Quantitative Mass Spectrometry Analysis of PD-L1 Protein Expression, N-glycosylation and Expression Stoichiometry with PD-1 and PD-L2 in Human Melanoma*

Quantitative assessment of key proteins that control the tumor-immune interface is one of the most formidable analytical challenges in immunotherapeutics. We developed a targeted MS platform to quantify programmed cell death-1 (PD-1), programmed cell death 1 ligand 1 (PD-L1), and programmed cell death 1 ligand 2 (PD-L2) at fmol/microgram protein levels in formalin fixed, paraffin-embedded sections from 22 human melanomas. PD-L1 abundance ranged 50-fold, from ∼0.03 to 1.5 fmol/microgram protein and the parallel reaction monitoring (PRM) data were largely concordant with total PD-L1-positive cell content, as analyzed by immunohistochemistry (IHC) with the E1L3N antibody. PD-1 was measured at levels up to 20-fold lower than PD-L1, but the abundances were not significantly correlated (r2 = 0.062, p = 0.264). PD-1 abundance was weakly correlated (r2 = 0.3057, p = 0.009) with the fraction of lymphocytes and histiocytes in sections. PD-L2 was measured from 0.03 to 1.90 fmol/microgram protein and the ratio of PD-L2 to PD-L1 abundance ranged from 0.03 to 2.58. In 10 samples, PD-L2 was present at more than half the level of PD-L1, which suggests that PD-L2, a higher affinity PD-1 ligand, is sufficiently abundant to contribute to T-cell downregulation. We also identified five branched mannose and N-acetylglucosamine glycans at PD-L1 position N192 in all 22 samples. Extent of PD-L1 glycan modification varied by ∼10-fold and the melanoma with the highest PD-L1 protein abundance and most abundant glycan modification yielded a very low PD-L1 IHC estimate, thus suggesting that N-glycosylation may affect IHC measurement and PD-L1 function. Additional PRM analyses quantified immune checkpoint/co-regulator proteins LAG3, IDO1, TIM-3, VISTA, and CD40, which all displayed distinct expression independent of PD-1, PD-L1, and PD-L2. Targeted MS can provide a next-generation analysis platform to advance cancer immuno-therapeutic research and diagnostics.

[1]  Ruchi Verma,et al.  A Support Vector Machine based method to distinguish proteobacterial proteins from eukaryotic plant proteins , 2012, BMC Bioinformatics.

[2]  P. Robbins,et al.  Separation of Glycopeptides by High Performance Liquid Chromatography , 1982, Journal of cellular biochemistry.

[3]  Yu Shyr,et al.  Targeted Next Generation Sequencing Identifies Markers of Response to PD-1 Blockade , 2016, Cancer Immunology Research.

[4]  I. Ganly,et al.  Recurrent squamous-cell carcinoma of the head and neck: overview of current therapy and future prospects. , 2000, Annals of oncology : official journal of the European Society for Medical Oncology.

[5]  G. Gao,et al.  Structural basis of anti-PD-L1 monoclonal antibody avelumab for tumor therapy , 2016, Cell Research.

[6]  Michael J MacCoss,et al.  Large-Scale Interlaboratory Study to Develop, Analytically Validate and Apply Highly Multiplexed, Quantitative Peptide Assays to Measure Cancer-Relevant Proteins in Plasma* , 2015, Molecular & Cellular Proteomics.

[7]  Jamey D. Young,et al.  Oncogenic KRAS and BRAF Drive Metabolic Reprogramming in Colorectal Cancer * , 2016, Molecular & Cellular Proteomics.

[8]  P. Massion,et al.  Methods for Peptide and Protein Quantitation by Liquid Chromatography-Multiple Reaction Monitoring Mass Spectrometry* , 2011, Molecular & Cellular Proteomics.

[9]  R. Noelle,et al.  VISTA Regulates the Development of Protective Antitumor Immunity. , 2014, Cancer research.

[10]  Xinning Jiang,et al.  Glycoproteomics analysis of human liver tissue by combination of multiple enzyme digestion and hydrazide chemistry. , 2009, Journal of proteome research.

[11]  A. Anderson Tim-3: An Emerging Target in the Cancer Immunotherapy Landscape , 2014, Cancer Immunology Research.

[12]  Hoguen Kim,et al.  Abundance-ratio-based semiquantitative analysis of site-specific N-linked glycopeptides present in the plasma of hepatocellular carcinoma patients. , 2014, Journal of proteome research.

[13]  L. Crinò,et al.  Nivolumab versus Docetaxel in Advanced Squamous-Cell Non-Small-Cell Lung Cancer. , 2015, The New England journal of medicine.

[14]  P. Sharma,et al.  Immune Checkpoint Targeting in Cancer Therapy: Toward Combination Strategies with Curative Potential , 2015, Cell.

[15]  D. Liebler,et al.  Assembly Dynamics and Stoichiometry of the Apoptosis Signal-regulating Kinase (ASK) Signalosome in Response to Electrophile Stress* , 2016, Molecular & Cellular Proteomics.

[16]  D. Fearon,et al.  T cell exclusion, immune privilege, and the tumor microenvironment , 2015, Science.

[17]  L. Chin,et al.  Analysis of Immune Signatures in Longitudinal Tumor Samples Yields Insight into Biomarkers of Response and Mechanisms of Resistance to Immune Checkpoint Blockade. , 2016, Cancer discovery.

[18]  R. Emerson,et al.  PD-1 blockade induces responses by inhibiting adaptive immune resistance , 2014, Nature.

[19]  A. Borczuk,et al.  Programmed Death Ligand-1 Immunohistochemistry--A New Challenge for Pathologists: A Perspective From Members of the Pulmonary Pathology Society. , 2016, Archives of pathology & laboratory medicine.

[20]  C. Rudin,et al.  Nivolumab versus Docetaxel in Advanced Nonsquamous Non-Small-Cell Lung Cancer. , 2015, The New England journal of medicine.

[21]  Rony Dahan,et al.  Therapeutic Activity of Agonistic, Human Anti-CD40 Monoclonal Antibodies Requires Selective FcγR Engagement. , 2016, Cancer cell.

[22]  Y. Shentu,et al.  Pembrolizumab versus Chemotherapy for PD-L1-Positive Non-Small-Cell Lung Cancer. , 2016, The New England journal of medicine.

[23]  Wei Zhou,et al.  Pembrolizumab versus investigator-choice chemotherapy for ipilimumab-refractory melanoma (KEYNOTE-002): a randomised, controlled, phase 2 trial. , 2015, The Lancet. Oncology.

[24]  J. Taube,et al.  PD-1/PD-L1 inhibitors. , 2015, Current opinion in pharmacology.

[25]  G. Gao,et al.  An unexpected N-terminal loop in PD-1 dominates binding by nivolumab , 2017, Nature Communications.

[26]  L. Sempere,et al.  VISTA Is a Novel Broad-Spectrum Negative Checkpoint Regulator for Cancer Immunotherapy , 2014, Cancer Immunology Research.

[27]  P. Sharma,et al.  Nivolumab monotherapy in recurrent metastatic urothelial carcinoma (CheckMate 032): a multicentre, open-label, phase 1/2 trial , 2016, The Lancet. Oncology.

[28]  Brendan MacLean,et al.  Bioinformatics Applications Note Gene Expression Skyline: an Open Source Document Editor for Creating and Analyzing Targeted Proteomics Experiments , 2022 .

[29]  Deepak R. Mani,et al.  Statistical characterization of multiple-reaction monitoring mass spectrometry (MRM-MS) assays for quantitative proteomics , 2012, BMC Bioinformatics.

[30]  R. Bourgon,et al.  Atezolizumab in patients with locally advanced and metastatic urothelial carcinoma who have progressed following treatment with platinum-based chemotherapy: a single-arm, multicentre, phase 2 trial , 2016, The Lancet.

[31]  A. Ravaud,et al.  Nivolumab versus Everolimus in Advanced Renal-Cell Carcinoma. , 2015, The New England journal of medicine.

[32]  Yu Shyr,et al.  Melanoma-specific MHC-II expression represents a tumour-autonomous phenotype and predicts response to anti-PD-1/PD-L1 therapy , 2016, Nature Communications.

[33]  M. Valsecchi Combined Nivolumab and Ipilimumab or Monotherapy in Untreated Melanoma. , 2015, The New England journal of medicine.

[34]  M. Washington,et al.  Precision of Multiple Reaction Monitoring Mass Spectrometry Analysis of Formalin-Fixed, Paraffin-Embedded Tissue , 2012, Journal of proteome research.

[35]  L. Nguyen,et al.  Clinical blockade of PD1 and LAG3 — potential mechanisms of action , 2014, Nature Reviews Immunology.

[36]  J. Larkin,et al.  Pembrolizumab versus Ipilimumab in Advanced Melanoma. , 2015, The New England journal of medicine.

[37]  Maria P. Pavlou,et al.  Quantitative Analysis of Energy Metabolic Pathways in MCF-7 Breast Cancer Cells by Selected Reaction Monitoring Assay* , 2012, Molecular & Cellular Proteomics.

[38]  G. Freeman,et al.  Combination cancer immunotherapy and new immunomodulatory targets , 2015, Nature Reviews Drug Discovery.

[39]  G. Freeman,et al.  PD-1 and its ligands in tolerance and immunity. , 2008, Annual review of immunology.

[40]  Christoph H Borchers,et al.  Multi-site assessment of the precision and reproducibility of multiple reaction monitoring–based measurements of proteins in plasma , 2009, Nature Biotechnology.

[41]  C. Paweletz,et al.  Multiparametric profiling of non-small-cell lung cancers reveals distinct immunophenotypes. , 2016, JCI insight.

[42]  J. Taube,et al.  Mechanism-driven biomarkers to guide immune checkpoint blockade in cancer therapy , 2016, Nature Reviews Cancer.

[43]  Drew M Pardoll,et al.  Differential binding properties of B7-H1 and B7-DC to programmed death-1. , 2003, Biochemical and biophysical research communications.

[44]  Ronald J. Moore,et al.  Antibody-free, targeted mass-spectrometric approach for quantification of proteins at low picogram per milliliter levels in human plasma/serum , 2012, Proceedings of the National Academy of Sciences.

[45]  D. Olive,et al.  A novel regulation of PD-1 ligands on mesenchymal stromal cells through MMP-mediated proteolytic cleavage , 2015, Oncoimmunology.

[46]  Drew M. Pardoll,et al.  The blockade of immune checkpoints in cancer immunotherapy , 2012, Nature Reviews Cancer.

[47]  J. Lunceford,et al.  Programmed Death-Ligand 1 Expression and Response to the Anti-Programmed Death 1 Antibody Pembrolizumab in Melanoma. , 2016, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[48]  K. Zak,et al.  Structure of the Complex of Human Programmed Death 1, PD-1, and Its Ligand PD-L1. , 2015, Structure.

[49]  Jun Yao,et al.  Glycosylation and stabilization of programmed death ligand-1 suppresses T-cell activity , 2016, Nature Communications.

[50]  Ludmila V. Danilova,et al.  Association of PD-1/PD-L axis expression with cytolytic activity, mutational load, and prognosis in melanoma and other solid tumors , 2016, Proceedings of the National Academy of Sciences.

[51]  D. Liebler,et al.  Quantitative Profiling of Protein Tyrosine Kinases in Human Cancer Cell Lines by Multiplexed Parallel Reaction Monitoring Assays* , 2015, Molecular & Cellular Proteomics.

[52]  T. Schumacher,et al.  Neoantigens in cancer immunotherapy , 2015, Science.

[53]  Richard Z. Liu,et al.  Quantification of beta-catenin signaling components in colon cancer cell lines, tissue sections, and microdissected tumor cells using reaction monitoring mass spectrometry. , 2010, Journal of proteome research.

[54]  W. Wick,et al.  Cancer Immunotherapy by Targeting IDO1/TDO and Their Downstream Effectors , 2015, Front. Immunol..

[55]  Jedd D. Wolchok,et al.  PD-L1 (B7-H1) and PD-1 pathway blockade for cancer therapy: Mechanisms, response biomarkers, and combinations , 2016, Science Translational Medicine.

[56]  J. Radford Nivolumab for recurrent squamous-cell carcinoma of the head and neck , 2016, BDJ.

[57]  Martin L. Miller,et al.  Mutational landscape determines sensitivity to PD-1 blockade in non–small cell lung cancer , 2015, Science.

[58]  Derek J. Bailey,et al.  Parallel Reaction Monitoring for High Resolution and High Mass Accuracy Quantitative, Targeted Proteomics* , 2012, Molecular & Cellular Proteomics.

[59]  Christoph H Borchers,et al.  Design, Implementation and Multisite Evaluation of a System Suitability Protocol for the Quantitative Assessment of Instrument Performance in Liquid Chromatography-Multiple Reaction Monitoring-MS (LC-MRM-MS)* , 2013, Molecular & Cellular Proteomics.

[60]  B. Domon,et al.  Targeted Proteomic Quantification on Quadrupole-Orbitrap Mass Spectrometer* , 2012, Molecular & Cellular Proteomics.

[61]  G. Freeman,et al.  PD-L2 is a second ligand for PD-1 and inhibits T cell activation , 2001, Nature Immunology.