Stat5 induces androgen receptor ( <i>AR</i>) gene transcription in prostate cancer and offers a druggable pathway to target AR signaling

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Abstract

Androgen receptor (AR) drives prostate cancer (PC) growth and progression, and targeting AR signaling is the mainstay of pharmacological therapies for PC. Resistance develops relatively fast as a result of refueled AR activity. A major gap in the field is the lack of understanding of targetable mechanisms that induce persistent AR expression in castrate-resistant PC (CRPC). This study uncovers an unexpected function of active Stat5 signaling, a known promoter of PC growth and clinical progression, as a potent inducer of AR gene transcription. Stat5 suppression inhibited AR gene transcription in preclinical PC models and reduced the levels of wild-type, mutated, and truncated AR proteins. Pharmacological Stat5 inhibition by a specific small-molecule Stat5 inhibitor down-regulated Stat5-inducible genes as well as AR and AR-regulated genes and suppressed PC growth. This work introduces the concept of Stat5 as an inducer of AR gene transcription in PC. Pharmacological Stat5 inhibitors may represent a new strategy for suppressing AR and CRPC growth.

Abstract

Pharmacological Stat5 inhibition provides a strategy to suppress androgen receptor gene transcription and prostate cancer growth.

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Most cited references 86

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Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

A. Subramanian, P. Tamayo, V. K. Mootha … (2005)

Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

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edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

Mark Robinson, Davis J. McCarthy, Gordon K. Smyth (2009)

Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au

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HISAT: a fast spliced aligner with low memory requirements.

Daehwan Kim, Ben Langmead, Steven L Salzberg (2018)

HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.

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Author and article information

Contributors

Cristina Maranto:

ORCID: https://orcid.org/0000-0002-9437-1958

Role: InvestigationRole: MethodologyRole: ValidationRole: Writing - original draft

Lavannya Sabharwal:

ORCID: https://orcid.org/0000-0001-6022-029X

Role: ConceptualizationRole: InvestigationRole: MethodologyRole: ResourcesRole: Validation

Vindhya Udhane: Role: InvestigationRole: Validation

Samuel P. Pitzen:

ORCID: https://orcid.org/0000-0001-6556-0461

Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ValidationRole: VisualizationRole: Writing - review & editing

Braedan McCluskey:

ORCID: https://orcid.org/0000-0001-6423-7048

Role: Formal analysisRole: MethodologyRole: SoftwareRole: Visualization

Songyan Qi:

ORCID: https://orcid.org/0009-0009-6157-006X

Role: Investigation

Christine O’Connor: Role: Formal analysisRole: Software

Savita Devi: Role: InvestigationRole: ValidationRole: VisualizationRole: Writing - review & editing

Scott Johnson:

ORCID: https://orcid.org/0000-0003-2946-0601

Role: ConceptualizationRole: Data curationRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: VisualizationRole: Writing - review & editing

Kenneth Jacobsohn:

ORCID: https://orcid.org/0000-0003-2039-2737

Role: ConceptualizationRole: InvestigationRole: Resources

Anjishnu Banerjee:

ORCID: https://orcid.org/0000-0002-6898-9148

Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing - review & editing

Kenneth A. Iczkowski: Role: InvestigationRole: MethodologyRole: Resources

Liang Wang:

ORCID: https://orcid.org/0000-0002-9364-8572

Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: Writing - review & editing

Scott M. Dehm:

ORCID: https://orcid.org/0000-0002-7827-5579

Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: SupervisionRole: VisualizationRole: Writing - review & editing

Marja T. Nevalainen:

ORCID: https://orcid.org/0009-0008-8593-078X

Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: VisualizationRole: Writing - original draft

Journal

Journal ID (nlm-ta): Sci Adv

Journal ID (iso-abbrev): Sci Adv

Journal ID (publisher-id): sciadv

Journal ID (hwp): advances

Title: Science Advances

Publisher: American Association for the Advancement of Science

ISSN (Electronic): 2375-2548

Publication date Collection: 01 March 2024

Publication date (Electronic, pub): 28 February 2024

Volume: 10

Issue: 9

Electronic Location Identifier: eadi2742

Affiliations

[ ¹ ]Department of Pathology, Medical College of Wisconsin, Milwaukee, WI 53226, USA.

[ ² ]Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455, USA.

[ ³ ]Graduate Program in Molecular, Cellular, and Developmental Biology and Genetics, University of Minnesota, Minneapolis, MN 55455, USA.

[ ⁴ ]Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55455, USA.

[ ⁵ ]Graduate Program in Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA.

[ ⁶ ]Department of Urology, Medical College of Wisconsin, Milwaukee, WI 53226, USA.

[ ⁷ ]Institute for Health and Equity, Medical College of Wisconsin, Milwaukee, WI 53226, USA.

[ ⁸ ]Department of Tumor Biology, Moffitt Cancer Center, 12902 USF Magnolia Drive, Tampa, FL 33612, USA.

[ ⁹ ]Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN 55455, USA.

[ ¹⁰ ]Department of Urology, University of Minnesota, Minneapolis, MN 55455, USA.

[ ¹¹ ]Department of Pharmacology, Physiology and Cancer Biology, Sidney Kimmel Cancer Center at Jefferson Health, Thomas Jefferson University, Philadelphia, PA 19107, USA.

Author notes

[* ]Corresponding author. Email: marja.nevalainen@ 123456jefferson.edu

[†]

These authors contributed equally to this work.

Author information

Cristina Maranto https://orcid.org/0000-0002-9437-1958

Lavannya Sabharwal https://orcid.org/0000-0001-6022-029X

Samuel P. Pitzen https://orcid.org/0000-0001-6556-0461

Braedan McCluskey https://orcid.org/0000-0001-6423-7048

Songyan Qi https://orcid.org/0009-0009-6157-006X

Scott Johnson https://orcid.org/0000-0003-2946-0601

Kenneth Jacobsohn https://orcid.org/0000-0003-2039-2737

Anjishnu Banerjee https://orcid.org/0000-0002-6898-9148

Liang Wang https://orcid.org/0000-0002-9364-8572

Scott M. Dehm https://orcid.org/0000-0002-7827-5579

Marja T. Nevalainen https://orcid.org/0009-0008-8593-078X

Article

Publisher ID: adi2742

DOI: 10.1126/sciadv.adi2742

PMC ID: 10901378

PubMed ID: 38416822

SO-VID: 194008c4-d339-41c0-8b02-0f9703f4e123

Copyright © Copyright © 2024 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

History

Date received : 14 April 2023

Date accepted : 24 January 2024

Funding

Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;

Award ID: 1RO1CA2622570

Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;

Award ID: 1RO1CA212097

Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;

Award ID: 1RO1CA212097

Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;

Award ID: 1RO1CA2622570

Funded by: Wisconsin Cancer Showhouse MTN;

Award ID: FP15437

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Copyeditor Mjoy Toledo

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Stat5 induces androgen receptor ( AR) gene transcription in prostate cancer and offers a druggable pathway to target AR signaling

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Most cited references 86

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

HISAT: a fast spliced aligner with low memory requirements.

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Contributors

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