Yu-An Chen

Oxidative stress response (OSR) is suggested as a therapeutic target in triple negative breast cancer (TNBC). However, the cell type specificity and spatial distribution of OSR genes and proteins remain poorly defined. We re-analyzed public datasets, demostrating high OSR gene expression in TNBC tumor cells. We generated a multiplex immunofluorescence dataset of 345 TNBC samples that showed OSR proteins GCLC and NQO1 are differentially expressed across tumor, immune, and stromal cells, and elevated in inflamed regions. We identified a mesenchymal-like tumor cell state characterized by co-expression of GCLC and vimentin (GCLC⁺VIM⁺), with increased proliferation and reduced anastasis-associated signatures, suggesting a vulnerability to chemotherapy. Consistently, TNBC cell lines with higher GCLC expression showed increased sensitivity to cisplatin. Moreover, patients with higher proportions of GCLC⁺VIM⁺ tumor cells had longer overall survival. Our findings indicate that OSR proteins are expressed in spatially and phenotypically distinct tumor microenvironments, and that the GCLC⁺VIM⁺ tumor cell state is associated with favorable outcomes in TNBC.

Supplementary Figures provide additional evidence and validation of copy number changes from the ORION-FISH assay.

Table S1 provides the details of antibodies (ORION) and FISH probes used in this manuscript.

Table S2 provides the cohort description.

Detecting chromosomal copy-number alterations together with protein-defined cell states in intact tissue is critical for understanding early clonal evolution and microenvironmental interactions in cancer. We developed ORION-FISH, which integrates high-plex tissue imaging with a morphology-preserving DNA fluorescence in situ hybridization (DNA-FISH) workflow and single-cell registration, yielding measurements concordant with clinical FISH. In high-grade serous ovarian carcinoma (HGSOC), ORION-FISH recapitulated known chromosomal changes while revealing subclonal heterogeneity missed by targeted sequencing. Applied to serous tubal intraepithelial carcinomas, precursors of HGSOC, ORION-FISH identified intermixed epithelial cells with MYC or CCNE1 copy-number gains, as well as concurrent alterations associated with distinct immune microenvironments. In addition, epithelial cells with MYC and CCNE1 copy-number gains were detected in morphologically normal fallopian tube epithelium, along with rare MDM4 increases across epithelial lineages. Together, ORION-FISH provides a framework linking chromosomal copy-number states to protein-defined phenotypes within preserved tissue architecture, enabling context-aware interrogation of early copy-number diversification at single-cell resolution. Significance:We introduce ORION-FISH, a spatially resolved workflow integrating multiplexed protein imaging with DNA-FISH to map genomic alterations within intact tissues. Applying this approach to ovarian cancer precursors reveals early copy-number diversification and associations with the local immune context, providing a foundation for studying how genomic and microenvironmental states coevolve during tumor initiation.

Table S4 provides the statistics summary.

Table S3 provides the FISH scores by BioView for HGSOC cases.

Detecting chromosomal copy-number alterations together with protein-defined cell states in intact tissue is critical for understanding early clonal evolution and microenvironmental interactions in cancer. We developed ORION-FISH, which integrates high-plex tissue imaging with a morphology-preserving DNA-FISH workflow and single-cell registration, yielding measurements concordant with clinical FISH. In High Grade Serous Ovarian Carcinoma (HGSOC), ORION-FISH recapitulated known chromosomal changes while revealing subclonal heterogeneity missed by targeted sequencing. Applied to serous tubal intraepithelial carcinomas (STICs), precursors of HGSOC, ORION-FISH identified intermixed epithelial cells with MYC or CCNE1 copy-number gains, as well as concurrent alterations associated with distinct immune microenvironments. In addition, epithelial cells with MYC and CCNE1 copy-number gains were detected in morphologically normal fallopian tube epithelium, along with rare MDM4 increases across epithelial lineages. Together, ORION-FISH provides a framework linking chromosomal copy number states to protein-defined phenotypes within preserved tissue architecture, enabling context-aware interrogation of early copy-number diversification at single-cell resolution.

Inhibiting oxidative stress response (OSR) proteins has been suggested as a therapeutic strategy in triple negative breast cancer (TNBC). However, the cell type specificity and spatial distribution of OSR genes and proteins, such as GCLC and NQO1, is unknown. Using single cell and spatial transcriptomics datasets we found that OSR genes were highly expressed in TNBC tumor cells, which localized in spatial clusters. Multiplex immunofluorescence imaging of 345 TNBC samples from 186 patients demonstrated that OSR proteins GCLC and NQO1 exhibit distinct expression profiles across tumor, immune, and stromal cell populations and are elevated in inflamed histological regions. Tumor cell OSR protein expression was associated with the composition of the adjacent cellular neighborhood. Furthermore, we identified GCLC and vimentin positive (GCLC+VIM+) mesenchymal-like tumor cells, residing near immune cells and exhibiting increased proliferation and decreased anastasis signatures, suggesting sensitivity to chemotherapy. Across a panel of thirteen TNBC cell lines, GCLC expression was positively correlated with sensitivity to cisplatin. Cox regression analysis revealed that patients with higher proportions of GCLC+VIM+ tumor cells had a longer overall survival. Collectively, our results demonstrate that individual OSR proteins are expressed in distinct microenvironments and tumor cell states, potentially contributing to patient outcomes.

Spatial proteomics (highly multiplexed tissue imaging) provides unprecedented insight into the types, states, and spatial organization of cells within preserved tissue environments. To enable single-cell analysis, high-plex images are typically segmented using algorithms that assign marker signals to individual cells. However, conventional segmentation is often imprecise and susceptible to signal spillover between adjacent cells, interfering with accurate cell type identification. Segmentation-based methods also fail to capture the morphological detail that histopathologists rely on for disease diagnosis and staging. Here, we present a method that combines unsupervised, pixel-level machine learning using autoencoders with traditional segmentation to generate single-cell data that captures information on protein abundance, morphology, and local neighborhood in a manner analogous to human experts while overcoming signal spillover. We demonstrate the generality of this approach by applying it to CyCIF, Lunaphore COMET, and CODEX data, and show that it can learn histological features across multiple spatial scales.