Integrating spatial gene expression and breast tumour morphology via deep learning

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QC 20201217
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
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

Bergenstråhle, LudvigKTH Royal Institute of Technology,KTH,Genteknologi(Swepub:kth)u1f4ltny (author)
Stenbeck, LinneaKTH Royal Institute of Technology,KTH,Genteknologi(Swepub:kth)u1v835xb (author)
Abid, A.Stanford University (author)
Andersson, AlmaKTH Royal Institute of Technology,KTH,Genteknologi(Swepub:kth)u15avt37 (author)
Borg, ÅkeLund 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(Swepub:lu)onk-abo (author)
Maaskola, JonasKTH Royal Institute of Technology,KTH,Genteknologi,Science for Life Laboratory, SciLifeLab(Swepub:kth)u1a8gise (author)
Lundeberg, JoakimKTH Royal Institute of Technology,KTH,Science for Life Laboratory, SciLifeLab,Genteknologi(Swepub:kth)u1qkn9kw (author)
Zou, J.Chan–Zuckerberg Biohub,Stanford University (author)
Stanford UniversityGenteknologi (creator_code:org_t)

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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