Metabolic tumor constitution is superior to tumor regression grading for evaluating response to neoadjuvant therapy of esophageal adenocarcinoma patients.
Adenocarcinoma
/ drug therapy
Biomarkers, Tumor
/ metabolism
Chemoradiotherapy, Adjuvant
Chemotherapy, Adjuvant
Energy Metabolism
Esophageal Neoplasms
/ drug therapy
Esophagectomy
Germany
Humans
Machine Learning
Metabolome
Metabolomics
Neoadjuvant Therapy
/ adverse effects
Neoplasm Grading
Predictive Value of Tests
Reproducibility of Results
Retrospective Studies
Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
Switzerland
Time Factors
Treatment Outcome
artificial intelligence
esophageal cancer
imaging mass spectrometry
machine learning
metabolic response evaluation
patient stratification
spatial metabolomics
tumor regression grading
Journal
The Journal of pathology
ISSN: 1096-9896
Titre abrégé: J Pathol
Pays: England
ID NLM: 0204634
Informations de publication
Date de publication:
02 2022
02 2022
Historique:
revised:
04
10
2021
received:
20
07
2021
accepted:
28
10
2021
pubmed:
2
11
2021
medline:
22
2
2022
entrez:
1
11
2021
Statut:
ppublish
Résumé
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.
Substances chimiques
Biomarkers, Tumor
0
Types de publication
Comparative Study
Journal Article
Multicenter Study
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
202-213Subventions
Organisme : Deutsche Forschungsgemeinschaft
ID : SFB 824TP C04
Organisme : European Commission
ID : ERA NET
Organisme : European Commission
ID : TRANSCAN 2
Informations de copyright
© 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.
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