PAX8 regulon in human ovarian cancer links lineage dependency with epigenetic vulnerability to HDAC inhibitors

  1. Kaixuan Shi
  2. Xia Yin
  3. Mei-Chun Cai
  4. Ying Yan
  5. Chenqiang Jia
  6. Pengfei Ma
  7. Shengzhe Zhang
  8. Zhenfeng Zhang
  9. Zhenyu Gu
  10. Meiying Zhang  Is a corresponding author
  11. Wen Di  Is a corresponding author
  12. Guanglei Zhuang  Is a corresponding author
  1. Shanghai Jiao Tong University, China
  2. Shanghai Cancer Institute, China
  3. GenenDesign Co Ltd, China

Abstract

PAX8 is a prototype lineage-survival oncogene in epithelial ovarian cancer. However, neither its underlying pro-tumorigenic mechanisms nor potential therapeutic implications have been adequately elucidated. Here, we identified an ovarian lineage-specific PAX8 regulon using modified cancer outlier profile analysis, in which PAX8-FGF18 axis was responsible for promoting cell migration in an autocrine fashion. An image-based drug screen pinpointed that PAX8 expression was potently inhibited by small-molecules against histone deacetylases (HDACs). Mechanistically, HDAC blockade altered histone H3K27 acetylation occupancies and perturbed the super-enhancer topology associated with PAX8 gene locus, resulting in epigenetic downregulation of PAX8 transcripts and related targets. HDAC antagonists efficaciously suppressed ovarian tumor growth and spreading as single agents, and exerted synergistic effects in combination with standard chemotherapy. These findings provide mechanistic and therapeutic insights for PAX8-addicted ovarian cancer. More generally, our analytic and experimental approach represents an expandible paradigm for identifying and targeting lineage-survival oncogenes in diverse human malignancies.

Data availability

The sequencing data have been deposited in NCBI SRA database(http://www.ncbi.nlm.nih.gov/sra/) under the accession number SRP153266.

The following data sets were generated

Article and author information

Author details

  1. Kaixuan Shi

    School of Medicine, Shanghai Jiao Tong University, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.
  2. Xia Yin

    School of Medicine, Shanghai Jiao Tong University, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.
  3. Mei-Chun Cai

    State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.
  4. Ying Yan

    Precision Oncology Lab, GenenDesign Co Ltd, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.
  5. Chenqiang Jia

    School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.
  6. Pengfei Ma

    School of Medicine, Shanghai Jiao Tong University, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.
  7. Shengzhe Zhang

    School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.
  8. Zhenfeng Zhang

    State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.
  9. Zhenyu Gu

    Precision Oncology Lab, GenenDesign Co Ltd, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.
  10. Meiying Zhang

    School of Medicine, Shanghai Jiao Tong University, Shanghai, China
    For correspondence
    fudoczhang82@126.com
    Competing interests
    The authors declare that no competing interests exist.
  11. Wen Di

    School of Medicine, Shanghai Jiao Tong University, Shanghai, China
    For correspondence
    diwen163@163.com
    Competing interests
    The authors declare that no competing interests exist.
  12. Guanglei Zhuang

    School of Medicine, Shanghai Jiao Tong University, Shanghai, China
    For correspondence
    zhuanglab@163.com
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8141-5096

Funding

National Natural Science Foundation of China (81472537)

  • Guanglei Zhuang

Shanghai Municipal Commission of Health and Family Planning (20174Y0043)

  • Mei-Chun Cai

Program of Shanghai Hospital Development Center (16CR2001A)

  • Wen Di

Shanghai Jiao Tong University School of Medicine (YG2016MS51)

  • Xia Yin

The State Key Laboratory of Oncogenes and Related Genes (SB17-06)

  • Mei-Chun Cai

Shanghai Sailing Program (18YF1413200)

  • Pengfei Ma

National Key R&D Program of China (2016YFC1302900)

  • Wen Di

Science and Technology Commission of Shanghai Municipality (18441904800)

  • Wen Di

The Shanghai Institutions of Higher Learning (Eastern Scholar)

  • Guanglei Zhuang

National Natural Science Foundation of China (81672714)

  • Guanglei Zhuang

National Natural Science Foundation of China (81772770)

  • Wen Di

National Natural Science Foundation of China (81802584)

  • Meiying Zhang

National Natural Science Foundation of China (81802734)

  • Pengfei Ma

National Natural Science Foundation of China (81802809)

  • Mei-Chun Cai

Shanghai Municipal Education Commission-Gaofeng Clinical Medicine Grant Support (20161313)

  • Guanglei Zhuang

Shanghai Rising-Star Program (16QA1403600)

  • Guanglei Zhuang

Shanghai Municipal Commission of Health and Family Planning (2017ZZ02016,ZY(2018-2020)-FWTX-3006)

  • Wen Di

Science and Technology Commission of Shanghai Municipality (16140904401)

  • Xia Yin

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Animal experimentation: The institutional animal care and use committee of Ren Ji Hospital approved all animal protocols (permit-number: m20170205) and all animal experiments were in accordance with Ren Ji Hospital policies on the care, welfare, and treatment of laboratory animals.

Copyright

© 2019, Shi et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 3,232
    views
  • 543
    downloads
  • 37
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Kaixuan Shi
  2. Xia Yin
  3. Mei-Chun Cai
  4. Ying Yan
  5. Chenqiang Jia
  6. Pengfei Ma
  7. Shengzhe Zhang
  8. Zhenfeng Zhang
  9. Zhenyu Gu
  10. Meiying Zhang
  11. Wen Di
  12. Guanglei Zhuang
(2019)
PAX8 regulon in human ovarian cancer links lineage dependency with epigenetic vulnerability to HDAC inhibitors
eLife 8:e44306.
https://doi.org/10.7554/eLife.44306

Share this article

https://doi.org/10.7554/eLife.44306

Further reading

    1. Cancer Biology
    2. Cell Biology
    Kourosh Hayatigolkhatmi, Chiara Soriani ... Simona Rodighiero
    Tools and Resources

    Understanding the cell cycle at the single-cell level is crucial for cellular biology and cancer research. While current methods using fluorescent markers have improved the study of adherent cells, non-adherent cells remain challenging. In this study, we addressed this gap by combining a specialized surface to enhance cell attachment, the FUCCI(CA)2 sensor, an automated image analysis pipeline, and a custom machine learning algorithm. This approach enabled precise measurement of cell cycle phase durations in non-adherent cells. This method was validated in acute myeloid leukemia cell lines NB4 and Kasumi-1, which have unique cell cycle characteristics, and we tested the impact of cell cycle-modulating drugs on NB4 cells. Our cell cycle analysis system, which is also compatible with adherent cells, is fully automated and freely available, providing detailed insights from hundreds of cells under various conditions. This report presents a valuable tool for advancing cancer research and drug development by enabling comprehensive, automated cell cycle analysis in both adherent and non-adherent cells.

    1. Cancer Biology
    2. Computational and Systems Biology
    Rosalyn W Sayaman, Masaru Miyano ... Mark LaBarge
    Research Article

    Effects from aging in single cells are heterogenous, whereas at the organ- and tissue-levels aging phenotypes tend to appear as stereotypical changes. The mammary epithelium is a bilayer of two major phenotypically and functionally distinct cell lineages: luminal epithelial and myoepithelial cells. Mammary luminal epithelia exhibit substantial stereotypical changes with age that merit attention because these cells are the putative cells-of-origin for breast cancers. We hypothesize that effects from aging that impinge upon maintenance of lineage fidelity increase susceptibility to cancer initiation. We generated and analyzed transcriptomes from primary luminal epithelial and myoepithelial cells from younger <30 (y)ears old and older >55y women. In addition to age-dependent directional changes in gene expression, we observed increased transcriptional variance with age that contributed to genome-wide loss of lineage fidelity. Age-dependent variant responses were common to both lineages, whereas directional changes were almost exclusively detected in luminal epithelia and involved altered regulation of chromatin and genome organizers such as SATB1. Epithelial expression of gap junction protein GJB6 increased with age, and modulation of GJB6 expression in heterochronous co-cultures revealed that it provided a communication conduit from myoepithelial cells that drove directional change in luminal cells. Age-dependent luminal transcriptomes comprised a prominent signal that could be detected in bulk tissue during aging and transition into cancers. A machine learning classifier based on luminal-specific aging distinguished normal from cancer tissue and was highly predictive of breast cancer subtype. We speculate that luminal epithelia are the ultimate site of integration of the variant responses to aging in their surrounding tissue, and that their emergent phenotype both endows cells with the ability to become cancer-cells-of-origin and represents a biosensor that presages cancer susceptibility.