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.

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

  • 1,020
    views
  • 142
    downloads
  • 3
    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. 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. Cell Biology
    Kourosh Hayatigolkhatmi, Chiara Soriani ... Simona Rodighiero
    Tools and Resources

    Understanding the cell cycle at the single-cell level is crucial for cellular biology and cancer research. While current methods using fluorescent markers have improved the study of adherent cells, non-adherent cells remain challenging. In this study, we addressed this gap by combining a specialized surface to enhance cell attachment, the FUCCI(CA)2 sensor, an automated image analysis pipeline, and a custom machine learning algorithm. This approach enabled precise measurement of cell cycle phase durations in non-adherent cells. This method was validated in acute myeloid leukemia cell lines NB4 and Kasumi-1, which have unique cell cycle characteristics, and we tested the impact of cell cycle-modulating drugs on NB4 cells. Our cell cycle analysis system, which is also compatible with adherent cells, is fully automated and freely available, providing detailed insights from hundreds of cells under various conditions. This report presents a valuable tool for advancing cancer research and drug development by enabling comprehensive, automated cell cycle analysis in both adherent and non-adherent cells.

    1. Cancer Biology
    2. Computational and Systems Biology
    Rosalyn W Sayaman, Masaru Miyano ... Mark LaBarge
    Research Article

    Effects from aging in single cells are heterogenous, whereas at the organ- and tissue-levels aging phenotypes tend to appear as stereotypical changes. The mammary epithelium is a bilayer of two major phenotypically and functionally distinct cell lineages: luminal epithelial and myoepithelial cells. Mammary luminal epithelia exhibit substantial stereotypical changes with age that merit attention because these cells are the putative cells-of-origin for breast cancers. We hypothesize that effects from aging that impinge upon maintenance of lineage fidelity increase susceptibility to cancer initiation. We generated and analyzed transcriptomes from primary luminal epithelial and myoepithelial cells from younger <30 (y)ears old and older >55y women. In addition to age-dependent directional changes in gene expression, we observed increased transcriptional variance with age that contributed to genome-wide loss of lineage fidelity. Age-dependent variant responses were common to both lineages, whereas directional changes were almost exclusively detected in luminal epithelia and involved altered regulation of chromatin and genome organizers such as SATB1. Epithelial expression of gap junction protein GJB6 increased with age, and modulation of GJB6 expression in heterochronous co-cultures revealed that it provided a communication conduit from myoepithelial cells that drove directional change in luminal cells. Age-dependent luminal transcriptomes comprised a prominent signal that could be detected in bulk tissue during aging and transition into cancers. A machine learning classifier based on luminal-specific aging distinguished normal from cancer tissue and was highly predictive of breast cancer subtype. We speculate that luminal epithelia are the ultimate site of integration of the variant responses to aging in their surrounding tissue, and that their emergent phenotype both endows cells with the ability to become cancer-cells-of-origin and represents a biosensor that presages cancer susceptibility.