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Objectives:  Tumor pathology examination especially epidermal growth factor receptor ( EGFR ) mutations molecular testing has been integral part of lung cancer clinical practices. However, the  EGFR  mutations spatial distribution characteristics remains poorly investigated, which is critical to tumor heterogeneity analysis and precision diagnosis. Here, we conducted an exploratory study for label-free lung cancer pathology diagnosis and mapping of  EGFR  mutation spatial distribution using ambient mass spectrometry imaging (MSI).   Materials and Methods:  MSI analysis were performed in 55 post-operative non-small cell lung cancer (NSCLC) tumor and paired normal tissues to distinguish tumor from normal and classify pathology. We then compared diagnostic sensitivity of MSI and ADx-amplification refractory mutation system (ARMS) for the detection of  EGFR  mutation in pathological confirmed lung adenocarcinoma (AC) and explored  EGFR  mutations associated biomarkers to depict  EGFR  spatial distribution base on ambient MSI.   Results:  Of 55 pathological confirmed NSCLC, MSI achieved a diagnostic sensitivity of 85.2% (23/27) and 82.1% (23/28) for AC and squamous cell carcinoma (SCC), respectively. Among 27 AC, there were 17  EGFR- wild-type and 10  EGFR- mutated-positive samples detected by ARMS, and MSI achieved a diagnostic sensitivity of 82.3% (14/17) and 80% (8/10) for these two groups. Several phospholipids were specially enriched in AC compared with SCC tissues, with the higher ions intensity of phospholipids in  EGFR- mutated-positive compared with  EGFR- wild-type AC tissues. We also found  EGFR  mutations distribution was heterogeneous in different regions of same tumor by multi-regions ARMS detection, and only the regions with higher ions intensity of phospholipids were  EGFR- mutated-positive.   Conclusion:  MSI method could accurately distinguish tumor pathology and subtypes, and phospholipids were reliable  EGFR  mutations associated biomarkers, phospholipids imaging could intuitively visualize  EGFR  mutations spatial distribution, may facilitate our understanding of tumor heterogeneity.

frontiers in oncology

Accurate Classification of Non-small Cell Lung Cancer (NSCLC) Pathology and Mapping of EGFR Mutation Spatial Distribution by Ambient Mass Spectrometry Imaging