Lipidomics-based tissue heterogeneity in specimens of luminal breast cancer revealed by clustering analysis of mass spectrometry imaging: A preliminary study

Abstract

Cancer tissues reflect a greater number of pathological characteristics of cancer compared to cancer cells, so the evaluation of cancer tissues can be effective in determining cancer treatment strategies. Mass spectrometry imaging (MSI) can evaluate cancer tissues and even identify molecules while preserving spatial information. Cluster analysis of cancer tissues’ MSI data is currently used to evaluate the phenotype heterogeneity of the tissues. Interestingly, it has been reported that phenotype heterogeneity does not always coincide with genotype heterogeneity in HER2-positive breast cancer. We thus investigated the phenotype heterogeneity of luminal breast cancer, which is generally known to have few gene mutations. As a result, we identified phenotype heterogeneity based on lipidomics in luminal breast cancer tissues. Clusters were composed of phosphatidylcholine (PC), triglycerides (TG), phosphatidylethanolamine, sphingomyelin, and ceramide. It was found that mainly the proportion of PC and TG correlated with the proportion of cancer and stroma on HE images. Furthermore, the number of carbons in these lipid class varied from cluster to cluster. This was consistent with the fact that enzymes that synthesize long-chain fatty acids are increased through cancer metabolism. It was then thought that clusters containing PCs with high carbon counts might reflect high malignancy. These results indicate that lipidomics-based phenotype heterogeneity could potentially be used to classify cancer for which genetic analysis alone is insufficient for classification.

Desorption electrospray ionization mass spectrometry imaging (DESI-MSI) in disease diagnosis: an overview

“…This group of lipids also differed from cluster to cluster in their carbon composition. 110 This was in line with the notion that cancer metabolism results in an increase in the number of enzymes that synthesize long-chain fatty acids. Clusters of PCs could indicate high malignancy if they had high carbon counts.…”

“…Aramaki et al (2023) used cluster assessment of MSI data depending on the characteristics of lipid molecules and the levels of their expression to investigate variability in luminal breast cancer tissue sections. 110 The clusters were composed of phosphatidylethanolamine, sphingomyelin, (PC) phosphatidylcholine, (TG) triglycerides, and ceramide. It was discovered that the percentage of TG and PC mainly correlated with the percentage of stroma and cancer on HE images.…”

“…These results imply that cancers for which genetic testing alone is inadequate may be classified using phenotype variability depending on lipidomics. 110…”

Recent Contributions of Mass Spectrometry-Based “Omics” in the Studies of Breast Cancer

“…Aramaki (2023) reported lipidome profiles seen in imaging MS clustered for a predictive analysis. High amounts of phosphocholine and triacylglycerols were reflective of malignancy . Phosphocholine with a higher carbon count seemed more associated with neoplasms.…”

“…High amounts of phosphocholine and triacylglycerols were reflective of malignancy. 134 Phosphocholine with a higher carbon count seemed more associated with neoplasms. Phenotypic correlation with genotypes exists more in luminal cancers and is not such in HER2+ subtypes.…”