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Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions

Abstract

In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.

Type Journal
ISBN 2041-1723 (Electronic) 2041-1723 (Linking)
Authors Andersson, A.; Larsson, L.; Stenbeck, L.; Salmen, F.; Ehinger, A.; Wu, S. Z.; Al-Eryani, G.; Roden, D.; Swarbrick, A.; Borg, A.; Frisen, J.; Engblom, C.; Lundeberg, J.
Responsible Garvan Author A/Prof Alex Swarbrick
Publisher Name Nature Communications
Published Date 2021-10-31
Published Volume 12
Published Issue 1
Published Pages 6012
Status Published in-print
DOI 10.1038/s41467-021-26271-2
URL link to publisher's version https://www.ncbi.nlm.nih.gov/pubmed/34650042