Spatial proteomic profiling elucidates immune determinants of neoadjuvant chemo-immunotherapy in esophageal squamous cell carcinoma

生物 免疫系统 免疫疗法 新辅助治疗 免疫学 肿瘤微环境 CD8型 肿瘤科 癌症研究 内科学 医学 癌症 乳腺癌
作者
Chao Wu,Guoqing Zhang,Lin Wang,Jinlong Hu,Zhongjian Ju,Haitao Tao,Qing Li,Jian Li,Wei Zhang,Jianpeng Sheng,Xiaobin Hou,Yi Hu
出处
期刊:Oncogene [Springer Nature]
卷期号:43 (37): 2751-2767 被引量:27
标识
DOI:10.1038/s41388-024-03123-z
摘要

Esophageal squamous cell carcinoma (ESCC) presents significant clinical and therapeutic challenges due to its aggressive nature and generally poor prognosis. We initiated a Phase II clinical trial (ChiCTR1900027160) to assess the efficacy of a pioneering neoadjuvant chemo-immunotherapy regimen comprising programmed death-1 (PD-1) blockade (Toripalimab), nanoparticle albumin-bound paclitaxel (nab-paclitaxel), and the oral fluoropyrimidine derivative S-1, in patients with locally advanced ESCC. This study uniquely integrates clinical outcomes with advanced spatial proteomic profiling using Imaging Mass Cytometry (IMC) to elucidate the dynamics within the tumor microenvironment (TME), focusing on the mechanistic interplay of resistance and response. Sixty patients participated, receiving the combination therapy prior to surgical resection. Our findings demonstrated a major pathological response (MPR) in 62% of patients and a pathological complete response (pCR) in 29%. The IMC analysis provided a detailed regional assessment, revealing that the spatial arrangement of immune cells, particularly CD8+ T cells and B cells within tertiary lymphoid structures (TLS), and S100A9+ inflammatory macrophages in fibrotic regions are predictive of therapeutic outcomes. Employing machine learning approaches, such as support vector machine (SVM) and random forest (RF) analysis, we identified critical spatial features linked to drug resistance and developed predictive models for drug response, achieving an area under the curve (AUC) of 97%. These insights underscore the vital role of integrating spatial proteomics into clinical trials to dissect TME dynamics thoroughly, paving the way for personalized and precise cancer treatment strategies in ESCC. This holistic approach not only enhances our understanding of the mechanistic basis behind drug resistance but also sets a robust foundation for optimizing therapeutic interventions in ESCC.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
科研通AI6.4应助Y.X采纳,获得10
1秒前
1秒前
夏姬宁静完成签到,获得积分10
1秒前
xinyi发布了新的文献求助30
2秒前
零度蓝莓发布了新的文献求助10
2秒前
科研通AI6.4应助囫囵觉采纳,获得10
3秒前
缥缈慕青完成签到,获得积分10
3秒前
桐桐应助因心采纳,获得10
3秒前
3秒前
4秒前
4秒前
5秒前
5秒前
Criminology34应助zzz采纳,获得30
5秒前
风之梦完成签到 ,获得积分10
6秒前
6秒前
蟹味虾条完成签到,获得积分10
6秒前
7秒前
有何可不完成签到,获得积分10
8秒前
8秒前
Sharley发布了新的文献求助10
8秒前
Pises发布了新的文献求助10
10秒前
10秒前
蟹味虾条发布了新的文献求助10
10秒前
龚宇完成签到,获得积分10
11秒前
12秒前
12秒前
13秒前
13秒前
希望天下0贩的0应助WWTWM采纳,获得10
14秒前
科研通AI6.4应助沉默采纳,获得10
15秒前
贪玩的秋柔完成签到,获得积分0
15秒前
陈睿完成签到 ,获得积分10
17秒前
变化球完成签到,获得积分10
17秒前
jundading发布了新的文献求助10
17秒前
因心发布了新的文献求助10
17秒前
17秒前
19秒前
脑洞疼应助健壮的凝安采纳,获得10
20秒前
21秒前
高分求助中
Principles of Economics, 11th Edition 10000
University Physics with Modern Physics, 16th edition 10000
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Molecular Mechanisms of Photosynthesis, 4th Edition 1000
Organic Reactions, Volume 116 1000
Matrix Methods in Data Mining and Pattern Recognition 510
Social Skills Improvement System-Rating Scales--Chinese Version 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7256133
求助须知:如何正确求助?哪些是违规求助? 8878255
关于积分的说明 18750802
捐赠科研通 6936413
什么是DOI,文献DOI怎么找? 3200785
关于科研通互助平台的介绍 2374970
邀请新用户注册赠送积分活动 2176314