免疫检查点抑制剂（ICI）在特定的癌症中表现出高效性，其中抗肿瘤免疫的典型标志被用于患者选择。需要改进T细胞炎症的预测因子，以在其他癌症类型中识别对ICI有响应的肿瘤亚组。我们调查了4种趋化因子表达特征（c-分数：CCL4，CCL5，CXCL9，CXCL10）与肿瘤免疫抗性指标的关联。通过从癌症基因组图谱中选择的癌症实体，肿瘤亚组显示出c-分数的高表达（c-分数高），并伴随着免疫检查点（IC）基因和癌症免疫周期的转录标志的增加表达。c-分数与高肿瘤突变负荷（TMB）之间的相关性不完整，仅有15％的c-分数高肿瘤显示出每兆碱基≥10个突变。在一个异质的全癌症患者队列中（包括82例先进和曾经治疗过的实体肿瘤），与低c-分数表达病例相比，c-分数高肿瘤在ICI治疗开始后具有更长的中位进展时间（103天对比72天，P = 0.012）和总生存期（382天对比196天，P = 0.038）。我们还发现，相对于TMB分配，c-分数分层对于总生存预测更有效（HR = 0.42 [0.22-0.79]，P = 0.008对比HR = 0.60 [0.29-1.27]，P = 0.18）。使用TIDE和PredictIO数据库评估c-分数，这些数据库包含来自10种肿瘤类型的ICI治疗结果，进一步支持了c-分数作为预测性ICI治疗生物标志物的有效性。总而言之，c-分数可以识别出在多种癌症类型中具有T细胞炎症标志和对ICI治疗潜在响应的患者，而TMB分配则没有检测到这一点。© 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.© 2023. Nature Publishing Group UK.