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. 2021 Sep 21:11:741993.
doi: 10.3389/fonc.2021.741993. eCollection 2021.

Digital Quantification of Tumor PD-L1 Predicts Outcome of PD-1-Based Immune Checkpoint Therapy in Metastatic Melanoma

Jan-Malte Placke  1 , Camille Soun  2 , Jenny Bottek  2 , Rudolf Herbst  3 , Patrick Terheyden  4 , Jochen Utikal  5   6 , Claudia Pföhler  7 , Jens Ulrich  8 , Alexander Kreuter  9 , Christiane Pfeiffer  10 , Peter Mohr  11 , Ralf Gutzmer  12 , Friedegund Meier  6   13 , Edgar Dippel  14 , Michael Weichenthal  15 , Lisa Zimmer  1 , Elisabeth Livingstone  1 , Jürgen C Becker  1   6   16 , Georg Lodde  1 , Antje Sucker  1 , Klaus Griewank  1 , Susanne Horn  1 , Eva Hadaschik  1 , Alexander Roesch  1   6 , Dirk Schadendorf  1   6 , Daniel Robert Engel  2 , Selma Ugurel  1
Affiliations

Digital Quantification of Tumor PD-L1 Predicts Outcome of PD-1-Based Immune Checkpoint Therapy in Metastatic Melanoma

Jan-Malte Placke et al. Front Oncol. .

Abstract

Background: PD-1-based immune checkpoint blockade (ICB) is a highly effective therapy in metastatic melanoma. However, 40-60% of patients are primarily resistant, with valid predictive biomarkers currently missing. This study investigated the digitally quantified tumor PD-L1 expression for ICB therapy outcome prediction.

Patients and methods: Tumor tissues taken prior to PD-1-based ICB for unresectable metastatic disease were collected within the prospective multicenter Tissue Registry in Melanoma (TRIM). PD-L1 expression (clone 28-8; cut-off=5%) was determined by digital and physician quantification, and correlated with therapy outcome (best overall response, BOR; progression-free survival, PFS; overall survival, OS).

Results: Tissue samples from 156 patients were analyzed (anti-PD-1, n=115; anti-CTLA-4+anti-PD-1, n=41). Patients with PD-L1-positive tumors showed an improved response compared to patients with PD-L1-negative tumors, by digital (BOR 50.5% versus 32.2%; p=0.026) and physician (BOR 54.2% versus 36.6%; p=0.032) quantification. Tumor PD-L1 positivity was associated with a prolonged PFS and OS by either digital (PFS, 9.9 versus 4.6 months, p=0.021; OS, not reached versus 13.0 months, p=0.001) or physician (PFS, 10.6 versus 5.6 months, p=0.051; OS, not reached versus 15.6 months, p=0.011) quantification. Multivariable Cox regression revealed digital (PFS, HR=0.57, p=0.007; OS, HR=0.44, p=0.001) and physician (OS, HR=0.54, p=0.016) PD-L1 quantification as independent predictors of survival upon PD-1-based ICB. The combination of both methods identified a patient subgroup with particularly favorable therapy outcome (PFS, HR=0.53, p=0.011; OS, HR=0.47, p=0.008).

Conclusion: Pre-treatment tumor PD-L1 positivity predicted a favorable outcome of PD-1-based ICB in melanoma. Herein, digital quantification was not inferior to physician quantification, and should be further validated for clinical use.

Keywords: PD-L1 quantification; immune checkpoint blockade therapy; melanoma; response; survival.

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Conflict of interest statement

J-MP served as consultant and/or has received honoraria from Bristol-Myers Squibb, Novartis and received travel support from Bristol-Myers Squibb, Novartis and Therakos. PT declares Invited Speaker´s honoraria from Bristol-Myers Squibb, Novartis, MSD, Pierre-Fabre, CureVac, Roche, Kyowa Kirin, Biofrontera, Advisory Board honoraria from Bristol-Myers Squibb, Novartis, Pierre-Fabre, Merck Serono, Sanofi, Roche, Kyowa Kirin, and Travel support from Bristol-Myers Squibb, and Pierre-Fabre. JoU is on the advisory board or has received honoraria and travel support from Amgen, Bristol Myers Squibb, GSK, LeoPharma, Merck Sharp and Dohme, Novartis, Pierre Fabre, Roche, Sanofi outside the submitted work. ClP received honoraria (speaker honoraria or honoraria as a consultant) and travel support from: Novartis, BMS, Roche, Merck Serono, MSD, Celgene, AbbVie, AMGEN, SUNPHARMA, Allergy Therapeutics and LEO. LZ served as consultant and/or has received honoraria from Roche, Bristol-Myers Squibb, Merck Sharp & Dohme, Novartis, Pierre-Fabre, and Sanofi; Research funding to institution: Novartis; travel support from Merck Sharp & Dohme, Bristol-Myers Squibb, Amgen, Pierre-Fabre, and Novartis, outside the submitted work. EL served as consultant and/or has received honoraria from Amgen, Actelion, Roche, Bris-tol-Myers Squibb, Merck Sharp & Dohme, Novartis, Janssen, Medac, Sanofi, Sunpharma and travel support from Amgen, Merck Sharp & Dohme, Bristol-Myers Squibb, Amgen, Pierre Fabre, Sunpharma and Novartis, outside the submitted work. JB is receiving speaker’s bureau honoraria from Amgen, Pfizer, MerckSerono, Recordati and Sanofi, is a paid consultant/advisory board member/DSMB member for 4SC, Almirall, Boehringer Ingelheim, ICON, InProTher, MerckSerono, Pfizer, and Sanofi/Regeneron. His group receives research grants from Merck Serono, HTG, IQVIA, and Alcedis. GL has received travel support from Sun Pharma. AR reported grants from Novartis, Bristol Myers Squibb, and Adtec; personal fees from Merck Sharp & Dohme; and nonfinancial support from Amgen, Roche, Merck Sharp & Dohme, Novartis, Bristol Myers Squibb, and Teva. DS received grants and other support from Bristol-Myers Squibb, personal fees from Bristol-Myers Squibb during the conduct of the study; personal fees from Amgen; personal fees from Boehringer Ingelheim; personal fees from InFlarX; personal fees and other support from Roche; grants, personal fees and other support from Novartis; personal fees from Incyte; personal fees and other support from Regeneron; personal fees from 4SC; personal fees from Sanofi; personal fees from Neracare; personal fees from Pierre-Fabre; personal fees and other support from Merck-EMD; personal fees from Pfizer; personal fees and other support from Philiogen; personal fees from Array, personal fees and other support from MSD Sharp & Dohme, outside the submitted work. SU declares research support from Bristol Myers Squibb and Merck Serono; speakers and advisory board honoraria from Bristol Myers Squibb, Merck Sharp & Dohme, Merck Serono, Novartis and Roche, and travel support from Bristol Myers Squibb, and Merck Sharp & Dohme. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The authors declare that this study received funding from Bristol Myers Squibb. The funder had the following involvement with the study: Financing of test material.

Figures

Figure 1
Figure 1
Exemplary presentation of the functioning of the digital algorithm on the basis of a sample from the patient group. Digital quantification of PD-L1 expression demonstrated on representative tissue slides from a subcutaneous melanoma metastasis. (A, B) Manual selection of the tumor regions of interest on an anti-PD-L1-stained slide and a consecutive negative control IgG-stained slide. (C, D) Binary masks of (A, B).
Figure 2
Figure 2
Study flow. Schematic presentation of the study flow. P-values <0.05 are in bold.
Figure 3
Figure 3
Comparison of PD-L1 calculation by physician and digital algorithm. (A) Distribution of PD-L1 quantification in tumor tissue specimen of n=156 melanoma patients by the physician and the digital algorithm. (B) Correlation of PD-L1 quantification by the physician (x axis) versus the digital algorithm (y axis) in n=156 patients (Pearson’s correlation; r = 0.39; p < 0.001).
Figure 4
Figure 4
Survival analysis based on PD-L1 expression analysis by physician or digital algorithm. Kaplan-Meier curves showing the probability of progression-free (A, C) and overall (B, D) survival of n=156 melanoma patients upon treatment with PD-1-based immune checkpoint blockade by tumor PD-L1 expression. Tumor PD-L1 expression was assessed by physician’s quantification (A, B) and digital quantification (C, D), respectively. Censored observations are indicated by vertical bars; P values were calculated using the log-rank test.
Figure 5
Figure 5
Therapy response and survival analysis based on PD-L1 expression analysis by physician and digital algorithm. Best overall response, BOR (A) and survival (B, C) of n=156 melanoma patients upon PD-1-based immune checkpoint inhibition by tumor PD-L1 expression. Tumor PD-L1 expression is presented as a combination of physician and digital quantification. (A) BOR is highest in patients with tumor PD-L1 positivity by both physician and digital quantification (CR/PR=60.4%; right), compared to patients with tumor PD-L1 positivity by only one of both quantification methods (CR/PR=37.5%; center), and patients whose tumors are classified as PD-L1 negative by both physician and digital quantification (CR/PR=33.4%; left); Chi-square test P = 0.015. (B, C) Progression-free (B) and overall survival (C) by tumor PD-L1 expression combined of physician and digital quantification. P values were calculated using the log-rank test.
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