Conserved principles of spatial biology define tumor heterogeneity and response to immunotherapy DOI Creative Commons
Vivek Behera,

Hannah Giba,

Ue-Yu Pen

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 22, 2024

Abstract The tumor microenvironment (TME) is an immensely complex ecosystem 1,2 . This complexity underlies difficulties in elucidating principles of spatial organization and using molecular profiling the TME for clinical use 3 Through statistical analysis 96 transcriptomic (ST-seq) datasets spanning twelve diverse types, we found a conserved distribution multicellular, transcriptionally covarying units termed ‘Spatial Groups’ (SGs). SGs were either dependent on hierarchical local context – enriched cell-extrinsic processes such as immune regulation signal transduction or independent from cell-intrinsic protein RNA metabolism, DNA repair, cell cycle regulation. We used to define measure gene heterogeneity ‘spatial lability’ categorized all tumors by their lability profiles. resulting classification captured variation versus biology motivated class-specific strategies therapeutic intervention. Using this characterize pre-treatment biopsy samples 16 non-small lung cancer (NSCLC) patients outside our database distinguished responders non-responders checkpoint blockade while programmed death-ligand 1 (PD-L1) status spatially unaware bulk transcriptional markers did not. Our findings show that are both biologically clinically significant.

Language: Английский

Statistical design of a synthetic microbiome that clears a multi-drug resistant gut pathogen DOI Creative Commons
Rita A. Oliveira, Bipul Pandey, Kiseok Lee

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Feb. 29, 2024

Abstract Microbiomes perform critical functions across many environments on Earth 1–3 . However, elucidating principles of their design is immensely challenging 4–7 Using a diverse bank human gut commensal strains and clearance multi-drug resistant Klebsiella pneumoniae as target, we engineered functional synthetic microbiome using process that was agnostic to mechanism action, bacterial interactions, or compositions natural microbiomes. Our strategy modified ‘Design-Build-Test-Learn’ approach (‘DBTL+’) coupled with statistical inference learned by considering only the strain presence-absence designed communities. In just single round DBTL+, converged generative model K. suppression. Statistical performed our identified 15 were key for community function. Combining these into (‘SynCom15’) suppressed unrelated in vitro matched ability whole stool transplant pre-clinically relevant mouse infection. Considering metabolic profiles communities instead yielded poor model, demonstrating advantage deriving design. work introduces concept ‘statistical design’ engineering microbiomes, opening possibility ecology more broadly.

Language: Английский

Citations

2
Conserved principles of spatial biology define tumor heterogeneity and response to immunotherapy DOI Creative Commons
Vivek Behera,

Hannah Giba,

Ue-Yu Pen

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 22, 2024

Abstract The tumor microenvironment (TME) is an immensely complex ecosystem 1,2 . This complexity underlies difficulties in elucidating principles of spatial organization and using molecular profiling the TME for clinical use 3 Through statistical analysis 96 transcriptomic (ST-seq) datasets spanning twelve diverse types, we found a conserved distribution multicellular, transcriptionally covarying units termed ‘Spatial Groups’ (SGs). SGs were either dependent on hierarchical local context – enriched cell-extrinsic processes such as immune regulation signal transduction or independent from cell-intrinsic protein RNA metabolism, DNA repair, cell cycle regulation. We used to define measure gene heterogeneity ‘spatial lability’ categorized all tumors by their lability profiles. resulting classification captured variation versus biology motivated class-specific strategies therapeutic intervention. Using this characterize pre-treatment biopsy samples 16 non-small lung cancer (NSCLC) patients outside our database distinguished responders non-responders checkpoint blockade while programmed death-ligand 1 (PD-L1) status spatially unaware bulk transcriptional markers did not. Our findings show that are both biologically clinically significant.

Language: Английский

Citations

0