Validation of a multiomic model of plasma extracellular vesicle PD-L1 and radiomics for prediction of response to immunotherapy in NSCLC
Background Immune-checkpoint inhibitors (ICIs) have showed unprecedent efficacy in the treatment of patients with advanced non-small cell lung cancer (NSCLC). However, not all patients manifest clinical benefit due to the lack of reliable predictive biomarkers. We showed preliminary data on the predictive role of the combination of radiomics and plasma extracellular vesicle (EV) PD-L1 to predict durable response to ICIs. Main body Here, we validated this model in a prospective cohort of patients receiving ICIs plus chemotherapy and compared it with patients undergoing chemotherapy alone. This multiparametric model showed high sensitivity and specificity at identifying non-responders to ICIs and outperformed tissue PD-L1, being directly correlated with tumor change. Short conclusion These findings indicate that the combination of radiomics and EV PD-L1 dynamics is a minimally invasive and promising biomarker for the stratification of patients to receive ICIs..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:43 |
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| Enthalten in: |
Journal of experimental & clinical cancer research - 43(2024), 1 vom: 15. März |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
de Miguel‑Perez, Diego [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Themen: |
Biomarker |
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| Anmerkungen: |
© The Author(s) 2024 |
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| doi: |
10.1186/s13046-024-02997-x |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
SPR055154042 |
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| 520 | |a Background Immune-checkpoint inhibitors (ICIs) have showed unprecedent efficacy in the treatment of patients with advanced non-small cell lung cancer (NSCLC). However, not all patients manifest clinical benefit due to the lack of reliable predictive biomarkers. We showed preliminary data on the predictive role of the combination of radiomics and plasma extracellular vesicle (EV) PD-L1 to predict durable response to ICIs. Main body Here, we validated this model in a prospective cohort of patients receiving ICIs plus chemotherapy and compared it with patients undergoing chemotherapy alone. This multiparametric model showed high sensitivity and specificity at identifying non-responders to ICIs and outperformed tissue PD-L1, being directly correlated with tumor change. Short conclusion These findings indicate that the combination of radiomics and EV PD-L1 dynamics is a minimally invasive and promising biomarker for the stratification of patients to receive ICIs. | ||
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| 700 | 1 | |a Mamindla, Priyadarshini |4 aut | |
| 700 | 1 | |a Russo, Alessandro |4 aut | |
| 700 | 1 | |a Zenkin, Serafettin |4 aut | |
| 700 | 1 | |a Ak, Nursima |4 aut | |
| 700 | 1 | |a Peddagangireddy, Vishal |4 aut | |
| 700 | 1 | |a Lara‑Mejia, Luis |4 aut | |
| 700 | 1 | |a Gunasekaran, Muthukumar |4 aut | |
| 700 | 1 | |a Cardona, Andres F. |4 aut | |
| 700 | 1 | |a Naing, Aung |4 aut | |
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| 700 | 1 | |a Arrieta, Oscar |4 aut | |
| 700 | 1 | |a Colen, Rivka R. |4 aut | |
| 700 | 1 | |a Rolfo, Christian |4 aut | |
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