Geospatial immune variability illuminates differential evolution of lung adenocarcinoma
- Submitting institution
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The University of Warwick
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 12484
- Type
- D - Journal article
- DOI
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10.1038/s41591-020-0900-x
- Title of journal
- Nature Medicine
- Article number
- -
- First page
- 1054
- Volume
- 26
- Issue
- -
- ISSN
- 1078-8956
- Open access status
- Compliant
- Month of publication
- July
- Year of publication
- 2020
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
22
- Research group(s)
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A - Applied Computing
- Citation count
- 13
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This is the first work to use multi-region, multi-stain histology data and combine AI analysis from histology with genomics to study cancer evolution. It is the largest multi-sample digital pathology application so far that links spatial variability of immune-tumour-stromal interface with intra-tumour genetic heterogeneity and discusses evolutionary pressures in the tumour microenvironment. This paper featured in an editors' choice article in Science Translational Medicine, and in several newspaper articles (e.g. Evening Standard, Medical Express). An extension of this work to identify mechanisms by which pre-cancerous lesions evade immune detection during early stages of carcinogenesis has been published in Cancer Discovery.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -