Viktor H Koelzer1,2, Aline Gisler1, Jonathan C Hanhart1, Johannes Griss3, Stephan N Wagner3, Niels Willi1, Gieri Cathomas1, Melanie Sachs1, Werner Kempf4, Daniela S Thommen5,6, Kirsten D Mertz1. 1. Cantonal Hospital Baselland, Institute of Pathology, Liestal, Switzerland. 2. Translational Research Unit (TRU), Institute of Pathology, University of Bern, Bern, Switzerland. 3. Division of Immunology, Allergy and Infectious Diseases (DIAID), Department of Dermatology, Medical University of Vienna, Vienna, Austria. 4. Kempf und Pfaltz Histologische Diagnostik, Research Unit, Zürich, Switzerland. 5. Cancer Immunology, Department of Biomedicine, University Hospital Basel, Basel, Switzerland. 6. Division of Molecular Oncology and Immunology, the Netherlands Cancer Institute, Amsterdam, the Netherlands.
Abstract
AIMS: 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. CONCLUSION: Digital evaluation of PD-L1 reduces scoring variability and may facilitate patient stratification in clinical practice.
AIMS: 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. CONCLUSION: Digital evaluation of PD-L1 reduces scoring variability and may facilitate patient stratification in clinical practice.
Authors: Yoshimi Endo Greer; Samuel F Gilbert; Brunilde Gril; Rajesh Narwal; Danielle L Peacock Brooks; David A Tice; Patricia S Steeg; Stanley Lipkowitz Journal: Breast Cancer Res Date: 2019-02-18 Impact factor: 6.466