PO.MCB06.03 · 分子与细胞生物学
表观基因组液体活检分子肺分型与晚期 NSCLC（aNSCLC）的真实世界（RW）患者结局
Epigenomic liquid biopsy molecular lung subtyping and real-world (RW) patient outcomes in advanced NSCLC (aNSCLC)
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
引言：NSCLC 亚型包括腺癌（LUAD）、鳞状细胞癌（LUSC）和小细胞癌（SCLC）。混合组织学、取样有限和解读不一致常常延误治疗。为解决这一问题，开发了一种基于血浆的分子肺亚型预测器（MLSP），用于从循环高甲基化 DNA 中定量 LUAD、LUSC 和 SCLC（Guardant360 Liquid，Guardant Health）。我们报告 MLSP 与检测申请单（TRF）组织学之间的一致性、按治疗类型划分的真实世界（RW）结局，以及亚型特异性基因组特征。
方法：InfinityAI 数据库将去标识化的基因组/表观基因组结果与纵向理赔数据关联。当 ≥90% 信号来自某一亚型时，样本被视为“纯”样本。队列按 MLSP-TRF 一致性和 Guardant360 Liquid 检测后的一线治疗进行分层：化疗（chemo）、免疫治疗（IO）、化疗-IO 或靶向治疗。使用 Kaplan-Meier 和 log-rank 检验分析真实世界至治疗中断时间（RW-TTD）和至下一次治疗时间（RW-TTNT）。
结果：在 8,559 例 MLSP 结果中，69.4% 为 LUAD，12.4% 为 LUSC，3.7% 为 SCLC，14.6% 为混合型。与 TRF 的一致性为 91.6%（LUAD）、75.1%（LUSC）和 56.1%（SCLC）。在 MLSP-LUAD 中，Tier 1 突变包括 KRAS G12C（11.9%）、EGFR ex19del（9.5%）和 BRAF V600E（2.1%）；PIK3CA E545K（6%）在 MLSP-LUSC 中常见，RB1（19%）在 MLSP-SCLC 中常见。可靶向生物标志物在 43.5% 的 MLSP-LUAD 中出现，而 LUSC 为 3.4%，SCLC 为 2.6%。在 MLSP-TRF 不一致的病例中，基因组特征更倾向于 MLSP 预测。KRAS G12C（7%）、PIK3CA E545K（5%）和 RB1（39%）是最常见的亚型特异性改变。靶向治疗改善了 MLSP-LUAD 的 RW 结局；化疗-IO 在 MLSP-LUSC 中获得最佳结局，IO 使 MLSP-SCLC 获益。接受 IO 或靶向治疗的不一致病例显示 RW-TTNT 和 RW-TTD 延长，与 MLSP 预测的生物学特征一致。
结论：本研究证明了 MLS 与 TRF 之间的高度一致性，这与既往分析一致。不一致病例的基因组学和 RW 结局与 MLSP 预测的亚型比与 TRF 报告的组织学更为一致。不一致患者表现出的基因组模式和 RW 结局与 MLSP 预测更为密切吻合，提示 MLSP 可能比标准组织学更准确地反映潜在的肿瘤生物学特征。
查看英文原文 English abstract
Introduction: NSCLC subtypes include adenocarcinoma (LUAD), squamous cell (LUSC), and small cell (SCLC). Mixed histology, limited sampling, and discordant interpretations often delay treatment. To address this, a plasma-based Molecular Lung Subtype Predictor (MLSP) was developed to quantify LUAD, LUSC, and SCLC from circulating hypermethylated DNA (Guardant360 Liquid, Guardant Health). We report concordance between MLSP and histology from test requisition forms (TRF), real-world (RW) outcomes by therapy type, and subtype-specific genomic profiles.
Methods: The InfinityAI Data Library links de-identified genomic/epigenomic results with longitudinal claims data. Samples were considered “pure” when ≥90% signal was from one subtype. Cohorts were stratified by MLSP-TRF concordance and first-line therapy post-Guardant360 Liquid: chemotherapy (chemo), immunotherapy (IO), chemo-IO, or targeted therapy. RW time to treatment discontinuation (RW-TTD) and time to next treatment (RW-TTNT) were analyzed using Kaplan-Meier and log-rank tests.
Results: Among 8,559 MLSP results, 69.4% were LUAD, 12.4% LUSC, 3.7% SCLC, and 14.6% mixed. Concordance with TRF was 91.6% (LUAD), 75.1% (LUSC), and 56.1% (SCLC). In MLSP-LUAD, Tier 1 mutations included KRAS G12C (11.9%), EGFR ex19del (9.5%), and BRAF V600E (2.1%); PIK3CA E545K (6%) was common in MLSP-LUSC, and RB1 (19%) in MLSP-SCLC. Targetable biomarkers occurred in 43.5% of MLSP-LUAD vs 3.4% LUSC and 2.6% SCLC. In discordant MLSP-TRF cases, genomic profiles favored MLSP predictions. KRAS G12C (7%), PIK3CA E545K (5%), and RB1 (39%) were the most frequent subtype-specific alterations. Targeted therapy improved RW outcomes in MLSP-LUAD; chemo-IO yielded best outcomes in MLSP-LUSC, and IO benefited MLSP-SCLC. Discordant cases treated with IO or targeted therapy showed extended RW-TTNT and RW-TTD, aligning with MLSP-predicted biology.
Conclusion: This study demonstrated high concordance between MLS and TRF, which is consistent with previous analyses. The genomics and RW outcomes of discordant cases were more consistent with MLSP-predicted subtype vs. TRF-reported histology. Discordant pts exhibited genomic patterns and RW outcomes more aligned closely with the MLSP-predictions, suggesting MLSP may more accurately represent underlying tumor biology than standard histology.
利益披露 Disclosure
J. Saha,
Guardant Health Employment, Stock.
N. Zhang, None.
S. R. Solomon,
Guardant Health Employment, Stock.
S. Forbes, None..
M. Ellis, None.