肿瘤免疫浸润的空间分布可预测高危软组织肉瘤患者接受新辅助化疗后的预后。

IF 9.7 1区 医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL
EBioMedicine Pub Date : 2024-08-01 Epub Date: 2024-07-16 DOI:10.1016/j.ebiom.2024.105220
Sandro Pasquali, Viviana Vallacchi, Luca Lalli, Paola Collini, Marta Barisella, Cleofe Romagosa, Silvia Bague, Jean Michel Coindre, Angelo Paolo Dei Tos, Emanuela Palmerini, Vittorio Quagliuolo, Javier Martin-Broto, Antonio Lopez-Pousa, Giovanni Grignani, Jean-Yves Blay, Robert Diaz Beveridge, Elena Casiraghi, Silvia Brich, Salvatore Lorenzo Renne, Laura Bergamaschi, Barbara Vergani, Marta Sbaraglia, Paolo Giovanni Casali, Licia Rivoltini, Silvia Stacchiotti, Alessandro Gronchi
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引用次数: 0

摘要

背景:以蒽环类为基础的新辅助化疗(NAC)可能会改变肿瘤的免疫浸润。本研究描述了原发性高危软组织肉瘤(STS)接受新辅助化疗后免疫浸润的空间分布,并探讨了其与预后的关系:ISG-STS 1001试验将STS患者随机分为蒽环类加伊佛斯酰胺(AI)或组织学定制(HT)NAC。对肿瘤标本的四个区域进行取样:H&E淋巴细胞浸润最高的区域(HI);治疗后无变化的区域(最高级别,HG);治疗后有变化的区域(最低级别,LG);以及肿瘤边缘(TE)。CD3、CD8、PD-1、CD20、FOXP3和CD163通过免疫组化和数字病理进行分析。利用机器学习方法生成肉瘤免疫指数评分(SIS),预测患者的无病生存率和总生存率(DFS和OS):研究结果:与单纯核型相比,肿瘤浸润淋巴细胞、PD-1+细胞和CD163+细胞在STS组织学中的比例更高,而CD20+B细胞在这两组组织学中都能检测到。无论其空间分布如何,PD-1+细胞都具有负面预后价值。CD20+B细胞在HI和TE区域的富集与更好的患者预后相关。我们为每个肿瘤区域生成了预后SIS,其中HI-SIS表现最佳。这种预后价值受人工智能治疗的驱动:免疫群体的不同空间分布及其与预后的不同关联支持 NAC 作为 STS 肿瘤免疫浸润的调节剂:Pharmamar;意大利卫生部[RF-2019-12370923;GR-2016-02362609];意大利卫生部5×1000基金-2016;AIRC资助[ID#28546]。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spatial distribution of tumour immune infiltrate predicts outcomes of patients with high-risk soft tissue sarcomas after neoadjuvant chemotherapy.

Background: Anthracycline-based neoadjuvant chemotherapy (NAC) may modify tumour immune infiltrate. This study characterized immune infiltrate spatial distribution after NAC in primary high-risk soft tissue sarcomas (STS) and investigate association with prognosis.

Methods: The ISG-STS 1001 trial randomized STS patients to anthracycline plus ifosfamide (AI) or a histology-tailored (HT) NAC. Four areas of tumour specimens were sampled: the area showing the highest lymphocyte infiltrate (HI) at H&E; the area with lack of post-treatment changes (highest grade, HG); the area with post-treatment changes (lowest grade, LG); and the tumour edge (TE). CD3, CD8, PD-1, CD20, FOXP3, and CD163 were analyzed at immunohistochemistry and digital pathology. A machine learning method was used to generate sarcoma immune index scores (SIS) that predict patient disease-free and overall survival (DFS and OS).

Findings: Tumour infiltrating lymphocytes and PD-1+ cells together with CD163+ cells were more represented in STS histologies with complex compared to simple karyotype, while CD20+ B-cells were detected in both these histology groups. PD-1+ cells exerted a negative prognostic value irrespectively of their spatial distribution. Enrichment in CD20+ B-cells at HI and TE areas was associated with better patient outcomes. We generated a prognostic SIS for each tumour area, having the HI-SIS the best performance. Such prognostic value was driven by treatment with AI.

Interpretation: The different spatial distribution of immune populations and their different association with prognosis support NAC as a modifier of tumour immune infiltrate in STS.

Funding: Pharmamar; Italian Ministry of Health [RF-2019-12370923; GR-2016-02362609]; 5 × 1000 Funds-2016, Italian Ministry of Health; AIRC Grant [ID#28546].

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来源期刊
EBioMedicine
EBioMedicine Biochemistry, Genetics and Molecular Biology-General Biochemistry,Genetics and Molecular Biology
CiteScore
17.70
自引率
0.90%
发文量
579
审稿时长
5 weeks
期刊介绍: eBioMedicine is a comprehensive biomedical research journal that covers a wide range of studies that are relevant to human health. Our focus is on original research that explores the fundamental factors influencing human health and disease, including the discovery of new therapeutic targets and treatments, the identification of biomarkers and diagnostic tools, and the investigation and modification of disease pathways and mechanisms. We welcome studies from any biomedical discipline that contribute to our understanding of disease and aim to improve human health.
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