| Abstract: |
Multiplex immunofluorescence (mIF) is a promising tool for immunotherapy biomarker discovery in melanoma and other solid tumors. mIF captures detailed phenotypic information of immune cells in the tumor microenvironment, as well as spatial data that can reveal biologically relevant interactions among cell types. Given the complexity of mIF data, the development of automated analysis pipelines is crucial for advancing biomarker discovery. In pre-treatment melanoma samples from 50 patients treated with immune checkpoint inhibitors (ICIs), a higher stromal B cell percentage is associated with the clinical benefit of ICI therapy. The automatic detection of B cell aggregates with DBSCAN, a novel application of a computer-aided machine learning algorithm, demonstrates the potential for enhanced accuracy compared to pathologist assessment of lymphoid aggregates. TCF1+ and LAG3− T cell subpopulations are enriched near stromal B cells, suggesting potential functional interactions. These analyses provide a roadmap for the further development of spatial immunotherapy biomarkers in melanoma and other diseases. © 2025 The Author(s) |
