Microenvironment of metastasis reveals key predictors of PD-1 blockade response in renal cell carcinoma
Abstract Immune checkpoint blockade (ICB) therapies have improved the overall survival (OS) of many patients with advanced cancers. However, the response rate to ICB varies widely among patients, exposing non-responders to potentially severe immune-related adverse events. The discovery of new biomarkers to identify patients responding to ICB is now a critical need in the clinic. We therefore investigated the tumor microenvironment (TME) of advanced clear cell renal cell carcinoma (ccRCC) samples from primary and metastatic sites to identify molecular and cellular markers of response to ICB. We revealed a significant discrepancy in treatment response between subgroups based on cell fractions inferred from metastatic sites. One of the subgroups was enriched in non-responders and harbored a lower fraction of CD8+ T cells and plasma cells, as well as a decreased expression of immunoglobulin genes. In addition, we developed the Tumor-Immunity Differential (TID) score which combines features from tumor cells and the TME to accurately predict response to anti-PD-1 immunotherapy (AUC-ROC=0.88, log-rank tests for PFS P < 0.0001, OS P = 0.01). Finally, we also defined TID-related genes (YWHAE,CXCR6andBTF3), among whichYWHAEwas validated as a robust predictive marker of ICB response in independent cohorts of pre- or on-treatment biopsies of melanoma and lung cancers. Overall, these results provide a rationale to further explore variations in the cell composition of metastatic sites, and underlying gene signatures, to predict patient response to ICB treatments.One Sentence Summary Tumor microenvironment balance of metastasis and associated genes are key predictors of immunotherapy patient response in kidney cancer..
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Preprint| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
bioRxiv.org - (2024) vom: 13. Jan. Zur Gesamtaufnahme - year:2024 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Jeanneret, Florian [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| doi: |
10.1101/2023.07.17.548676 |
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| PPN (Katalog-ID): |
XBI040257304 |
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| 520 | |a Abstract Immune checkpoint blockade (ICB) therapies have improved the overall survival (OS) of many patients with advanced cancers. However, the response rate to ICB varies widely among patients, exposing non-responders to potentially severe immune-related adverse events. The discovery of new biomarkers to identify patients responding to ICB is now a critical need in the clinic. We therefore investigated the tumor microenvironment (TME) of advanced clear cell renal cell carcinoma (ccRCC) samples from primary and metastatic sites to identify molecular and cellular markers of response to ICB. We revealed a significant discrepancy in treatment response between subgroups based on cell fractions inferred from metastatic sites. One of the subgroups was enriched in non-responders and harbored a lower fraction of CD8+ T cells and plasma cells, as well as a decreased expression of immunoglobulin genes. In addition, we developed the Tumor-Immunity Differential (TID) score which combines features from tumor cells and the TME to accurately predict response to anti-PD-1 immunotherapy (AUC-ROC=0.88, log-rank tests for PFS P < 0.0001, OS P = 0.01). Finally, we also defined TID-related genes (YWHAE,CXCR6andBTF3), among whichYWHAEwas validated as a robust predictive marker of ICB response in independent cohorts of pre- or on-treatment biopsies of melanoma and lung cancers. Overall, these results provide a rationale to further explore variations in the cell composition of metastatic sites, and underlying gene signatures, to predict patient response to ICB treatments.One Sentence Summary Tumor microenvironment balance of metastasis and associated genes are key predictors of immunotherapy patient response in kidney cancer. | ||
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| 700 | 1 | |a Schoch, Sarah |4 aut | |
| 700 | 1 | |a Pillet, Catherine |4 aut | |
| 700 | 1 | |a Um, In Hwa |4 aut | |
| 700 | 1 | |a Bouzit, Assilah |4 aut | |
| 700 | 1 | |a Evrard, Bertrand |4 aut | |
| 700 | 1 | |a Seffar, Evan |4 aut | |
| 700 | 1 | |a Chalmel, Frédéric |4 aut | |
| 700 | 1 | |a Alfaro, Javier A |4 aut | |
| 700 | 1 | |a Pesquita, Catia |4 aut | |
| 700 | 1 | |a Zanzotto, Fabio Massimo |4 aut | |
| 700 | 1 | |a Stares, Mark |4 aut | |
| 700 | 1 | |a Symeonides, Stefan N |4 aut | |
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| 700 | 1 | |a Long, Jean-Alexandre |4 aut | |
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| 700 | 1 | |a Pflieger, Delphine |4 aut | |
| 700 | 1 | |a Harrison, David J |4 aut | |
| 700 | 1 | |a Filhol, Odile |4 aut | |
| 700 | 1 | |a Axelson, Håkan |4 aut | |
| 700 | 1 | |a Battail, Christophe |4 aut | |
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