Details

Autor(en) / Beteiligte

• Romero, Joan Miguel
• Titmuss, Emma
• Wang, Yifan
• Vafiadis, James
• Pacis, Alain
• Jang, Gun Ho
• Zhang, Amy
• Golesworthy, Bryn
• Lenko, Tatiana
• Williamson, Laura M
• Grünwald, Barbara
• O'Kane, Grainne M
• Jones, Steven J M
• Marra, Marco A
• Wilson, Julie M
• Gallinger, Steven
• Laskin, Janessa
• Zogopoulos, George

Titel

Chemokine expression predicts T cell-inflammation and improved survival with checkpoint inhibition across solid cancers

Ist Teil von

• NPJ precision oncology, 2023-08, Vol.7 (1), p.73-73, Article 73

Ort / Verlag

England: Nature Publishing Group

Erscheinungsjahr

2023

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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-Score ) 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-Score tumors displaying ≥10 mutations per megabase. In a heterogeneous pan-cancer cohort of 82 patients, with advanced and previously treated solid cancers, c-Score 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.