{"title":"Single-cell RNA sequencing and machine learning provide candidate drugs against drug-tolerant persister cells in colorectal cancer","authors":"Yosui Nojima , Ryoji Yao , Takashi Suzuki","doi":"10.1016/j.bbadis.2025.167693","DOIUrl":null,"url":null,"abstract":"<div><div>Drug resistance often stems from drug-tolerant persister (DTP) cells in cancer. These cells arise from various lineages and exhibit complex dynamics. However, effectively targeting DTP cells remains challenging. We used single-cell RNA sequencing (scRNA-Seq) data and machine learning (ML) models to identify DTP cells in patient-derived organoids (PDOs) and computationally screened candidate drugs targeting these cells in familial adenomatous polyposis (FAP), associated with a high risk of colorectal cancer. Three PDOs (benign and malignant tumor organoids and a normal organoid) were evaluated using scRNA-Seq. ML models constructed based on public scRNA-Seq data classified DTP versus non-DTP cells. Candidate drugs for DTP cells in a malignant tumor organoid were identified from public drug sensitivity data. From FAP scRNA-Seq data, a specific TC1 cell cluster in tumor organoids was identified. The ML model identified up to 36 % of TC1 cells as DTP cells, a higher proportion than those for other clusters. A viability assay using a malignant tumor organoid demonstrated that YM-155 and THZ2 exert synergistic effects with trametinib. The constructed ML model is effective for DTP cell identification based on scRNA-Seq data for FAP and provides candidate treatments. This approach may improve DTP cell targeting in the treatment of colorectal and other cancers.</div></div>","PeriodicalId":8821,"journal":{"name":"Biochimica et biophysica acta. Molecular basis of disease","volume":"1871 3","pages":"Article 167693"},"PeriodicalIF":4.2000,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biochimica et biophysica acta. Molecular basis of disease","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0925443925000389","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Drug resistance often stems from drug-tolerant persister (DTP) cells in cancer. These cells arise from various lineages and exhibit complex dynamics. However, effectively targeting DTP cells remains challenging. We used single-cell RNA sequencing (scRNA-Seq) data and machine learning (ML) models to identify DTP cells in patient-derived organoids (PDOs) and computationally screened candidate drugs targeting these cells in familial adenomatous polyposis (FAP), associated with a high risk of colorectal cancer. Three PDOs (benign and malignant tumor organoids and a normal organoid) were evaluated using scRNA-Seq. ML models constructed based on public scRNA-Seq data classified DTP versus non-DTP cells. Candidate drugs for DTP cells in a malignant tumor organoid were identified from public drug sensitivity data. From FAP scRNA-Seq data, a specific TC1 cell cluster in tumor organoids was identified. The ML model identified up to 36 % of TC1 cells as DTP cells, a higher proportion than those for other clusters. A viability assay using a malignant tumor organoid demonstrated that YM-155 and THZ2 exert synergistic effects with trametinib. The constructed ML model is effective for DTP cell identification based on scRNA-Seq data for FAP and provides candidate treatments. This approach may improve DTP cell targeting in the treatment of colorectal and other cancers.
期刊介绍:
BBA Molecular Basis of Disease addresses the biochemistry and molecular genetics of disease processes and models of human disease. This journal covers aspects of aging, cancer, metabolic-, neurological-, and immunological-based disease. Manuscripts focused on using animal models to elucidate biochemical and mechanistic insight in each of these conditions, are particularly encouraged. Manuscripts should emphasize the underlying mechanisms of disease pathways and provide novel contributions to the understanding and/or treatment of these disorders. Highly descriptive and method development submissions may be declined without full review. The submission of uninvited reviews to BBA - Molecular Basis of Disease is strongly discouraged, and any such uninvited review should be accompanied by a coverletter outlining the compelling reasons why the review should be considered.