Interrogating the precancerous evolution of pathway dysfunction in lung squamous cell carcinoma using XTABLE

  1. Matthew Roberts
  2. Julia Ogden
  3. A S Md Mukarram Hossain
  4. Anshuman Chaturvedi
  5. Alastair RW Kerr
  6. Caroline Dive
  7. Jennifer Ellen Beane
  8. Carlos Lopez-Garcia  Is a corresponding author
  1. University of Manchester, United Kingdom
  2. Cancer Research UK Manchester Institute, United Kingdom
  3. The Christie Hospital, United Kingdom
  4. Boston University, United States

Abstract

Lung squamous cell carcinoma (LUSC) is a type of lung cancer with a dismal prognosis that lacks adequate therapies and actionable targets. This disease is characterized by a sequence of low and high-grade preinvasive stages with increasing probability of malignant progression. Increasing our knowledge about the biology of these premalignant lesions (PMLs) is necessary to design new methods of early detection and prevention, and to identify the molecular processes that are key for malignant progression. To facilitate this research, we have designed XTABLE, an open-source application that integrates the most extensive transcriptomic databases of PMLs published so far. With this tool, users can stratify samples using multiple parameters and interrogate PML biology in multiple manners, such as two and multiple group comparisons, interrogation of genes of interests and transcriptional signatures. Using XTABLE, we have carried out a comparative study of the potential role of chromosomal instability scores as biomarkers of PML progression and mapped the onset of the most relevant LUSC pathways to the sequence of LUSC developmental stages. XTABLE will critically facilitate new research for the identification of early detection biomarkers and acquire a better understanding of the LUSC precancerous stages.

Data availability

The current manuscript makes use of previously published databases, so no data have been generated for this manuscript. All analyses shown in the manuscript has been carried out using XTABLE and can be reproduced easily by any user.

The following previously published data sets were used

Article and author information

Author details

  1. Matthew Roberts

    Cancer Biomarker Centre, University of Manchester, Alderley Edge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  2. Julia Ogden

    Cancer Research UK Manchester Institute, Alderley Edge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  3. A S Md Mukarram Hossain

    Cancer Biomarker Centre, University of Manchester, Alderley Edge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  4. Anshuman Chaturvedi

    Department of Histopathology, The Christie Hospital, Manchester, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  5. Alastair RW Kerr

    Cancer Biomarker Centre, University of Manchester, Edinburgh, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9207-6050
  6. Caroline Dive

    Cancer Biomarker Centre, University of Manchester, Alderley Edge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  7. Jennifer Ellen Beane

    School of Medicine, Boston University, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Carlos Lopez-Garcia

    Cancer Research UK Manchester Institute, Alderley Edge, United Kingdom
    For correspondence
    carlos.lopezgarcia@cruk.manchester.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9848-8216

Funding

Cancer Research UK (A25146)

  • Julia Ogden
  • A S Md Mukarram Hossain
  • Anshuman Chaturvedi
  • Alastair RW Kerr
  • Caroline Dive
  • Carlos Lopez-Garcia

Manchester Biomedical Research Centre

  • Matthew Roberts

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

Reviewing Editor

  1. W Kimryn Rathmell, Vanderbilt University Medical Center, United States

Version history

  1. Received: February 2, 2022
  2. Preprint posted: May 6, 2022 (view preprint)
  3. Accepted: March 9, 2023
  4. Accepted Manuscript published: March 9, 2023 (version 1)
  5. Version of Record published: March 24, 2023 (version 2)
  6. Version of Record updated: April 26, 2023 (version 3)

Copyright

© 2023, Roberts 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

  • 753
    Page views
  • 102
    Downloads
  • 0
    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. Matthew Roberts
  2. Julia Ogden
  3. A S Md Mukarram Hossain
  4. Anshuman Chaturvedi
  5. Alastair RW Kerr
  6. Caroline Dive
  7. Jennifer Ellen Beane
  8. Carlos Lopez-Garcia
(2023)
Interrogating the precancerous evolution of pathway dysfunction in lung squamous cell carcinoma using XTABLE
eLife 12:e77507.
https://doi.org/10.7554/eLife.77507

Share this article

https://doi.org/10.7554/eLife.77507

Further reading

    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.

    1. Cancer Biology
    Carolyn M Jablonowski, Waise Quarni ... Jun Yang
    Research Article

    Dysregulated pre-mRNA splicing and metabolism are two hallmarks of MYC-driven cancers. Pharmacological inhibition of both processes has been extensively investigated as potential therapeutic avenues in preclinical and clinical studies. However, how pre-mRNA splicing and metabolism are orchestrated in response to oncogenic stress and therapies is poorly understood. Here, we demonstrate that jumonji domain containing 6, arginine demethylase, and lysine hydroxylase, JMJD6, acts as a hub connecting splicing and metabolism in MYC-driven human neuroblastoma. JMJD6 cooperates with MYC in cellular transformation of murine neural crest cells by physically interacting with RNA binding proteins involved in pre-mRNA splicing and protein homeostasis. Notably, JMJD6 controls the alternative splicing of two isoforms of glutaminase (GLS), namely kidney-type glutaminase (KGA) and glutaminase C (GAC), which are rate-limiting enzymes of glutaminolysis in the central carbon metabolism in neuroblastoma. Further, we show that JMJD6 is correlated with the anti-cancer activity of indisulam, a ‘molecular glue’ that degrades splicing factor RBM39, which complexes with JMJD6. The indisulam-mediated cancer cell killing is at least partly dependent on the glutamine-related metabolic pathway mediated by JMJD6. Our findings reveal a cancer-promoting metabolic program is associated with alternative pre-mRNA splicing through JMJD6, providing a rationale to target JMJD6 as a therapeutic avenue for treating MYC-driven cancers.