LBPO.CL02 · 临床研究 · Late-Breaking

疾病进展的驱动因素和模式作为转移性非小细胞肺癌风险分层的新框架

Drivers and patterns of disease progression as a novel schema for risk stratification in metastatic non-small cell lung cancer

编号 LB127 展板 14 时间 4/20 09:00–12:00 区域 Section 52 主讲 Gabriela Esnaola, BA

分会场 Late-Breaking Research: Clinical Research 2

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作者与单位 Authors & Affiliations

Gabriela R. Esnaola¹, Mengru Wang², Jeffrey J. Ishizuka¹, Benjamin J. Resio¹, Alexander Pan¹, Daniel Lee³, Aaron Cohen², Madeleine Schmitter², Kelly L. Olino¹

¹Yale School of Medicine, New Haven, CT,²Flatiron Health, New York, NY,³Dana-Farber Cancer Institute, Boston, MA

摘要 Abstract

中文摘要

背景：靶向治疗（TT）和免疫治疗（IO）已改变了非小细胞肺癌（NSCLC）的系统治疗（ST）格局，但总体预后仍然较差。特定器官转移可能预示生存期缩短；然而，转移进展的根本驱动因素和模式尚未充分明确，ST的部位特异性效应也知之甚少。我们利用了一个包含转移部位、基因组和治疗信息的大规模真实世界数据库，来表征并评估转移模式和基因组相关因素对接受ST的转移性NSCLC患者真实世界总生存期（rwOS）的预后价值。方法：本回顾性研究利用了Flatiron Health-Foundation Medicine临床-基因组数据库中接受一线（1L）ST治疗的转移性NSCLC患者。使用卡方检验比较患者、肿瘤和治疗变量。通过Kaplan-Meier法估计rwOS并使用log-rank检验进行比较；使用多变量Cox回归计算校正后的风险比。使用伯努利混合模型，借助R软件包“flexmix”，根据1L治疗开始时的转移部位对患者进行聚类。结果：对10,571例患者的疾病部位进行了评估。具有脾、皮肤、肝、肾和骨转移的患者OS较差。来自18个转移部位的数据揭示了8个簇：高转移负荷（HMB，≥3个部位且频率≥0.5，n=933）、骨（n=2,889）、胸膜（n=2,469）、肺（n=1,710）、肝（n=1,345）、脑（n=1,163）、淋巴结（LN，n=748）和肾上腺（n=659）。虽然已知HMB（8.4个月）和肝转移（8.9个月）与较差的中位OS相关，但我们还发现骨（11.9个月）、肾上腺（13.4个月）、胸膜（14.0个月）和LN（14.4个月）簇的中位OS与肺（17.7个月）和脑（16.5个月，p<0.001）相比更差。某些突变在部位特异性簇中具有更高的比值（OR>1.5且p<0.05），包括：肾上腺（SRC、ARID1A、BCL2L1、ATR、EPHA3、CCND3、KEAP1）、肝（AXL、AKT1、AKT2、EPHB1）、脑（MAP2K4、GLI1、PIK3CB、KDR）和骨（ERBB3）。有趣的是，EP300、EPHB4和PBRM1突变与HMB相关，但与任何单个部位无关。肾上腺和脑簇在各簇中具有最高的组织肿瘤突变负荷。在肺、胸膜和骨簇中，TT与更长的OS相关（与其他ST相比，HR 0.68-0.80，p<0.01）。结论：可定义的转移模式可预测肺癌的生存期和治疗反应。基因组突变模式可预测部位特异性转移扩散。本研究是迄今为止对转移部位和遗传标志物对NSCLC患者生存结局影响最全面的评估。我们提出的簇可能代表了晚期NSCLC机制探究和风险分层的新框架。

查看英文原文 English abstract

Background: Targeted therapy (TT) and immunotherapy (IO) have transformed the systemic therapy (ST) landscape for non-small cell lung cancer (NSCLC), but overall prognosis remains poor. Select organ metastases may be predictive of decreased survival; however, the fundamental drivers and patterns of metastatic progression are not well established, and the site-specific effects of ST are poorly understood. We leveraged a large-scale, real-world database containing metastatic site, genomic, and treatment information to characterize and assess the prognostic value of patterns of metastasis and genomic correlates on real-world overall survival (rwOS) in patients with metastatic NSCLC receiving ST. Methods: This retrospective study utilized the Flatiron Health-Foundation Medicine Clinico-Genomic Database of patients with metastatic NSCLC treated with first-line (1L) ST. Patient, tumor, and treatment variables were compared with chi-squared tests. rwOS was estimated via Kaplan Meier method and compared with logrank test; adjusted hazard ratios were computed with multivariable Cox regression. Bernoulli mixture models were used to cluster patients by metastatic sites at 1L therapy start using the R package “flexmix”. Results: Sites of disease were evaluated for 10,571 patients. Patients with spleen, skin, liver, kidney, and bone metastases had worse OS. Data from 18 metastatic sites revealed 8 clusters: high metastatic burden (HMB, ≥3 sites with frequency ≥0.5, n = 933), bone (n = 2,889), pleura (n = 2,469), lung (n = 1,710), liver (n = 1,345), brain (n = 1,163), lymph node (LN, n = 748), and adrenal avid (n = 659). While HMB (8.4 months) and liver mets (8.9 months) are known to be associated with poor median OS, we also found that bone (11.9 months), adrenal (13.4 months), pleura (14.0 months), and LN (14.4 months) clusters had worse median OS compared to lung (17.7 months) and brain (16.5 months, p<0.001). Certain mutations had increased odds (OR>1.5 and p < 0.05) in site-specific clusters, including: adrenal (SRC, ARID1A, BCL2L1, ATR, EPHA3, CCND3, KEAP1), liver (AXL, AKT1, AKT2, EPHB1), brain (MAP2K4, GLI1, PIK3CB, KDR), and bone (ERBB3). Interestingly, EP300, EPHB4, and PBRM1 mutations were associated with HMB, but no individual sites. Adrenal and brain clusters had the highest tissue tumor mutational burden of the clusters. TT was associated with greater OS compared to other ST in lung, pleura, and bone clusters (HR 0.68-0.80, p < 0.01). Conclusions: Definable patterns of metastasis predict survival and treatment response in lung cancer. Genomic patterns of mutation predict site-specific metastatic spread. This study is the most comprehensive evaluation of the impact of sites of metastasis and genetic markers on survival outcomes in patients with NSCLC to date. Our proposed clusters may represent a novel framework for mechanistic inquiry and risk stratification in advanced NSCLC.

利益披露 Disclosure

G. R. Esnaola, None. M. Wang, Flatiron Health (Independent subsidiary of Roche) Employment. J. J. Ishizuka, Curios Therapeutics Stock, Other Business Ownership. Jounce Therapeutics Stock, Other Business Ownership. Kronos Bio Stock, Other Business Ownership. Danger Bio Other, Counseling or advisory role. Fortress Biotech Other, Counseling or advisory role. Ono Pharmaceutical Other, Counseling or advisory role. Phenomic AI Other, Counseling or advisory role. Rheos Medicines Other, Counseling or advisory role. Tango Therapeutics Other, Counseling or advisory role. Two River Group Other, Counseling or advisory role. AstraZeneca/MedImmune ). MODULATING dsRNA EDITING, SENSING, AND METABOLISM TO INCREASE TUMOR IMMUNITY AND IMPROVE THE EFFICACY OF CANCER IMMUNOTHERAPY AND/OR MODULATORS OF INTRATUMORAL INTERFERON Patent, Other Intellectual Property. B. J. Resio, None.. A. Pan, None.. D. Lee, None. A. Cohen, Flatiron Health (Independent subsidiary of Roche) Employment. Roche Stock. M. Schmitter, Flatiron Health (Independent subsidiary of Roche) Employment. K. L. Olino, Patent pending for the use of mRNA vaccine for the treatment of virally associated cancers Patent, Other Intellectual Property.

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