Role of mass spectrometry-based serum proteomics signatures in predicting clinical outcomes and toxicity in patients with cancer treated with immunotherapy
Immunotherapy has fundamentally changed the landscape of cancer treatment. However, only a subset of patients respond to immunotherapy, and a significant portion experience immune-related adverse events (irAEs). In addition, the predictive ability of current biomarkers such as programmed death-ligan...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMJ Publishing Group
2022-03-01
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| Series: | Journal for ImmunoTherapy of Cancer |
| Online Access: | https://jitc.bmj.com/content/10/3/e003566.full |
| _version_ | 1827893883519893504 |
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| author | Young Kwang Chae Emma Yu Na Hyun Kim Min Jeong Kim Leeseul Kim Hyung-Gyo Cho Yeonggyeong Park Yoonhee Choi Seung Pyo Daniel Hong |
| author_facet | Young Kwang Chae Emma Yu Na Hyun Kim Min Jeong Kim Leeseul Kim Hyung-Gyo Cho Yeonggyeong Park Yoonhee Choi Seung Pyo Daniel Hong |
| author_sort | Young Kwang Chae |
| collection | DOAJ |
| description | Immunotherapy has fundamentally changed the landscape of cancer treatment. However, only a subset of patients respond to immunotherapy, and a significant portion experience immune-related adverse events (irAEs). In addition, the predictive ability of current biomarkers such as programmed death-ligand 1 (PD-L1) remains unreliable and establishing better potential candidate markers is of great importance in selecting patients who would benefit from immunotherapy. Here, we focus on the role of serum-based proteomic tests in predicting the response and toxicity of immunotherapy. Serum proteomic signatures refer to unique patterns of proteins which are associated with immune response in patients with cancer. These protein signatures are derived from patient serum samples based on mass spectrometry and act as biomarkers to predict response to immunotherapy. Using machine learning algorithms, serum proteomic tests were developed through training data sets from advanced non-small cell lung cancer (Host Immune Classifier, Primary Immune Response) and malignant melanoma patients (PerspectIV test). The tests effectively stratified patients into groups with good and poor treatment outcomes independent of PD-L1 expression. Here, we review current evidence in the published literature on three liquid biopsy tests that use biomarkers derived from proteomics and machine learning for use in immuno-oncology. We discuss how these tests may inform patient prognosis as well as guide treatment decisions and predict irAE of immunotherapy. Thus, mass spectrometry-based serum proteomics signatures play an important role in predicting clinical outcomes and toxicity. |
| first_indexed | 2024-03-12T21:58:17Z |
| format | Article |
| id | doaj.art-a9af539960c14eebb6591d661e6d999e |
| institution | Directory Open Access Journal |
| issn | 2051-1426 |
| language | English |
| last_indexed | 2024-03-12T21:58:17Z |
| publishDate | 2022-03-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | Journal for ImmunoTherapy of Cancer |
| spelling | doaj.art-a9af539960c14eebb6591d661e6d999e2023-07-25T12:15:07ZengBMJ Publishing GroupJournal for ImmunoTherapy of Cancer2051-14262022-03-0110310.1136/jitc-2021-003566Role of mass spectrometry-based serum proteomics signatures in predicting clinical outcomes and toxicity in patients with cancer treated with immunotherapyYoung Kwang Chae0Emma Yu1Na Hyun Kim2Min Jeong Kim3Leeseul Kim4Hyung-Gyo Cho5Yeonggyeong Park6Yoonhee Choi7Seung Pyo Daniel Hong8Robert H. Lurie Comprehensive Cancer Center of Northwestern University, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA1Northwestern University Feinberg School of Medicine, Chicago, IL, USAAMITA Health Saint Joseph Hospital Chicago, Chicago, Illinois, USA1 Section of Cardiovascular Imaging, Division of Cardiology, Cardiovascular Center, Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea1AMITA health Saint Francis Hospital Evanston, Chicago, IL, USA1Northwestern University, Feinberg School of Medicine, Chicago, IL, USA1 Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA2 Department of Internal Medicine, NewYork-Presbyterian Queens, Flushing, New York, USA1 Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USAImmunotherapy has fundamentally changed the landscape of cancer treatment. However, only a subset of patients respond to immunotherapy, and a significant portion experience immune-related adverse events (irAEs). In addition, the predictive ability of current biomarkers such as programmed death-ligand 1 (PD-L1) remains unreliable and establishing better potential candidate markers is of great importance in selecting patients who would benefit from immunotherapy. Here, we focus on the role of serum-based proteomic tests in predicting the response and toxicity of immunotherapy. Serum proteomic signatures refer to unique patterns of proteins which are associated with immune response in patients with cancer. These protein signatures are derived from patient serum samples based on mass spectrometry and act as biomarkers to predict response to immunotherapy. Using machine learning algorithms, serum proteomic tests were developed through training data sets from advanced non-small cell lung cancer (Host Immune Classifier, Primary Immune Response) and malignant melanoma patients (PerspectIV test). The tests effectively stratified patients into groups with good and poor treatment outcomes independent of PD-L1 expression. Here, we review current evidence in the published literature on three liquid biopsy tests that use biomarkers derived from proteomics and machine learning for use in immuno-oncology. We discuss how these tests may inform patient prognosis as well as guide treatment decisions and predict irAE of immunotherapy. Thus, mass spectrometry-based serum proteomics signatures play an important role in predicting clinical outcomes and toxicity.https://jitc.bmj.com/content/10/3/e003566.full |
| spellingShingle | Young Kwang Chae Emma Yu Na Hyun Kim Min Jeong Kim Leeseul Kim Hyung-Gyo Cho Yeonggyeong Park Yoonhee Choi Seung Pyo Daniel Hong Role of mass spectrometry-based serum proteomics signatures in predicting clinical outcomes and toxicity in patients with cancer treated with immunotherapy Journal for ImmunoTherapy of Cancer |
| title | Role of mass spectrometry-based serum proteomics signatures in predicting clinical outcomes and toxicity in patients with cancer treated with immunotherapy |
| title_full | Role of mass spectrometry-based serum proteomics signatures in predicting clinical outcomes and toxicity in patients with cancer treated with immunotherapy |
| title_fullStr | Role of mass spectrometry-based serum proteomics signatures in predicting clinical outcomes and toxicity in patients with cancer treated with immunotherapy |
| title_full_unstemmed | Role of mass spectrometry-based serum proteomics signatures in predicting clinical outcomes and toxicity in patients with cancer treated with immunotherapy |
| title_short | Role of mass spectrometry-based serum proteomics signatures in predicting clinical outcomes and toxicity in patients with cancer treated with immunotherapy |
| title_sort | role of mass spectrometry based serum proteomics signatures in predicting clinical outcomes and toxicity in patients with cancer treated with immunotherapy |
| url | https://jitc.bmj.com/content/10/3/e003566.full |
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