Single-cell RNA sequencing and machine learning provide candidate drugs against drug-tolerant persister cells in colorectal cancer

Nojima, Yosui; Yao, Ryoji; Suzuki, Takashi

doi:https://doi.org/10.1016/j.bbadis.2025.167693

Single-cell RNA sequencing and machine learning provide candidate drugs against drug-tolerant persister cells in colorectal cancer

Nojima, Yosui; Yao, Ryoji; Suzuki, Takashi

Biochimica et Biophysica Acta - Molecular Basis of Disease, 2025, 1871(3), 167693

アクセス数:270件（2025-07-19 06:51 集計）

固定URL: https://hdl.handle.net/11094/100580

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タイトル	Single-cell RNA sequencing and machine learning provide candidate drugs against drug-tolerant persister cells in colorectal cancer
著者	NRID 1000030815717 1000030815717 ORCID 0000-0002-6821-0093 0000-0002-6821-0093 researchmap permalink yosui_nojima yosui_nojima Scopus Author ID 56533374600 56533374600 著者名 Nojima, Yosui
	NRID 1000080291095 1000080291095 著者名 Yao, Ryoji
	NRID 1000040114516 1000040114516 researchmap permalink read0043227 read0043227 Scopus Author ID 55628569951 55628569951 著者名 Suzuki, Takashi
キーワード等	Colorectal cancer
	Drug-tolerant persister cell
	Familial adenomatous polyposis
	Machine learning
	Patient-derived organoid
	Single-cell RNA sequencing
内容	Nojima Y., Yao R., Suzuki T. Single-cell RNA sequencing and machine learning provide candidate drugs against drug-tolerant persister cells in colorectal cancer. Biochimica et Biophysica Acta - Molecular Basis of Disease 1871, 167693 (2025); https://doi.org/10.1016/j.bbadis.2025.167693.
抄録	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.
出版者	Elsevier B.V.
収録物名	Biochimica et Biophysica Acta - Molecular Basis of Disease
巻(号)	1871(3)
ページ	167693
刊行年月	2025-03-01
言語	英語
ハンドルURL	https://hdl.handle.net/11094/100580
PISSN	09254439
eISSN	1879260X
関連情報 (isIdenticalTo)	DOI (出版社版) https://doi.org/10.1016/j.bbadis.2025.167693
アクセス権	open access
権利情報	This article is licensed under a Creative Commons Attribution 4.0 International License.
出版タイプ	Version of Record
カテゴリ	学術雑誌論文 Journal Article

資源タイプ	学術雑誌論文
ローカル資源タイプ	学術雑誌論文
DCTERMS.bibliographicCitation	Biochimica et Biophysica Acta - Molecular Basis of Disease. 2025, 1871(3), p. 167693
DC.title	Single-cell RNA sequencing and machine learning provide candidate drugs against drug-tolerant persister cells in colorectal cancer
DC.creator	Nojima, Yosui
	Yao, Ryoji
	Suzuki, Takashi
DC.publisher	Elsevier B.V.
DC.language' scheme='DCTERMS.RFC1766	英語
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2025-03-01
DC.identifier	https://doi.org/10.1016/j.bbadis.2025.167693
DC.subject	Colorectal cancer
	Drug-tolerant persister cell
	Familial adenomatous polyposis
	Machine learning
	Patient-derived organoid
	Single-cell RNA sequencing
DC.description	Nojima Y., Yao R., Suzuki T. Single-cell RNA sequencing and machine learning provide candidate drugs against drug-tolerant persister cells in colorectal cancer. Biochimica et Biophysica Acta - Molecular Basis of Disease 1871, 167693 (2025); https://doi.org/10.1016/j.bbadis.2025.167693.
DCTERMS.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.
DC.rights	This article is licensed under a Creative Commons Attribution 4.0 International License.
DC.format	100580
citation_title	Single-cell RNA sequencing and machine learning provide candidate drugs against drug-tolerant persister cells in colorectal cancer
citation_author	Nojima, Yosui
	Yao, Ryoji
	Suzuki, Takashi
citation_publisher	Elsevier B.V.
citation_language	英語
citation_date	2025-03-01
citation_journal_title	Biochimica et Biophysica Acta - Molecular Basis of Disease
citation_volume	1871
citation_issue	3
citation_firstpage	167693
citation_doi	https://doi.org/10.1016/j.bbadis.2025.167693
citation_public_url	https://hdl.handle.net/11094/100580

Single-cell RNA sequencing and machine learning provide candidate drugs against drug-tolerant persister cells in colorectal cancer

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