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Background: Tumour immunoprofiling captures tumour-microenvironment interactions and enables precision medicine. Multiplex immunofluorescence (mIF) imaging can provide comprehensive quantitative and spatial information for multiple immune markers. However, application at scale to clinical trial samples sourced from multiple institutions is challenging due to pre-analytical heterogeneity. Methods: We analysed 12'592 tissue microarray (TMA) spots from 3'545 colorectal cancers (CRC) sourced from more than 240 institutions in two clinical trials (QUASAR 2 and SCOT) stained for CD4, CD8, CD20, CD68, FoxP3, pan-cytokeratin and DAPI by mIF. TMA cores were multi-spectrally imaged by digital pathology and analysed by cell-based and pixel-based marker analysis. We developed and validated an adaptive thresholding method to account for inter- and intra-slide intensity variation in TMA analysis, compared the numerical results between analysis approaches and validated against the current gold standard of single-plex chromogenic immunohistochemistry (IHC). Results: Adaptive thresholding at a slide and spot level effectively ameliorated inter- and intra-slide intensity variation enabling high-throughput analysis of mIF-stained TMA cores by digital pathology and improving the image analysis results compared to methods using a single global threshold. Comparison of our mIF approach with CD8 IHC data derived from subsequent sections indicates the validity of our method (Spearman's rank correlation coefficients (SCC) between 0.63 and 0.66, p-value << 0.01). Evaluation of correlation between cell-based and pixel-based analysis results confirms equivalency (SCC > 0.8, p<< 0.01, except for CD20 in epithelium region) of both analytical approaches for precision immunoprofiling by mIF. Conclusions: This study reports an analytical approach to the largest multiparameter immunoprofiling study of clinical trial samples to date and establishes an adaptive thresholding method to account for inter- and intra-slide variation introduced by pre-analytical heterogeneity. This approach can enable the application of mIF immunoprofiling of clinical trial TMA datasets at scale.

CD8+ tumor-infiltrating T cells can be regarded as one of the most relevant predictive biomarkers in immune-oncology. Highly infiltrated tumors, referred to as inflamed (clinically “hot”), show the most favorable response to immune checkpoint inhibitors in contrast to tumors with a scarce immune infiltrate called immune desert or excluded (clinically “cold”). Nevertheless, quantitative and reproducible methods examining their prevalence within tumors are lacking. We therefore established a computational diagnostic algorithm to quantitatively measure spatial densities of tumor-infiltrating CD8+ T cells by digital pathology within the three known tumor compartments as recommended by the International Immuno-Oncology Biomarker Working Group in 116 prospective metastatic melanomas of the Swiss Tumor Profiler cohort. Workflow robustness was confirmed in 33 samples of an independent retrospective validation cohort. The introduction of the intratumoral tumor center compartment proved to be most relevant for establishing an immune diagnosis in metastatic disease, independent of metastatic site. Cut-off values for reproducible classification were defined and successfully assigned densities into the respective immune diagnostic category in the validation cohort with high sensitivity, specificity, and precision. We provide a robust diagnostic algorithm based on intratumoral and stromal CD8+ T-cell densities in the tumor center compartment that translates spatial densities of tumor-infiltrating CD8+ T cells into the clinically relevant immune diagnostic categories “inflamed”, “excluded”, and “desert”. The consideration of the intratumoral tumor center compartment allows immune phenotyping in the clinically highly relevant setting of metastatic lesions, even if the invasive margin compartment is not captured in biopsy material., The authors present a robust diagnostic algorithm based on digital pathology and image analysis that quantifies intratumoral and stromal CD8+ T-cell densities in the tumor center and invasive margin compartment in metastatic melanoma. Spatial CD8+ T-cell densities are translated into the clinically relevant immune diagnostic categories “inflamed”, “excluded”, and “desert”. Their approach also allows efficient immune phenotyping of metastatic lesions, on biopsy material or even in the absence of material from the invasive margin.

Future Medicine: Digital Pathology