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Spatial Deconvolution of HER2-positive Breast Tumors Reveals Novel Intercellular Relationships

Andersson, Alma ; Larsson, Ludvig ; Stenbeck, Linnea ; Salmén, Fredrik ; Ehinger, Anna LU orcid ; Wu, Sunny ; Al-Eryani, Ghamdan ; Roden, Daniel ; Swarbrick, Alex and Borg, Åke LU , et al. (2020)
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. We integrate and spatially map tumor-associated types from single cell data to find: segregated epithelial cells, interactions between B and T-cells and... (More)
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. We integrate and spatially map tumor-associated types from single cell data to find: segregated epithelial cells, interactions between B and T-cells and myeloid cells, co-localization of macrophage and T-cell subsets. A model is constructed to infer presence of tertiary lymphoid structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define novel interactions between tumor-infiltrating cells in breast cancer and provide tools generalizing across tissues and diseases. (Less)
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publication status
published
subject
publisher
bioRxiv
DOI
10.1101/2020.07.14.200600
language
English
LU publication?
yes
id
d320706b-699c-435d-aa31-ab1e105d2ca5
date added to LUP
2020-07-30 12:05:25
date last changed
2023-05-26 12:04:03
@misc{d320706b-699c-435d-aa31-ab1e105d2ca5,
  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. We integrate and spatially map tumor-associated types from single cell data to find: segregated epithelial cells, interactions between B and T-cells and myeloid cells, co-localization of macrophage and T-cell subsets. A model is constructed to infer presence of tertiary lymphoid structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define novel interactions between tumor-infiltrating cells in breast cancer and provide tools generalizing across tissues and diseases.}},
  author       = {{Andersson, Alma and Larsson, Ludvig and Stenbeck, Linnea and Salmén, Fredrik and Ehinger, Anna and Wu, Sunny and Al-Eryani, Ghamdan and Roden, Daniel and Swarbrick, Alex and Borg, Åke and Frisén, Jonas and Engblom, Camilla and Lundeberg, Joakim}},
  language     = {{eng}},
  publisher    = {{bioRxiv}},
  title        = {{Spatial Deconvolution of HER2-positive Breast Tumors Reveals Novel Intercellular Relationships}},
  url          = {{http://dx.doi.org/10.1101/2020.07.14.200600}},
  doi          = {{10.1101/2020.07.14.200600}},
  year         = {{2020}},
}