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

Alma Andersson, Ludvig Larsson, Linnea Stenbeck, Fredrik Salmén, Anna Ehinger, Sunny Z. Wu, Ghamdan Al-Eryani, Daniel Roden, Alex Swarbrick, Åke Borg, Jonas Frisén, Camilla Engblom and Joakim Lundeberg ()
Additional contact information
Alma Andersson: Science for Life Laboratory, Division of Gene Technology, KTH Royal Institute of Technology
Ludvig Larsson: Science for Life Laboratory, Division of Gene Technology, KTH Royal Institute of Technology
Linnea Stenbeck: Science for Life Laboratory, Division of Gene Technology, KTH Royal Institute of Technology
Fredrik Salmén: Science for Life Laboratory, Division of Gene Technology, KTH Royal Institute of Technology
Anna Ehinger: Laboratory Medicine Region Skåne
Sunny Z. Wu: The Kinghorn Cancer Centre, Garvan Institute of Medical Research
Ghamdan Al-Eryani: The Kinghorn Cancer Centre, Garvan Institute of Medical Research
Daniel Roden: The Kinghorn Cancer Centre, Garvan Institute of Medical Research
Alex Swarbrick: The Kinghorn Cancer Centre, Garvan Institute of Medical Research
Åke Borg: Division of Oncology, Lund University
Jonas Frisén: Karolinska Institutet
Camilla Engblom: Karolinska Institutet
Joakim Lundeberg: Science for Life Laboratory, Division of Gene Technology, KTH Royal Institute of Technology

Nature Communications, 2021, vol. 12, issue 1, 1-14

Abstract: 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.

Date: 2021
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DOI: 10.1038/s41467-021-26271-2

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