DataSheet_1_A Novel Computational Framework for Predicting the Survival of Cancer Patients With PD-1/PD-L1 Checkpoint Blockade Therapy.zip

BackgroundImmune checkpoint inhibitors (ICIs) induce durable responses, but only a minority of patients achieve clinical benefits. The development of gene expression profiling of tumor transcriptomes has enabled identifying prognostic gene expression signatures and patient selection with targeted therapies.
MethodsImmune exclusion score (IES) was built by elastic net-penalized Cox proportional hazards (PHs) model in the discovery cohort and validated via four independent cohorts. The survival differences between the two groups were compared using Kaplan-Meier analysis. Both GO and KEGG analyses were performed for functional annotation. CIBERSORTx was also performed to estimate the relative proportion of immune-cell types.
ResultsA fifteen-genes immune exclusion score (IES) was developed in the discovery cohort of 65 patients treated with anti-PD-(L)1 therapy. The ROC efficiencies of 1- and 3- year prognosis were 0.842 and 0.82, respectively. Patients with low IES showed a longer PFS (p=0.003) and better response rate (ORR: 43.8% vs 18.2%, p=0.03). We found that patients with low IES enriched with high expression of immune eliminated cell genes, such as CD8+ T cells, CD4+ T cells, NK cells and B cells. IES was positively correlated with other immune exclusion signatures. Furthermore, IES was successfully validated in four independent cohorts (Riaz’s SKCM, Liu’s SKCM, Nathanson’s SKCM and Braun’s ccRCC, n = 367). IES was also negatively correlated with T cell–inflamed signature and independent of TMB.
ConclusionsThis novel IES model encompassing immune-related biomarkers might serve as a promising tool for the prognostic prediction of immunotherapy.

背景：免疫检查点抑制剂（Immune checkpoint inhibitors, ICIs）可诱导持久的临床应答，但仅少数患者可从中获得临床获益。肿瘤转录组基因表达谱技术的发展，已实现预后基因表达特征的识别，以及通过靶向疗法实现患者筛选。

方法：本研究通过发现队列构建弹性网络正则化Cox比例风险（Cox proportional hazards, PHs）模型，以建立免疫排斥评分（Immune exclusion score, IES），并通过4个独立队列完成验证。采用Kaplan-Meier分析比较两组间的生存差异；同时开展基因本体（Gene Ontology, GO）与京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）富集分析以进行功能注释；此外借助CIBERSORTx工具评估免疫细胞类型的相对占比。

结果：在65例接受抗PD-(L)1治疗的患者组成的发现队列中，本研究构建了15基因免疫排斥评分（IES）。该评分对1年和3年预后的ROC曲线效能分别为0.842和0.82。低IES组患者的无进展生存期（Progression-Free Survival, PFS）更长（p=0.003），客观缓解率（Objective Response Rate, ORR）更高（43.8% vs 18.2%，p=0.03）。研究发现，低IES组患者的免疫清除相关细胞基因呈高表达状态，例如CD8+ T细胞、CD4+ T细胞、自然杀伤细胞（Natural Killer cells, NK cells）及B细胞。IES与其他免疫排斥特征呈正相关，且可在4个独立队列（Riaz的皮肤黑色素瘤（Skin Cutaneous Melanoma, SKCM）队列、Liu的皮肤黑色素瘤（Skin Cutaneous Melanoma, SKCM）队列、Nathanson的皮肤黑色素瘤（Skin Cutaneous Melanoma, SKCM）队列及Braun的肾透明细胞癌（clear cell Renal Cell Carcinoma, ccRCC）队列，共n=367例患者）中得到有效验证。此外，IES与T细胞炎性特征呈负相关，且独立于肿瘤突变负荷（Tumor Mutational Burden, TMB）。

结论：这款包含免疫相关生物标志物的新型IES模型，有望成为免疫治疗预后预测的可靠工具。