ReviewJournal for immunotherapy of cancer2022
Role of mass spectrometry-based serum proteomics signatures in predicting clinical outcomes and toxicity in patients with cancer treated with immunotherapy.
Review in Journal for immunotherapy of cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
27 citing papers in PubMed, 50 citations in OpenAlex.
- Multimodal Artificial Intelligence in Lung Cancer: From Data Integration to Precision Oncology.Cancers · 2026Review
- Liquid Biopsy Biomarkers for Predicting and Monitoring Immunotherapy Response in Lung Cancer.Cancers · 2026Review
- Artificial Intelligence for Predicting Immunotherapy Efficacy in Non-Small Cell Lung Cancer.Journal of inflammation research · 2026Review
- Evolving non-invasive biomarkers in NSCLC immunotherapy: integrating liquid biopsy and multi-omics profiling for precision oncology.Frontiers in immunology · 2026Review
- The Evolution and Recent Advances in Diagnostic Criteria for Idiopathic Multicentric Castleman Disease.American journal of hematology · 2025Review
- Evolving stratification and biomarker discovery in cancer research with technological advancement of proteomics: 35 years and counting.Bioscience reports · 2025Review
- Proteomics in pancreatic cancer.Biomarker research · 2025Review
- Evaluating Biocompatibility: From Classical Techniques to State-of-the-Art Functional Proteomics.Nanomaterials (Basel, Switzerland) · 2025Review
- In-depth plasma proteomics reveals the dynamic changes and prognostic biomarkers for chimeric antigen receptor-glypican-3 T-cell therapy in patients with hepatocellular carcinoma.Gastroenterology report · 2025Article
- Identifying cancer prognosis genes through causal learning.Briefings in bioinformatics · 2024Article
- Evaluation of Serum Proteome Sample Preparation Methods to Support Clinical Proteomics Applications.Journal of the American Society for Mass Spectrometry · 2024Article
- Biomarkers associated with immune-related adverse events induced by immune checkpoint inhibitors.World journal of clinical oncology · 2024Review
- Review
- Unveiling the Influence of AI Predictive Analytics on Patient Outcomes: A Comprehensive Narrative Review.Cureus · 2024Review
- Plasma proteomics-based biomarkers for predicting response to mesenchymal stem cell therapy in severe COVID-19.Stem cell research & therapy · 2023Article
- [Advances in Predictive Research of Immune Checkpoint Inhibitors-related Adverse Events].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2023Article
- Proteomics-Driven Biomarkers in Pancreatic Cancer.Proteomes · 2023Review
- Phospho-DIGE Identified Phosphoproteins Involved in Pathways Related to Tumour Growth in Endometrial Cancer.International journal of molecular sciences · 2023Article
- Potential non-invasive biomarkers in tumor immune checkpoint inhibitor therapy: response and prognosis prediction.Biomarker research · 2023Review
- The artificial intelligence and machine learning in lung cancer immunotherapy.Journal of hematology & oncology · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.