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Digital image analysis improves precision of PD‐L1 scoring in cutaneous melanoma

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Digital image analysis improves precision of PD‐L1 scoring in cutaneous melanoma



Immune checkpoint inhibitors have become a successful treatment in metastatic melanoma. The high response rates in a subset of patients suggest that a sensitive companion diagnostic test is required. The predictive value of programmed death ligand 1 (PD‐L1) staining in melanoma has been questioned due to inconsistent correlation with clinical outcome. Whether this is due to predictive irrelevance of PD‐L1 expression or inaccurate assessment techniques remains unclear. The aim of this study was to develop a standardised digital protocol for the assessment of PD‐L1 staining in melanoma and to compare the output data and reproducibility to conventional assessment by expert pathologists.


Methods and results
In two cohorts with a total of 69 cutaneous melanomas, a highly significant correlation was found between pathologist‐based consensus reading and automated PD‐L1 analysis (r = 0.97, P < 0.0001). Digital scoring captured the full diagnostic spectrum of PD‐L1 expression at single cell resolution. An average of 150 472 melanoma cells (median 38 668 cells; range = 733–1 078 965) were scored per lesion. Machine learning was used to control for heterogeneity introduced by PD‐L1‐positive inflammatory cells in the tumour microenvironment. The PD‐L1 image analysis protocol showed excellent reproducibility (r = 1.0, P < 0.0001) when carried out on independent workstations and reduced variability in PD‐L1 scoring of human observers. When melanomas were grouped by PD‐L1 expression status, we found a clear correlation of PD‐L1 positivity with CD8‐positive T cell infiltration, but not with tumour stage, metastasis or driver mutation status.


Digital evaluation of PD‐L1 reduces scoring variability and may facilitate patient stratification in clinical practice.


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Viktor H. Koelzer, Aline Gisler, Jonathan C. Hanhart, Johannes Griss, Stephan N. Wagner, Niels Willi, Gieri Cathomas, Melanie Sachs, Werner Kempf, Daniela S. Thommen, Kirsten D. Mertz


Nature Medicine | First published April 16th, 2018 | DOI