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

Cancer Communications 2022年第6期42卷 517-535页

作者： Jian Shen Na Sun Philipp Zens Thomas Kunzke Achim Buck Verena M.Prade Jun Wang Qian Wang Ronggui Hu Annette Feuchtinger Sabina Berezowska Axel Walch Research Unit Analytical Pathology Helmholtz Zentrum Munchen-German Research Center for Environmental HealthNeuherberg 85764Germany Institute of Pathology University of BernBern 3008Switzerland Graduate School for Health Sciences University of BernMittelstrasse 43Bern 3012Switzerland Center for Excellence in Molecular Cell Science Chinese Academy of SciencesShanghai 200031P.R.China Department of Laboratory Medicine and Pathology Institute of PathologyLausanne University Hospital and University of LausanneLausanne 1011Switzerland

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 patientsNSCLC *** spatial metabolomics is a promising tool for evaluating metabolic phenotypes,it has not yet been utilized to assess therapy responses in patients with *** 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 ***: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 *** specificities of the generated tumor cell and stroma classifiers were validated by applying this approach to a cohort of biologically matched chemotherapy-naive 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%*** contrast,the accuracies of MPR and TNM staging for stratification were 62.5%and 54.1%,*** combination of metabolic and MPR classifiers showed slightly lower accuracy than either individual metabolic *** 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),whereasMPR(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 *** Kaplan-Meier survival analyses,both tumor and stroma metabolic classifiers were able to further stratify patients as NAC responders(P<0.001)and non

关键词： cancer metabolism machine learning mass spectrometry imaging metabolic classifier Nonsmall cell lung cancer prognosis spatial metabolomics treatment response

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