Clone-by-Clone Therapy Guidance in Luminal Breast Cancer via Spatial Proteomics and Patient-Derived Tumoroid

Breast cancer (BC) remains the leading cause of cancer-related death among women worldwide, with 2.26 million new cases and ∼685,000 deaths reported in 2020 (Globocan 2020). A major challenge in treating BC, particularly luminal subtypes, is the pronounced molecular heterogeneity both between patients and within individual tumours. This intratumoural diversity underlies many cases of therapy resistance and relapse. Here, we present a theranostic approach that integrates spatial proteomics and patient-derived tumoroids (PDTs) to guide personalized treatment in luminal BC. Formalin-fixed, paraffin-embedded (FFPE) tumour tissues from three patients were analyzed by MALDI mass spectrometry imaging (MSI) and spatially-resolved micro-proteomics, mapping distinct clonal subpopulations and their protein expression profiles. Proteomic pathway analysis revealed subclone-specific vulnerabilities that are not apparent from standard histopathology. We exploited these insights to design alternative combination therapies tailored to each tumour's molecular makeup. The efficacy of proteomics-guided regimens was then evaluated in vitro using PDTs established from the same patients, in direct comparison to conventional chemotherapy. Proteomic-informed treatments demonstrated significantly enhanced anti-tumour activity in the PDT models, yielding lower IC₅₀ values and greater cell death than standard treatments. In several cases, PDTs exhibited resistance to conventional therapy, which was explained by the presence of proteomic resistance markers (such as EDIL3, CA12, PGK1, and CapG) in the corresponding tumour clones. These results underscore the potential of spatial proteomic profiling to expose actionable intratumoural heterogeneity and to inform more effective, clone-specific therapies. Our study highlights an innovative pipeline for luminal BC therapy guidance, combining MALDI MSI and PDT drug testing, and advocates for the integration of spatial proteomics into precision oncology to improve treatment outcomes.