Targeting of vulnerabilities of drug-tolerant persisters identified through functional genetics delays tumor relapse

Chen, Mengnuo; Mainardi, Sara; Lieftink, Cor; Velds, Arno; de Rink, Iris; Yang, Chen; Kuiken, Hendrik J.; Morris, Ben; Edwards, Finn; Jochems, Fleur; van Tellingen, Olaf; Boeije, Manon; Proost, Natalie; Jansen, Robin A.; Qin, Shifan; Jin, Haojie; Koen van der Mijn, J.C.; Schepers, Arnout; Venkatesan, Subramanian; Qin, Wenxin; Beijersbergen, Roderick L.; Wang, Liqin; Bernards, René

doi:10.1016/j.xcrm.2024.101471

Cell Reports Medicine

2024

DOI: 10.1016/j.xcrm.2024.101471

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Targeting of vulnerabilities of drug-tolerant persisters identified through functional genetics delays tumor relapse

Mengnuo Chen,

Sara Mainardi,

Cor Lieftink

et al.

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Cited by 5 publications

References 34 publications

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Drug tolerant persister cell plasticity in cancer: A revolutionary strategy for more effective anticancer therapies

He,

Qiu,

Fan

et al. 2024

Sig Transduct Target Ther

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Non-genetic mechanisms have recently emerged as important drivers of anticancer drug resistance. Among these, the drug tolerant persister (DTP) cell phenotype is attracting more and more attention and giving a predominant non-genetic role in cancer therapy resistance. The DTP phenotype is characterized by a quiescent or slow-cell-cycle reversible state of the cancer cell subpopulation and inert specialization to stimuli, which tolerates anticancer drug exposure to some extent through the interaction of multiple underlying mechanisms and recovering growth and proliferation after drug withdrawal, ultimately leading to treatment resistance and cancer recurrence. Therefore, targeting DTP cells is anticipated to provide new treatment opportunities for cancer patients, although our current knowledge of these DTP cells in treatment resistance remains limited. In this review, we provide a comprehensive overview of the formation characteristics and underlying drug tolerant mechanisms of DTP cells, investigate the potential drugs for DTP (including preclinical drugs, novel use for old drugs, and natural products) based on different medicine models, and discuss the necessity and feasibility of anti-DTP therapy, related application forms, and future issues that will need to be addressed to advance this emerging field towards clinical applications. Nonetheless, understanding the novel functions of DTP cells may enable us to develop new more effective anticancer therapy and improve clinical outcomes for cancer patients.

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Drug tolerant persister cell plasticity in cancer: A revolutionary strategy for more effective anticancer therapies

He,

Qiu,

Fan

et al. 2024

Sig Transduct Target Ther

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Cancer drug-tolerant persister cells: from biological questions to clinical opportunities

Russo,

Chen,

Mariella

et al. 2024

Nat Rev Cancer

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Robust self-supervised learning strategy to tackle the inherent sparsity in single-cell RNA-seq data

Park,

Lee

2024

Briefings in Bioinformatics

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Single-cell RNA sequencing (scRNA-seq) is a powerful tool for elucidating cellular heterogeneity and tissue function in various biological contexts. However, the sparsity in scRNA-seq data limits the accuracy of cell type annotation and transcriptomic analysis due to information loss. To address this limitation, we present scRobust, a robust self-supervised learning strategy to tackle the inherent sparsity of scRNA-seq data. Built upon the Transformer architecture, scRobust employs a novel self-supervised learning strategy comprising contrastive learning and gene expression prediction tasks. We demonstrated the effectiveness of scRobust using nine benchmarks, additional dropout scenarios, and combined datasets. scRobust outperformed recent methods in cell-type annotation tasks and generated cell embeddings that capture multi-faceted clustering information (e.g. cell types and HbA1c levels). In addition, cell embeddings of scRobust were useful for detecting specific marker genes related to drug tolerance stages. Furthermore, when we applied scRobust to scATAC-seq data, high-quality cell embedding vectors were generated. These results demonstrate the representational power of scRobust.

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Targeting of vulnerabilities of drug-tolerant persisters identified through functional genetics delays tumor relapse

Cited by 5 publications

References 34 publications

Drug tolerant persister cell plasticity in cancer: A revolutionary strategy for more effective anticancer therapies

Drug tolerant persister cell plasticity in cancer: A revolutionary strategy for more effective anticancer therapies

Cancer drug-tolerant persister cells: from biological questions to clinical opportunities

Robust self-supervised learning strategy to tackle the inherent sparsity in single-cell RNA-seq data

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