Header

UZH-Logo

Maintenance Infos

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


Koelzer, Viktor H; Gisler, Aline; Hanhart, Jonathan C; Griss, Johannes; Wagner, Stephan N; Willi, Niels; Cathomas, Gieri; Sachs, Melanie; Kempf, Werner; Thommen, Daniela S; Mertz, Kirsten D (2018). Digital image analysis improves precision of PD-L1 scoring in cutaneous melanoma. Histopathology, 73(3):397-406.

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.

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.

Statistics

Citations

Dimensions.ai Metrics
10 citations in Web of Science®
11 citations in Scopus®
Google Scholar™

Altmetrics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Institute of Pathology and Molecular Pathology
Dewey Decimal Classification:610 Medicine & health
Language:English
Date:2018
Deposited On:26 Sep 2019 10:11
Last Modified:29 Sep 2019 05:59
Publisher:Wiley-Blackwell Publishing, Inc.
ISSN:0309-0167
OA Status:Closed
Publisher DOI:https://doi.org/10.1111/his.13528
PubMed ID:29660160

Download

Full text not available from this repository.
View at publisher

Get full-text in a library