Integrating spatial gene expression and breast tumour morphology via deep learning

• 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

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