PO.MD01.01 · 分子诊断与数据
利用计算推理对 GENIE BPC NSCLC 队列进行分子信息驱动的治疗疗效预测
Molecularly-informed prediction of treatment efficacy in the GENIE BPC NSCLC cohort using computational reasoning
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
背景：数字药物分配（DDA）是一种计算推理模型，它基于肿瘤的完整分子谱对癌症疗法进行评分，并按预测疗效对其分层（Petak 等，2021）。在一项针对 111 例肺癌患者的既往研究中，DDA 得出的高分分子靶向药物（MTAs）与临床结局改善相关（Dirner 等，2025）。在此，我们将该分析扩展至 GENIE BPC NSCLC 队列，以评估 DDA 更广泛的临床有效性。
方法：从 Synapse 上可获得的 GENIE BPC NSCLC 队列数据中，我们纳入了 1,078 例具有单样本基因组谱、可用的初始治疗数据和生存结局的患者（共 2,103 条治疗线，纳入疗法包括：afatinib、erlotinib、osimertinib、crizotinib、nivolumab、pembrolizumab、atezolizumab、bevacizumab+化疗、ramucirumab+化疗；以及单用化疗）。为所有病例生成 DDA 评分，并使用所给予 MTAs（含免疫检查点抑制剂）的个体评分将结局分层为低（<0）、中等和高 DDA 评分（≥1000）三档。采用 Kaplan-Meier 统计分析无进展生存期（PFS，通过影像学评估）和总生存期（OS）。
结果：中位 PFS 和 OS 在各 DDA 评分档之间存在显著差异，并随评分升高而增加（见表）。中等档药物的中位 PFS 值与化疗相近（3.9 对 4.2 个月）。6 个月 PFS 率和 12 个月 OS 率随 DDA 档次升高而增加，且经 χ² 检验均有显著差异。DDA-高疗法在各治疗类型中提供的获益均大于评分较低的对应疗法。
结论：在一个大型真实世界 NSCLC 队列中，DDA 基于每位患者的完整分子谱有效区分了具有更高临床疗效的疗法。这些结果强化了 DDA 在精准肿瘤学中基于 NGS 诊断增强个性化治疗选择的潜力。
DDA-低 DDA-中等 DDA-高 统计检验 化疗 中位 PFS（月）1.7（n = 72）3.9（n = 303）5.1（n = 554）log-rank p<0.0001；HR 高对低 = 0.52 4.2（n = 709）中位 OS（月）9.0（n = 74）16.2（n = 327）23.3（n = 601）log-rank p<0.0001；HR 高对低 = 0.49 23.5（n = 1094）6 个月 PFS 率 14% 28% 40% χ² p<0.0001 25% 12 个月 OS 率 36% 53% 63% χ² p<0.0001 65%
查看英文原文 English abstract
Background: Digital Drug Assignment (DDA) is a computational reasoning model that scores cancer therapies based on the complete molecular profile of a tumor, and stratifies them by predicted efficacy (Petak et al., 2021). In a prior study of 111 lung cancer patients, DDA-derived high-score molecularly targeted agents (MTAs) were associated with improved clinical outcomes (Dirner et al., 2025). Here, we extend this analysis to the GENIE BPC NSCLC cohort to assess the broader clinical validity of DDA.
Methods: From the GENIE BPC NSCLC cohort data available on Synapse, we included 1,078 patients with a single-sample genomic profile, available primary treatment data and survival outcomes (total 2,103 treatment lines, therapies included: afatinib, erlotinib, osimertinib, crizotinib, nivolumab, pembrolizumab, atezolizumab, bevacizumab+chemo, ramucirumab+chemo; and chemotherapy alone). DDA scores were generated for all cases, and the individual score of the administered MTAs (incl. immune checkpoint inhibitors) was used to stratify outcomes into low (<0), intermediate, and high DDA-score (≥1000) tiers. Progression-free survival (PFS, by imaging) and overall survival (OS) were analyzed using Kaplan-Meier statistics.
Results: Median PFS and OS differed significantly across DDA score tiers, increasing with higher scores (see table). Intermediate-tier drugs had similar mPFS values as chemotherapies (3.9 vs 4.2 months). Six-month PFS and twelve-month OS rates increased with DDA-tiers and were all significantly different by χ² test. DDA-high therapies provided greater benefit across treatment types than lower-score counterparts.
Conclusions: Across a large, real-world NSCLC cohort, DDA effectively distinguished therapies with higher clinical efficacy based on the full molecular profile of each patient. These results reinforce the potential of DDA to enhance personalized treatment selection based on NGS diagnostics in precision oncology.
DDA-low DDA-intermediate DDA-high Statistical test Chemo mPFS (months) 1.7 (n = 72) 3.9 (n = 303) 5.1 (n = 554) log-rank p<0.0001; HR high vs low = 0.52 4.2 (n = 709) mOS (months) 9.0 (n = 74) 16.2 (n = 327) 23.3 (n = 601) log-rank p<0.0001; HR high vs low = 0.49 23.5 (n = 1094) 6-month PFS rate 14% 28% 40% Χ² p<0.0001 25% 12-month OS rate 36% 53% 63% Χ² p<0.0001 65%
利益披露 Disclosure
B. Vodicska,
Genomate Health Employment.
E. Kispeter,
Genomate Health Employment.
D. Lakatos,
Genomate Health Employment.
G. G. Kalmar,
Genomate Health Employment.
R. Doczi,
Genomate Health Employment.
D. Gorog-Tihanyi,
Genomate Health Employment.
A. Dirner,
Genomate Health Employment.
R. Szalkai-Denes,
Genomate Health Employment.
W. T. Beck,
Genomate Health Stock, Stock Option.
A. Z. Dudek,
Iovance Other, Honorarium for participation in Advisory Board.
C. Le Tourneau,
Transgene, MSD, LEO Pharma, BMS, J&J, DOB Pharmaceuticals, Bicara, Merus, Immutep, Owkin, Roche, GSK, Clinigen, Merck Serono, Aveon, ALX Oncology, Seagen Other, Advisory Board.
I. Petak,
Genomate Health Employment.