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 a...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 afterNAC.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-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%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),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 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.展开更多
基金supported bv the National Natural Science Foundation of China(20877011)State Key Laboratory of Environmental Chemistry and Ecotoxicology,Research Center for Eco-Environmental Sciences,Chinese Academy of Science(KF2009-17)+1 种基金the Key Laboratory of Industrial Ecology and Environmental Engineering,China Ministry of Education(0802)Scientific Research Foundation for Returned Overseas Chinese Scholars~~
基金Ministry of Education and Research of the Federal Republic of Germany,Grant/Award Numbers:BMBF,01ZX1610B,01KT1615Deutsche Forschungsgmeinschaft,Grant/Award Numbers:SFB 824 C4,CRC/Transregio 205/1+3 种基金Deutsche Krebshilfe,Grant/Award Number:70112617Stiftung zur Krebsbekampfung,Grant/Award Number:SKB425Cancer Research Switzerland,Grant/Award Number:KFS-4694-02-2019Cancer Research Switzerland,Grant/Award Number:MD-PhD-5088-06-2020。
文摘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 afterNAC.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-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%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),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 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.
