PO.CL05.07 · 临床研究

基于免疫相关RNA-seq生物标志物的聚类揭示转移性NSCLC中免疫治疗应答的异质性并指导亚型特异性策略

Immune-related RNA-seq biomarker-based clustering reveals heterogeneous immunotherapy responses and guides subtype-specific strategies in metastatic NSCLC

编号 7748 展板 8 时间 4/22 09:00–12:00 区域 Section 42 主讲 Sanghwa Kim, MD

分会场 Immune Response to Therapies

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作者与单位 Authors & Affiliations

Jiyon Lyu¹, Sebastià Franch-Expósito², Sanghwa Kim¹, Liam Il-Young Chung¹, Ronald Min¹, Sung Hwan Lee³, Shinkyo Yoon¹, Michelle M. Stein², JACOB MERCER², Paul Fields², Bella Kim⁴, Young Kwang Chae¹

¹Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL,²Tempus AI, Chicago, IL,³Department of Surgery, CHA Bundang Medical Center, Seongnam, Korea, Republic of,⁴Northwestern University, Chicago, IL

摘要 Abstract

中文摘要

转移性非小细胞肺癌（mNSCLC）是一种高度异质性的疾病，在一线免疫治疗联合化疗下临床结局各异。为更好地理解与免疫治疗异质性应答相关的免疫景观特征，我们使用已知的免疫相关标志物TIGIT、FOXP3、CD274（PD-L1）和肿瘤相关巨噬细胞（TAM）评分，进行了生物标志物驱动的RNA-seq分子聚类。我们分析了去标识化Tempus数据库中2,235例接受一线PD-(L)1联合化疗、具有治疗前肿瘤活检的mNSCLC患者的真实世界队列。对RNA-seq数据进行无监督聚类，定义出四个不同的免疫亚型。通过Kaplan-Meier分析结合log-rank检验评估真实世界总生存期（rwOS）和无进展生存期（rwPFS）。分析了使用hallmark基因集的通路富集、肿瘤突变负荷（TMB）以及使用QuantiSeq的免疫细胞组成。 RNA-seq生物标志物的表达水平和TAM评分在所识别的各聚类间存在显著差异（ANOVA；p<0.001）。这些聚类还显示出TMB-高和PD-L1阳性（IHC）的显著差异性患病率（卡方检验；分别p<0.001），以及特征性的通路富集和免疫谱。非鳞癌/从不吸烟者在聚类2中更常见，而鳞癌/当前吸烟者在聚类1中占主导（卡方检验；组织学/吸烟，分别p<0.05）。生存期存在显著差异，聚类1最差，聚类3最佳（rwOS/rwPFS，p<0.001）（表1）。这项生物标志物驱动的RNA-seq分析识别出mNSCLC的四个免疫聚类，具有不同的生存结局。本研究为理解肿瘤异质性提供了基础，并支持使用免疫生物标志物对患者进行分层以指导治疗联合。表1。聚类1（免疫荒漠型）N=713 聚类2（TAM富集型）N=402 聚类3（免疫热型）N=813 聚类4（髓系炎症型，PD-L1高）N=302 p值中位生存时间（月）rwOS/rwPFS 11.5/5.95个月 14.8/7.33个月 18.1/8.15个月 16.7/6.84个月 Log-rank检验；p<0.001 RNA-seq生物标志物（TIGIT、FOXP3、CD274（PD-L1））和TAM评分所有标志物均一致低表达 TAM高但TIGIT/FOXP3/PD-L1低 TIGIT/FOXP3/PD-L1高且TAM升高 PD-L1高而TIGIT/FOXP3低 ANOVA检验；p<0.001 通路富集 TME：肿瘤微环境 ↑致癌信号和增殖 ↓免疫相关通路 ↑TME重塑通路 ↑免疫/炎症信号（如IFNγ）和TME重塑通路 ↑增殖和DNA修复通路 Welch ANOVA+Games-Howell或Kruskal-Wallis和Dunn（BH）检验；校正p<0.05 免疫细胞组成 ↓淋巴系和髓系细胞浸润 ↑M1/M2巨噬细胞广泛浸润（↑CD8、CD4、Treg、B、NK）↑髓系细胞浸润 Kruskal-Wallis和Dunn（BH）检验；校正p<0.05 TMB-高（TMB≥10 mut/Mb）283（34%）92（20%）254（26%）124（35%）卡方检验；p<0.001 PD-L1阳性（IHC；TPS≥1%）203（36%）174（54%）421（68%）213（94%）卡方检验；p<0.001 肿瘤组织学卡方检验；p<0.001 鳞癌 232（33%）73（18%）221（27%）84（28%）非鳞癌 444（62%）315（78%）555（68%）201（67%）NOS 37（5.2%）14（3.5%）37（4.6%）17（5.6%）吸烟状态卡方检验；p<0.05 当前吸烟者 118（62%）44（46%）113（55%）48（59%）从不吸烟者 16（8.4%）23（24%）32（15%）12（15%）既往吸烟者 56（29%）29（30%）62（30%）21（26%）未知 523 306 606 221

查看英文原文 English abstract

Metastatic non-small cell lung cancer (mNSCLC) represents a highly heterogeneous disease with variable clinical outcomes under first-line immunotherapy plus chemotherapy. To better understand immune landscape features associated with heterogeneous response to immunotherapy, we performed biomarker-driven RNA-seq molecular clustering using known immune-related markers TIGIT, FOXP3, CD274 ( PD-L1 ), and tumor-associated macrophage (TAM) score. We analyzed a real-world cohort of 2,235 mNSCLC patients with pre-treatment tumor biopsies in the de-identified Tempus database treated with first-line PD-(L)1 plus chemotherapy. Unsupervised clustering of RNA-seq data defined four distinct immune subtypes. Real-world overall survival (rwOS) and progression-free survival (rwPFS) were assessed via Kaplan-Meier analysis with a log-rank test. Pathway enrichment using hallmark gene sets, tumor mutational burden (TMB), and immune cell composition using QuantiSeq were analyzed. Expression levels of RNA-seq biomarkers and TAM score were significantly different across identified clusters (ANOVA; p <0.001). These clusters also showed significantly differential prevalence of TMB-high and PD-L1-positive (IHC) (Chi-squared; p < 0.001, respectively), as well as characteristic pathway enrichment and immune profiles. Non-squamous/Never smoker were more frequent in Cluster 2, whereas Squamous/Current smoker were predominant in Cluster 1 (Chi-squared; Histology/Smoking, p <0.05, respectively). Survival differed significantly, being poorest in Cluster 1 and best in Cluster 3 (rwOS/rwPFS, p <0.001) (Table 1). This biomarker-driven RNA-seq analysis identified four immune clusters of mNSCLC with differential survival outcomes. This study provides a foundation for understanding tumor heterogeneity and supports the use of immune biomarkers to enable patient stratification for therapeutic combinations. Table 1. Cluster 1 (Immune-desert) N=713 Cluster 2 (TAM-enriched) N=402 Cluster 3 (Immune-hot) N=813 Cluster 4 (Myeloid-inflamed, PD-L1-high) N=302 p -value Median Survival Time (Months) rwOS/rwPFS 11.5/5.95 mo 14.8/7.33 mo 18.1/8.15 mo 16.7/6.84 mo Log-rank test; p <0.001 RNA-Seq Biomarkers (TIGIT, FOXP3, CD274 (PD-L1)) and TAM Score Uniformly low expression of all markers High TAM but low TIGIT/FOXP3/PD-L1 High TIGIT/FOXP3/PD-L1 with elevated TAM High PD-L1 with low TIGIT/FOXP3 ANOVA test; p <0.001 Pathway Enrichment TME: tumor microenvironment ↑ Oncogenic signalings and proliferation ↓ Immune -related pathways ↑ TME remodeling pathways ↑ Immune/inflammatory signaling (e.g., IFNgamma) and TME remodeling pathways ↑ Proliferation and DNA-repair pathways Welch ANOVA + Games-Howell or Kruskal-Wallis and Dunn (BH) test; adjusted p <0.05 Immune Cell Composition ↓ Lymphoid and myeloid cell infiltration ↑ M1/M2 macrophage Broad infiltration (↑ CD8, CD4, Treg, B, NK) ↑ Myeloid cell infiltration Kruskal-Wallis and Dunn (BH) test; adjusted p <0.05 TMB-High (TMB ≥10 mut/Mb) 283 (34%) 92 (20%) 254 (26%) 124 (35%) Chi-squared test; p <0.001 PD-L1-Positive (IHC; TPS ≥ 1%) 203 (36%) 174 (54%) 421 (68%) 213 (94%) Chi-squared test; p <0.001 Tumor Histology Chi-squared test; p <0.001 Squamous 232 (33%) 73 (18%) 221 (27%) 84 (28%) Non-Squamous 444 (62%) 315 (78%) 555 (68%) 201 (67%) NOS 37 (5.2%) 14 (3.5%) 37 (4.6%) 17 (5.6%) Smoking Status Chi-squared test; p <0.05 Current Smoker 118 (62%) 44 (46%) 113 (55%) 48 (59%) Never Smoker 16 (8.4%) 23 (24%) 32 (15%) 12 (15%) Ex-Smoker 56 (29%) 29 (30%) 62 (30%) 21 (26%) Unknown 523 306 606 221

利益披露 Disclosure

J. Lyu, None. S. Franch-Expósito, Tempus Employment, Stock. S. Kim, None.. L. I. Chung, None.. R. Min, None.. S. Lee, None.. S. Yoon, None. M. M. Stein, Tempus Employment, Stock. J. Mercer, Tempus Employment. P. Fields, Tempus Employment, Stock. Adaptive Biotechnologies Stock. B. Kim, None. Y. K. Chae, AbbVie ). Bristol Myers ). Squibb ). Biodesix ). Freenome ). Predicine ). Picture Health ). Roche/Genentech Other, Consulting fees, payments, and/or honoraria. AstraZeneca Other, Consulting fees, payments, and/or honoraria. Foundation Medicine Other, Consulting fees, payments, and/or honoraria. Neogenomics Other, Consulting fees, payments, and/or honoraria. Guardant Health Other, Consulting fees, payments, and/or honoraria. Boehringer Ingelheim Other, Consulting fees, payments, and/or honoraria. Biodesix Other, Consulting fees, payments, and/or honoraria. ImmuneOncia Other, Consulting fees, payments, and/or honoraria. Lilly Oncology Other, Consulting fees, payments, and/or honoraria. Merck Other, Consulting fees, payments, and/or honoraria. Takeda Other, Consulting fees, payments, and/or honoraria. Lunit Other, Consulting fees, payments, and/or honoraria. Jazz Pharmaceutical, Tempus, Bristol Myers Squibb, Regeneron, NeoImmunTech, Esai, and Novocure. Other, Consulting fees, payments, and/or honoraria.

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