Regulation of protein complex partners as a compensatory mechanism in aneuploid tumors

  1. Gökçe Senger
  2. Stefano Santaguida
  3. Martin H Schaefer  Is a corresponding author
  1. European Institute of Oncology, Italy

Abstract

Aneuploidy, a state of chromosome imbalance, is a hallmark of human tumors, but its role in cancer still remains to be fully elucidated. To understand the consequences of whole-chromosome-level aneuploidies on the proteome, we integrated aneuploidy, transcriptomic and proteomic data from hundreds of TCGA/CPTAC tumor samples. We found a surprisingly large number of expression changes happened on other, non-aneuploid chromosomes. Moreover, we identified an association between those changes and co-complex members of proteins from aneuploid chromosomes. This co-abundance association is tightly regulated for aggregation-prone aneuploid proteins and those involved in a smaller number of complexes. On the other hand, we observe that complexes of the cellular core machinery are under functional selection to maintain their stoichiometric balance in aneuploid tumors. Ultimately, we provide evidence that those compensatory and functional maintenance mechanisms are established through post-translational control and that the degree of success of a tumor to deal with aneuploidy-induced stoichiometric imbalance impacts the activation of cellular protein degradation programs and patient survival.

Data availability

The code performing all analyses in this study is available at https://github.com/SengerG/Coregulation-of-complexes-in-Aneuploidtumors.git

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

Article and author information

Author details

  1. Gökçe Senger

    Department of Experimental Oncology, European Institute of Oncology, Milan, Italy
    Competing interests
    The authors declare that no competing interests exist.
  2. Stefano Santaguida

    Department of Experimental Oncology, European Institute of Oncology, Milan, Italy
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1501-6190
  3. Martin H Schaefer

    Department of Experimental Oncology, European Institute of Oncology, Milan, Italy
    For correspondence
    martin.schaefer@ieo.it
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7503-6364

Funding

Fondazione AIRC (MFAG 21791)

  • Martin H Schaefer

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

Reviewing Editor

  1. Elana J Fertig, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, United States

Version history

  1. Received: November 12, 2021
  2. Preprint posted: December 9, 2021 (view preprint)
  3. Accepted: May 13, 2022
  4. Accepted Manuscript published: May 16, 2022 (version 1)
  5. Version of Record published: May 26, 2022 (version 2)

Copyright

© 2022, Senger 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

  • 1,537
    Page views
  • 268
    Downloads
  • 6
    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. Gökçe Senger
  2. Stefano Santaguida
  3. Martin H Schaefer
(2022)
Regulation of protein complex partners as a compensatory mechanism in aneuploid tumors
eLife 11:e75526.
https://doi.org/10.7554/eLife.75526

Share this article

https://doi.org/10.7554/eLife.75526

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.