Literature DB >> 31289135

Single-Cell Analysis Reveals a Preexisting Drug-Resistant Subpopulation in the Luminal Breast Cancer Subtype.

Marta Prieto-Vila1,2, Wataru Usuba1,3, Ryou-U Takahashi1,4, Iwao Shimomura1, Hideo Sasaki3, Takahiro Ochiya1,2, Yusuke Yamamoto5.   

Abstract

Drug resistance is a major obstacle in the treatment of breast cancer. Surviving cells lead to tumor recurrence and metastasis, which remains the main cause of cancer-related mortality. Breast cancer is also highly heterogeneous, which hinders the identification of individual cells with the capacity to survive anticancer treatment. To address this, we performed extensive single-cell gene-expression profiling of the luminal-type breast cancer cell line MCF7 and its derivatives, including docetaxel-resistant cells. Upregulation of epithelial-to-mesenchymal transition and stemness-related genes and downregulation of cell-cycle-related genes, which were mainly regulated by LEF1, were observed in the drug-resistant cells. Interestingly, a small number of cells in the parental population exhibited a gene-expression profile similar to that of the drug-resistant cells, indicating that the untreated parental cells already contained a rare subpopulation of stem-like cells with an inherent predisposition toward docetaxel resistance. Our data suggest that during chemotherapy, this population may be positively selected, leading to treatment failure. SIGNIFICANCE: This study highlights the role of breast cancer intratumor heterogeneity in drug resistance at a single-cell level. ©2019 American Association for Cancer Research.

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Year:  2019        PMID: 31289135     DOI: 10.1158/0008-5472.CAN-19-0122

Source DB:  PubMed          Journal:  Cancer Res        ISSN: 0008-5472            Impact factor:   12.701


  17 in total

Review 1.  Single-Cell Techniques and Deep Learning in Predicting Drug Response.

Authors:  Zhenyu Wu; Patrick J Lawrence; Anjun Ma; Jian Zhu; Dong Xu; Qin Ma
Journal:  Trends Pharmacol Sci       Date:  2020-11-02       Impact factor: 14.819

2.  BAF Complexes and the Glucocorticoid Receptor in Breast Cancers.

Authors:  Nicholas Dietrich; Jackson A Hoffman; Trevor K Archer
Journal:  Curr Opin Endocr Metab Res       Date:  2020-09-06

Review 3.  Application and prospects of single cell sequencing in tumors.

Authors:  Ruo Han Huang; Le Xin Wang; Jing He; Wen Gao
Journal:  Biomark Res       Date:  2021-12-11

Review 4.  Advancing Cancer Research and Medicine with Single-Cell Genomics.

Authors:  Bora Lim; Yiyun Lin; Nicholas Navin
Journal:  Cancer Cell       Date:  2020-04-13       Impact factor: 31.743

5.  Single-cell qPCR Assay with Massively Parallel Microfluidic System.

Authors:  Marta Prieto-Vila; Takahiro Ochiya; Yusuke Yamamoto
Journal:  Bio Protoc       Date:  2020-03-20

Review 6.  Anticancer drug resistance: An update and perspective.

Authors:  Ruth Nussinov; Chung-Jung Tsai; Hyunbum Jang
Journal:  Drug Resist Updat       Date:  2021-12-16       Impact factor: 18.500

Review 7.  Single cell metabolomics using mass spectrometry: Techniques and data analysis.

Authors:  Renmeng Liu; Zhibo Yang
Journal:  Anal Chim Acta       Date:  2020-11-25       Impact factor: 6.558

Review 8.  Markers and Reporters to Reveal the Hierarchy in Heterogeneous Cancer Stem Cells.

Authors:  Amrutha Mohan; Reshma Raj Rajan; Gayathri Mohan; Padmaja Kollenchery Puthenveettil; Tessy Thomas Maliekal
Journal:  Front Cell Dev Biol       Date:  2021-06-03

9.  Epithelial-Mesenchymal-Transition-Like Circulating Tumor Cell-Associated White Blood Cell Clusters as a Prognostic Biomarker in HR-Positive/HER2-Negative Metastatic Breast Cancer.

Authors:  Xiuwen Guan; Chunxiao Li; Yiqun Li; Jiani Wang; Zongbi Yi; Binliang Liu; Hongyan Chen; Jiasen Xu; Haili Qian; Binghe Xu; Fei Ma
Journal:  Front Oncol       Date:  2021-06-02       Impact factor: 6.244

Review 10.  Applications of Single-Cell Omics in Tumor Immunology.

Authors:  Junwei Liu; Saisi Qu; Tongtong Zhang; Yufei Gao; Hongyu Shi; Kaichen Song; Wei Chen; Weiwei Yin
Journal:  Front Immunol       Date:  2021-06-09       Impact factor: 7.561

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