PO.MCB08.04 · 分子与细胞生物学
大规模分析揭示真实世界胃癌数据中的不同分子亚型
Large-scale analysis reveals distinct molecular subtypes in real-world gastric cancer data
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
引言
胃癌(GC)仍是一个全球性健康问题,尽管有靶向治疗,其结局改善仍然有限。在此,我们使用了一个涵盖多样疾病分期和肿瘤部位的大型真实世界数据集,以刻画GC的分子和临床格局。
方法
分析了经Tempus xT(DNA)和xR(RNA)测序检测所描绘的GC患者的去标识化临床-基因组记录。分子分型对来自胃和食管原发肿瘤的xR数据(n=1,385;89.9%为III/IV期)使用了非负矩阵分解。将一个基于肿瘤内在特征训练的分类器应用于转移部位(n=640;99%为III/IV期),并排除亚型概率较低的样本(n=159)。在有可用数据的患者中(31.5%)评估了从一线治疗到死亡的真实世界总生存期(rwOS)。针对延迟入组的敏感性分析纳入了在治疗前接收/测序样本的类前瞻性患者。仅报告贡献rwOS数据的患者≥20例的亚型。
结果
为增强可解释性和生物学相关性,基于质心距离鉴定出九个聚类,并合并为七个具有预后差异(p=0.01)的分子亚型。G1亚型(25.6%;中位OS 12.2个月,95% CI 6.4-16.1)与染色体不稳定性(CIN)(78% CIN-High)、IV期(91%)、肝部位样本(26%)、微卫星稳定(MSS;97%)和低肿瘤突变负荷(TMB<10;97%)相关。G1表现出TP53突变(81.9%)、CCNE1扩增(16.7%)和CDKN2A/B-MTAP缺失(约15%)。G2亚型(21.8%;中位OS 20.6个月,95% CI 14-22.2)具有最高的TMB(TMB≥10;21%)、微卫星不稳定(MSI;20%)、频繁的ERBB2(14.3%)和FGFR2(5.9%)扩增,以及较低的TP53突变(62.6%)。G3亚型(21.8%;中位OS 18.1个月,95% CI 11.6-27.4)具有弥漫型组织学(58%)、CDH1突变(21.2%)、低TMB(94%)和胃部位富集(54%)。G4亚型(5.6%)富集KRAS突变(11.4%)和扩增(9.6%)、TP53突变(75.4%)以及CIN(62%,接近显著性)。G5亚型(6.4%)具有最高的PD-L1(CPS≥10;40%)、EBV阳性(16%)、高TMB(17%)和低CIN(55%)。G5表现出ARID1A/B(41.9%/9.3%)、PIK3CA(21.7%)和KRAS突变(18.6%),并具有最低的TP53突变比例(46.5%)。G6亚型(4.9%)富集III期(22%)和CDKN2A/B-MTAP缺失(约13%)。G7亚型(17.1%;中位OS 20.6个月,95% CI 7.6-15.5%)与弥漫型组织学(67%)、CDH1突变(23.9%)、MSS(97%)、低TMB(97%)和低CIN(53%)相关。
结论
本研究表明,真实世界数据整合能够识别出具有临床相关性、生物学上不同且具有预后差异的GC亚型。这些发现为定制治疗策略和改善患者结局提供了基础。
查看英文原文 English abstract
Introduction
Gastric cancer (GC) remains a global health concern, with limited outcome improvement despite targeted therapy. Here, a large real-world dataset of diverse disease stages and tumor sites was used to profile the molecular and clinical landscape of GC.
Methods
De-identified clinico-genomic records from GC patients profiled with Tempus xT (DNA) and xR (RNA)-seq assays were analyzed. Molecular subtyping used non-negative matrix factorization on xR data from primary tumors of the stomach and esophagus (n=1,385; 89.9% Stage III/IV). A classifier trained on tumor-intrinsic features was applied to metastatic sites (n=640; 99% Stage III/IV) and samples with low subtype probability were excluded (n=159). Real-world overall survival (rwOS), from first-line therapy to death was evaluated in patients with available data (31.5%). Sensitivity analysis for delayed entry included prospective-like patients with samples received/sequenced before treatment. Only subtypes with ≥20 patients contributing rwOS data were reported.
Results
To enhance interpretability and biological relevance, nine clusters were identified and merged into seven molecular subtypes with prognostic differences (p=0.01) based on centroid distance. The G1 subtype (25.6%; median OS 12.2 months, 95% CI 6.4-16.1) was associated with chromosomal instability (CIN) (78% CIN-High), Stage IV (91%), liver site samples (26%), microsatellite stability (MSS; 97%), and low tumor mutation burden (TMB<10; 97%). G1 exhibited TP53 mutations (81.9%), CCNE1 amplifications (16.7%), and CDKN2A / B - MTAP deletions (~15%). The G2 subtype (21.8%; median OS 20.6 months, 95% CI 14-22.2) had the highest TMB (TMB≥10; 21%), microsatellite instability (MSI; 20%), frequent ERBB2 (14.3%) and FGFR2 (5.9%) amplifications, and lower TP53 mutations (62.6%). The G3 subtype (21.8%; median OS 18.1 months, 95% CI 11.6-27.4) had diffuse histology (58%), CDH1 mutations (21.2%), low TMB (94%), and stomach site enrichment (54%). The G4 subtype (5.6%) was enriched for KRAS mutations (11.4%) and amplifications (9.6%), TP53 mutations (75.4%), and CIN (62%, trending significance). The G5 subtype (6.4%) had the highest PD-L1 (CPS≥10; 40%), EBV positivity (16%), high TMB (17%), and low CIN (55%). G5 exhibited ARID1A / B (41.9%/9.3%), PIK3CA (21.7%), and KRAS mutations (18.6%), and had the lowest TP53 mutation proportion (46.5%). The G6 subtype (4.9%) was enriched for Stage III (22%) and CDKN2A / B - MTAP deletions (~13%). The G7 subtype (17.1%; median OS 20.6 months, 95% CI 7.6-15.5%) was associated with diffuse histology (67%), CDH1 mutations (23.9%), MSS (97%), low TMB (97%), and low CIN (53%).
Conclusions
This study shows that real-world data integration enables identification of clinically relevant, biologically distinct GC subtypes with prognostic differences. These findings provide a foundation for tailoring therapeutic strategies and improving patient outcomes.
利益披露 Disclosure
A. Singhania,
Tempus AI Employment, Stock.
S. Kaushik,
Tempus AI Employment, Stock.
B. L. Mapes,
Tempus AI Employment, Stock, Patent.
L. F. Langer,
Tempus AI Employment, Stock, Patent.
K. R. Bastian,
Tempus AI Employment, Stock, Patent.
S. Cowher,
Tempus AI Employment, Stock.
Y. E. Stern,
Tempus AI Employment, Stock.
B. Hu,
Bristol Myers Squibb Employment, Stock.
G. Lopez,
Bristol Myers Squibb Employment, Stock.
R. Novosiadly,
Bristol Myers Squibb Employment, Stock.
Eli Lilly Stock.
M. Ortiz-Estevez,
Bristol Myers Squibb Employment, Stock.
K. Wang,
Bristol Myers Squibb Employment, Stock.
N. Callamaras,
Tempus AI Employment, Stock, Patent.
R. A. Klinghoffer,
Tempus AI Employment, Stock.
Presage Biosciences Employment, Stock, Stock Option.
J. Guinney,
Tempus AI Employment, Stock, Patent.
R. M. Johnson,
Tempus AI Employment, Stock, Patent.
Gilead Sciences Stock.