Highly multiplexed imaging reveals prognostic immune and stromal spatial biomarkers in breast cancer.

Abstract

Spatial profiling of tissues promises to elucidate tumor-microenvironment interactions and generate prognostic and predictive biomarkers. We analyzed single-cell, spatial data from three multiplex imaging technologies: cyclic immunofluorescence (CycIF) data we generated from 102 breast cancer patients with clinical follow-up, and publicly available imaging mass cytometry and multiplex ion-beam imaging datasets. Similar single-cell phenotyping results across imaging platforms enabled combined analysis of epithelial phenotypes to delineate prognostic subtypes among estrogen-receptor positive (ER+) patients. We utilized discovery and validation cohorts to identify biomarkers with prognostic value. Increased lymphocyte infiltration was independently associated with longer survival in triple-negative (TN) and high-proliferation ER+ breast tumors. An assessment of ten spatial analysis methods revealed robust spatial biomarkers. In ER+ disease, quiescent stromal cells close to tumor were abundant in good prognosis tumors, while tumor cell neighborhoods containing mixed fibroblast phenotypes were enriched in poor prognosis tumors. In TN disease, macrophage/tumor and B/T lymphocyte neighbors were enriched and lymphocytes were dispersed in good prognosis tumors, while tumor cell neighborhoods containing vimentin-positive fibroblasts were enriched in poor prognosis tumors. In conclusion, we generated comparable single-cell spatial proteomic data from several clinical cohorts to enable prognostic spatial biomarker identification and validation.