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Studentova H, Hola K, Melichar B, Spisarova M. Neopterin as a potential prognostic and predictive biomarker in metastatic renal cell carcinoma treated with immune checkpoint inhibitors. Expert Rev Anticancer Ther 2024; 24:339-345. [PMID: 38596831 DOI: 10.1080/14737140.2024.2341734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 04/08/2024] [Indexed: 04/11/2024]
Abstract
INTRODUCTION Immunotherapy represents a significant and essential component of renal carcinoma therapy (RCC), but the selection of an optimal regimen for an individual patient remains unclear. Despite significant improvements in therapeutic options for RCC, predictive biomarkers for immunotherapeutic agents remain elusive. Neopterin is a biomarker of cell-mediated immune response, with concentrations increased in different disorders, including cancer. High neopterin levels herald, in general, a poor prognosis. AREAS COVERED This review briefly overviews the contemporary clinical data on biomarkers in metastatic RCC therapy, focusing on neopterin. EXPERT OPINION Elevated neopterin levels have been observed in tumors of different primary locations. Research indicates that neopterin may serve as a potential biomarker for assessing the inflammatory status associated with certain cancers. However, it is necessary to interpret neopterin levels in the context of a comprehensive clinical evaluation, as elevated neopterin alone is not specific to cancer and can be influenced by other factors, including comorbid conditions. Neopterin has also been identified as a prognostic biomarker. An increasing neopterin level in serum and urine is associated with advanced cancer, but the role as a potential predictor of response to immunotherapy has yet to be established. A reliable biomarker for optimal therapy selection in metastatic RCC is still putative.
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Affiliation(s)
- Hana Studentova
- Department of Oncology, University Hospital, Olomouc, Czech Republic
- Department of Oncology, Faculty of Medicine and Dentistry, Palacký University, Olomouc, Czech Republic
| | - Katerina Hola
- Department of Oncology, University Hospital, Olomouc, Czech Republic
- Department of Oncology, Faculty of Medicine and Dentistry, Palacký University, Olomouc, Czech Republic
| | - Bohuslav Melichar
- Department of Oncology, University Hospital, Olomouc, Czech Republic
- Department of Oncology, Faculty of Medicine and Dentistry, Palacký University, Olomouc, Czech Republic
| | - Martina Spisarova
- Department of Oncology, University Hospital, Olomouc, Czech Republic
- Department of Oncology, Faculty of Medicine and Dentistry, Palacký University, Olomouc, Czech Republic
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Qin S, Sun S, Wang Y, Li C, Fu L, Wu M, Yan J, Li W, Lv J, Chen L. Immune, metabolic landscapes of prognostic signatures for lung adenocarcinoma based on a novel deep learning framework. Sci Rep 2024; 14:527. [PMID: 38177198 PMCID: PMC10767103 DOI: 10.1038/s41598-023-51108-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 12/30/2023] [Indexed: 01/06/2024] Open
Abstract
Lung adenocarcinoma (LUAD) is a malignant tumor with high lethality, and the aim of this study was to identify promising biomarkers for LUAD. Using the TCGA-LUAD dataset as a discovery cohort, a novel joint framework VAEjMLP based on variational autoencoder (VAE) and multilayer perceptron (MLP) was proposed. And the Shapley Additive Explanations (SHAP) method was introduced to evaluate the contribution of feature genes to the classification decision, which helped us to develop a biologically meaningful biomarker potential scoring algorithm. Nineteen potential biomarkers for LUAD were identified, which were involved in the regulation of immune and metabolic functions in LUAD. A prognostic risk model for LUAD was constructed by the biomarkers HLA-DRB1, SCGB1A1, and HLA-DRB5 screened by Cox regression analysis, dividing the patients into high-risk and low-risk groups. The prognostic risk model was validated with external datasets. The low-risk group was characterized by enrichment of immune pathways and higher immune infiltration compared to the high-risk group. While, the high-risk group was accompanied by an increase in metabolic pathway activity. There were significant differences between the high- and low-risk groups in metabolic reprogramming of aerobic glycolysis, amino acids, and lipids, as well as in angiogenic activity, epithelial-mesenchymal transition, tumorigenic cytokines, and inflammatory response. Furthermore, high-risk patients were more sensitive to Afatinib, Gefitinib, and Gemcitabine as predicted by the pRRophetic algorithm. This study provides prognostic signatures capable of revealing the immune and metabolic landscapes for LUAD, and may shed light on the identification of other cancer biomarkers.
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Affiliation(s)
- Shimei Qin
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, China
| | - Shibin Sun
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, China
| | - Yahui Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, China
| | - Chao Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, China
| | - Lei Fu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, China
| | - Ming Wu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, China
| | - Jinxing Yan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, China
| | - Wan Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, China
| | - Junjie Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, China.
| | - Lina Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, China.
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Zdrenka M, Kowalewski A, Ahmadi N, Sadiqi RU, Chmura Ł, Borowczak J, Maniewski M, Szylberg Ł. Refining PD-1/PD-L1 assessment for biomarker-guided immunotherapy: A review. BIOMOLECULES & BIOMEDICINE 2024; 24:14-29. [PMID: 37877810 PMCID: PMC10787614 DOI: 10.17305/bb.2023.9265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 06/23/2023] [Accepted: 06/23/2023] [Indexed: 10/26/2023]
Abstract
Anti-programmed cell death ligand 1 (anti-PD-L1) immunotherapy is an increasingly crucial in cancer treatment. To date, the Federal Drug Administration (FDA) has approved four PD-L1 immunohistochemistry (IHC) staining protocols, commercially available in the form of "kits", facilitating testing for PD-L1 expression. These kits comprise four PD-L1 antibodies on two separate IHC platforms, each utilizing distinct, non-interchangeable scoring systems. Several factors, including tumor heterogeneity and the size of the tissue specimens assessed, can lead to PD-L1 status misclassification, potentially hindering the initiation of therapy. Therefore, the development of more accurate predictive biomarkers to distinguish between responders and non-responders prior to anti-PD-1/PD-L1 therapy warrants further research. Achieving this goal necessitates refining sampling criteria, enhancing current methods of PD-L1 detection, and deepening our understanding of the impact of additional biomarkers. In this article, we review potential solutions to improve the predictive accuracy of PD-L1 assessment in order to more precisely anticipate patients' responses to anti-PD-1/PD-L1 therapy, monitor disease progression and predict clinical outcomes.
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Affiliation(s)
- Marek Zdrenka
- Department of Tumor Pathology and Pathomorphology, Oncology Centre-Prof. Franciszek Łukaszczyk Memorial Hospital, Bydgoszcz, Poland
| | - Adam Kowalewski
- Department of Tumor Pathology and Pathomorphology, Oncology Centre-Prof. Franciszek Łukaszczyk Memorial Hospital, Bydgoszcz, Poland
| | - Navid Ahmadi
- Department of Cardiothoracic Surgery, Royal Papworth Hospital, Cambridge, UK
| | | | - Łukasz Chmura
- Department of Pathomorphology, Jagiellonian University Medical College, Kraków, Poland
| | - Jędrzej Borowczak
- Department of Obstetrics, Gynaecology and Oncology, Chair of Pathomorphology and Clinical Placentology, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, Bydgoszcz, Poland
| | - Mateusz Maniewski
- Department of Obstetrics, Gynaecology and Oncology, Chair of Pathomorphology and Clinical Placentology, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, Bydgoszcz, Poland
| | - Łukasz Szylberg
- Department of Tumor Pathology and Pathomorphology, Oncology Centre-Prof. Franciszek Łukaszczyk Memorial Hospital, Bydgoszcz, Poland
- Department of Obstetrics, Gynaecology and Oncology, Chair of Pathomorphology and Clinical Placentology, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, Bydgoszcz, Poland
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Koc MA, Wiles TA, Weinhold DC, Rightmyer S, Weaver AL, McDowell CT, Roder J, Asmellash S, Pestano GA, Roder H, Georgantas III RW. Molecular and translational biology of the blood-based VeriStrat® proteomic test used in cancer immunotherapy treatment guidance. J Mass Spectrom Adv Clin Lab 2023; 30:51-60. [PMID: 38074293 PMCID: PMC10709509 DOI: 10.1016/j.jmsacl.2023.11.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 10/16/2023] [Accepted: 11/08/2023] [Indexed: 03/09/2024] Open
Abstract
INTRODUCTION The VeriStrat® test (VS) is a blood-based assay that predicts a patient's response to therapy by analyzing eight features in a spectrum obtained from matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) analysis of human serum and plasma. In a recent analysis of the INSIGHT clinical trial (NCT03289780), it was found that the VS labels, VS Good and VS Poor, can effectively predict the responsiveness of non-small cell lung cancer (NSCLC) patients to immune checkpoint inhibitor (ICI) therapy. However, while VS measures the intensities of spectral features using MALDI-TOF analysis, the specific proteoforms underlying these features have not been comprehensively identified. OBJECTIVES The objective of this study was to identify the proteoforms that are measured by VS. METHODS To resolve the features obtained from the low-resolution MALDI-TOF procedure used to acquire mass spectra for VS DeepMALDI® analysis of serum was employed. This technique allowed for the identification of finer peaks within these features. Additionally, a combination of reversed-phase fractionation and liquid chromatography-tandem mass spectrometry (LC-MS/MS) was then used to identify the proteoforms associated with these peaks. RESULTS The analysis revealed that the primary constituents of the spectrum measured by VS are serum amyloid A1, serum amyloid A2, serum amyloid A4, C-reactive protein, and beta-2 microglobulin. CONCLUSION Proteoforms involved in host immunity were identified as significant components of these features. This newly acquired information improves our understanding of how VS can accurately predict patient response to therapy. It opens up additional studies that can expand our understanding even further.
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Affiliation(s)
| | | | - Daniel C. Weinhold
- Biodesix Inc., 2970 Wilderness Place Suite 100, Boulder, CO 80301, United States
| | - Steven Rightmyer
- Biodesix Inc., 2970 Wilderness Place Suite 100, Boulder, CO 80301, United States
| | - Amanda L. Weaver
- Biodesix Inc., 2970 Wilderness Place Suite 100, Boulder, CO 80301, United States
| | - Colin T. McDowell
- Biodesix Inc., 2970 Wilderness Place Suite 100, Boulder, CO 80301, United States
| | - Joanna Roder
- Biodesix Inc., 2970 Wilderness Place Suite 100, Boulder, CO 80301, United States
| | - Senait Asmellash
- Biodesix Inc., 2970 Wilderness Place Suite 100, Boulder, CO 80301, United States
| | - Gary A. Pestano
- Biodesix Inc., 2970 Wilderness Place Suite 100, Boulder, CO 80301, United States
| | - Heinrich Roder
- Biodesix Inc., 2970 Wilderness Place Suite 100, Boulder, CO 80301, United States
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Rugambwa TK, Abdihamid O, Zhang X, Peng Y, Cai C, Shen H, Zeng S, Qiu W. Neutrophil-lymphocyte ratio and platelet-lymphocyte ratio as potential predictive markers of treatment response in cancer patients treated with immune checkpoint inhibitors: a systematic review and meta-analysis. Front Oncol 2023; 13:1181248. [PMID: 38023176 PMCID: PMC10646751 DOI: 10.3389/fonc.2023.1181248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 09/26/2023] [Indexed: 12/01/2023] Open
Abstract
Background The role of platelet-lymphocyte ratio (PLR) and neutrophil-lymphocyte ratio (NLR) as independent prognostic markers in different tumors is well established. However, there is a limited review of the potential of NLR and PLR as predictors of treatment outcomes from immune checkpoint inhibitors (ICIs). Objective To establish a correlation between NLR and PLR and the potential of clinical benefit from ICIs. Methods The literature search was performed for studies that reported the association between NLR, PLR, and treatment outcomes among cancer patients treated with ICIs. The outcomes of interest were objective response rate (ORR), disease control rate (DCR), and progressive disease (PD). ORR was the summation of patients who achieved complete response and partial response. DCR included patients who achieved stable disease. PD was the proportion of patients who progressed, relapsed, or discontinued the treatment. Statistical analysis was performed using the STATA 12.0 package. Heterogeneity was determined by the I2 value. Quality assessment was performed using the Newcastle-Ottawa Scale. Egger's test was used to establish publication bias and sensitivity analysis. Results A total of 40 papers that met the inclusion criteria were included in the systematic review. However, only 17 studies were used in the meta-analysis to determine the correlation between NLR, PLR, and treatment response. We found that treatment with ICIs and monitoring of outcomes and adverse events using PLR and NLR parameters have been studied in different tumors. Our analysis showed that low NLR correlated with higher ORR (OR = 0.62 (95% CI 0.47-0.81, p = 0.001) and higher DCR (OR = 0.23, 95% CI 0.14-0.36, p < 0.001). Higher NLR predicted a higher probability of PD (OR = 3.12, 95% CI 1.44, 6.77, p = 0.004). Similarly, low PLR correlated with higher ORR (OR = 0.69, 95% CI 0.5, 0.95, p = 0.025). Generally, patients with low NLR and PLR were more likely to achieve clinical benefit and better response (p-value < 0.001). Meanwhile, patients with high ratios were more likely to progress (p-value < 0.005), although there was significant heterogeneity among studies. There was no significant publication bias observed. Conclusion The study showed that high NLR and PLR either at baseline or during treatment is associated with poorer treatment outcome. Therefore, these ratios can be utilized in clinical practice with other markers to determine treatment efficacy from immunotherapy.
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Affiliation(s)
- Tibera K. Rugambwa
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Internal Medicine, Mbeya Zonal Referral Hospital and Mbeya College of Health and Allied Sciences, University of Dar-es-salaam, Mbeya, Tanzania
| | - Omar Abdihamid
- Garissa Cancer Center, Garissa County Referral Hospital, Garissa, Kenya
| | - Xiangyang Zhang
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yinghui Peng
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Changjing Cai
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Hong Shen
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Shan Zeng
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Wei Qiu
- Department of Oncology, The First People's Hospital of Loudi, Loudi, Hunan, China
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Janho Dit Hreich S, Hofman P, Vouret-Craviari V. The Role of IL-18 in P2RX7-Mediated Antitumor Immunity. Int J Mol Sci 2023; 24:ijms24119235. [PMID: 37298187 DOI: 10.3390/ijms24119235] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 05/22/2023] [Accepted: 05/23/2023] [Indexed: 06/12/2023] Open
Abstract
Cancer is the leading cause of death worldwide despite the variety of treatments that are currently used. This is due to an innate or acquired resistance to therapy that encourages the discovery of novel therapeutic strategies to overcome the resistance. This review will focus on the role of the purinergic receptor P2RX7 in the control of tumor growth, through its ability to modulate antitumor immunity by releasing IL-18. In particular, we describe how the ATP-induced receptor activities (cationic exchange, large pore opening and NLRP3 inflammasome activation) modulate immune cell functions. Furthermore, we recapitulate our current knowledge of the production of IL-18 downstream of P2RX7 activation and how IL-18 controls the fate of tumor growth. Finally, the potential of targeting the P2RX7/IL-18 pathway in combination with classical immunotherapies to fight cancer is discussed.
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Affiliation(s)
- Serena Janho Dit Hreich
- Faculty of Medicine, Université Côte d'Azur, CNRS, INSERM, IRCAN, 06108 Nice, France
- IHU RespirEREA, Université Côte d'Azur, 06108 Nice, France
- FHU OncoAge, 06108 Nice, France
| | - Paul Hofman
- IHU RespirEREA, Université Côte d'Azur, 06108 Nice, France
- Laboratory of Clinical and Experimental Pathology and Biobank, Pasteur Hospital, 06108 Nice, France
- Hospital-Related Biobank, Pasteur Hospital, 06108 Nice, France
| | - Valérie Vouret-Craviari
- Faculty of Medicine, Université Côte d'Azur, CNRS, INSERM, IRCAN, 06108 Nice, France
- IHU RespirEREA, Université Côte d'Azur, 06108 Nice, France
- FHU OncoAge, 06108 Nice, France
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