Improving drug discovery using image-based multiparametric analysis of the epigenetic landscape

  1. Chen Farhy
  2. Santosh Hariharan
  3. Jarkko Ylanko
  4. Luis Orozco
  5. Fu-Yue Zeng
  6. Ian Pass
  7. Fernando Ugarte
  8. E Camilla Forsberg
  9. Chun-Teng Huang
  10. David W Andrews
  11. Alexey V Terskikh  Is a corresponding author
  1. Sanford Burnham Prebys Medical Discovery Institute, United States
  2. University of Toronto, Canada
  3. University of California, Santa Cruz, United States

Abstract

High-content phenotypic screening has become the approach of choice for drug discovery due to its ability to extract drug-specific multi-layered data. In the field of epigenetics, such screening methods have suffered from a lack of tools sensitive to selective epigenetic perturbations. Here we describe a novel approach, Microscopic Imaging of Epigenetic Landscapes (MIEL), which captures the nuclear staining patterns of epigenetic marks and employs machine learning to accurately distinguish between such patterns. We validated the MIEL platform across multiple cells lines and using dose-response curves, to insure the fidelity and robustness of this approach for high content high throughput drug discovery. Focusing on noncytotoxic glioblastoma treatments, we demonstrated that MIEL can identify and classify epigenetically active drugs. Furthermore, we show MIEL was able to accurately rank candidate drugs by their ability to produce desired epigenetic alterations consistent with increased sensitivity to chemotherapeutic agents or with induction of glioblastoma differentiation.

Data availability

Sequencing data have been deposited in GEO under accession code GSE134045

The following data sets were generated

Article and author information

Author details

  1. Chen Farhy

    Sanford Burnham Prebys Medical Discovery Institute, La Jolla, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Santosh Hariharan

    Biological Sciences Platform, Sunnybrook Research Institute, University of Toronto, Toronto, Canada
    Competing interests
    The authors declare that no competing interests exist.
  3. Jarkko Ylanko

    Biological Sciences Platform, Sunnybrook Research Institute, University of Toronto, Toronto, Canada
    Competing interests
    The authors declare that no competing interests exist.
  4. Luis Orozco

    Sanford Burnham Prebys Medical Discovery Institute, La Jolla, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Fu-Yue Zeng

    Sanford Burnham Prebys Medical Discovery Institute, La Jolla, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Ian Pass

    Sanford Burnham Prebys Medical Discovery Institute, La Jolla, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Fernando Ugarte

    Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. E Camilla Forsberg

    Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Chun-Teng Huang

    Sanford Burnham Prebys Medical Discovery Institute, La Jolla, United States
    Competing interests
    The authors declare that no competing interests exist.
  10. David W Andrews

    Biological Sciences Platform, Sunnybrook Research Institute, University of Toronto, Toronto, Canada
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9266-7157
  11. Alexey V Terskikh

    Sanford Burnham Prebys Medical Discovery Institute, La Jolla, United States
    For correspondence
    terskikh@sbpdiscovery.org
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4641-3997

Funding

California Institute for Regenerative Medicine (TG2-01162)

  • Chen Farhy

Celgene (SCRA)

  • Alexey V Terskikh

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

Copyright

© 2019, Farhy 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

  • 5,063
    views
  • 808
    downloads
  • 25
    citations

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

Download links

Share this article

https://doi.org/10.7554/eLife.49683

Further reading

    1. Cancer Biology
    Han V Han, Richard Efem ... Richard Z Lin
    Research Article

    Most human pancreatic ductal adenocarcinoma (PDAC) are not infiltrated with cytotoxic T cells and are highly resistant to immunotherapy. Over 90% of PDAC have oncogenic KRAS mutations, and phosphoinositide 3-kinases (PI3Ks) are direct effectors of KRAS. Our previous study demonstrated that ablation of Pik3ca in KPC (KrasG12D; Trp53R172H; Pdx1-Cre) pancreatic cancer cells induced host T cells to infiltrate and completely eliminate the tumors in a syngeneic orthotopic implantation mouse model. Now, we show that implantation of Pik3ca−/− KPC (named αKO) cancer cells induces clonal enrichment of cytotoxic T cells infiltrating the pancreatic tumors. To identify potential molecules that can regulate the activity of these anti-tumor T cells, we conducted an in vivo genome-wide gene-deletion screen using αKO cells implanted in the mouse pancreas. The result shows that deletion of propionyl-CoA carboxylase subunit B gene (Pccb) in αKO cells (named p-αKO) leads to immune evasion, tumor progression, and death of host mice. Surprisingly, p-αKO tumors are still infiltrated with clonally enriched CD8+ T cells but they are inactive against tumor cells. However, blockade of PD-L1/PD1 interaction reactivated these clonally enriched T cells infiltrating p-αKO tumors, leading to slower tumor progression and improve survival of host mice. These results indicate that Pccb can modulate the activity of cytotoxic T cells infiltrating some pancreatic cancers and this understanding may lead to improvement in immunotherapy for this difficult-to-treat cancer.

    1. Cancer Biology
    2. Immunology and Inflammation
    Almudena Mendez-Perez, Andres M Acosta-Moreno ... Esteban Veiga
    Short Report

    In this study, we present a proof-of-concept classical vaccination experiment that validates the in silico identification of tumor neoantigens (TNAs) using a machine learning-based platform called NAP-CNB. Unlike other TNA predictors, NAP-CNB leverages RNA-seq data to consider the relative expression of neoantigens in tumors. Our experiments show the efficacy of NAP-CNB. Predicted TNAs elicited potent antitumor responses in mice following classical vaccination protocols. Notably, optimal antitumor activity was observed when targeting the antigen with higher expression in the tumor, which was not the most immunogenic. Additionally, the vaccination combining different neoantigens resulted in vastly improved responses compared to each one individually, showing the worth of multiantigen-based approaches. These findings validate NAP-CNB as an innovative TNA identification platform and make a substantial contribution to advancing the next generation of personalized immunotherapies.