Abstract
AbstractCD8+ 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.
Highlights
Melanoma is one of the prime examples for the success of immune checkpoint inhibition in the clinic
Digital pathology For digital immune phenotyping, we developed an end-to-end image analysis pipeline in the HALOAI platform consisting of (1) expert pathologist review and annotation of tumor center and invasive border according to the recommendations of the International Immuno-Oncology Biomarker Working Group, followed by automated (2) deep learning-based tissue classification with (3) cell segmentation, and (4) spatially resolved detection and scoring of CD8+ T-cell infiltrates as visualized by immunohistochemistry stains to achieve highly accurate and automated differentiation of immune cell infiltrates in each case
The focus on CD8+ T cells is in line with the notion that CD8+ T cells represent currently one of the most actionable targets of immune checkpoint inhibitors as observed in antiPD1 treated metastatic melanoma patients[21]
Summary
Melanoma is one of the prime examples for the success of immune checkpoint inhibition in the clinic. Three distinct distribution patterns of CD8+ T cells were identified: (1) high densities of intraepithelial CD8+ T cells corresponding to inflamed tumors associated with a favorable response to immune checkpoint inhibition; (2) a poor infiltrate corresponding to non-inflamed/desert tumors, or (3) high densities of CD8+ T cells at the tumor margin without tumor infiltration, referred to as immune excluded[6,10]. Both noninflamed/desert and excluded immune phenotypes were strongly correlated with non-response to immune checkpoint inhibitors in the clinic. Neither for melanoma nor for any other entity are tumorinfiltrating CD8+ T cells currently assessed in routine diagnostic practice despite their essential role to predict immunotherapy success
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