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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.K.

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

Cancer Comm., DOI: 10.1002/cac2.12310 (2022)
Publ. Version/Full Text Research data DOI
Open Access Gold
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BACKGROUND: 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. METHODS: 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). RESULTS: 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). CONCLUSIONS: 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.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Non-small Cell Lung Cancer ; Cancer Metabolism ; Machine Learning ; Mass Spectrometry Imaging ; Metabolic Classifier ; Prognosis ; Spatial Metabolomics ; Treatment Response
ISSN (print) / ISBN 2523-3548
e-ISSN 2523-3548
Publisher BioMed Central
Publishing Place [London]
Reviewing status Peer reviewed
Grants Ministry of Education and Research of the Federal Republic of Germany
Deutsche Forschungsgmeinschaft
Stiftung zur Krebsbekampfung
Cancer Research Switzerland