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

Sep 24, 2026

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Cancer tissues contain diverse pathological features beyond cancer cells, making tissue-level evaluation valuable for guiding treatment strategy. Mass spectrometry imaging (MSI) assesses cancer tissues and can identify molecules while retaining spatial context. Cluster analysis of MSI data is used to study tissue phenotype heterogeneity, though it may not match genotype heterogeneity. Here, phenotype heterogeneity of luminal breast cancer was investigated using lipidomics to reveal lipid-driven clusters.

LanguageEnglish

CategoryHealthcare

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Abstract

Introduction

PLOS ONE OPEN ACCESS Citation: Aramaki S, Tsuge S, Islam A, Eto F, Sakamoto T, Oyama S, et al. (2023) Lipidomicsbased tissue heterogeneity in specimens of luminal breast cancer revealed by clustering analysis of mass spectrometry imaging: A preliminary study. PLoS ONE 18(5): e0283155 . [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.1371/journal.pone](10.1371/journal.pone).0283155 Editor: Kenji Fujiwara, Kimura Hoospital, JAPAN Received: November 15, 2022 Accepted: March 2, 2023 Published: May 10, 2023 Copyright: © 2023 Aramaki et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the paper and its Supporting Information files. Funding: Japan’s MEXT/JSPS KAKENHI, JP15H05898B1, Dr. Mitsutoshi Setou Japan’s MEXT/JSPS KAKENHI, JP21464465, Shuhei Aramaki Development Programs for Medical Innovation (CREST) of the Japan Agency for Medical Research and Development, AMED, 921910520, Dr. Mitsutoshi Setou Japan’s MEXT/ RESEARCH ARTICLE Lipidomics-based tissue heterogeneity in specimens of luminal breast cancer revealed by clustering analysis of mass spectrometry imaging: A preliminary study Shuhei Aramaki1,2,3, Shogo Tsuge1, Ariful Islam1, Fumihiro Eto1, Takumi Sakamoto1, Soho Oyama1, Wenxin Li2, Chi Zhang1, Shinichi Yamaguchi4, Daiki Takatsuka5, Yuko Hosokawa5, A. S. M. Waliullah1, Yutaka Takahashi1, Kenji Kikushima1, Tomohito Sato1,5,6, Kei Koizumi5, Hiroyuki Ogura5, Tomoaki Kahyo1,6, Satoshi Baba7, Norihiko Shiiya5, Haruhiko Sugimura3, Katsumasa Nakamura2, Mitsutoshi Setou1,6 * 1 Department of Cellular and Molecular Anatomy, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan, 2 Department of Radiation Oncology, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan, 3 First Department of Pathology, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan, 4 Analytical & Measuring Instruments Division, Shimadzu Corporation, Kyoto, Japan, 5 1st Department of Surgery, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan, 6 International Mass Imaging Center, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan, 7 Department of Diagnostic Pathology, Hamamatsu University School of Medicine, Hamamatsu, Japan * [setou@hama-med.ac.jp](setou@hama-med.ac.jp) 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 lipidomicsbased pheno

FAQs

Why is evaluating cancer tissues important for treatment strategy?

Cancer tissues reflect more pathological characteristics than cancer cells alone. Tissue-level evaluation can therefore help determine cancer treatment strategies.

How does mass spectrometry imaging (MSI) contribute to the analysis?

MSI can evaluate cancer tissues, identify molecules, and preserve spatial information on tissue sections.

What did the study find about phenotype heterogeneity in luminal breast cancer?

The study used lipidomics to identify phenotype heterogeneity in luminal breast cancer specimens, forming clusters based on lipid classes and showing links between lipid proportions and cancer/stroma composition.

Sep 24, 2026

Uploaded by Asher