Integrated mPD‐L1 and metabolic analysis identifies new prognostic subgroups in lung cancers with wild‐type EGFR

Guosheng Wang; Weilei Hu; Yundi Chen; Yuan Wan; Qiang Li

摘要

Dear Editor, Metabolic reprogramming, especially changes in glycolysis and cholesterogenesis pathways, has been reported to affect tumour prognosis. ~(1) Interestingly, there is an intricate relationship between metabolic changes and immune checkpoints in the tumour microenvironment (TME), ~(2) but their interaction and effects on the prognosis of lung cancer patients remain poorly understood. In this work, we established a novel stratification framework based on combined analysis of PD‐L1 mRNA (mPD‐L1) expression and glycolysis/cholesterol metabolic signatures, which stratified epidermal growth factor receptor (EGFR) wild‐type lung cancers into metabolic subtypes with significantly different prognoses. We also created a visualization website called glycolysis/cholesterol metabolism axis and PD‐L1 mRNA expression (GCP) ( https://www.liqlab.cn/gcp ) for this stratification approach. We first investigated the impact of the three metabolic subtypes on prognosis at different PD‐L1 expression levels. In the mPD‐L1 ~(low) group, cholesterogenic cases had a significantly worse OS and progression free survival (FPS) (mOS: 2.9 years; mFPS: 3.7 years) compared to glycolytic (mOS and mPFS were not reached) and quiescent cases (mOS: 5.5 years, mPFS was not reached). However, in the mPD‐L1 ~(high) group, cases belonging to the glycolytic subtype had a significantly worse OS and FPS (mOS: 3.3 years; mFPS: 2.3 years) than the cholesterogenic and quiescent subtypes (mOS: 7.1 years, mPFS: 7.2 years). Of note, no difference was observed among metabolic subtypes for the mPD‐L1 ~(med) group (Figure? 1C and Table S4 ). Furthermore, for the cholesterogenic subtype, higher PD‐L1 levels were associated with a better prognosis, whereas opposite findings were observed for the glycolytic subtype (Figure? 1D ). FIGURE 1 Clinical prognostic of EGFR wild type lung cancers with different metabolic subtypes. (A) Stratification of mPD‐L1 ~(low), mPD‐L1 ~(med), and mPD‐L1 ~(high) groups based on gene expression of glycolysis/cholesterol synthesis axis. Heatmap (upper) showing results of consensus clustering analysis for genes involved in glycolytic and cholesterogenic processes in mPD‐L1 ~(low) ( k ?=?4, n ?=?192), mPD‐L1 ~(med) ( k ?=?4, n ?=?383), and mPD‐L1 ~(high) ( k ?=?4, n ?=?208) groups of EGFR wild‐type lung cancers. Scatter plot (down) illustrating median expression level of co‐expressed genes associated with glycolytic (x‐axis) and cholesterogenic (y‐axis) processes for all samples. The expression of these genes was used to establish metabolic subtypes. (B) Overall survival for groups with highly expressed cholesterogenic genes and high or low glycolytic gene expression. (C) Kaplan–Meier survival analysis in mPD‐L1 ~(low), mPD‐L1 ~(med), or mPD‐L1 ~(high) groups stratified by metabolic subtype. Upper panel, overall survival (OS) analysis; lower panel, progression free survival (PFS) analysis; Log‐rank test p values are shown. (D) Overall survival for glycolytic and cholesterogenic groups with different mPD‐L1 expression For the mPD‐L1 ~(high) group, univariate cox regression analysis revealed that glycolytic subtype, pT stage and pTNM stage were correlated with OS and PFS. During multivariate Cox regression analysis, glycolytic subtype (hazard ratio [HR], 2.62; 95% confidence interval [CI], 1.19–5.73; p ?=?0.02) and pT stage (HR, 2.69; 95% CI, 1.08–6.73; p ?=?0.03) were found to be independent predictors of OS after adjusting for typical clinicopathologic factors (Table? 1 ). For the mPD‐L1 ~(low) group, univariate cox regression analysis uncovered that age, gender, smoking, glycolytic subtype and cholesterogenic subtype were correlated with OS, and only cholesterogenic subtype was related to PFS. Multivariate analysis using the Cox regression model further demonstrated that only the cholesterogenic subtype was independently prognostic for OS (HR, 2.07; 95% CI, 1.13–3.78; p ?=?0.02) (Table? 1 ). Overall, the above findings suggest that in EGFR wild‐type non‐small cell lung cancer (NSCLC), different metabolic subtypes have distinct prognostic outcomes based on PD‐L1 expression levels. A more aggressive phenotype could be associated with predominantly cholesterogenic tumours than those with predominantly glycolytic phenotype in EGFR wild‐type NSCLC poorly expressing PD‐L1. TABLE 1 Univariate and multivariate regression analysis of different clinical parameters and metabolic subtypes PD‐L1 ~(low) OS PFS Variable Univariate, p Multivariate (HR, 95% CI) p ‐value Univariate, p Multivariate (HR, 95% CI) p ‐value Age (&60?vs. ≤60) 0.026 1.003 0.971 1.036 0.866 0.52 1.004 0.97 1.04 0.812 Gender (male vs. female) 0.015 1.668 0.937 2.969 0.082 0.16 1.834 0.977 3.443 0.059 pT_stage (T3/T4 vs. T1/T2) 0.73 1.135 0.523 2.46 0.749 0.646 2.049 0.85 4.935 0.11 pN_stage (N1/N2/N3 vs. N0) 0.569 0.776 0.425 1.418 0.409 0.184 0.577 0.291 1.143 0.115 pM_stage (M1/MX vs. M0) 0.223 1.719 0.791 3.735 0.171 0.402 1.419 0.601 3.35 0.425 pTNM_stage (

机译：据报道，亲爱的编辑，代谢重编程，尤其是糖酵解和胆固化途径的变化，以影响肿瘤预后。〜（1）有趣的是，肿瘤微环境（TME）的代谢变化和免疫检查点之间存在复杂的关系，但它们的相互作用和对肺癌患者预后的影响仍然明白。在这项工作中，我们建立了一种基于PD-L1 mRNA（MPD-L1）表达和糖酵解/胆固醇代谢签名的组合分析的新型分层框架，其分层表皮生长因子受体（EGFR）野生型肺癌与代谢亚型显着不同的预测。我们还创建了一种称为糖酵解/胆固醇代谢轴和PD-L1 mRNA表达（GCP）（HTTPS://WWW.LIQLAB.CN/GCP）的可视化网站。我们首先研究了三种代谢亚型对不同PD-L1表达水平的预后的影响。在MPD-L1〜（低）组中，与甘露糖尿病（未达到的MOS和MPFS没有达到）和静止案件相比，胆固醇发生病例具有显着更差的操作系统和进展免费存活（FPS：2.9岁; MFPS：3.7岁）（MOS：5.5年，未达到MPFS）。然而，在MPD-L1〜（高）组中，属于糖酵解亚型的病例具有比胆固醇和静态亚型（MOS：7.1岁，MOS：7.1岁，MOS：7.1岁，MOS：3.3岁以下）具有显着更差的操作系统和FPS（MOS：3.3岁以下）：7.2岁）。值得注意的是，MPD-L1〜（MED）组的代谢亚型中没有观察到差异（图？1C和表S4）。此外，对于胆固醇生成的亚型，较高的PD-L1水平与更好的预后相关，而对糖酵解亚型观察到相反的发现（图？1D）。图1具有不同代谢亚型的EGFR野生型肺癌的临床预后。（a）基于基于糖酵解/胆固醇合成轴的基因表达的MPD-L1〜（低），MPD-L1〜（HID）和MPD-L1〜（高）基团分层。 Heatmap（上部）显示在MPD-L1〜（低）中参与糖酵解和胆固醇生成过程中涉及的基因的共识聚类分析结果（K？=Δ4，n？= 192），MPD-L1〜（MED）（K？ =？4，n？=？383），MPD-L1〜（高）（k？=Δ4，n？=α208）egfr野生型肺癌。散点图（下），说明与糖酵解（X轴）和所有样品相关的共表达基因的中值表达水平。这些基因的表达用于建立代谢亚型。（b）具有高表达胆固醇基因的基团的总存活和高或低糖酵解基因表达。（c）Kaplan-Meier存活分析在MPD-L1〜（低），MPD-L1〜（MED），或MPD-L1〜（高）组由代谢亚型分层分层。上面板，整体存活（OS）分析;小面板，进展免费生存（PFS）分析;显示日志级别测试P值。（d）糖酵解和胆固醇生成的总存活具有不同MPD-L1〜（高）组的不同MPD-L1表达，单变量COX回归分析显示糖酵解亚型，PT阶段和PTNM阶段与OS和PFS相关。在多变量Cox回归分析期间，糖酵解亚型（危险比[HR]，2.62; 95％置信区间[CI]，1.19-5.73; P？=Δ02）和Pt阶段（HR，2.69; 95％CI，1.08-6.73 ; P？=β03）被发现在调整典型临床病理因子后是OS的独立预测因子（表？1）。对于MPD-L1〜（低）组，单变量COX回归分析未发现该年龄，性别，吸烟，糖酵母亚型和胆固醇生成亚型与OS相关，并且只有胆固醇亚型与PFS有关。使用Cox回归模型的多变量分析进一步证明，只有胆固醇生成的亚型独立于OS预后（HR，2.07; 95％CI，1.13-3.78; P？=？02）（表？1）。总体而言，上述研究结果表明，在EGFR野生型非小细胞肺癌（NSCLC）中，不同的代谢亚型基于PD-L1表达水平具有不同的预后结果。更具侵略性的表型可以与EGFR野生型NSCLC中表达PD-L1中主要糖酵解表型的胆固醇成表型相关联。表1不同临床参数的单变量和多元回归分析和代谢亚型PD-L1〜（低）OS PFS可变单变量，P多变量（HR，95％CI）P -Value单变量，P多变量（HR，95％CI）P. - 值年龄（＆amp; 60？vs。≤60）0.026 1.003 0.971 1.036 0.866 0.52 1.004 0.97 1.04 0.812性别（男性与雌性）0.015 1.668 0.12.969 0.082 0.16 1.834 0.087 3.443 0.059 PT_STAGE（T3 / T4 Vs. T1 / t2）0.73 1.135 0.523 2.46 0.749 0.646 2.049 0.85 4.935 0.12 0.85 4.935 0.11 PN_stage（N1 / N2 / N3与N0）0.569 0.776 0.425 1.418 0.425 1.418 0.291 1.143 0.115 0.291 1.143 0.115 PM_STAGE（M1 / MX与M0）0.223 1.719 0.491 3.735 0.171 0.402 1.419 0.601 3.35 0.425 PTNM_STAGE（

Integrated mPD‐L1 and metabolic analysis identifies new prognostic subgroups in lung cancers with wild‐type EGFR

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