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代谢肿瘤成分优于肿瘤退缩分级,可用于评估食管腺癌患者新辅助治疗的反应。

Metabolic tumor constitution is superior to tumor regression grading for evaluating response to neoadjuvant therapy of esophageal adenocarcinoma patients.

机构信息

Research Unit Analytical Pathology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.

Department of Visceral Surgery and Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.

出版信息

J Pathol. 2022 Feb;256(2):202-213. doi: 10.1002/path.5828. Epub 2021 Dec 4.

Abstract

The response to neoadjuvant therapy can vary widely between individual patients. Histopathological tumor regression grading (TRG) is a strong factor for treatment response and survival prognosis of esophageal adenocarcinoma (EAC) patients following neoadjuvant treatment and surgery. However, TRG systems are usually based on the estimation of residual tumor but do not consider stromal or metabolic changes after treatment. Spatial metabolomics analysis is a powerful tool for molecular tissue phenotyping but has not been used so far in the context of neoadjuvant treatment of esophageal cancer. We used imaging mass spectrometry to assess the potential of spatial metabolomics on tumor and stroma tissue for evaluating therapy response of neoadjuvant-treated EAC patients. With an accuracy of 89.7%, the binary classifier trained on spatial tumor metabolite data proved to be superior for stratifying patients when compared with histopathological response assessment, which had an accuracy of 70.5%. Sensitivities and specificities for the poor and favorable survival patient groups ranged from 84.9% to 93.3% using the metabolic classifier and from 62.2% to 78.1% using TRG. The tumor classifier was the only significant prognostic factor (HR 3.38, 95% CI 1.40-8.12, p = 0.007) when adjusted for clinicopathological parameters such as TRG (HR 1.01, 95% CI 0.67-1.53, p = 0.968) or stromal classifier (HR 1.86, 95% CI 0.81-4.25, p = 0.143). The classifier even allowed us to further stratify patients within the TRG1-3 categories. The underlying mechanisms of response to treatment have been figured out through network analysis. In summary, metabolic response evaluation outperformed histopathological response evaluation in our study with regard to prognostic stratification. This finding indicates that the metabolic constitution of the tumor may have a greater impact on patient survival than the quantity of residual tumor cells or the stroma. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

摘要

新辅助治疗的反应在个体患者之间差异很大。组织病理学肿瘤消退分级(TRG)是新辅助治疗和手术后食管腺癌(EAC)患者治疗反应和生存预后的重要因素。然而,TRG 系统通常基于对残留肿瘤的估计,但不考虑治疗后的基质或代谢变化。空间代谢组学分析是一种强大的分子组织表型分析工具,但迄今为止尚未在食管癌新辅助治疗的背景下使用。我们使用成像质谱法评估空间代谢组学在肿瘤和基质组织中评估新辅助治疗的 EAC 患者治疗反应的潜力。基于空间肿瘤代谢物数据训练的二进制分类器的准确性为 89.7%,与组织病理学反应评估相比,证明在分层患者方面更具优势,组织病理学反应评估的准确性为 70.5%。使用代谢分类器,不良和良好生存患者组的敏感性和特异性范围为 84.9%至 93.3%,而使用 TRG 的敏感性和特异性范围为 62.2%至 78.1%。在调整了组织病理学参数(如 TRG[风险比 1.01,95%置信区间 0.67-1.53,p=0.968]或基质分类器[风险比 1.86,95%置信区间 0.81-4.25,p=0.143])后,肿瘤分类器是唯一显著的预后因素(风险比 3.38,95%置信区间 1.40-8.12,p=0.007)。该分类器甚至允许我们在 TRG1-3 类别内进一步分层患者。通过网络分析,已经确定了治疗反应的潜在机制。总之,与组织病理学反应评估相比,代谢反应评估在我们的研究中在预后分层方面表现更好。这一发现表明,肿瘤的代谢组成可能比残留肿瘤细胞或基质的数量对患者的生存有更大的影响。

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