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Pickering C, Aiyetan P, Xu G, Mitchell A, Rice R, Najjar YG, Markowitz J, Ebert LM, Brown MP, Tapia-Rico G, Frederick D, Cong X, Serie D, Lindpaintner K, Schwarz F, Boland GM. Plasma glycoproteomic biomarkers identify metastatic melanoma patients with reduced clinical benefit from immune checkpoint inhibitor therapy. Front Immunol 2023; 14:1187332. [PMID: 37388743 PMCID: PMC10302726 DOI: 10.3389/fimmu.2023.1187332] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 05/23/2023] [Indexed: 07/01/2023] Open
Abstract
The clinical success of immune-checkpoint inhibitors (ICI) in both resected and metastatic melanoma has confirmed the validity of therapeutic strategies that boost the immune system to counteract cancer. However, half of patients with metastatic disease treated with even the most aggressive regimen do not derive durable clinical benefit. Thus, there is a critical need for predictive biomarkers that can identify individuals who are unlikely to benefit with high accuracy so that these patients may be spared the toxicity of treatment without the likely benefit of response. Ideally, such an assay would have a fast turnaround time and minimal invasiveness. Here, we utilize a novel platform that combines mass spectrometry with an artificial intelligence-based data processing engine to interrogate the blood glycoproteome in melanoma patients before receiving ICI therapy. We identify 143 biomarkers that demonstrate a difference in expression between the patients who died within six months of starting ICI treatment and those who remained progression-free for three years. We then develop a glycoproteomic classifier that predicts benefit of immunotherapy (HR=2.7; p=0.026) and achieves a significant separation of patients in an independent cohort (HR=5.6; p=0.027). To understand how circulating glycoproteins may affect efficacy of treatment, we analyze the differences in glycosylation structure and discover a fucosylation signature in patients with shorter overall survival (OS). We then develop a fucosylation-based model that effectively stratifies patients (HR=3.5; p=0.0066). Together, our data demonstrate the utility of plasma glycoproteomics for biomarker discovery and prediction of ICI benefit in patients with metastatic melanoma and suggest that protein fucosylation may be a determinant of anti-tumor immunity.
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Affiliation(s)
- Chad Pickering
- InterVenn Biosciences, South San Francisco, CA, United States
| | - Paul Aiyetan
- InterVenn Biosciences, South San Francisco, CA, United States
| | - Gege Xu
- InterVenn Biosciences, South San Francisco, CA, United States
| | - Alan Mitchell
- InterVenn Biosciences, South San Francisco, CA, United States
| | - Rachel Rice
- InterVenn Biosciences, South San Francisco, CA, United States
| | - Yana G. Najjar
- Department of Medicine, University of Pittsburgh Medical Center (UPMC) Hillman Cancer Center, Pittsburgh, PA, United States
| | - Joseph Markowitz
- Department of Cutaneous Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States
- Immuno-Oncology Program, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States
| | - Lisa M. Ebert
- Centre for Cancer Biology, South Australia (SA) Pathology and University of South Australia, Adelaide, SA, Australia
- Cancer Clinical Trials Unit, Royal Adelaide Hospital, Adelaide, SA, Australia
- Adelaide Medical School, The University of Adelaide, Adelaide, SA, Australia
| | - Michael P. Brown
- Centre for Cancer Biology, South Australia (SA) Pathology and University of South Australia, Adelaide, SA, Australia
- Cancer Clinical Trials Unit, Royal Adelaide Hospital, Adelaide, SA, Australia
- Adelaide Medical School, The University of Adelaide, Adelaide, SA, Australia
| | - Gonzalo Tapia-Rico
- Cancer Clinical Trials Unit, Royal Adelaide Hospital, Adelaide, SA, Australia
| | - Dennie Frederick
- Department of Surgery, Massachusetts General Hospital, Boston, MA, United States
| | - Xin Cong
- InterVenn Biosciences, South San Francisco, CA, United States
| | - Daniel Serie
- InterVenn Biosciences, South San Francisco, CA, United States
| | | | - Flavio Schwarz
- InterVenn Biosciences, South San Francisco, CA, United States
| | - Genevieve M. Boland
- Department of Surgery, Massachusetts General Hospital, Boston, MA, United States
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Seyhan AA, Carini C. Insights and Strategies of Melanoma Immunotherapy: Predictive Biomarkers of Response and Resistance and Strategies to Improve Response Rates. Int J Mol Sci 2022; 24:ijms24010041. [PMID: 36613491 PMCID: PMC9820306 DOI: 10.3390/ijms24010041] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 12/10/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022] Open
Abstract
Despite the recent successes and durable responses with immune checkpoint inhibitors (ICI), many cancer patients, including those with melanoma, do not derive long-term benefits from ICI therapies. The lack of predictive biomarkers to stratify patients to targeted treatments has been the driver of primary treatment failure and represents an unmet medical need in melanoma and other cancers. Understanding genomic correlations with response and resistance to ICI will enhance cancer patients' benefits. Building on insights into interplay with the complex tumor microenvironment (TME), the ultimate goal should be assessing how the tumor 'instructs' the local immune system to create its privileged niche with a focus on genomic reprogramming within the TME. It is hypothesized that this genomic reprogramming determines the response to ICI. Furthermore, emerging genomic signatures of ICI response, including those related to neoantigens, antigen presentation, DNA repair, and oncogenic pathways, are gaining momentum. In addition, emerging data suggest a role for checkpoint regulators, T cell functionality, chromatin modifiers, and copy-number alterations in mediating the selective response to ICI. As such, efforts to contextualize genomic correlations with response into a more insightful understanding of tumor immune biology will help the development of novel biomarkers and therapeutic strategies to overcome ICI resistance.
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Affiliation(s)
- Attila A. Seyhan
- Laboratory of Translational Oncology and Experimental Cancer Therapeutics, Warren Alpert Medical School, Brown University, Providence, RI 02912, USA
- Department of Pathology and Laboratory Medicine, Warren Alpert Medical School, Brown University, Providence, RI 02912, USA
- Joint Program in Cancer Biology, Lifespan Health System and Brown University, Providence, RI 02912, USA
- Legorreta Cancer Center, Brown University, Providence, RI 02912, USA
- Correspondence:
| | - Claudio Carini
- School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences & Medicine, New Hunt’s House, Guy’s Campus, King’s College London, London SE1 1UL, UK
- Biomarkers Consortium, Foundation of the National Institute of Health, Bethesda, MD 20892, USA
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Du K, Wei S, Wei Z, Frederick DT, Miao B, Moll T, Tian T, Sugarman E, Gabrilovich DI, Sullivan RJ, Liu L, Flaherty KT, Boland GM, Herlyn M, Zhang G. Pathway signatures derived from on-treatment tumor specimens predict response to anti-PD1 blockade in metastatic melanoma. Nat Commun 2021; 12:6023. [PMID: 34654806 PMCID: PMC8519947 DOI: 10.1038/s41467-021-26299-4] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 09/27/2021] [Indexed: 02/05/2023] Open
Abstract
Both genomic and transcriptomic signatures have been developed to predict responses of metastatic melanoma to immune checkpoint blockade (ICB) therapies; however, most of these signatures are derived from pre-treatment biopsy samples. Here, we build pathway-based super signatures in pre-treatment (PASS-PRE) and on-treatment (PASS-ON) tumor specimens based on transcriptomic data and clinical information from a large dataset of metastatic melanoma treated with anti-PD1-based therapies as the training set. Both PASS-PRE and PASS-ON signatures are validated in three independent datasets of metastatic melanoma as the validation set, achieving area under the curve (AUC) values of 0.45-0.69 and 0.85-0.89, respectively. We also combine all test samples and obtain AUCs of 0.65 and 0.88 for PASS-PRE and PASS-ON signatures, respectively. When compared with existing signatures, the PASS-ON signature demonstrates more robust and superior predictive performance across all four datasets. Overall, we provide a framework for building pathway-based signatures that is highly and accurately predictive of response to anti-PD1 therapies based on on-treatment tumor specimens. This work would provide a rationale for applying pathway-based signatures derived from on-treatment tumor samples to predict patients' therapeutic response to ICB therapies.
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Affiliation(s)
- Kuang Du
- Department of Computer Science, Ying Wu College of Computing, New Jersey Institute of Technology, Newark, NJ, 07102, USA
| | - Shiyou Wei
- Department of Thoracic Surgery, West China Hospital, Sichuan University, 610041, Chengdu, Sichuan, China
- Department of Neurosurgery, Duke University School of Medicine, Durham, NC, 27710, USA
- The Preston Robert Tisch Brain Tumor Center, Duke University Medical Center, Durham, NC, 27710, USA
| | - Zhi Wei
- Department of Computer Science, Ying Wu College of Computing, New Jersey Institute of Technology, Newark, NJ, 07102, USA.
| | | | - Benchun Miao
- Massachusetts General Hospital Cancer Center, Boston, MA, 02114, USA
| | - Tabea Moll
- Department of Surgery, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Tian Tian
- Department of Computer Science, Ying Wu College of Computing, New Jersey Institute of Technology, Newark, NJ, 07102, USA
| | - Eric Sugarman
- Philadelphia College of Osteopathic Medicine, Philadelphia, PA, 19131, USA
| | | | - Ryan J Sullivan
- Massachusetts General Hospital Cancer Center, Boston, MA, 02114, USA
| | - Lunxu Liu
- Department of Thoracic Surgery, West China Hospital, Sichuan University, 610041, Chengdu, Sichuan, China
| | - Keith T Flaherty
- Massachusetts General Hospital Cancer Center, Boston, MA, 02114, USA
| | - Genevieve M Boland
- Department of Surgery, Massachusetts General Hospital, Boston, MA, 02114, USA.
| | - Meenhard Herlyn
- Molecular and Cellular Oncogenesis Program and Melanoma Research Center, The Wistar Institute, Philadelphia, PA, 19104, USA.
| | - Gao Zhang
- Department of Neurosurgery, Duke University School of Medicine, Durham, NC, 27710, USA.
- The Preston Robert Tisch Brain Tumor Center, Duke University Medical Center, Durham, NC, 27710, USA.
- Department of Pathology, Duke University School of Medicine, Durham, NC, 27710, USA.
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