sets. Our consensus subtyping approach provided near-perfect concordance with other molecular classifiers (κ > 0.85) and reclassified 30% of samples into subtypes with distinct prognostic implications. Consensus risk groups mitigated misclassification of single samples and provided prognostic information about both early and late relapse. High risk patients had enriched innate and adaptive immune signatures, which predicted response to neoadjuvant immunotherapy. Furthermore, we identified patients responsive to ADCs, as evidenced by a 19-feature classifier for T-DM1 sensitivity, validated on the multicenter, phase II, prospective I-SPY2 trial. To demonstrate the clinical potential, we deployed mFISHseq as a research use only test on 48 patients, revealing insights into the efficacy of novel targeted therapies, such as CDK4/6 inhibitors, immune checkpoint inhibitors, and ADCs. Conclusion: The mFISHseq method solves a long-standing challenge in the precise diagnosis and classification of BCa subtypes/prognostic risk groups, and allows accurate response prediction for patients, including those treated with immunotherapies and ADCs.