肿瘤细胞治疗研究
英文原题：Inflammatory response signature score model for predicting immunotherapy response and pan-cancer prognosis.
在本研究中，我们开发了一个炎症反应基因特征模型，用于预测多种恶性肿瘤患者的生存和免疫治疗反应。我们进一步发现，在六种不同的恶性肿瘤亚组中，非小细胞肺癌和胃癌组的预测性能最高。与现有特征相比，治疗中样本的炎症反应基因特征评分是转移性黑色素瘤CIT反应更稳健的预测因子。
炎症反应通过调节宿主免疫影响免疫治疗和肿瘤发生的结果。然而，用于预测跨人类癌症的癌症免疫治疗（CIT）反应和生存的系统性炎症反应评估模型仍未被探索。在此，我们在一项泛癌分析中研究了一种炎症反应评分模型，以预测CIT反应和患者生存。
我们从Gene Expression Omnibus数据库（GSE78220、GSE19423、GSE100797、GSE126044、GSE35640、GSE67501、GSE115821和GSE168204）、Tumor Immune Dysfunction and Exclusion数据库（PRJEB23709、PRJEB25780和phs000452.v2.p1）、European Genome-phenome Archive数据库（EGAD00001005738）以及IMvigor210队列中检索了12个CIT应答基因表达数据集。肿瘤样本来自六种癌症类型：转移性尿路上皮癌、转移性黑色素瘤、胃癌、原发性膀胱癌、肾细胞癌和非小细胞肺癌。我们进一步使用最小绝对收缩和选择算子（LASSO）计算算法建立了二元分类模型来预测CIT应答。
该模型在训练队列和验证队列中均具有较高的预测准确性。在亚组分析中，非小细胞肺癌、胃癌、转移性尿路上皮癌、原发性膀胱癌、转移性黑色素瘤和肾细胞癌队列的曲线下面积（AUC）值分别为0.82、0.80、0.71、0.7、0.67和0.64。高评分训练队列受试者的CIT缓解率（51%）高于低评分受试者（27%）。训练队列高评分组和低评分组的五年生存率分别为62%和21%，而验证队列分别为54%和22%（所有情况下P < 0·001）。从治疗中肿瘤标本衍生的炎症反应特征评分对转移性黑色素瘤患者的CIT缓解具有高度预测性。观察到炎症反应评分与肿瘤纯度之间存在显著相关性。无论肿瘤纯度如何，低评分组患者的预后均显著差于高评分组。免疫细胞浸润分析表明，在高评分队列中，TIL（肿瘤浸润淋巴细胞）显著富集，尤其是效应细胞和NK 细胞。炎症反应评分与免疫检查点基因呈正相关，提示免疫检查点抑制剂可能使高评分患者获益。对来自The Cancer Genome Atlas的不同癌症类型特征评分的分析显示，炎症反应评分对未接受免疫治疗患者生存的预后性能可能受到肿瘤纯度的影响。白细胞介素21（IL21）在炎症反应模型中的权重最高，表明其在预测模式中发挥关键作用。由于转移性黑色素瘤患者数量（n = 429）在CIT队列中相对较多，我们进一步使用黑色素瘤细胞系和由外周血单核细胞生成的CD8 + T细胞群进行了共培养实验。结果显示，IL21治疗联合抗PD1（程序性细胞死亡1）抗体（trepril单克隆抗体）显著增强了CD8 + T细胞对黑色素瘤细胞系的细胞毒性活性。
BACKGROUND: Inflammatory responses influence the outcome of immunotherapy and tumorigenesis by modulating host immunity. However, systematic inflammatory response assessment models for predicting cancer immunotherapy (CIT) responses and survival across human cancers remain unexplored. Here, we investigated an inflammatory response score model to predict CIT responses and patient survival in a pan-cancer analysis. METHODS: We retrieved 12 CIT response gene expression datasets from the Gene Expression Omnibus database (GSE78220, GSE19423, GSE100797, GSE126044, GSE35640, GSE67501, GSE115821 and GSE168204), Tumor Immune Dysfunction and Exclusion database (PRJEB23709, PRJEB25780 and phs000452.v2.p1), European Genome-phenome Archive database (EGAD00001005738), and IMvigor210 cohort. The tumor samples from six cancers types: metastatic urothelial cancer, metastatic melanoma, gastric cancer, primary bladder cancer, renal cell carcinoma, and non-small cell lung cancer.We further established a binary classification model to predict CIT responses using the least absolute shrinkage and selection operator (LASSO) computational algorithm. FINDINGS: The model had high predictive accuracy in both the training and validation cohorts. During sub-group analysis, area under the curve (AUC) values of 0.82, 0.80, 0.71, 0.7, 0.67, and 0.64 were obtained for the non-small cell lung cancer, gastric cancer, metastatic urothelial cancer, primary bladder cancer, metastatic melanoma, and renal cell carcinoma cohorts, respectively. CIT response rates were higher in the high-scoring training cohort subjects (51%) than the low-scoring subjects (27%). The five-year survival rates in the high- and low score groups of the training cohorts were 62% and 21%, respectively, while those of the validation cohorts were 54% and 22%, respectively ( P < 0·001 in all cases). Inflammatory response signature score derived from on-treatment tumor specimens are highly predictive of response to CIT in patients with metastatic melanoma. A significant correlation was observed between the inflammatory response scores and tumor purity. Regardless of the tumor purity, patients in the low score group had a significantly poorer prognosis than those in the high score group. Immune cell infiltration analysis indicated that in the high score cohort, tumor-infiltrating lymphocytes were significantly enriched, particularly effector and natural killer cells. Inflammatory response scores were positively correlated with immune checkpoint genes, suggesting that immune checkpoint inhibitors may have benefited patients with high scores. Analysis of signature scores across different cancer types from The Cancer Genome Atlas revealed that the prognostic performance of inflammatory response scores for survival in patients who have not undergone immunotherapy can be affected by tumor purity. Interleukin 21 (IL21) had the highest weight in the inflammatory response model, suggesting its vital role in the prediction mode. Since the number of metastatic melanoma patients (n = 429) was relatively large among CIT cohorts, we further performed a co-culture experiment using a melanoma cell line and CD8 + T cell populations generated from peripheral blood monocytes. The results showed that IL21 therapy combined with anti-PD1 (programmed cell death 1) antibodies (trepril monoclonal antibodies) significantly enhanced the cytotoxic activity of CD8 + T cells against the melanoma cell line. CONCLUSION: In this study, we developed an inflammatory response gene signature model that predicts patient survival and immunotherapy response in multiple malignancies. We further found that the predictive performance in the non-small cell lung cancer and gastric cancer group had the highest value among the six different malignancy subgroups. When compared with existing signatures, the inflammatory response gene signature scores for on-treatment samples were more robust predictors of the response to CIT in metastatic melano
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