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Integrating spatial...
Integrating spatial gene expression and breast tumour morphology via deep learning
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- He, B. (författare)
- Stanford University
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- Bergenstråhle, Ludvig (författare)
- KTH Royal Institute of Technology,KTH,Genteknologi
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- Stenbeck, Linnea (författare)
- KTH Royal Institute of Technology,KTH,Genteknologi
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- Abid, A. (författare)
- Stanford University
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- Andersson, Alma (författare)
- KTH Royal Institute of Technology,KTH,Genteknologi
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- Borg, Åke (författare)
- Lund University,Lunds universitet,Familjär bröstcancer,Forskargrupper vid Lunds universitet,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Familial Breast Cancer,Lund University Research Groups,LUCC: Lund University Cancer Centre,Other Strong Research Environments
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- Maaskola, Jonas (författare)
- KTH Royal Institute of Technology,KTH,Genteknologi,Science for Life Laboratory, SciLifeLab
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- Lundeberg, Joakim (författare)
- KTH Royal Institute of Technology,KTH,Science for Life Laboratory, SciLifeLab,Genteknologi
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- Zou, J. (författare)
- Chan–Zuckerberg Biohub,Stanford University
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(creator_code:org_t)
- 2020-06-22
- 2020
- Engelska.
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Ingår i: Nature Biomedical Engineering. - : Nature Research. - 2157-846X. ; 4:8, s. 827-834
- Relaterad länk:
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http://dx.doi.org/10...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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https://lup.lub.lu.s...
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Abstract
Ämnesord
Stäng
- Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
Nyckelord
- Biomarkers
- Diagnosis
- Diseases
- Gene expression
- Learning algorithms
- Medical imaging
- Morphology
- Tumors
- Co-localizations
- Gene Expression Data
- High spatial resolution
- Image-based screenings
- Immune activation
- Molecular biomarker
- Spatial variations
- Spatially resolved
- Deep learning
- transcriptome
- tumor marker
- Article
- breast cancer
- breast tissue
- cancer tissue
- clinical article
- clinician
- gene identification
- histopathology
- human
- human tissue
- protein localization
- st net
- transcriptomics
- tumor growth
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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