Developing a risk score using liquid biopsy biomarkers for selecting Immunotherapy responders and stratifying disease progression risk in metastatic melanoma patients
Abstract Background Despite the high response rate to PD-1 blockade therapy in metastatic melanoma (MM) patients, a significant proportion of patients do not respond. Identifying biomarkers to predict patient response is crucial, ideally through non-invasive methods such as liquid biopsy. Methods So...
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BMC
2025-02-01
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| Series: | Journal of Experimental & Clinical Cancer Research |
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| Online Access: | https://doi.org/10.1186/s13046-025-03306-w |
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| author | Amalia Azzariti Simona De Summa Tommaso M. Marvulli Ivana De Risi Giuseppe De Palma Roberta Di Fonte Rossella Fasano Simona Serratì Sabino Strippoli Letizia Porcelli Michele Guida |
| author_facet | Amalia Azzariti Simona De Summa Tommaso M. Marvulli Ivana De Risi Giuseppe De Palma Roberta Di Fonte Rossella Fasano Simona Serratì Sabino Strippoli Letizia Porcelli Michele Guida |
| author_sort | Amalia Azzariti |
| collection | DOAJ |
| description | Abstract Background Despite the high response rate to PD-1 blockade therapy in metastatic melanoma (MM) patients, a significant proportion of patients do not respond. Identifying biomarkers to predict patient response is crucial, ideally through non-invasive methods such as liquid biopsy. Methods Soluble forms of PD1, PD-L1, LAG-3, CTLA-4, CD4, CD73, and CD74 were quantified using ELISA assay in plasma of a cohort of 110 MM patients, at baseline, to investigate possible correlations with clinical outcomes. A clinical risk prediction model was applied and validated in pilot studies. Results No biomarker showed statistically significant differences between responders and non-responders. However, high number of significant correlations were observed among certain biomarkers in non-responders. Through univariate and multivariate Cox analyses, we identified sPD-L1, sCTLA-4, sCD73, and sCD74 as independent biomarkers predicting progression-free survival and overall survival. According to ROC analysis we discovered that, except for sCD73, values of sPD-L1, sCTLA-4, and sCD74 lower than the cut-off predicted lower disease progression and reduced mortality. A comprehensive risk score for predicting progression-free survival was developed by incorporating the values of the two identified independent factors, sCTLA-4 and sCD74, which significantly improved the accuracy of outcome prediction. Pilot validations highlighted the potential use of the risk score in treatment-naive individuals and long responders. Conclusion In summary, risk score based on circulating sCTLA-4 and sCD74 reflects the response to immune checkpoint inhibitor (ICI) therapy in MM patients. If confirmed, through further validation, these findings could assist in recommending therapy to patients likely to experience a long-lasting response. |
| format | Article |
| id | doaj-art-16d8193e1d2143a89087d15af53aa0ca |
| institution | Kabale University |
| issn | 1756-9966 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Experimental & Clinical Cancer Research |
| spelling | doaj-art-16d8193e1d2143a89087d15af53aa0ca2025-02-09T12:59:51ZengBMCJournal of Experimental & Clinical Cancer Research1756-99662025-02-0144111410.1186/s13046-025-03306-wDeveloping a risk score using liquid biopsy biomarkers for selecting Immunotherapy responders and stratifying disease progression risk in metastatic melanoma patientsAmalia Azzariti0Simona De Summa1Tommaso M. Marvulli2Ivana De Risi3Giuseppe De Palma4Roberta Di Fonte5Rossella Fasano6Simona Serratì7Sabino Strippoli8Letizia Porcelli9Michele Guida10Experimental Pharmacology Laboratory, IRCCS Istituto Tumori Giovanni Paolo IIBiostatistic and Bioinformatic Laboratory, IRCCS Istituto Tumori Giovanni Paolo IIMolecular Diagnostics and Pharmacogenetics Unit, IRCCS Istituto Tumori Giovanni Paolo IIRare Tumors and Melanoma Unit, IRCCS Istituto Tumori Giovanni Paolo IIBiobank, IRCCS Istituto Tumori Giovanni Paolo IIExperimental Pharmacology Laboratory, IRCCS Istituto Tumori Giovanni Paolo IIExperimental Pharmacology Laboratory, IRCCS Istituto Tumori Giovanni Paolo IIExperimental Pharmacology Laboratory, IRCCS Istituto Tumori Giovanni Paolo IIRare Tumors and Melanoma Unit, IRCCS Istituto Tumori Giovanni Paolo IIExperimental Pharmacology Laboratory, IRCCS Istituto Tumori Giovanni Paolo IIRare Tumors and Melanoma Unit, IRCCS Istituto Tumori Giovanni Paolo IIAbstract Background Despite the high response rate to PD-1 blockade therapy in metastatic melanoma (MM) patients, a significant proportion of patients do not respond. Identifying biomarkers to predict patient response is crucial, ideally through non-invasive methods such as liquid biopsy. Methods Soluble forms of PD1, PD-L1, LAG-3, CTLA-4, CD4, CD73, and CD74 were quantified using ELISA assay in plasma of a cohort of 110 MM patients, at baseline, to investigate possible correlations with clinical outcomes. A clinical risk prediction model was applied and validated in pilot studies. Results No biomarker showed statistically significant differences between responders and non-responders. However, high number of significant correlations were observed among certain biomarkers in non-responders. Through univariate and multivariate Cox analyses, we identified sPD-L1, sCTLA-4, sCD73, and sCD74 as independent biomarkers predicting progression-free survival and overall survival. According to ROC analysis we discovered that, except for sCD73, values of sPD-L1, sCTLA-4, and sCD74 lower than the cut-off predicted lower disease progression and reduced mortality. A comprehensive risk score for predicting progression-free survival was developed by incorporating the values of the two identified independent factors, sCTLA-4 and sCD74, which significantly improved the accuracy of outcome prediction. Pilot validations highlighted the potential use of the risk score in treatment-naive individuals and long responders. Conclusion In summary, risk score based on circulating sCTLA-4 and sCD74 reflects the response to immune checkpoint inhibitor (ICI) therapy in MM patients. If confirmed, through further validation, these findings could assist in recommending therapy to patients likely to experience a long-lasting response.https://doi.org/10.1186/s13046-025-03306-wMetastatic melanomaPredictor of anti-PD1 responseAnti-PD1 resistancesPD1sPD-L1sLAG-3 |
| spellingShingle | Amalia Azzariti Simona De Summa Tommaso M. Marvulli Ivana De Risi Giuseppe De Palma Roberta Di Fonte Rossella Fasano Simona Serratì Sabino Strippoli Letizia Porcelli Michele Guida Developing a risk score using liquid biopsy biomarkers for selecting Immunotherapy responders and stratifying disease progression risk in metastatic melanoma patients Journal of Experimental & Clinical Cancer Research Metastatic melanoma Predictor of anti-PD1 response Anti-PD1 resistance sPD1 sPD-L1 sLAG-3 |
| title | Developing a risk score using liquid biopsy biomarkers for selecting Immunotherapy responders and stratifying disease progression risk in metastatic melanoma patients |
| title_full | Developing a risk score using liquid biopsy biomarkers for selecting Immunotherapy responders and stratifying disease progression risk in metastatic melanoma patients |
| title_fullStr | Developing a risk score using liquid biopsy biomarkers for selecting Immunotherapy responders and stratifying disease progression risk in metastatic melanoma patients |
| title_full_unstemmed | Developing a risk score using liquid biopsy biomarkers for selecting Immunotherapy responders and stratifying disease progression risk in metastatic melanoma patients |
| title_short | Developing a risk score using liquid biopsy biomarkers for selecting Immunotherapy responders and stratifying disease progression risk in metastatic melanoma patients |
| title_sort | developing a risk score using liquid biopsy biomarkers for selecting immunotherapy responders and stratifying disease progression risk in metastatic melanoma patients |
| topic | Metastatic melanoma Predictor of anti-PD1 response Anti-PD1 resistance sPD1 sPD-L1 sLAG-3 |
| url | https://doi.org/10.1186/s13046-025-03306-w |
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