cancers Article Proteomics of Primary Uveal Melanoma: Insights into Metastasis and Protein Biomarkers Geeng-Fu Jang 1,2,† , Jack S. Crabb 1,2,† , Bo Hu 3 , Belinda Willard 4 , Helen Kalirai 5 , Arun D. Singh 1,6 , Sarah E. Coupland 5,7 and John W. Crabb 1,2,6, *   Citation: Jang, G.-F.; Crabb, J.S.; Hu, B.; Willard, B.; Kalirai, H.; Singh, A.D.; Coupland, S.E.; Crabb, J.W. Proteomics of Primary Uveal Melanoma: Insights into Metastasis and Protein Biomarkers. Cancers 2021, 13, 3520. https://doi.org/10.3390/ cancers13143520 Academic Editor: Ellen Kapiteijn Received: 21 May 2021 Accepted: 9 July 2021 Published: 14 July 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 Cole Eye Institute, Cleveland Clinic, Cleveland, OH 44195, USA; jangg@ccf.org (G.-F.J.); crabbj1@ccf.org (J.S.C.); singha@ccf.org (A.D.S.) 2 Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA 3 Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; hub@ccf.org 4 Proteomics and Metabolomics Facility, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; willarb@ccf.org 5 Liverpool Ocular Oncology Research Centre, Department of Molecular and Clinical Cancer Medicine, University of Liverpool, William Henry Duncan Building, West Derby Street, Liverpool L7 8TX, UK; h.kalirai@liverpool.ac.uk (H.K.); s.e.coupland@liverpool.ac.uk (S.E.C.) 6 Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH 44106, USA 7 Liverpool Clinical Laboratories, Liverpool University Hospitals NHS Foundation Trust, Duncan Building, Daulby Street, Liverpool L69 3GA, UK * Correspondence: crabbj@ccf.org; Tel.: +1-216-318-7298 † These two authors contributed equally to this work. Simple Summary: This study pursued the proteomic analysis of primary uveal melanoma (pUM) for insights into the mechanisms of metastasis and protein biomarkers. Liquid chromatography tandem mass spectrometry quantitative proteomic technology was used to analyze 53 metastasizing and 47 non-metastasizing pUM. The determined proteome of 3935 proteins was very similar between the metastasizing and non-metastasizing pUM, but included the identification of 402 differentially expressed (DE) proteins. Bioinformatic analyses suggest significant differences in the immune response between metastasizing and non-metastasizing pUM. Immune protein profiling results were consistent with transcriptomic studies, showing the immune-suppressive nature and low abundance of immune checkpoint regulators in pUM, and suggest CDH1, HLA-DPA1, and several DE immune kinases and phosphatases as potential targets for immune therapy checkpoint blockade. Prediction modeling of the proteomic data identified 32 proteins capable of predicting metastasizing versus non-metastasizing pUM with 93% discriminatory accuracy. Abstract: Uveal melanoma metastases are lethal and remain incurable. A quantitative proteomic analysis of 53 metastasizing and 47 non-metastasizing primary uveal melanoma (pUM) was pursued for insights into UM metastasis and protein biomarkers. The metastatic status of the pUM specimens was defined based on clinical data, survival histories, prognostic analyses, and liver histopathology. LC MS/MS iTRAQ technology, the Mascot search engine, and the UniProt human database were used to identify and quantify pUM proteins relative to the normal choroid excised from UM donor eyes. The determined proteomes of all 100 tumors were very similar, encompassing a total of 3935 pUM proteins. Proteins differentially expressed (DE) between metastasizing and non-metastasizing pUM (n = 402) were employed in bioinformatic analyses that predicted significant differences in the immune system between metastasizing and non-metastasizing pUM. The immune proteins (n = 778) identified in this study support the immune-suppressive nature and low abundance of immune checkpoint regulators in pUM, and suggest CDH1, HLA-DPA1, and several DE immune kinases and phosphatases as possible candidates for immune therapy checkpoint blockade. Prediction modeling identified 32 proteins capable of predicting metastasizing versus non-metastasizing pUM with 93% discriminatory accuracy, supporting the potential for protein-based prognostic methods for detecting UM metastasis. Cancers 2021, 13, 3520. https://doi.org/10.3390/cancers13143520 https://www.mdpi.com/journal/cancers