Functional heterogeneity of lymphocytic patterns in primary melanoma dissected through single-cell multiplexing

Abstract

In melanoma, the lymphocytic infiltrate is a prognostic parameter classified morphologically into 'brisk', 'non-brisk' and 'absent' entailing a functional association that has never been proved. Recently, it has been shown that lymphocytic populations can be very heterogeneous, and that anti-PD-1 immunotherapy supports activated T cells. Here, we characterize the immune landscape in primary melanoma by high-dimensional single cell multiplex analysis in tissue sections (MILAN technique) followed by image analysis, RT-PCR and shotgun proteomics. We observed that the brisk and non-brisk patterns are heterogeneous functional categories that can be further sub-classified into active, transitional or exhausted. The classification of primary melanomas based on the functional paradigm also shows correlation with spontaneous regression, and an improved prognostic value when compared to that of the brisk classification. Finally, the main inflammatory cell subpopulations that are present in the microenvironment associated with activation and exhaustion and their spatial relationships are described using neighbourhood analysis.

Data availability

All data generated or analysed during this study are included in the submission as source data files. We also included the codes to ease the review in process.

The following previously published data sets were used

Article and author information

Author details

  1. Francesca Maria Bosisio

    Laboratory of Translational Cell and Tissue Research and Pathology Department, KU Leuven and UZ Leuven, Leuven, Belgium
    For correspondence
    f.bosisio1@gmail.com
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8874-2003
  2. Asier Antoranz

    National Technical University of Athens, ProtATonce Ltd, Athens, Greece
    For correspondence
    asierantoranz91@gmail.com
    Competing interests
    Asier Antoranz, Asier Antoranz is affiliated with ProtATonce Ltd. The author has no other competing interests to declare.
  3. Yannick van Herck

    Laboratory of Experimental Oncology, KU Leuven and UZ Leuven, Leuven, Belgium
    Competing interests
    No competing interests declared.
  4. Maddalena Maria Bolognesi

    Department of Medicine and Surgery, Università degli studi di Milano-Bicocca, Milan, Italy
    Competing interests
    Maddalena Maria Bolognesi, Maddalena Maria Bolognesi has received funding from GlaxoSmithKline . The author has no other competing interests to declare.
  5. Lukas Marcelis

    Laboratory of Translational Cell and Tissue Research and Pathology Department, KU Leuven, Leuven, Belgium
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5446-1801
  6. Clizia Chinello

    Department of Medicine and Surgery, Università degli studi di Milano-Bicocca, Milan, Italy
    Competing interests
    No competing interests declared.
  7. Jasper Wouters

    Laboratory of Translational Cell and Tissue Research and Pathology Department, KU Leuven and UZ Leuven, Leuven, Belgium
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7129-2990
  8. Fulvio Magni

    Department of Medicine and Surgery, Università degli studi di Milano-Bicocca, Milan, Italy
    Competing interests
    No competing interests declared.
  9. Leonidas Alexopoulos

    National Technical University of Athens, ProtATonce Ltd, Athens, Greece
    Competing interests
    No competing interests declared.
  10. Marguerite Stas

    Department of Surgical Oncology, KU Leuven and UZ Leuven, Leuven, Belgium
    Competing interests
    No competing interests declared.
  11. Veerle Boecxstaens

    Department of Surgical Oncology, KU Leuven and UZ Leuven, Leuven, Belgium
    Competing interests
    No competing interests declared.
  12. Oliver Bechter

    Laboratory of Experimental Oncology, KU Leuven and UZ Leuven, Leuven, Belgium
    Competing interests
    No competing interests declared.
  13. Giorgio Cattoretti

    Department of Medicine and Surgery, Università degli studi di Milano-Bicocca, Milan, Italy
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3799-3221
  14. Joost van den Oord

    Laboratory of Translational Cell and Tissue Research and Pathology Department, KU Leuven and UZ Leuven, Leuven, Belgium
    Competing interests
    No competing interests declared.

Funding

Horizon 2020 Framework Programme (642295)

  • Francesca Maria Bosisio

Horizon 2020 Framework Programme (675585)

  • Asier Antoranz

BEL114054 (HGS1006-C1121)

  • Maddalena Maria Bolognesi

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

Ethics

Human subjects: Ethical approval was obtained from the Ethical Committee/IRB OG032 of the University Hospital of Leuven. After the approval, the study was identified with the number S57266. According to the Clinical Trial regalement no informed consent was needed due to the use of post-diagnostic left-over material.

Copyright

© 2020, Bosisio 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

  • 2,528
    views
  • 383
    downloads
  • 46
    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. Francesca Maria Bosisio
  2. Asier Antoranz
  3. Yannick van Herck
  4. Maddalena Maria Bolognesi
  5. Lukas Marcelis
  6. Clizia Chinello
  7. Jasper Wouters
  8. Fulvio Magni
  9. Leonidas Alexopoulos
  10. Marguerite Stas
  11. Veerle Boecxstaens
  12. Oliver Bechter
  13. Giorgio Cattoretti
  14. Joost van den Oord
(2020)
Functional heterogeneity of lymphocytic patterns in primary melanoma dissected through single-cell multiplexing
eLife 9:e53008.
https://doi.org/10.7554/eLife.53008

Share this article

https://doi.org/10.7554/eLife.53008

Further reading

    1. Cancer Biology
    2. Medicine
    Anastasia D Komarova, Snezhana D Sinyushkina ... Marina V Shirmanova
    Research Article

    Heterogeneity of tumor metabolism is an important, but still poorly understood aspect of tumor biology. Present work is focused on the visualization and quantification of cellular metabolic heterogeneity of colorectal cancer using fluorescence lifetime imaging (FLIM) of redox cofactor NAD(P)H. FLIM-microscopy of NAD(P)H was performed in vitro in four cancer cell lines (HT29, HCT116, CaCo2 and CT26), in vivo in the four types of colorectal tumors in mice and ex vivo in patients’ tumor samples. The dispersion and bimodality of the decay parameters were evaluated to quantify the intercellular metabolic heterogeneity. Our results demonstrate that patients’ colorectal tumors have significantly higher heterogeneity of energy metabolism compared with cultured cells and tumor xenografts, which was displayed as a wider and frequently bimodal distribution of a contribution of a free (glycolytic) fraction of NAD(P)H within a sample. Among patients’ tumors, the dispersion was larger in the high-grade and early stage ones, without, however, any association with bimodality. These results indicate that cell-level metabolic heterogeneity assessed from NAD(P)H FLIM has a potential to become a clinical prognostic factor.

    1. Cancer Biology
    2. Epidemiology and Global Health
    Chelsea L Hansen, Cécile Viboud, Lone Simonsen
    Research Article

    Cancer is considered a risk factor for COVID-19 mortality, yet several countries have reported that deaths with a primary code of cancer remained within historic levels during the COVID-19 pandemic. Here, we further elucidate the relationship between cancer mortality and COVID-19 on a population level in the US. We compared pandemic-related mortality patterns from underlying and multiple cause (MC) death data for six types of cancer, diabetes, and Alzheimer’s. Any pandemic-related changes in coding practices should be eliminated by study of MC data. Nationally in 2020, MC cancer mortality rose by only 3% over a pre-pandemic baseline, corresponding to ~13,600 excess deaths. Mortality elevation was measurably higher for less deadly cancers (breast, colorectal, and hematological, 2–7%) than cancers with a poor survival rate (lung and pancreatic, 0–1%). In comparison, there was substantial elevation in MC deaths from diabetes (37%) and Alzheimer’s (19%). To understand these differences, we simulated the expected excess mortality for each condition using COVID-19 attack rates, life expectancy, population size, and mean age of individuals living with each condition. We find that the observed mortality differences are primarily explained by differences in life expectancy, with the risk of death from deadly cancers outcompeting the risk of death from COVID-19.