bioRxivCancer BiologyPreprint
A spatial atlas of chemoradiation therapy in pancreatic cancer identifies cellular and microenvironmental determinants of persister populations
Vincent BernardiD, Li-Ting KuiD, Tianyu Wang, Ariana Acevedo-Diaz, Kimal RajapaksheiD, Galia JacobsoniD, Daniela Tovar, Jimin MiniD, Guangsheng PeiiD, Candise L. TatiD, Ayush Suresh, Ching-Wei David TzengiD, Matthew H. G. KatziD, Manoop S. BhutaniiD, Huaming WangiD, Robert A. WolffiD, Cara HaymakeriD, Ethan Bernard LudmiriD, Huocong HuangiD, Xiongfeng CheniD, Liang LiiD, Albert C. KoongiD, Linghua WangiD, Nicholas E. NaviniD, Dadi JiangiD, , Eugene Jon KoayiD
Abstract
The molecular pathways involved in the response to radiation therapy in pancreatic ductal adenocarcinoma (PDAC) remain poorly understood. We aimed to elucidate the adaptive mechanisms and cellular interactions within PDAC to radiation therapy (RT). We constructed a transcriptomic landscape of the cellular subtypes and spatially resolved neighborhoods from 50 patient samples, including 16 longitudinally matched single cell RNA sequencing and 34 spatial transcriptomics specimens. To resolve shortcomings of cell-type mixtures in spatial data, we developed a novel statistical method called SpaCCI (spatially aware analysis of cell-cell interactions) to profile cell-cell interactions and ligand-receptor enrichment. This revealed CXCL12/TGF{beta}-driven persister cell niches where activated fibroblasts reprogram tumor- associated macrophages and spatially exclude stress-response CD8 T cells after RT. Persister cancer cells displayed transcriptional evidence of recalcitrance to metal-induced cell death pathways of ferroptosis and cuproptosis which were recapitulated in preclinical models. Our study reveals the selective pressures experienced by PDAC following RT that may help provide insight for future multimodal therapeutic strategies.
- organism: human
- assay: spatial transcriptomics
- assay: scRNA-seq
- research focus: cell-cell communication
- research focus: spatial domain detection
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 5
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