Chemokine expression predicts T cell-inflammation and improved survival with checkpoint inhibition across solid cancers
© 2023. Nature Publishing Group UK..
Immune checkpoint inhibitors (ICI) are highly effective in specific cancers where canonical markers of antitumor immunity are used for patient selection. Improved predictors of T cell-inflammation are needed to identify ICI-responsive tumor subsets in additional cancer types. We investigated associations of a 4-chemokine expression signature (c-Score: CCL4, CCL5, CXCL9, CXCL10) with metrics of antitumor immunity across tumor types. Across cancer entities from The Cancer Genome Atlas, subgroups of tumors displayed high expression of the c-Score (c-Scorehi) with increased expression of immune checkpoint (IC) genes and transcriptional hallmarks of the cancer-immunity cycle. There was an incomplete association of the c-Score with high tumor mutation burden (TMB), with only 15% of c-Scorehi tumors displaying ≥10 mutations per megabase. In a heterogeneous pan-cancer cohort of 82 patients, with advanced and previously treated solid cancers, c-Scorehi tumors had a longer median time to progression (103 versus 72 days, P = 0.012) and overall survival (382 versus 196 days, P = 0.038) following ICI therapy initiation, compared to patients with low c-Score expression. We also found c-Score stratification to outperform TMB assignment for overall survival prediction (HR = 0.42 [0.22-0.79], P = 0.008 versus HR = 0.60 [0.29-1.27], P = 0.18, respectively). Assessment of the c-Score using the TIDE and PredictIO databases, which include ICI treatment outcomes from 10 tumor types, provided further support for the c-Score as a predictive ICI therapeutic biomarker. In summary, the c-Score identifies patients with hallmarks of T cell-inflammation and potential response to ICI treatment across cancer types, which is missed by TMB assignment.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:7 |
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| Enthalten in: |
NPJ precision oncology - 7(2023), 1 vom: 09. Aug., Seite 73 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Romero, Joan Miguel [VerfasserIn] |
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Date Revised 23.11.2023 published: Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1038/s41698-023-00428-2 |
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| Förderinstitution / Projekttitel: |
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NLM360583458 |
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| 520 | |a Immune checkpoint inhibitors (ICI) are highly effective in specific cancers where canonical markers of antitumor immunity are used for patient selection. Improved predictors of T cell-inflammation are needed to identify ICI-responsive tumor subsets in additional cancer types. We investigated associations of a 4-chemokine expression signature (c-Score: CCL4, CCL5, CXCL9, CXCL10) with metrics of antitumor immunity across tumor types. Across cancer entities from The Cancer Genome Atlas, subgroups of tumors displayed high expression of the c-Score (c-Scorehi) with increased expression of immune checkpoint (IC) genes and transcriptional hallmarks of the cancer-immunity cycle. There was an incomplete association of the c-Score with high tumor mutation burden (TMB), with only 15% of c-Scorehi tumors displaying ≥10 mutations per megabase. In a heterogeneous pan-cancer cohort of 82 patients, with advanced and previously treated solid cancers, c-Scorehi tumors had a longer median time to progression (103 versus 72 days, P = 0.012) and overall survival (382 versus 196 days, P = 0.038) following ICI therapy initiation, compared to patients with low c-Score expression. We also found c-Score stratification to outperform TMB assignment for overall survival prediction (HR = 0.42 [0.22-0.79], P = 0.008 versus HR = 0.60 [0.29-1.27], P = 0.18, respectively). Assessment of the c-Score using the TIDE and PredictIO databases, which include ICI treatment outcomes from 10 tumor types, provided further support for the c-Score as a predictive ICI therapeutic biomarker. In summary, the c-Score identifies patients with hallmarks of T cell-inflammation and potential response to ICI treatment across cancer types, which is missed by TMB assignment | ||
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| 700 | 1 | |a Titmuss, Emma |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Yifan |e verfasserin |4 aut | |
| 700 | 1 | |a Vafiadis, James |e verfasserin |4 aut | |
| 700 | 1 | |a Pacis, Alain |e verfasserin |4 aut | |
| 700 | 1 | |a Jang, Gun Ho |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Amy |e verfasserin |4 aut | |
| 700 | 1 | |a Golesworthy, Bryn |e verfasserin |4 aut | |
| 700 | 1 | |a Lenko, Tatiana |e verfasserin |4 aut | |
| 700 | 1 | |a Williamson, Laura M |e verfasserin |4 aut | |
| 700 | 1 | |a Grünwald, Barbara |e verfasserin |4 aut | |
| 700 | 1 | |a O'Kane, Grainne M |e verfasserin |4 aut | |
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| 700 | 1 | |a Wilson, Julie M |e verfasserin |4 aut | |
| 700 | 1 | |a Gallinger, Steven |e verfasserin |4 aut | |
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| 700 | 1 | |a Zogopoulos, George |e verfasserin |4 aut | |
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