Limited inhibition of multiple nodes in a driver network blocks metastasis

  1. Ali Ekrem Yesilkanal
  2. Dongbo Yang
  3. Andrea Valdespino
  4. Payal Tiwari
  5. Alan U Sabino
  6. Long Chi Nguyen
  7. Jiyoung Lee
  8. Xiao-He Xie
  9. Siqi Sun
  10. Christopher Dann
  11. Lydia Robinson-Mailman
  12. Ethan Steinberg
  13. Timothy Stuhlmiller
  14. Casey Frankenberger
  15. Elizabeth Goldsmith
  16. Gary L Johnson
  17. Alexandre F Ramos  Is a corresponding author
  18. Marsha R Rosner  Is a corresponding author
  1. University of Chicago, United States
  2. University of São Paulo, Brazil
  3. University of North Carolina at Chapel Hill, United States
  4. University of Texas Southwestern Medical Center, United States

Abstract

Metastasis suppression by high-dose, multi-drug targeting is unsuccessful due to network heterogeneity and compensatory network activation. Here we show that targeting driver network signaling capacity by limited inhibition of core pathways is a more effective anti-metastatic strategy. This principle underlies the action of a physiological metastasis suppressor, Raf Kinase Inhibitory Protein (RKIP), that moderately decreases stress-regulated MAP kinase network activity, reducing output to transcription factors such as pro-metastastic BACH1 and motility-related target genes. We developed a low-dose four-drug mimic that blocks metastatic colonization in mouse breast cancer models and increases survival. Experiments and network flow modeling show limited inhibition of multiple pathways is required to overcome variation in MAPK network topology and suppress signaling output across heterogeneous tumor cells. Restricting inhibition of individual kinases dissipates surplus signal, preventing threshold activation of compensatory kinase networks. This low-dose multi-drug approach to decrease signaling capacity of driver networks represents a transformative, clinically-relevant strategy for anti-metastatic treatment.

Data availability

RNA sequencing data have been deposited in GEO under the accession code GSE128983.

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Ali Ekrem Yesilkanal

    Ben May Department for Cancer Research, University of Chicago, Chicago, United States
    Competing interests
    No competing interests declared.
  2. Dongbo Yang

    Ben May Department for Cancer Research, University of Chicago, Chicago, United States
    Competing interests
    No competing interests declared.
  3. Andrea Valdespino

    Ben May Department for Cancer Research, University of Chicago, Chicago, United States
    Competing interests
    No competing interests declared.
  4. Payal Tiwari

    Ben May Department for Cancer Research, University of Chicago, Chicago, United States
    Competing interests
    No competing interests declared.
  5. Alan U Sabino

    Instituto do Câncer do Estado de São Paulo, University of São Paulo, São Paulo, Brazil
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1094-5078
  6. Long Chi Nguyen

    Ben May Department for Cancer Research, University of Chicago, Chicago, United States
    Competing interests
    No competing interests declared.
  7. Jiyoung Lee

    Ben May Department for Cancer Research, University of Chicago, Chicago, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8503-4805
  8. Xiao-He Xie

    Ben May Department for Cancer Research, University of Chicago, Chicago, United States
    Competing interests
    No competing interests declared.
  9. Siqi Sun

    Ben May Department for Cancer Research, University of Chicago, Chicago, United States
    Competing interests
    No competing interests declared.
  10. Christopher Dann

    Ben May Department for Cancer Research, University of Chicago, Chicago, United States
    Competing interests
    No competing interests declared.
  11. Lydia Robinson-Mailman

    Ben May Department for Cancer Research, University of Chicago, Chicago, United States
    Competing interests
    No competing interests declared.
  12. Ethan Steinberg

    Ben May Department for Cancer Research, University of Chicago, Chicago, United States
    Competing interests
    No competing interests declared.
  13. Timothy Stuhlmiller

    Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, United States
    Competing interests
    No competing interests declared.
  14. Casey Frankenberger

    Ben May Department for Cancer Research, University of Chicago, Chicago, United States
    Competing interests
    No competing interests declared.
  15. Elizabeth Goldsmith

    Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, United States
    Competing interests
    No competing interests declared.
  16. Gary L Johnson

    Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2867-0551
  17. Alexandre F Ramos

    Instituto do Câncer do Estado de São Paulo, University of São Paulo, São Paulo, Brazil
    For correspondence
    alex.ramos@usp.br
    Competing interests
    No competing interests declared.
  18. Marsha R Rosner

    Ben May Department for Cancer Research, University of Chicago, Chicago, United States
    For correspondence
    m-rosner@uchicago.edu
    Competing interests
    Marsha R Rosner, This research is also the subject of a pending US patent application # 17/048,282..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6586-8335

Funding

National Institutes of Health (R01 GM121735-01)

  • Marsha R Rosner

National Institutes of Health (CA058223)

  • Gary L Johnson

Rustandy Fund for Innovative Cancer Research

  • Marsha R Rosner

University of Chicago Women's Board Grants Fund

  • Ali Ekrem Yesilkanal

University of Sao Paulo (Use of Intelligent Systems,18.5.245.86.7)

  • Alexandre F Ramos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (88881.062174/2014-01)

  • Alexandre F Ramos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

  • Alan U Sabino

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

Reviewing Editor

  1. Maureen E Murphy, The Wistar Institute, United States

Ethics

Animal experimentation: All animal protocols related to mouse experiments were approved by the University of Chicago Institutional Animal Care and Use Committee (IACUC #72228).

Version history

  1. Received: June 5, 2020
  2. Accepted: April 29, 2021
  3. Accepted Manuscript published: May 11, 2021 (version 1)
  4. Version of Record published: May 17, 2021 (version 2)

Copyright

© 2021, Yesilkanal 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

  • 2,562
    Page views
  • 339
    Downloads
  • 16
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

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. Ali Ekrem Yesilkanal
  2. Dongbo Yang
  3. Andrea Valdespino
  4. Payal Tiwari
  5. Alan U Sabino
  6. Long Chi Nguyen
  7. Jiyoung Lee
  8. Xiao-He Xie
  9. Siqi Sun
  10. Christopher Dann
  11. Lydia Robinson-Mailman
  12. Ethan Steinberg
  13. Timothy Stuhlmiller
  14. Casey Frankenberger
  15. Elizabeth Goldsmith
  16. Gary L Johnson
  17. Alexandre F Ramos
  18. Marsha R Rosner
(2021)
Limited inhibition of multiple nodes in a driver network blocks metastasis
eLife 10:e59696.
https://doi.org/10.7554/eLife.59696

Share this article

https://doi.org/10.7554/eLife.59696

Further reading

    1. Cancer Biology
    Wanyoung Lim, Inwoo Hwang ... Sungsu Park
    Research Article

    Chemoresistance is a major cause of treatment failure in many cancers. However, the life cycle of cancer cells as they respond to and survive environmental and therapeutic stress is understudied. In this study, we utilized a microfluidic device to induce the development of doxorubicin-resistant (DOXR) cells from triple negative breast cancer (TNBC) cells within 11 days by generating gradients of DOX and medium. In vivo chemoresistant xenograft models, an unbiased genome-wide transcriptome analysis, and a patient data/tissue analysis all showed that chemoresistance arose from failed epigenetic control of the nuclear protein-1 (NUPR1)/histone deacetylase 11 (HDAC11) axis, and high NUPR1 expression correlated with poor clinical outcomes. These results suggest that the chip can rapidly induce resistant cells that increase tumor heterogeneity and chemoresistance, highlighting the need for further studies on the epigenetic control of the NUPR1/HDAC11 axis in TNBC.

    1. Cancer Biology
    2. Computational and Systems Biology
    Bingrui Li, Fernanda G Kugeratski, Raghu Kalluri
    Research Article

    Non-invasive early cancer diagnosis remains challenging due to the low sensitivity and specificity of current diagnostic approaches. Exosomes are membrane-bound nanovesicles secreted by all cells that contain DNA, RNA, and proteins that are representative of the parent cells. This property, along with the abundance of exosomes in biological fluids makes them compelling candidates as biomarkers. However, a rapid and flexible exosome-based diagnostic method to distinguish human cancers across cancer types in diverse biological fluids is yet to be defined. Here, we describe a novel machine learning-based computational method to distinguish cancers using a panel of proteins associated with exosomes. Employing datasets of exosome proteins from human cell lines, tissue, plasma, serum, and urine samples from a variety of cancers, we identify Clathrin Heavy Chain (CLTC), Ezrin, (EZR), Talin-1 (TLN1), Adenylyl cyclase-associated protein 1 (CAP1), and Moesin (MSN) as highly abundant universal biomarkers for exosomes and define three panels of pan-cancer exosome proteins that distinguish cancer exosomes from other exosomes and aid in classifying cancer subtypes employing random forest models. All the models using proteins from plasma, serum, or urine-derived exosomes yield AUROC scores higher than 0.91 and demonstrate superior performance compared to Support Vector Machine, K Nearest Neighbor Classifier and Gaussian Naive Bayes. This study provides a reliable protein biomarker signature associated with cancer exosomes with scalable machine learning capability for a sensitive and specific non-invasive method of cancer diagnosis.