PO.CL01.07 · 临床研究

利用细胞游离DNA片段组和蛋白生物标志物进行肺癌亚型分型

Lung cancer subtyping using cell-free DNA fragmentomes and protein biomarkers

编号 1135 展板 16 时间 4/19 02:00–05:00 区域 Section 44 主讲 Jamie Medina

分会场 Liquid Biopsies: Circulating Nucleic Acids 1

查看 PDF 下载 PDF 🔒 查看 / 下载完整 PDF 需登录并开通下载套餐 · 查看套餐 / 开通 AACR 官方页面

作者与单位 Authors & Affiliations

Stephen Cristiano¹, Paul van der Leest², Jamie Medina¹, Zachary Skidmore¹, Milou M. Schuurbiers³, Garrett Graham¹, Alessandro Leal⁴, Bryan Chesnick¹, Kim Monkhors², Nicholas C. Dracopoli¹, Robert Scharpf⁵, Peter B. Bach¹, Daan van den Broek², Amoolya Singh¹, Victor E. Velculescu⁵, Sian Jones¹, Michel M. van den Heuvel³, Lorenzo Rinaldi¹

¹Delfi Diagnostics, Palo Alto, CA,²Netherlands Cancer Institute, Amsterdam, Netherlands,³Department of Pulmonary Diseases, Radboud University Medical Center, Nijmegen, Netherlands,⁴NYU Langone Health Perlmutter Comprehensive Cancer Center, New York, NY,⁵Sidney Kimmel Comprehensive Cancer Ctr., Baltimore, MD

摘要 Abstract

中文摘要

引言：肺癌是全球癌症相关死亡的首要原因。准确的组织学亚型分型以区分肺腺癌（LUAD）、肺鳞状细胞癌（LUSC）和小细胞肺癌（SCLC）对于指导最佳治疗策略至关重要。然而，高达20%的患者缺乏足够组织进行常规组织病理学分类。当组织不可获得时，使用细胞游离DNA（cfDNA）片段组学的液体活检为癌症表征提供了一种有前景的无创替代方案。方法：我们检查了761例新诊断、初治的各分期肺癌患者，包括肺腺癌（n=468）、鳞状细胞癌（n=156）、小细胞癌（n=42）、大细胞癌（n=15）和其他亚型（n=80），均来自前瞻性肺癌早期分子评估试验（LEMA，NCT02894853）。对cfDNA血浆样本进行低覆盖度全基因组测序，以获得全基因组片段化特征。使用DELFI-TF方法从片段化估计循环肿瘤DNA（ctDNA）负荷。我们开发了一种机器学习分类器，仅在来自临床肺癌基因组项目（CLCGP）的基于组织的拷贝数特征上进行训练，并将其应用于患者cfDNA样本以预测肺癌亚型。结果：这一基于组织训练的亚型分型算法在所有可用血浆样本上进行评估，在区分NSCLC与SCLC方面达到0.99的AUC（95% CI = 0.98-1.00），在区分LUAD与LUSC方面达到0.91的AUC（95% CI=0.87-0.95）。在肿瘤分数≥0.3%的病例（n=276）中，该模型正确分类了88%的SCLC、80%的LUAD和87%的LUSC病例。在361例NSCLC患者的亚组中，整合五种血液蛋白生物标志物形成的多模态模型在所有肿瘤分数下均以高性能区分LUAD与LUSC（AUC=0.85，95% CI=0.80-0.90），优于仅cfDNA模型（p<0.01；AUC=0.78，95% CI=0.74-0.82）或仅蛋白分类器（p<0.001；AUC=0.70，95% CI=0.62-0.78）。结论：这些发现确立了cfDNA片段化和蛋白生物标志物作为组织不可获得时肺癌亚型分型的可行无创方法，具有加速亚型特异性治疗选择和改善临床结局的潜力。

查看英文原文 English abstract

Introduction: Lung cancer is the leading cause of cancer-related mortality worldwide. Accurate histological subtyping to differentiate between lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), and small cell lung cancer (SCLC) is critical for guiding optimal therapeutic strategies. However, up to 20% of patients lack sufficient tissue for conventional histopathological classification. Liquid biopsies using cell-free DNA (cfDNA) fragmentomics offer a promising non-invasive alternative for cancer characterization when tissue is not available. Methods: We examined 761 patients with newly diagnosed, treatment-naive lung cancer of all stages, including lung adenocarcinoma (n=468), squamous cell carcinoma (n=156), small cell carcinoma (n=42), large cell carcinoma (n=15) and other subtypes (n=80) from the prospective Lung Cancer Early Molecular Assessment trial (LEMA, NCT02894853). Low-coverage whole genome sequencing of cfDNA plasma samples was performed to derive genome-wide fragmentation features. Circulating tumor DNA (ctDNA) burden was estimated from fragmentation using the DELFI-TF method. We developed a machine learning classifier trained exclusively on the tissue-based copy number signatures from the Clinical Lung Cancer Genome Project (CLCGP) and applied it to patient cfDNA samples to predict lung cancer subtypes. Results: This tissue-trained subtyping algorithm was evaluated on all available plasma samples, achieving an AUC of 0.99 (95% CI = 0.98-1.00) for distinguishing NSCLC from SCLC and an AUC of 0.91 (95% CI=0.87-0.95) for differentiating LUAD from LUSC. The model correctly classified 88% of SCLC, 80% of LUAD and 87% of LUSC cases where the tumor fraction was ≥0.3% (n=276). Among a subset of 361 NSCLC patients, integration of five blood protein biomarkers resulted in a multimodal model that differentiated LUAD from LUSC across all tumor fractions with high performance (AUC=0.85, 95% CI=0.80-0.90), an improvement over cfDNA (p<0.01; AUC=0.78, 95% CI=0.74-0.82) or protein-only classifiers (p<0.001; AUC=0.70, 95% CI=0.62-0.78). Conclusions: These findings establish cfDNA fragmentation and protein biomarkers as a viable non-invasive approach for lung cancer subtyping when tissue is unavailable, with potential to expedite subtype-specific treatment selection and improve clinical outcomes

利益披露 Disclosure

S. Cristiano, Delfi Diagnostics Employment, Stock. P. van der Leest, None. J. Medina, Delfi Diagnostics Employment, Stock. Z. Skidmore, DELFI Diagnostics Employment, Stock. M. M. Schuurbiers, None. G. Graham, Delfi Diagnostics Employment, Stock. A. Leal, None. B. Chesnick, Delfi Diagnostics Employment, Stock. K. Monkhors, None. N. C. Dracopoli, Delfi Diagnostics Employment, Stock. R. Scharpf, Delfi Diagnostics Other, co-founder. P. B. Bach, Delfi Diagnostics Employment, Stock. D. van den Broek, None. A. Singh, Delfi Diagnostics Employment, Stock. S. Jones, Delfi Diagnostics Employment, Stock. M. M. van den Heuvel, None. L. Rinaldi, DELFI Diagnostics Employment, Independent Contractor, Stock.

← 返回 AACR 2026 检索