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.

Comprehensive analysis of spatial architecture in primary liver cancer

“…7A and fig. S11, A and B) ( 45 , 46 ). In addition, we found high expression of CD52 (ranked in the top five of the TLS-50), CD53 (ranked sixth from the bottom of the TLS-50), and HLA-DR (the major histocompatibility complex, class II, DR) ( HLA-DQB1 and HLA-DQA1 are ranked the 42nd and 50th in the TLS-50 signature, respectively) in TLS regions by immunohistochemistry ( 47 ) on HCC tissue microarrays (fig.…”

uniPort: a unified computational framework for single-cell data integration with optimal transport

“…4D). Myeloid cells, as an innate part of the immune system, also displayed a distribution concordant with the expression of CD68 [42]. Furthermore, cancer epithelial cells protruded along the invasive ductal carcinoma region, corresponding with the expression of ERBB2 as well.…”

A Landmark-based Common Coordinate Framework for Spatial Transcriptomics Data

“…Cell types with less than 25 members were excluded, for cell types with more than 500 members, a subset consisting of 500 cells were randomly sampled from these. We also used a curated list of genes in the analysis consisting of 5540 members, representing a union of the 5000 highest expressed genes and cell type specific marker genes, see Supplementary Data 13 in [15]. We used 50000 epochs and a batch size of 2048 for the single cell parameter estimation as well as the proportion inference.…”