Cancer tissues reflect a greater number of pathological characteristics of cancer comparedto cancer cells, so the evaluation of cancer tissues can be effective in determining cancertreatment strategies. Mass spectrometry imaging (MSI) can evaluate cancer tissues andeven 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 coincidewith genotype heterogeneity in HER2-positive breast cancer. We thus investigated the phenotype heterogeneity of luminal breast cancer, which is generally known to have few genemutations. As a result, we identified phenotype heterogeneity based on lipidomics in luminalbreast cancer tissues. Clusters were composed of phosphatidylcholine (PC), triglycerides(TG), phosphatidylethanolamine, sphingomyelin, and ceramide. It was found that mainly theproportion 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. Thiswas consistent with the fact that enzymes that synthesize long-chain fatty acids areincreased through cancer metabolism. It was then thought that clusters containing PCs withhigh carbon counts might reflect high malignancy. These results indicate that lipidomicsbased phenotype heterogeneity could potentially be used to classify cancer for whichgenetic analysis alone is insufficient for classification.