Integrating spatial gene expression and breast tumour morphology via deep learning

Bergenstråhle, Ludvig (author): KTH Royal Institute of Technology,KTH,Genteknologi

Stenbeck, Linnea (author): KTH Royal Institute of Technology,KTH,Genteknologi

Andersson, Alma (author): KTH Royal Institute of Technology,KTH,Genteknologi

Borg, Åke (author): 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

Maaskola, Jonas (author): KTH Royal Institute of Technology,KTH,Genteknologi,Science for Life Laboratory, SciLifeLab

Lundeberg, Joakim (author): KTH Royal Institute of Technology,KTH,Science for Life Laboratory, SciLifeLab,Genteknologi

(creator_code:org_t)

2020-06-22
2020
English.
In: Nature Biomedical Engineering. - : Nature Research. - 2157-846X. ; 4:8, s. 827-834

Related links:: http://dx.doi.org/10...; show more...; https://urn.kb.se/re...; https://doi.org/10.1...; https://lup.lub.lu.s...; show less...

Abstract Subject headings

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.

TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)

By the author/editor: He, B.; Bergenstråhle, L ...; Stenbeck, Linnea; Abid, A.; Andersson, Alma; Borg, Åke; show more...; Maaskola, Jonas; Lundeberg, Joaki ...; Zou, J.; show less...

About the subject

ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Medical Engineer ...; and Medical Image Pr ...

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