Ontogeny and Vulnerabilities of Drug-Tolerant Persisters in HER2+ Breast Cancer

Chang, Chewei Anderson; Jiang, Shaosong; Sayad, Azin; Brown, Kevin R.; Nixon, Allison M.L.; Dhabaria, Avantika; Tang, Kwan Ho; Moffat, Jason; Ueberheide, Beatrix; Khodadadi‐Jamayran, Alireza; Tsirigos, Aristotelis; Neel, Benjamin G.

doi:10.1101/2020.08.28.273029

Cited by 15 publications

(22 citation statements)

References 100 publications

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“…As a proven target of miR-335, ALDOA and OCT4 regulate glucose metabolism and homeostasis [25,26,43]. With the increasing incidence of drug-tolerant in various breast cancer subtypes [44][45][46], new therapies or diagnostics could be developed for distant metastasis based on the relationship between circKIF4A and glucose metabolism. Accordingly, our study found that circKIF4A is biologically active in BC and revealed the precise role of the circKIF4A-miR-335- ALDOA/OCT4-HK2/PKM2 axis in the metabolic reprogramming during the development of breast cancer and liver metastasis.…”

Section: Discussionmentioning

confidence: 99%

Circular RNA KIF4A Promotes Liver Metastasis of Breast Cancer by Reprogramming Glucose Metabolism

Huang

Deng

Chen

et al. 2022

Journal of Oncology

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Background. Circular RNAs (circRNAs) regulate complex functional processes and play crucial roles in cancer development and progression. It was reported that circKIF4 regulates the progression of triple-negative breast cancer (TNBC). This study evaluates the role of circKIF4 in breast cancer distant metastasis and metabolic reprogramming. Methods. RT-qPCR was performed to verify the expression of circKIF4A in breast cancer, liver metastatic tissues, and cell lines. The function of circKIF4A in metastasis was evaluated both in vitro and in vivo through a series of experiments, including cell migration and glucose intake experiments. Additionally, we conducted molecular experiments to clarify the regulatory role of circKIF4A. We then conducted a Luciferase reporter assay and an RNA immunoprecipitation assay to identify the molecular interactions between circKIF4A and miRNA. Results. circKIF4A was overexpressed in breast cancer cell lines and tissues, inhibiting its expression and suppressing breast cancer growth and metastasis. Interestingly, we observed that circKIF4A reprogrammed the glucose metabolism of breast cancer, and silencing circKIF4A greatly affected glucose uptake and lactate production in breast cancer cells. miR-335 can be sponged by circKIF4A, which affected the expression of ALDOA/OCT4 protein and regulated HK2/PKM2 expression. Conclusions. This study demonstrated that the circKIF4A-miR-335-OCT4/ALDOA-HK2/PKM2 axis is critical to breast cancer metabolic reprogramming, indicating that this axis could be a novel therapeutic target for the treatment of liver metastasis of breast cancer.

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Section: Discussionmentioning

confidence: 99%

Circular RNA KIF4A Promotes Liver Metastasis of Breast Cancer by Reprogramming Glucose Metabolism

Huang

Deng

Chen

et al. 2022

Journal of Oncology

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“…Heterogeneous mRNA levels for given genes are often transient, but the expression levels of some genes can be preserved through cell division 7 . The existence of such gene expression memory is of major fundamental and biomedical interest, as it is involved in cancer resistance to drugs [8][9][10][11][12][13][14] , disease onset 11,15 , and differentiation trajectories in development, homeostasis and regeneration [16][17][18][19][20][21] . To study gene expression over cell divisions, scRNA-seq has been combined with different lineage tracing techniques.…”

Section: Introductionmentioning

confidence: 99%

Barcode-free prediction of cell lineages from scRNA-seq datasets

Eisele

Tarbier

Dormann

et al. 2022

Preprint

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The integration of lineage tracing with scRNA-seq has transformed our understanding of gene expression heritability during development, regeneration, and disease. However, lineage tracing is technically demanding and most existing scRNA-seq datasets are devoid of lineage information. Here we introduce Gene Expression Memory-based Lineage Inference (GEMLI), a computational pipeline allowing to predict cell lineages over several cell divisions solely from scRNA-seq datasets. GEMLI leverages genes displaying conserved expression levels over cell divisions, and allows i.a. identifying cell lineages in a broad range of cultured cell types, in intestinal organoids, and in crypts from adult mice. GEMLI recovers GO-terms enriched for heritable gene expression, allows to discriminate symmetric and asymmetric cell fate decisions and to reconstruct individual cellular structures from pooled scRNA-seq datasets. GEMLI considerably extends the pool of datasets from which lineage information can be obtained, thereby facilitating the study of gene expression heritability in a broad range of contexts. GEMLI is available at (https://github.com/UPSUTER/GEMLI).

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“…While the approach only needs a single time point, performing the assay at several different time points is important for validating the inferred model. We have recently exploited his approach for diverse applications include characterizing drug-tolerant cancer cells [63]- [65], activation of human viruses such as HIV [66], and innate immune response in individual human cells [67].…”

Section: Discussionmentioning

confidence: 99%

A fluctuation-based approach to infer kinetics and topology of cell-state switching

Saint-Antoine

Grima

Singh

2022

Preprint

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In the noisy cellular environment, RNAs and proteins are subject to considerable stochastic fluctuations in copy numbers over time. As a consequence, single cells within the same isoclonal population can differ in their expression profile and reside in different phenotypic states. The dynamic nature of this intercellular variation, where individual cells can transition between different states over time makes it a particularly hard phenomenon to characterize. Here we propose a novel fluctuation-test approach to infer the kinetics of transitions between cell states. More specifically, single cells are randomly drawn from the population and grown into cell colonies. After growth for a fixed number of generations, the number of cells residing in different states is assayed for each colony. In a simple system with reversible switching between two cell states, our analysis shows that the extent of colony-to-colony fluctuations in the fraction of cells in a given state is monotonically related to the switching kinetics. Several closed-form formulas for inferring the switching rates from experimentally quantified fluctuations are presented. We further extend this approach to multiple cell states where harnessing fluctuation signatures can reveal both the topology and the rates of cell-state switching. In summary, our analysis provides a powerful approach for dissecting cell-state transitions based on a single time point measurement. This is especially important for scenarios where a measurement involves killing the cell (for example, performing single-cell RNA-seq or assaying whether a microbial/cancer cell is in a drug-sensitive or drug-tolerant state), and hence the state of the same cell cannot be measured at different time points.

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Ontogeny and Vulnerabilities of Drug-Tolerant Persisters in HER2+ Breast Cancer

Cited by 15 publications

References 100 publications

Circular RNA KIF4A Promotes Liver Metastasis of Breast Cancer by Reprogramming Glucose Metabolism

Circular RNA KIF4A Promotes Liver Metastasis of Breast Cancer by Reprogramming Glucose Metabolism

Barcode-free prediction of cell lineages from scRNA-seq datasets

A fluctuation-based approach to infer kinetics and topology of cell-state switching

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