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The prognostic impact of the tumour stroma fraction: A machine learning-based analysis in 16 human solid tumour types
- Source :
- EBioMedicine, Vol 65, Iss, Pp 103269-(2021), Dipòsit Digital de la UB, Universidad de Barcelona, EBioMedicine
- Publication Year :
- 2021
- Publisher :
- Elsevier, 2021.
-
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 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 Sjoberg Foundation, Sweden.
- Subjects :
- 0301 basic medicine
Oncology
medicine.medical_specialty
Stromal cell
Pronòstic mèdic
lcsh:Medicine
General Biochemistry, Genetics and Molecular Biology
Machine Learning
03 medical and health sciences
0302 clinical medicine
Breast cancer
Stroma
Neoplasms
Internal medicine
Aprenentatge automàtic
Machine learning
Periampullary cancer
Humans
Medicine
Proportional Hazards Models
Retrospective Studies
Tumors
Cancer och onkologi
lcsh:R5-920
Clinical Laboratory Medicine
business.industry
Proportional hazards model
lcsh:R
Cancer
General Medicine
Prognosis
medicine.disease
Survival Analysis
people.cause_of_death
Klinisk laboratoriemedicin
030104 developmental biology
Cancer and Oncology
030220 oncology & carcinogenesis
Tumour stroma
Stromal Cells
business
people
lcsh:Medicine (General)
Kidney cancer
Research Paper
Subjects
Details
- Language :
- English
- ISSN :
- 23523964
- Volume :
- 65
- Database :
- OpenAIRE
- Journal :
- EBioMedicine
- Accession number :
- edsair.doi.dedup.....7d734701992427eb042ea1f98d204805