Spatial metabolomics for evaluating response to neoadjuvant therapy in non-small cell lung cancer patients.

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Version: Final published version
License: CC BY-NC-ND 4.0
Serval ID
serval:BIB_43545258FA26
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Spatial metabolomics for evaluating response to neoadjuvant therapy in non-small cell lung cancer patients.
Journal
Cancer communications
Author(s)
Shen J., Sun N., Zens P., Kunzke T., Buck A., Prade V.M., Wang J., Wang Q., Hu R., Feuchtinger A., Berezowska S., Walch A.
ISSN
2523-3548 (Electronic)
ISSN-L
2523-3548
Publication state
Published
Issued date
06/2022
Peer-reviewed
Oui
Volume
42
Number
6
Pages
517-535
Language
english
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Abstract
The response to neoadjuvant chemotherapy (NAC) differs substantially among individual patients with non-small cell lung cancer (NSCLC). Major pathological response (MPR) is a histomorphological read-out used to assess treatment response and prognosis in patients NSCLC after NAC. Although spatial metabolomics is a promising tool for evaluating metabolic phenotypes, it has not yet been utilized to assess therapy responses in patients with NSCLC. We evaluated the potential application of spatial metabolomics in cancer tissues to assess the response to NAC, using a metabolic classifier that utilizes mass spectrometry imaging combined with machine learning.
Resected NSCLC tissue specimens obtained after NAC (n = 88) were subjected to high-resolution mass spectrometry, and these data were used to develop an approach for assessing the response to NAC in patients with NSCLC. The specificities of the generated tumor cell and stroma classifiers were validated by applying this approach to a cohort of biologically matched chemotherapy-naïve patients with NSCLC (n = 85).
The developed tumor cell metabolic classifier stratified patients into different prognostic groups with 81.6% accuracy, whereas the stroma metabolic classifier displayed 78.4% accuracy. By contrast, the accuracies of MPR and TNM staging for stratification were 62.5% and 54.1%, respectively. The combination of metabolic and MPR classifiers showed slightly lower accuracy than either individual metabolic classifier. In multivariate analysis, metabolic classifiers were the only independent prognostic factors identified (tumor: P = 0.001, hazards ratio [HR] = 3.823, 95% confidence interval [CI] = 1.716-8.514; stroma: P = 0.049, HR = 2.180, 95% CI = 1.004-4.737), whereas MPR (P = 0.804; HR = 0.913; 95% CI = 0.445-1.874) and TNM staging (P = 0.078; HR = 1.223; 95% CI = 0.977-1.550) were not independent prognostic factors. Using Kaplan-Meier survival analyses, both tumor and stroma metabolic classifiers were able to further stratify patients as NAC responders (P < 0.001) and non-responders (P < 0.001).
Our findings indicate that the metabolic constitutions of both tumor cells and the stroma are valuable additions to the classical histomorphology-based assessment of tumor response.
Keywords
Carcinoma, Non-Small-Cell Lung, Humans, Lung Neoplasms/pathology, Metabolomics, Neoadjuvant Therapy, Neoplasm Staging, Non-small cell lung cancer, cancer metabolism, machine learning, mass spectrometry imaging, metabolic classifier, prognosis, spatial metabolomics, treatment response
Pubmed
Web of science
Open Access
Yes
Create date
23/05/2022 8:01
Last modification date
25/06/2022 6:34
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