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Tumor infiltrated immune cell types support distinct immune checkpoint inhibitor outcomes in patients with advanced non‐small cell lung cancer
The evaluation of PD‐L1 expression alone has limitations in predicting clinical outcome in immune‐checkpoint inhibitors (ICI). This study aimed to evaluate the predictive and prognostic effects of the presence of various immune cells in pretreatment tissue samples and to identify determinants associ...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8248238/ https://www.ncbi.nlm.nih.gov/pubmed/33506525 http://dx.doi.org/10.1002/eji.202048966 |
Sumario: | The evaluation of PD‐L1 expression alone has limitations in predicting clinical outcome in immune‐checkpoint inhibitors (ICI). This study aimed to evaluate the predictive and prognostic effects of the presence of various immune cells in pretreatment tissue samples and to identify determinants associated with response in patients with advanced non‐small cell lung cancer (NSCLC) treated with PD‐1 blockade. Immune cell distribution was heterogeneous and the most dominant immune cell type was T cells. Patients with durable clinical benefit (DCB) showed significantly higher PD‐L1 expression. The ratio of tumor/stroma region of T cell, B cell, and macrophage was significantly higher in patient with DCB. High intratumoral T‐ and B‐cell density (≥median) was associated with DCB in the low PD‐L1 expression (<50%) group. In univariate analyses, the overall survival (OS) benefit was shown according to intratumoral B‐cell density (p = 0.0337). The incidence of hyperprogressive disease (HPD) was 13.0%. The Chi‐square test revealed that HPD was significantly associated with intratumoral B‐cell density but not T‐cell or macrophage density. Our results demonstrate different predictive and prognostic values for infiltrating immune cells in tumor tissue, which may help in selecting patients for ICI. |
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