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

Cancer Communications

article

Jian Shen¹  Na Sun¹  Philipp Zens³  Thomas Kunzke¹  Achim Buck¹  Verena M. Prade¹  Jun Wang¹  Qian Wang¹  Ronggui Hu⁵  Annette Feuchtinger¹  Sabina Berezowska³  Axel Walch¹ 

[1] Research Unit Analytical Pathology, Helmholtz Zentrum München – German Research Center for Environmental Health;These authors contributed equally to this work;Institute of Pathology, University of Bern;Graduate School for Health Sciences, University of Bern;Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences;Department of Laboratory Medicine and Pathology, Institute of Pathology, Lausanne University Hospital and University of Lausanne

关键词: cancer metabolism;    machine learning;    mass spectrometry imaging;    metabolic classifier;    Non-small cell lung cancer;    prognosis;    spatial metabolomics;    treatment response;   

DOI  :  10.1002/cac2.12310

学科分类：社会科学、人文和艺术（综合）

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY|CC BY-NC-ND