Abstract
BackgroundDrug mass spectrometry imaging (MSI) data contain knowledge about drug and several other molecular ions present in a biological sample. However, a proper approach to fully explore the potential of such type of data is still missing. Therefore, a computational pipeline that combines different spatial and non-spatial methods is proposed to link the observed drug distribution profile with tumor heterogeneity in solid tumor. Our data analysis steps include pre-processing of MSI data, cluster analysis, drug local indicators of spatial association (LISA) map, and ions selection.ResultsThe number of clusters identified from different tumor tissues. The spatial homogeneity of the individual cluster was measured using a modified version of our drug homogeneity method. The clustered image and drug LISA map were simultaneously analyzed to link identified clusters with observed drug distribution profile. Finally, ions selection was performed using the spatially aware method.ConclusionsIn this paper, we have shown an approach to correlate the drug distribution with spatial heterogeneity in untargeted MSI data. Our approach is freely available in an R package 'CorrDrugTumorMSI'.
Highlights
Heterogeneity of the tumor microenvironment directly affects drug penetration, reducing therapeutic efficacy and contributing to the development of resistance [1, 2]
Cluster 3 showed a high overlap with the necrosis area [12] and was noted to be present in a relatively higher fraction among the tumor models not pretreated with bevacizumab
One of the causes of drug therapy failure is tumor drug resistance often induced by incomplete drug penetration
Summary
Heterogeneity of the tumor microenvironment directly affects drug penetration, reducing therapeutic efficacy and contributing to the development of resistance [1, 2]. For these reasons, knowledge about tumor spatial heterogeneity is of paramount importance to optimize therapeutic outcomes [3]. Mass spectrometry imaging (MSI) is a molecular imaging technique that provides simultaneous information about spatial localization of drugs and other small molecules present in the biological sample [5, 6]. Drug mass spectrometry imaging (MSI) data contain knowledge about drug and several other molecular ions present in a biological sample. Conclusions: In this paper, we have shown an approach to correlate the drug distribution with spatial heterogeneity in untargeted MSI data. Our approach is freely available in an R package ’CorrDrugTumorMSI’
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