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Abstract The tumor microenvironment (TME) is an immensely complex ecosystem1,2. This complexity underlies difficulties in elucidating principles of spatial organization and using molecular profiling of the TME for clinical use3. Through statistical analysis of 96 spatial transcriptomic (ST-seq) datasets spanning twelve diverse tumor types, we found a conserved distribution of multicellular, transcriptionally covarying units termed ‘Spatial Groups’ (SGs). SGs were either dependent on a hierarchical local spatial context – enriched for cell-extrinsic processes such as immune regulation and signal transduction – or independent from local spatial context – enriched for cell-intrinsic processes such as protein and RNA metabolism, DNA repair, and cell cycle regulation. We used SGs to define a measure of gene spatial heterogeneity – ‘spatial lability’ – and categorized all 96 tumors by their TME spatial lability profiles. The resulting classification captured spatial variation in cell-extrinsic versus cell-intrinsic biology and motivated class-specific strategies for therapeutic intervention. Using this classification to characterize pre-treatment biopsy samples of 16 non-small cell lung cancer (NSCLC) patients outside our database distinguished responders and non-responders to immune checkpoint blockade while programmed death-ligand 1 (PD-L1) status and spatially unaware bulk transcriptional markers did not. Our findings show conserved principles of TME spatial biology that are both biologically and clinically significant.
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Topology-Guided Multi-Class Cell Context Generation for Digital Pathology
In digital pathology, the spatial context of cells is important for cell classification, cancer diagnosis and prognosis. To model such complex cell context, however, is challenging. Cells form different mixtures, lineages, clusters and holes. To model such structural patterns in a learnable fashion, we introduce several mathematical tools from spatial statistics and topological data analysis. We incorporate such structural descriptors into a deep generative model as both conditional inputs and a differentiable loss. This way, we are able to generate high quality multi-class cell layouts for the first time. We show that the topology-rich cell layouts can be used for data augmentation and improve the performance of downstream tasks such as cell classification.
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- Award ID(s):
- 2144901
- PAR ID:
- 10417482
- Date Published:
- Journal Name:
- IEEE Conference on Computer Vision and Pattern Recognition
- ISSN:
- 2163-6648
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
