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

Buck, Achim; Prade, Verena M; Kunzke, Thomas; Feuchtinger, Annette; Kröll, Dino; Feith, Marcus; Dislich, Bastian; Balluff, Benjamin; Langer, Rupert; Walch, Axel (2022). Metabolic tumor constitution is superior to tumor regression grading for evaluating response to neoadjuvant therapy of esophageal adenocarcinoma patients. The journal of pathology, 256(2), pp. 202-213. Wiley 10.1002/path.5828

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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 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.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Service Sector > Institute of Pathology > Clinical Pathology
04 Faculty of Medicine > Service Sector > Institute of Pathology
04 Faculty of Medicine > Department of Gastro-intestinal, Liver and Lung Disorders (DMLL) > Clinic of Visceral Surgery and Medicine > Visceral Surgery

UniBE Contributor:

Kröll, Dino, Dislich, Bastian, Langer, Rupert

Subjects:

500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health

ISSN:

1096-9896

Publisher:

Wiley

Language:

English

Submitter:

Rahel Fuhrer

Date Deposited:

11 Jan 2022 10:30

Last Modified:

05 Dec 2022 15:57

Publisher DOI:

10.1002/path.5828

PubMed ID:

34719782

Uncontrolled Keywords:

artificial intelligence esophageal cancer imaging mass spectrometry machine learning metabolic response evaluation patient stratification spatial metabolomics tumor regression grading

BORIS DOI:

10.48350/162378

URI:

https://boris.unibe.ch/id/eprint/162378

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