1. Cancer Biology

Supervised mutational signatures for obesity and other tissue-specific etiological factors in cancer

  1. Bahman Afsari
  2. Albert Kuo
  3. YiFan Zhang
  4. Lu Li
  5. Kamel Lahouel
  6. Ludmila Danilova
  7. Alexander Favorov
  8. Thomas A Rosenquist
  9. Arthur P Grollman
  10. Ken W Kinzler
  11. Leslie Cope
  12. Bert Vogelstein
  13. Cristian Tomasetti  Is a corresponding author
  1. Johns Hopkins University, United States
  2. Johns Hopkins School of Medicine, United States
  3. Stony Brook University, United States
  4. Howard Hughes Medical Institute, Ludwig Center, United States
https://doi.org/10.7554/eLife.61082
Share this article
Cite this article
  1. Bahman Afsari
  2. Albert Kuo
  3. YiFan Zhang
  4. Lu Li
  5. Kamel Lahouel
  6. Ludmila Danilova
  7. Alexander Favorov
  8. Thomas A Rosenquist
  9. Arthur P Grollman
  10. Ken W Kinzler
  11. Leslie Cope
  12. Bert Vogelstein
  13. Cristian Tomasetti
(2021)
Supervised mutational signatures for obesity and other tissue-specific etiological factors in cancer
eLife 10:e61082.
https://doi.org/10.7554/eLife.61082
  2. Download BibTeX
  3. Download .RIS

Abstract

Determining the etiologic basis of the mutations that are responsible for cancer is one of the fundamental challenges in modern cancer research. Different mutational processes induce different types of DNA mutations, providing 'mutational signatures' that have led to key insights into cancer etiology. The most widely used signatures for assessing genomic data are based on unsupervised patterns that are then retrospectively correlated with certain features of cancer. We show here that supervised machine-learning techniques can identify signatures, called SuperSigs, that are more predictive than those currently available. Surprisingly, we found that aging yields different SuperSigs in different tissues, and the same is true for environmental exposures. We were able to discover SuperSigs associated with obesity, the most important lifestyle factor contributing to cancer in Western populations.

Data availability

All data generated or analysed during this study are included in the manuscript and supporting files.

The following previously published data sets were used
    1. Weinstein
    (2013) TCGA
    CDG @ https://portal.gdc.cancer.gov/.
    https://portal.gdc.cancer.gov/

Article and author information

Author details

  1. Bahman Afsari

    Division of Biostatistics and Bioinformatics, Department of Oncology, Johns Hopkins University, Baltimore, United States
    Competing interests
    No competing interests declared.

  2. Albert Kuo

    Department of Biostatistics, Johns Hopkins University, Baltimore, United States
    Competing interests
    No competing interests declared.

  3. YiFan Zhang

    Department of Biostatistics, Johns Hopkins University, Baltimore, United States
    Competing interests
    No competing interests declared.

  4. Lu Li

    Department of Biostatistics, Johns Hopkins University, Baltimore, United States
    Competing interests
    No competing interests declared.

  5. Kamel Lahouel

    Division of Biostatistics and Bioinformatics, Department of Oncology, Johns Hopkins University, Baltimore, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4339-5749

  6. Ludmila Danilova

    Division of Biostatistics and Bioinformatics, Department of Oncology, Johns Hopkins School of Medicine, Baltimore, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2813-3094

  7. Alexander Favorov

    Division of Biostatistics and Bioinformatics, Department of Oncology, Johns Hopkins University, Baltimore, United States
    Competing interests
    No competing interests declared.

  8. Thomas A Rosenquist

    Department of Pharmacological Sciences, Stony Brook University, Stony Brook, United States
    Competing interests
    No competing interests declared.

  9. Arthur P Grollman

    Department of Pharmacological Sciences, Department of Medicine, Stony Brook University, Stony Brook, United States
    Competing interests
    No competing interests declared.

  10. Ken W Kinzler

    Howard Hughes Medical Institute, Ludwig Center, Baltimore, United States
    Competing interests
    Ken W Kinzler, K.W.K. is a founder of and hold equity, and serve as consultant to Thrive Earlier Detection and Personal Genome Diagnostics. K.W.K. is on the Board of Directors of Thrive Earlier Detection. K.W.K. is a consultant to Sysmex, Eisai, and CAGE Pharma and hold equity in CAGE Pharma. K.W.K. is a consultant to and hold equity in NeoPhore. The companies named above, as well as other companies, have licensed previously described technologies from Johns Hopkins University. K.W.K is an inventor on some of these technologies. Licenses to these technologies are or will be associated with equity or royalty payments to the inventors as well as to Johns Hopkins University. Patent applications on the work described in this paper have or may be filed by Johns Hopkins University. The terms of all these arrangements are being managed by Johns Hopkins University in accordance with its conflict of interest policies..

  11. Leslie Cope

    Division of Biostatistics and Bioinformatics, Department of Oncology, Johns Hopkins University, Baltimore, United States
    Competing interests
    No competing interests declared.

  12. Bert Vogelstein

    Ludwig Center & Howard Hughes Medical Institute, Johns Hopkins University, Baltimore, United States
    Competing interests
    Bert Vogelstein, B.V. is a founder of and hold equity, and serve as consultant to Thrive Earlier Detection and Personal Genome Diagnostics. B.V. is a consultant to Sysmex, Eisai, and CAGE Pharma and hold equity in CAGE Pharma. BV is also a consultant to Nexus, and is a consultant to and hold equity in NeoPhore. The companies named above, as well as other companies, have licensed previously described technologies from Johns Hopkins University. B.V. is an inventor on some of these technologies. Licenses to these technologies are or will be associated with equity or royalty payments to the inventors as well as to Johns Hopkins University. Patent applications on the work described in this paper have or may be filed by Johns Hopkins University. The terms of all these arrangements are being managed by Johns Hopkins University in accordance with its conflict of interest policies..

  13. Cristian Tomasetti

    Oncology, Johns Hopkins School of Medicine, Baltimore, United States
    For correspondence
    ctomasetti@jhu.edu
    Competing interests
    Cristian Tomasetti, C.T. is a consultant to Bayer and Johnson & Johnson. Thrive Earlier Detection has licensed previously described technologies from Johns Hopkins University. C.T. is an inventor on some of these technologies. Licenses to these technologies are or will be associated with equity or royalty payments to the inventors as well as to Johns Hopkins University. Patent applications on the work described in this paper have or may be filed by Johns Hopkins University. The terms of all these arrangements are being managed by Johns Hopkins University in accordance with its conflict of interest policies..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3277-4804

Funding

The John Templeton Foundation (#61471)

  • Bahman Afsari
  • Albert Kuo
  • YiFan Zhang
  • Lu Li
  • Kamel Lahouel
  • Ludmila Danilova
  • Cristian Tomasetti

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

Reviewing Editor

  1. William C Hahn, Dana-Farber Cancer Institue, United States

Version history

  1. Received: July 15, 2020
  2. Accepted: January 24, 2021
  3. Accepted Manuscript published: January 25, 2021 (version 1)
  4. Version of Record published: February 9, 2021 (version 2)

Copyright

© 2021, Afsari 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

  • 2,907
    views
  • 418
    downloads
  • 14
    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. Bahman Afsari
  2. Albert Kuo
  3. YiFan Zhang
  4. Lu Li
  5. Kamel Lahouel
  6. Ludmila Danilova
  7. Alexander Favorov
  8. Thomas A Rosenquist
  9. Arthur P Grollman
  10. Ken W Kinzler
  11. Leslie Cope
  12. Bert Vogelstein
  13. Cristian Tomasetti
(2021)
Supervised mutational signatures for obesity and other tissue-specific etiological factors in cancer
eLife 10:e61082.
https://doi.org/10.7554/eLife.61082

Share this article

https://doi.org/10.7554/eLife.61082

Categories and tags

Research organism

Further reading

    1. Cancer Biology
    2. Cell Biology

    A syngeneic spontaneous zebrafish model of tp53-deficient, EGFRvIII, and PI3KCAH1047R-driven glioblastoma reveals inhibitory roles for inflammation during tumor initiation and relapse in vivo

    Alex Weiss, Cassandra D'Amata ... Madeline N Hayes
    Research Article

    High-throughput vertebrate animal model systems for the study of patient-specific biology and new therapeutic approaches for aggressive brain tumors are currently lacking, and new approaches are urgently needed. Therefore, to build a patient-relevant in vivo model of human glioblastoma, we expressed common oncogenic variants including activated human EGFRvIII and PI3KCAH1047R under the control of the radial glial-specific promoter her4.1 in syngeneic tp53 loss-of-function mutant zebrafish. Robust tumor formation was observed prior to 45 days of life, and tumors had a gene expression signature similar to human glioblastoma of the mesenchymal subtype, with a strong inflammatory component. Within early stage tumor lesions, and in an in vivo and endogenous tumor microenvironment, we visualized infiltration of phagocytic cells, as well as internalization of tumor cells by mpeg1.1:EGFP+ microglia/macrophages, suggesting negative regulatory pressure by pro-inflammatory cell types on tumor growth at early stages of glioblastoma initiation. Furthermore, CRISPR/Cas9-mediated gene targeting of master inflammatory transcription factors irf7 or irf8 led to increased tumor formation in the primary context, while suppression of phagocyte activity led to enhanced tumor cell engraftment following transplantation into otherwise immune-competent zebrafish hosts. Altogether, we developed a genetically relevant model of aggressive human glioblastoma and harnessed the unique advantages of zebrafish including live imaging, high-throughput genetic and chemical manipulations to highlight important tumor-suppressive roles for the innate immune system on glioblastoma initiation, with important future opportunities for therapeutic discovery and optimizations.

    1. Cancer Biology
    2. Cell Biology

    Cancer: Modeling glioblastoma

    Ian Lorimer
    Insight

    Establishing a zebrafish model of a deadly type of brain tumor highlights the role of the immune system in the early stages of the disease.

    1. Cancer Biology

    YAP/TAZ enhances P-body formation to promote tumorigenesis

    Xia Shen, Xiang Peng ... Chen-Ying Liu
    Research Article

    The role of processing bodies (P-bodies) in tumorigenesis and tumor progression is not well understood. Here, we showed that the oncogenes YAP/TAZ promote P-body formation in a series of cancer cell lines. Mechanistically, both transcriptional activation of the P-body-related genes SAMD4A, AJUBA, and WTIP and transcriptional suppression of the tumor suppressor gene PNRC1 are involved in enhancing the effects of YAP/TAZ on P-body formation in colorectal cancer (CRC) cells. By reexpression of PNRC1 or knockdown of P-body core genes (DDX6, DCP1A, and LSM14A), we determined that disruption of P-bodies attenuates cell proliferation, cell migration, and tumor growth induced by overexpression of YAP5SA in CRC. Analysis of a pancancer CRISPR screen database (DepMap) revealed co-dependencies between YAP/TEAD and the P-body core genes and correlations between the mRNA levels of SAMD4A, AJUBA, WTIP, PNRC1, and YAP target genes. Our study suggests that the P-body is a new downstream effector of YAP/TAZ, which implies that reexpression of PNRC1 or disruption of P-bodies is a potential therapeutic strategy for tumors with active YAP.