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

Abstract

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.

Artificial intelligence assists precision medicine in cancer treatment

“…Besides the widely used omics data mentioned above, other omics data (metabolomics, immunomics and microbiome data) are also used ( 77 ). For instance, disposing of metabolomics data by AI can assist the diagnosis ( 78 , 79 ), the of treatment response evaluation ( 80 – 82 ), discovery of new biomarkers ( 83 , 84 ), and determination of patient tolerance ( 85 ) and cancer status (invasive or non-invasive) ( 86 ). Moreover, the AI model based on immunomics data can forecast the emergency immune characteristics of tumor patients ( 87 ).…”

State-of-the-art mass spectrometry imaging applications in biomedical research

“…In line with the latter, Shen et al have recently used MALDI-MSI to investigate non-small cell lung cancer tissue samples for metabolic changes after neoadjuvant chemotherapy. 113 Neoadjuvant therapy is administered before surgical intervention in order to reduce the tumour size and thereby facilitate its easier removal. While this strategy generally improves the survival time in patients, not all patients benefit equally from this therapy.…”

Patterns of Carbon-Bound Exogenous Compounds Impact Disease Pathophysiology in Lung Cancer Subtypes in Different Ways

“…The highly complex and heterogeneous chemical composition of carbon particles and carbon-bound exogenous compounds in human anthracosis within the natural histological context of lung tissues has been largely unexplored and methodologically challenging. High mass-resolution mass spectrometry imaging (MSI) is capable of detecting and visualizing the discrete spatial distributions of exogenous compounds and their related metabolites in tissue sections. − One advantage of MSI is the ability to directly overlay mass spectrometric analyses on tissue sections, allowing for correlations between molecular and histologic information . High-resolution imaging combined with molecular specificity facilitates the identification of exogenous compound characteristics at the single-pixel level.…”