PO.BCS01.04 · 生物信息与计算
通过转录谱解析mNSCLC的基线肿瘤微环境,识别可预测免疫治疗应答的肿瘤微环境条件
Dissecting the baseline tumor microenvironment in mNSCLC by transcriptional profiling identifies tumor microenvironmental conditions predictive for immunotherapy response
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摘要 Abstract
中文摘要
背景:MYSTIC试验(NCT02453282)是一项III期研究,在一线转移性NSCLC中比较durvalumab±tremelimumab与标准治疗(SOC)化疗,在意向治疗(ITT)人群中未达到总生存期(OS)的主要终点。本分析旨在识别基线肿瘤免疫微环境(TME)特征是否可预测该患者人群对durvalumab(D)±tremelimumab(T)治疗的有利或不良应答。
方法:对MYSTIC生物标志物可评估人群(BEP)中185例患者的基线肿瘤样本进行RNA测序。采用非负矩阵分解(NMF)根据变异度前10%的蛋白编码基因的表达解析出不同的聚类。分析所得聚类在临床结局、基因组改变、免疫细胞组成、通路活性以及T细胞和B细胞受体库方面的差异。
结果:NMF在MYSTIC BEP人群中识别出四个不同的聚类,其中两个聚类以肿瘤免疫分组为显著特征。聚类1(20%的患者)表现出促炎性TME的标志,具有高T细胞特征、巨噬细胞浸润和检查点抑制分子的表达。该聚类中接受D+T治疗的患者相比其他三个聚类中接受该治疗的患者,生存期延长(中位OS 19.78个月)(HR 0.39,95%CI 0.15-1)。聚类1还富集了PD-L1≥50%的患者。聚类3(20%的患者)显示出与免疫抑制相关的特征,包括高粒细胞和Treg浸润、低巨噬细胞特征,以及TGFbeta和干性通路的富集。这些患者接受D+T治疗时相比其他聚类经历了更差的生存结局(中位OS 2.17个月)(HR 3.13,95%CI 1.48-6.6)。最值得注意的是,聚类3中的患者在D+T治疗下的生存相比聚类1中的患者显著更差(HR 6.15,95%CI 2.02-18.8)。其余两个聚类可以组织学为特征,聚类2(35%的患者)以非鳞状组织学以及STK11和MYC改变的富集为特征,聚类4(25%的患者)以鳞状组织学、侵袭性增殖特征以及TP53、MLL2、PTEN和PIK3CA改变的富集为特征。这两个聚类均未对D+T治疗产生显著影响。
结论:对MYSTIC基线肿瘤样本的NMF聚类揭示了与免疫治疗应答分化相关的不同TME谱。促炎性聚类预示D+T的获益,而抑制性聚类则与不良结局相符。研究结果提示,应答不仅取决于整体免疫浸润,还取决于TME内特定的免疫细胞组成。
查看英文原文 English abstract
Background: The MYSTIC trial (NCT02453282), a phase 3 study of durvalumab ± tremelimumab vs. standard of care (SOC) chemotherapy in 1L metastatic NSCLC, did not meet its primary endpoint of overall survival (OS) in the intent-to-treat (ITT) population. This analysis aimed to identify whether baseline tumor-immune microenvironment (TME) features were predictive for either favorable or poor response to durvalumab (D) ± tremelimumab (T) treatment in this patient population.
Methods: RNA sequencing was performed on baseline tumor samples from 185 patients in the MYSTIC biomarker-evaluable population (BEP). Non-negative matrix factorization (NMF) was used to resolve distinct clusters based on the expression of the top 10% most variable protein-coding genes. The resulting clusters were analyzed for differences in clinical outcomes, genomic alterations, immune cell composition, pathway activity, and T-cell and B-cell repertoire.
Results: NMF identified four distinct clusters within the MYSTIC BEP population of which two clusters were notably characterized by tumor immune grouping. Cluster 1 (20% of patients) exhibited hallmarks of a pro-inflammatory TME, with high T-cell signatures, macrophage infiltration, and expression of checkpoint inhibitors. Patients in this cluster treated with D+T experienced a prolonged survival (mOS 19.78 months) compared with patients on this treatment in any of the other three clusters (HR 0.39, 95%CI 0.15-1). Cluster 1 was also enriched for patients with PD-L1≥50%. Cluster 3 (20% of patients) displayed features associated with immune suppression, including high granulocyte and Treg infiltration, low macrophage signature, and enrichment of TGFbeta and stemness pathways. These patients experienced worsened survival outcomes when treated with D+T (mOS 2.17 months), compared to other clusters (HR 3.13, 95%CI 1.48-6.6). Most notably, patients in cluster 3 had significantly worsened survival on D+T compared to patients in cluster 1 (HR 6.15, 95%CI 2.02-18.8). The remaining two clusters could be characterized by histology, with Cluster 2 (35% of patients) characterized by non-squamous histology and enrichment for STK11 and MYC alterations, and Cluster 4 (25% of patients) characterized by squamous histology, aggressive proliferation signatures, and enrichment for TP53, MLL2, PTEN, and PIK3CA alterations. Neither of these two clusters drove notable impact to D+T treatment.
Conclusions: NMF clustering of baseline MYSTIC tumor samples revealed distinct TME profiles linked to divergent immunotherapy responses. A pro‑inflammatory cluster predicted benefit with D+T, while a suppressive cluster aligned with poor outcomes. Findings suggest response depends not just on overall immune infiltration but on the specific immune cell composition within the TME.
利益披露 Disclosure
L. van Vlerken-Ysla,
Astrazeneca Employment.
A. Nabbi,
Astrazeneca Employment.
Z. Zhu,
Astrazeneca Employment.
R. Stewart,
Astrazeneca Employment.