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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 LU orcid ; Huvila, Jutta ; Sund, Malin ; Wärnberg, Fredrik ; Ponten, Fredrik and Glimelius, Bengt , et al. (2021) In EBioMedicine 65.
Abstract

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... (More)

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.

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Contribution to journal
publication status
published
subject
in
EBioMedicine
volume
65
article number
103269
publisher
Elsevier
external identifiers
  • scopus:85102125784
  • pmid:33706249
ISSN
2352-3964
DOI
10.1016/j.ebiom.2021.103269
language
English
LU publication?
yes
id
ef273d09-5417-442b-98c1-c7a75955763b
date added to LUP
2021-03-17 08:13:34
date last changed
2024-04-18 03:37:42
@article{ef273d09-5417-442b-98c1-c7a75955763b,
  abstract     = {{<p>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.</p>}},
  author       = {{Micke, Patrick and Strell, Carina and Mattsson, Johanna and Martín-Bernabé, Alfonso and Brunnström, Hans and Huvila, Jutta and Sund, Malin and Wärnberg, Fredrik and Ponten, Fredrik and Glimelius, Bengt and Hrynchyk, Ina and Mauchanski, Siarhei and Khelashvili, Salome and Garcia-Vicién, Gemma and Molleví, David G. and Edqvist, Per Henrik and O´Reilly, Aine and Corvigno, Sara and Dahlstrand, Hanna and Botling, Johan and Segersten, Ulrika and Krzyzanowska, Agnieszka and Bjartell, Anders and Elebro, Jacob and Heby, Margareta and Lundgren, Sebastian and Hedner, Charlotta and Borg, David and Brändstedt, Jenny and Sartor, Hanna and Malmström, Per Uno and Johansson, Martin and Nodin, Björn and Backman, Max and Lindskog, Cecilia and Jirström, Karin and Mezheyeuski, Artur}},
  issn         = {{2352-3964}},
  language     = {{eng}},
  publisher    = {{Elsevier}},
  series       = {{EBioMedicine}},
  title        = {{The prognostic impact of the tumour stroma fraction : A machine learning-based analysis in 16 human solid tumour types}},
  url          = {{http://dx.doi.org/10.1016/j.ebiom.2021.103269}},
  doi          = {{10.1016/j.ebiom.2021.103269}},
  volume       = {{65}},
  year         = {{2021}},
}