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The growing scale and dimensionality of multiplexed imaging require reproducible and comprehensive yet user-friendly computational pipelines. TRACERx-PHLEX performs deep learning-based cell segmentation (deep-imcyto), automated cell-type annotation (TYPEx) and interpretable spatial analysis (Spatial-PHLEX) as three independent but interoperable modules. PHLEX generates single-cell identities, cell densities within tissue compartments, marker positivity calls and spatial metrics such as cellular barrier scores, along with summary graphs and spatial visualisations. PHLEX was developed using imaging mass cytometry (IMC) in the TRACERx study, validated using published Co-detection by indexing (CODEX), IMC and orthogonal data and benchmarked against state-of-the-art approaches. We evaluated its use on different tissue types, tissue fixation conditions, image sizes and antibody panels. As PHLEX is an automated and containerised Nextflow pipeline, manual assessment, programming skills or pathology expertise are not essential. PHLEX offers an end-to-end solution in a growing field of highly multiplexed data and provides clinically relevant insights. Imaging dozens of proteins in situ and at large scales increases the complexity of data analysis. Here, the authors develop an end-to-end solution for multiplexed image analysis that facilitates data interpretation for clinically relevant insights. [ABSTRACT FROM AUTHOR]

The article presents a study on a therapeutic KRASG12C (glycine 12 to cysteine) inhibitor that drives effective interferon-mediated antitumor immunity in immunogenic lung cancers. It discusses that the in vivo KRASG12C inhibitor increases the T cell infiltration and activation. It also describes the mechanism of the KRASG12C inhibition reverses immunosuppression in several preclinical lung cancer models with varying levels of immunogenicity.