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Metabolic tumor constitution is superior to tumor regression grading for evaluating response to neoadjuvant therapy of esophageal adenocarcinoma patients.
J. Pathol. 256, 202-213 (2022)
Publ. Version/Full Text
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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 to 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 1.856, 95% CI = 0.81-4.25, P = 0.143). The classifier even allowed to further stratify patients within the TRG1-3 categories. The underlying mechanisms of response to treatment has 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 tumor may have a greater impact on patient survival than the quantity of residual tumor cells or the stroma. This article is protected by copyright. All rights reserved.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Esophageal Cancer ; Artificial Intelligence ; Imaging Mass Spectrometry ; Machine Learning ; Metabolic Response Evaluation ; Patient Stratification ; Spatial Metabolomics ; Tumor Regression Grading; Preoperative Chemoradiotherapy; Perioperative Chemotherapy; Nodal Status; Cancer; Carcinoma; Polymorphism; Multicenter; Expression; Pet
ISSN (print) / ISBN
0022-3417
e-ISSN
1096-9896
Journal
Journal of Pathology, The
Quellenangaben
Volume: 256,
Issue: 2,
Pages: 202-213
Publisher
Wiley
Publishing Place
111 River St, Hoboken 07030-5774, Nj Usa
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Research Unit Analytical Pathology (AAP)
Grants
Helmholtz Zentrum Munchen (Helmholtz Enterprise-2018-6)
Impulse and Networking Fund of the Helmholtz Association
Deutsche Forschungsgemeinschaft
European Union (ERA NET: TRANSCAN 2)
Impulse and Networking Fund of the Helmholtz Association
Deutsche Forschungsgemeinschaft
European Union (ERA NET: TRANSCAN 2)