Tumor-associated nonmyelinating Schwann cell–expressed <i>PVT1</i> promotes pancreatic cancer kynurenine pathway and tumor immune exclusion

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Abstract

One of the major obstacles to treating pancreatic ductal adenocarcinoma (PDAC) is its immunoresistant microenvironment. The functional importance and molecular mechanisms of Schwann cells in PDAC remains largely elusive. We characterized the gene signature of tumor-associated nonmyelinating Schwann cells (TASc) in PDAC and indicated that the abundance of TASc was correlated with immune suppressive tumor microenvironment and the unfavorable outcome of patients with PDAC. Depletion of pancreatic-specific TASc promoted the tumorigenesis of PDAC tumors. TASc-expressed long noncoding RNA (lncRNA) plasmacytoma variant translocation 1 ( PVT1) was triggered by the tumor cell–produced interleukin-6. Mechanistically, PVT1 modulated RAF proto-oncogene serine/threonine protein kinase–mediated phosphorylation of tryptophan 2,3-dioxygenase in TASc, facilitating its enzymatic activities in catalysis of tryptophan to kynurenine. Depletion of TASc-expressed PVT1 suppressed PDAC tumor growth. Furthermore, depletion of TASc using a small-molecule inhibitor effectively sensitized PDAC to immunotherapy, signifying the important roles of TASc in PDAC immune resistance.

Abstract

Targeting tumor-associated non-myelinating Schwann cells improves response of immunotherapy-refractory pancreatic cancer.

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Most cited references 67

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Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Michael Love, Wolfgang Huber, Simon Anders (2014)

In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.

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clusterProfiler: an R package for comparing biological themes among gene clusters.

Guangchuang Yu, Li-Gen Wang, Yanyan Han … (2012)

Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.

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Highly accurate protein structure prediction with AlphaFold

John Jumper, Richard Evans, Alexander Pritzel … (2021)

Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.

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Author and article information

Contributors

Chengcao Sun:

ORCID: https://orcid.org/0000-0002-0554-3165

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

Youqiong Ye:

ORCID: https://orcid.org/0000-0001-8332-4710

Role: Formal analysisRole: Software

Zhi Tan:

ORCID: https://orcid.org/0000-0003-1895-0285

Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ResourcesRole: SoftwareRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing

Yuan Liu:

ORCID: https://orcid.org/0000-0003-2030-1153

Role: InvestigationRole: Methodology

Yajuan Li:

ORCID: https://orcid.org/0000-0003-2328-6956

Role: InvestigationRole: Methodology

Wei Hu: Role: Formal analysisRole: InvestigationRole: MethodologyRole: Software

Ke Liang: Role: Investigation

Lisa Angela Huang: Role: Writing - review & editing

Zhao Zhang:

ORCID: https://orcid.org/0000-0002-3757-8469

Role: Formal analysisRole: Software

Yaohua Zhang:

ORCID: https://orcid.org/0000-0002-9741-591X

Role: InvestigationRole: Methodology

Jun Yao:

ORCID: https://orcid.org/0000-0003-1418-3576

Role: Data curationRole: Formal analysisRole: SoftwareRole: Visualization

Tina K. Nguyen: Role: Data curationRole: Validation

Zilong Zhao:

ORCID: https://orcid.org/0000-0001-6607-4596

Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Resources

Andrew Wu:

ORCID: https://orcid.org/0000-0001-7579-6363

Role: Writing - review & editing

Jeffrey R. Marks:

ORCID: https://orcid.org/0000-0002-2054-5468

Role: Data curationRole: MethodologyRole: Resources

Abigail S. Caudle: Role: ConceptualizationRole: Resources

Aysegul A. Sahin: Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Resources

Jianjun Gao:

ORCID: https://orcid.org/0000-0001-6138-4472

Role: ResourcesRole: Writing - review & editing

Seth T. Gammon:

ORCID: https://orcid.org/0000-0001-8647-0975

Role: Methodology

David Piwnica-Worms:

ORCID: https://orcid.org/0000-0002-2120-7217

Role: MethodologyRole: Supervision

Jian Hu:

ORCID: https://orcid.org/0000-0001-9760-2013

Role: Formal analysisRole: Methodology

Paul J. Chiao: Role: ConceptualizationRole: Resources

Dihua Yu:

ORCID: https://orcid.org/0000-0001-6231-9381

Role: Supervision

Mien-Chie Hung:

ORCID: https://orcid.org/0000-0003-4317-4740

Role: ConceptualizationRole: Supervision

Michael A. Curran:

ORCID: https://orcid.org/0000-0003-4996-7207

Role: Methodology

George A. Calin: Role: Methodology

Haoqiang Ying:

ORCID: https://orcid.org/0000-0003-0616-2310

Role: ResourcesRole: Writing - review & editing

Leng Han:

ORCID: https://orcid.org/0000-0002-7380-2640

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

Chunru Lin:

ORCID: https://orcid.org/0000-0002-6473-8229

Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: VisualizationRole: Writing - original draftRole: Writing - review & editing

Liuqing Yang:

ORCID: https://orcid.org/0000-0002-6518-474X

Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: Validation

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: February 2023

Publication date (Electronic, pub): 01 February 2023

Volume: 9

Issue: 5

Electronic Location Identifier: eadd6995

Affiliations

[ ¹ ]Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.

[ ² ]Department of Biochemistry and Molecular Biology, The University of Texas Health Science Center at Houston McGovern Medical School, Houston, TX 77030, USA.

[ ³ ]Center for Drug Discovery, Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX 77030, USA.

[ ⁴ ]Center for Epigenetics and Disease Prevention, Institute of Biosciences and Technology, Texas A&M University, Houston, TX 77030, USA.

[ ⁵ ]Institute of Translational Medicine, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 201620, China.

[ ⁶ ]Division of Surgical Science, Department of Surgery, Duke University, School of Medicine, Durham, NC 27710, USA.

[ ⁷ ]Department of Breast Surgical Oncology, Division of Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.

[ ⁸ ]Department of Pathology, Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.

[ ⁹ ]Department of Genitourinary Medical Oncology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.

[ ¹⁰ ]Department of Cancer Systems Imaging, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.

[ ¹¹ ]Department of Cancer Biology, Division of Basic Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.

[ ¹² ]The Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.

[ ¹³ ]Graduate Institute of Biomedical Sciences, Research Center for Cancer Biology, and Center for Molecular Medicine, China Medical University, Taichung 404, Taiwan.

[ ¹⁴ ]Department of Biotechnology, Asia University, Taichung 413, Taiwan.

[ ¹⁵ ]Department of Immunology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.

[ ¹⁶ ]Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.

[ ¹⁷ ]Center for RNA Interference and Non-Coding RNAs, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.

Author notes

[†]

These authors contributed equally to this work.

[‡]

Present address: Shanghai Institute of Immunology, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.

[§]

Present address: Incyte Corporation, Wilmington, DE 19803, USA.

[* ]Corresponding author. Email: leng.han@ 123456tamu.edu (L.H.); clin2@ 123456mdanderson.org (C.L.); lyang7@ 123456mdanderson.org (L.Y.)

Author information

Chengcao Sun https://orcid.org/0000-0002-0554-3165

Youqiong Ye https://orcid.org/0000-0001-8332-4710

Zhi Tan https://orcid.org/0000-0003-1895-0285

Yuan Liu https://orcid.org/0000-0003-2030-1153

Yajuan Li https://orcid.org/0000-0003-2328-6956

Zhao Zhang https://orcid.org/0000-0002-3757-8469

Yaohua Zhang https://orcid.org/0000-0002-9741-591X

Jun Yao https://orcid.org/0000-0003-1418-3576

Zilong Zhao https://orcid.org/0000-0001-6607-4596

Andrew Wu https://orcid.org/0000-0001-7579-6363

Jeffrey R. Marks https://orcid.org/0000-0002-2054-5468

Jianjun Gao https://orcid.org/0000-0001-6138-4472

Seth T. Gammon https://orcid.org/0000-0001-8647-0975

David Piwnica-Worms https://orcid.org/0000-0002-2120-7217

Jian Hu https://orcid.org/0000-0001-9760-2013

Dihua Yu https://orcid.org/0000-0001-6231-9381

Mien-Chie Hung https://orcid.org/0000-0003-4317-4740

Michael A. Curran https://orcid.org/0000-0003-4996-7207

Haoqiang Ying https://orcid.org/0000-0003-0616-2310

Leng Han https://orcid.org/0000-0002-7380-2640

Chunru Lin https://orcid.org/0000-0002-6473-8229

Liuqing Yang https://orcid.org/0000-0002-6518-474X

Article

Publisher ID: add6995

DOI: 10.1126/sciadv.add6995

PMC ID: 9891701

PubMed ID: 36724291

SO-VID: 7bba96f5-16d3-43dd-a9d6-9b7ce0593256

Copyright © Copyright © 2023 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 NonCommercial License 4.0 (CC BY-NC).

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

History

Date received : 28 June 2022

Date accepted : 03 January 2023

Funding

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

Award ID: P01-CA117969

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

Award ID: R01CA218025

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

Award ID: R01CA231011

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

Award ID: R01CA231011-03S1

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

Award ID: R01CA218036

Funded by: FundRef http://dx.doi.org/10.13039/100000043, American Association for Cancer Research;

Award ID: 20-60-51