PO.BCS01.11 · 生物信息与计算

基于血液整合表观基因组图谱、TMB和MSI以预测晚期非小细胞肺癌(aNSCLC)对免疫检查点抑制剂的应答

Blood-based integration of epigenomic profiles, TMB, and MSI to predict immune checkpoint inhibitor response in advanced non-small cell lung cancer (aNSCLC)

海报缩略图:基于血液整合表观基因组图谱、TMB和MSI以预测晚期非小细胞肺癌(aNSCLC)对免疫检查点抑制剂的应答
编号 100 展板 7 时间 4/19 02:00–05:00 区域 Section 5 主讲 Sean Gordon
分会场 Liquid Biopsy: Multi-Analyte and Multi-Omic
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作者与单位 Authors & Affiliations

Sean Gordon, Jing Wang, Shile Zhang, Marisa Juntilla, Tingting Jiang, Matthew Ellis, Vishnu Ramani, Reagan Barnett, Bernard Herrman, Justin Odegaard, Darya Chudova

Guardant Health, Palo Alto, CA

摘要 Abstract

中文摘要
引言。免疫检查点抑制剂(ICI)已变革了癌症治疗,但识别最可能获益的患者仍具挑战性。已确立的生物标志物,如肿瘤突变负荷(TMB)和微卫星不稳定性(MSI),存在性能局限。我们假设,将MSI和TMB与来自治疗前血浆的肿瘤内在免疫调节及肿瘤微环境的表观基因组特征相结合,将改善预测。我们开发并验证了一种多模态免疫治疗应答评分(MIRS-Score),它整合了MSI、TMB和表观基因组特征(Guardant360 Liquid),以识别可能对ICI单药或ICI联合化疗产生应答的患者。 方法。我们从去标识化的GuardantINFORM数据库中识别出695例接受一线或二线ICI单药或ICI+化疗治疗的晚期NSCLC(aNSCLC)患者,并将其随机分为训练集(n=483)和测试集(n=212)。将一个经文献整理、数据驱动、与真实世界治疗停药时间(rwTTD)相关的表观基因组特征与MSI和TMB相结合以训练该多模态模型。处于MIRS百分位≥80的患者被标记为MIRS-High。经性别、年龄、治疗类型(单药vs联合)、治疗线数和基线肿瘤分数校正的Cox比例风险模型提供了校正后风险比(aHR);中位rwTTD采用Kaplan-Meier法估算。 结果。在独立测试集(n=212)中,MIRS-High患者的rwTTD更长(中位8.7 vs 5.1个月;aHR 0.61,95% CI 0.41-0.93,p=0.02),总生存期(OS)改善(aHR 0.33,95% CI 0.16-0.68,p<0.005)。在ICI单药亚组(n=69)中,MIRS-High显示中位rwTTD为11.0 vs 4.9个月(aHR 0.31,95% CI 0.13-0.75,p=0.01),OS更长(aHR 0.18,95% CI 0.04-0.79,p=0.023)。在仅接受化疗的患者中,MIRS-High与rwTTD无关(aHR 1.16,95% CI 0.94-1.44,p=0.18)。 结论。一种整合MSI、TMB和表观基因组特征的治疗前血浆评分,能够识别出接受ICI治疗时结局更优的aNSCLC患者,且优于单独使用MSI或TMB。其对ICI治疗特异性的分层以及与rwTTD和OS的强关联,支持这一多模态方法的临床应用价值,值得进一步评估其指导ICI治疗决策的潜力。
查看英文原文 English abstract
Introduction. Immune checkpoint inhibitors (ICIs) have transformed cancer treatment, yet identifying patients most likely to benefit remains challenging. Established biomarkers such as tumor mutational burden (TMB) and microsatellite instability (MSI) have performance limitations. We hypothesized that combining MSI and TMB with epigenomic features of tumor-intrinsic immune regulation and the tumor microenvironment from pretreatment plasma would improve prediction. We developed and validated a multimodal immunotherapy response score (MIRS-Score) that integrates MSI, TMB, and epigenomic signatures (Guardant360 Liquid) to identify patients likely to respond to ICI alone or in combination with chemotherapy. Methods. From the de-identified GuardantINFORM database we identified 695 advanced NSCLC (aNSCLC) patients treated with first- or second-line ICI monotherapy or ICI+chemotherapy and randomly split them into training (n=483) and test (n=212) sets. A literature-curated, data-driven epigenomic signature associated with real-world time to treatment discontinuation (rwTTD) was combined with MSI and TMB to train the multimodal model. Patients ≥80th MIRS percentile were labeled MIRS-High. Cox proportional hazards models adjusted for sex, age, therapy type (mono vs combo), line of therapy, and baseline tumor fraction provided adjusted hazard ratios (aHR); median rwTTD was estimated by Kaplan-Meier. Results. In the independent test set (n=212), MIRS-High patients had longer rwTTD (median 8.7 vs 5.1 months; aHR 0.61, 95% CI 0.41-0.93, p=0.02) and improved overall survival (OS) (aHR 0.33, 95% CI 0.16-0.68, p<0.005). In the ICI monotherapy subgroup (n=69), MIRS-High showed median rwTTD 11.0 vs 4.9 months (aHR 0.31, 95% CI 0.13-0.75, p=0.01) and longer OS (aHR 0.18, 95% CI 0.04-0.79, p=0.023). MIRS-High was not associated with rwTTD in patients treated with chemotherapy alone (aHR 1.16, 95% CI 0.94-1.44, p=0.18). Conclusions. A pretreatment plasma-based score combining MSI, TMB, and epigenomic signatures identifies aNSCLC patients with superior outcomes on ICI and outperforms MSI or TMB alone. The ICI treatment-specific stratification and strong association with both rwTTD and OS support the clinical utility of this multimodal approach, warranting further evaluation to assess the potential for guiding ICI treatment decisions.
利益披露 Disclosure
S. Gordon, Guardant Health Employment, Stock. J. Wang, Guardant Health Employment, Stock. S. Zhang, Guardant Health Employment, Stock. M. Juntilla, Guardant Health Employment, Stock. T. Jiang, Guardant Health Employment, Stock. M. Ellis, Guardant Health Employment, Stock. V. Ramani, Guardant Health Employment, Stock. R. Barnett, Guardant Health Employment, Stock. B. Herrman, Guardant Health Employment, Stock. Guardant Health Employment, Stock. J. Odegaard, Guardant Health Employment, Stock. D. Chudova, Guardant Health Employment, Stock.

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