空间多组学揭示了肿瘤生态系统异质性对接受双特异性抗体治疗的晚期非小细胞肺癌患者免疫治疗疗效的影响。
Spatial multi-omics revealed the impact of tumor ecosystem heterogeneity on immunotherapy efficacy in patients with advanced non-small cell lung cancer treated with bispecific antibody.
机构信息
School of Medicine, Tongji University, Shanghai, China.
Department of Medical Oncology, Tongji University Affiliated Shanghai Pulmonary Hospital, Shanghai, China.
出版信息
J Immunother Cancer. 2023 Feb;11(2). doi: 10.1136/jitc-2022-006234.
BACKGROUND
Immunotherapy for malignant tumors has made great progress, but many patients do not benefit from it. The complex intratumoral heterogeneity (ITH) hindered the in-depth exploration of immunotherapy. Conventional bulk sequencing has masked intratumor complexity, preventing a more detailed discovery of the impact of ITH on treatment efficacy. Hence, we initiated this study to explore ITH at the multi-omics spatial level and to seek prognostic biomarkers of immunotherapy efficacy considering the presence of ITH.
METHODS
Using the segmentation strategy of digital spatial profiling (DSP), we obtained differential information on tumor and stromal regions at the proteomic and transcriptomic levels. Based on the consideration of ITH, signatures constructed by candidate proteins in different regions were used to predict the efficacy of immunotherapy.
RESULTS
Eighteen patients treated with a bispecific antibody (bsAb)-KN046 were enrolled in this study. The tumor and stromal areas of the same samples exhibited distinct features. Signatures consisting of 11 and 18 differentially expressed DSP markers from the tumor and stromal areas, respectively, were associated with treatment response. Furthermore, the spatially resolved signature identified from the stromal areas showed greater predictive power for bsAb immunotherapy response (area under the curve=0.838). Subsequently, our stromal signature was validated in an independent cohort of patients with non-small cell lung cancer undergoing immunotherapy.
CONCLUSION
We deciphered ITH at the spatial level and demonstrated for the first time that genetic information in the stromal region can better predict the efficacy of bsAb treatment.
TRIAL REGISTRATION NUMBER
NCT03838848.
背景
恶性肿瘤的免疫治疗已经取得了很大的进展,但许多患者并未从中获益。肿瘤内异质性(ITH)的复杂性阻碍了对免疫治疗的深入探索。传统的 bulk 测序掩盖了肿瘤内的复杂性,阻止了对 ITH 对治疗效果影响的更详细发现。因此,我们开展了这项研究,旨在探索多组学空间水平的 ITH,并考虑到 ITH 的存在,寻找免疫治疗疗效的预后生物标志物。
方法
我们使用数字空间分析(DSP)的分割策略,在蛋白质组学和转录组学水平上获得了肿瘤和基质区域的差异信息。基于 ITH 的考虑,使用不同区域候选蛋白构建的特征用于预测免疫治疗的疗效。
结果
本研究纳入了 18 名接受双特异性抗体(bsAb)KN046 治疗的患者。同一样本的肿瘤和基质区域表现出不同的特征。来自肿瘤和基质区域的分别包含 11 个和 18 个差异表达 DSP 标志物的特征与治疗反应相关。此外,从基质区域中确定的空间分辨特征对 bsAb 免疫治疗反应具有更大的预测能力(曲线下面积=0.838)。随后,我们在接受免疫治疗的非小细胞肺癌患者的独立队列中验证了我们的基质特征。
结论
我们在空间水平上解析了 ITH,并首次证明了基质区域的遗传信息可以更好地预测 bsAb 治疗的疗效。
试验注册号
NCT03838848。