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The prognostic impact of the tumour stroma fraction: A machine learning-based analysis in 16 human solid tumour types
BACKGROUND: The development of a reactive tumour stroma is a hallmark of tumour progression and pronounced tumour stroma is generally considered to be associated with clinical aggressiveness. The variability between tumour types regarding stroma fraction, and its prognosis associations, have not bee...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7960932/ https://www.ncbi.nlm.nih.gov/pubmed/33706249 http://dx.doi.org/10.1016/j.ebiom.2021.103269 |
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author | Micke, Patrick Strell, Carina Mattsson, Johanna Martín-Bernabé, Alfonso Brunnström, Hans Huvila, Jutta Sund, Malin Wärnberg, Fredrik Ponten, Fredrik Glimelius, Bengt Hrynchyk, Ina Mauchanski, Siarhei Khelashvili, Salome Garcia-Vicién, Gemma Molleví, David G. Edqvist, Per-Henrik O´Reilly, Aine Corvigno, Sara Dahlstrand, Hanna Botling, Johan Segersten, Ulrika Krzyzanowska, Agnieszka Bjartell, Anders Elebro, Jacob Heby, Margareta Lundgren, Sebastian Hedner, Charlotta Borg, David Brändstedt, Jenny Sartor, Hanna Malmström, Per-Uno Johansson, Martin Nodin, Björn Backman, Max Lindskog, Cecilia Jirström, Karin Mezheyeuski, Artur |
author_facet | Micke, Patrick Strell, Carina Mattsson, Johanna Martín-Bernabé, Alfonso Brunnström, Hans Huvila, Jutta Sund, Malin Wärnberg, Fredrik Ponten, Fredrik Glimelius, Bengt Hrynchyk, Ina Mauchanski, Siarhei Khelashvili, Salome Garcia-Vicién, Gemma Molleví, David G. Edqvist, Per-Henrik O´Reilly, Aine Corvigno, Sara Dahlstrand, Hanna Botling, Johan Segersten, Ulrika Krzyzanowska, Agnieszka Bjartell, Anders Elebro, Jacob Heby, Margareta Lundgren, Sebastian Hedner, Charlotta Borg, David Brändstedt, Jenny Sartor, Hanna Malmström, Per-Uno Johansson, Martin Nodin, Björn Backman, Max Lindskog, Cecilia Jirström, Karin Mezheyeuski, Artur |
author_sort | Micke, Patrick |
collection | PubMed |
description | BACKGROUND: The development of a reactive tumour stroma is a hallmark of tumour progression and pronounced tumour stroma is generally considered to be associated with clinical aggressiveness. The variability between tumour types regarding stroma fraction, and its prognosis associations, have not been systematically analysed. METHODS: Using an objective machine-learning method we quantified the tumour stroma in 16 solid cancer types from 2732 patients, representing retrospective tissue collections of surgically resected primary tumours. Image analysis performed tissue segmentation into stromal and epithelial compartment based on pan-cytokeratin staining and autofluorescence patterns. FINDINGS: The stroma fraction was highly variable within and across the tumour types, with kidney cancer showing the lowest and pancreato-biliary type periampullary cancer showing the highest stroma proportion (median 19% and 73% respectively). Adjusted Cox regression models revealed both positive (pancreato-biliary type periampullary cancer and oestrogen negative breast cancer, HR(95%CI)=0.56(0.34-0.92) and HR(95%CI)=0.41(0.17-0.98) respectively) and negative (intestinal type periampullary cancer, HR(95%CI)=3.59(1.49-8.62)) associations of the tumour stroma fraction with survival. INTERPRETATION: Our study provides an objective quantification of the tumour stroma fraction across major types of solid cancer. Findings strongly argue against the commonly promoted view of a general associations between high stroma abundance and poor prognosis. The results also suggest that full exploitation of the prognostic potential of tumour stroma requires analyses that go beyond determination of stroma abundance. FUNDING: The Swedish Cancer Society, The Lions Cancer Foundation Uppsala, The Swedish Government Grant for Clinical Research, The Mrs Berta Kamprad Foundation, Sweden, Sellanders foundation, P.O.Zetterling Foundation, and The Sjöberg Foundation, Sweden. |
format | Online Article Text |
id | pubmed-7960932 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-79609322021-03-19 The prognostic impact of the tumour stroma fraction: A machine learning-based analysis in 16 human solid tumour types Micke, Patrick Strell, Carina Mattsson, Johanna Martín-Bernabé, Alfonso Brunnström, Hans Huvila, Jutta Sund, Malin Wärnberg, Fredrik Ponten, Fredrik Glimelius, Bengt Hrynchyk, Ina Mauchanski, Siarhei Khelashvili, Salome Garcia-Vicién, Gemma Molleví, David G. Edqvist, Per-Henrik O´Reilly, Aine Corvigno, Sara Dahlstrand, Hanna Botling, Johan Segersten, Ulrika Krzyzanowska, Agnieszka Bjartell, Anders Elebro, Jacob Heby, Margareta Lundgren, Sebastian Hedner, Charlotta Borg, David Brändstedt, Jenny Sartor, Hanna Malmström, Per-Uno Johansson, Martin Nodin, Björn Backman, Max Lindskog, Cecilia Jirström, Karin Mezheyeuski, Artur EBioMedicine Research Paper BACKGROUND: The development of a reactive tumour stroma is a hallmark of tumour progression and pronounced tumour stroma is generally considered to be associated with clinical aggressiveness. The variability between tumour types regarding stroma fraction, and its prognosis associations, have not been systematically analysed. METHODS: Using an objective machine-learning method we quantified the tumour stroma in 16 solid cancer types from 2732 patients, representing retrospective tissue collections of surgically resected primary tumours. Image analysis performed tissue segmentation into stromal and epithelial compartment based on pan-cytokeratin staining and autofluorescence patterns. FINDINGS: The stroma fraction was highly variable within and across the tumour types, with kidney cancer showing the lowest and pancreato-biliary type periampullary cancer showing the highest stroma proportion (median 19% and 73% respectively). Adjusted Cox regression models revealed both positive (pancreato-biliary type periampullary cancer and oestrogen negative breast cancer, HR(95%CI)=0.56(0.34-0.92) and HR(95%CI)=0.41(0.17-0.98) respectively) and negative (intestinal type periampullary cancer, HR(95%CI)=3.59(1.49-8.62)) associations of the tumour stroma fraction with survival. INTERPRETATION: Our study provides an objective quantification of the tumour stroma fraction across major types of solid cancer. Findings strongly argue against the commonly promoted view of a general associations between high stroma abundance and poor prognosis. The results also suggest that full exploitation of the prognostic potential of tumour stroma requires analyses that go beyond determination of stroma abundance. FUNDING: The Swedish Cancer Society, The Lions Cancer Foundation Uppsala, The Swedish Government Grant for Clinical Research, The Mrs Berta Kamprad Foundation, Sweden, Sellanders foundation, P.O.Zetterling Foundation, and The Sjöberg Foundation, Sweden. Elsevier 2021-03-09 /pmc/articles/PMC7960932/ /pubmed/33706249 http://dx.doi.org/10.1016/j.ebiom.2021.103269 Text en © 2021 The Author(s) http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Research Paper Micke, Patrick Strell, Carina Mattsson, Johanna Martín-Bernabé, Alfonso Brunnström, Hans Huvila, Jutta Sund, Malin Wärnberg, Fredrik Ponten, Fredrik Glimelius, Bengt Hrynchyk, Ina Mauchanski, Siarhei Khelashvili, Salome Garcia-Vicién, Gemma Molleví, David G. Edqvist, Per-Henrik O´Reilly, Aine Corvigno, Sara Dahlstrand, Hanna Botling, Johan Segersten, Ulrika Krzyzanowska, Agnieszka Bjartell, Anders Elebro, Jacob Heby, Margareta Lundgren, Sebastian Hedner, Charlotta Borg, David Brändstedt, Jenny Sartor, Hanna Malmström, Per-Uno Johansson, Martin Nodin, Björn Backman, Max Lindskog, Cecilia Jirström, Karin Mezheyeuski, Artur The prognostic impact of the tumour stroma fraction: A machine learning-based analysis in 16 human solid tumour types |
title | The prognostic impact of the tumour stroma fraction: A machine learning-based analysis in 16 human solid tumour types |
title_full | The prognostic impact of the tumour stroma fraction: A machine learning-based analysis in 16 human solid tumour types |
title_fullStr | The prognostic impact of the tumour stroma fraction: A machine learning-based analysis in 16 human solid tumour types |
title_full_unstemmed | The prognostic impact of the tumour stroma fraction: A machine learning-based analysis in 16 human solid tumour types |
title_short | The prognostic impact of the tumour stroma fraction: A machine learning-based analysis in 16 human solid tumour types |
title_sort | prognostic impact of the tumour stroma fraction: a machine learning-based analysis in 16 human solid tumour types |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7960932/ https://www.ncbi.nlm.nih.gov/pubmed/33706249 http://dx.doi.org/10.1016/j.ebiom.2021.103269 |
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