ONECUT2 Activates Diverse Resistance Drivers of Androgen Receptor-Independent Heterogeneity in Prostate Cancer

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Abstract

Androgen receptor- (AR-) indifference is a mechanism of resistance to hormonal therapy in prostate cancer (PC). Here we demonstrate that the HOX/CUT transcription factor ONECUT2 (OC2) activates resistance through multiple drivers associated with adenocarcinoma, stem-like and neuroendocrine (NE) variants. Direct OC2 targets include the glucocorticoid receptor and the NE splicing factor SRRM4, among others. OC2 regulates gene expression by promoter binding, enhancement of chromatin accessibility, and formation of novel super-enhancers. OC2 also activates glucuronidation genes that irreversibly disable androgen, thereby evoking phenotypic heterogeneity indirectly by hormone depletion. Pharmacologic inhibition of OC2 suppresses lineage plasticity reprogramming induced by the AR signaling inhibitor enzalutamide. These results demonstrate that OC2 activation promotes a range of drug resistance mechanisms associated with treatment-emergent lineage variation in PC. Our findings support enhanced efforts to therapeutically target this protein as a means of suppressing treatment-resistant disease.

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STAR: ultrafast universal RNA-seq aligner.

Alexander Dobin, Carrie A. Davis, Felix Schlesinger … (2013)

Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.

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Fast gapped-read alignment with Bowtie 2.

Ben Langmead, Steven L Salzberg (2022)

As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.

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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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Author and article information

Journal

Journal ID (nlm-ta): bioRxiv

Journal ID (publisher-id): BIORXIV

Title: bioRxiv

Publisher: Cold Spring Harbor Laboratory

Publication date (Electronic): 12 October 2023

Electronic Location Identifier: 2023.09.28.560025

Affiliations

[1 ]Departments of Urology and Biomedical Sciences, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.

[2 ]Department of Urology and Computational Biomedicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.

[3 ]Department of Health Sciences, Public University of Navarre, Pamplona, Navarra, Spain.

[4 ]Department of Pathology and Laboratory Medicine, UCLA, Los Angeles, CA, 90095, USA.

[5 ]Department of Urology, David Geffen School of Medicine at UCLA, Box 951738, 10833 Le Conte Ave 66-188 CHS UCLA, Los Angeles, CA, 90095, USA.

[6 ]Department of Medicine Section Hematology/Oncology Baylor College of Medicine, Houston, 77030, TX.

[7 ]Division of Medical Oncology, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.

[8 ]Center for Prostate Disease Research, Mutha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences and the Walter Reed National Military Medical Center; The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD 20814, USA.

[9 ]Genitourinary Malignancies Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA.

[10 ]Department of Pathology and Laboratory Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.

[11 ]Department of Urology, University of Washington, Seattle, WA 98195, USA.

[12 ]Divisions of Human Biology and Clinical Research, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA.

[13 ]Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.

[14 ]Department of Pathology and Cancer Center, Stony Brook University, NY 11794, USA.

[15 ]Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah 84108, USA.

[16 ]Department of Pathology, University of Utah, Salt Lake City, Utah 84108, USA.

[17 ]Geriatric Research, Education and Clinical Center (GRECC), U.S. Department of Veterans Affairs Puget Sound Health Care System, Seattle, Washington 98133, USA.

[18 ]Department of Pathology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.

Author notes

[*]

These authors contributed equally

[# ] Corresponding authors: Michael R Freeman ( Michael.Freeman@ 123456cshs.org ) and Sungyong You ( Sungyong.You@ 123456cshs.org ).

Article

DOI: 10.1101/2023.09.28.560025

PMC ID: 10614109

PubMed ID: 37905039

SO-VID: dd41566d-f527-4f61-822a-896e474d534a

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ONECUT2 Activates Diverse Resistance Drivers of Androgen Receptor-Independent Heterogeneity in Prostate Cancer

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Microbiology Independent Research Journal (MIR Journal)

Most cited references 79

STAR: ultrafast universal RNA-seq aligner.

Fast gapped-read alignment with Bowtie 2.

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

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