{"database":"bioimages","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"submitter":["Inna Averbukh"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-BIAD2708"],"repository":["bioimages"],"figure_sub":["Specimen","Funding","Study Component","Biosample","organisation","Associations","Annotation","Image acquisition"],"pubmed_authors":["Sean Bendall","Michael Angelo","Inna Averbukh","Hadeesha Piyadasa"],"additional_accession":[]},"is_claimable":false,"name":"Topological encoding of risk in ductal carcinoma in situ","description":"Over 50,000 women in the US are diagnosed each year with ductal carcinoma in situ (DCIS), a preinvasive breast lesion of which only a subset will progress to invasive cancer. Because non-progressors cannot be identified at diagnosis, most patients undergo aggressive treatment with significant side effects. Here, we developed a high-dimensional spatial proteomic panel to comprehensively characterize epithelial and myoepithelial cell states and their surrounding microenvironment in a clinically annotated DCIS cohort (n = 162). Combined with deep learning–based segmentation, we profiled over one million single cells and mapped spatial niche organization across epithelial, stromal, and immune compartments. A composite spatial confidence score integrating epithelial organization, stromal desmoplasia, collagen branching and alignment, cell composition, and cell phenotypes, and compartmentalized ductal marker expression identified a high-confidence non-progressor group (46% of cases) with a negative predictive value of 100%. These data revealed conserved protective architectures and coordinated immune–stromal-tumor niches that maintain ductal integrity, establishing a framework for DCIS risk stratification and treatment de-escalation.","dates":{"release":"2026-01-19T00:00:00Z","modification":"2026-04-29T15:31:43.206Z","creation":"2026-01-19T19:49:56.123Z"},"accession":"S-BIAD2708","cross_references":{}}