bioRxivCancer BiologyPreprint
Cross-species graph-embedding unmasks the ageing microenvironment as a key determinant of pancreatic cancer malignant cell biology and therapy response
Joaquín Araos HenríqueziD, Muntadher JihadiD, Amir Jassim, Eloise G. LloydiD, Weike LuoiD, Judhell S. ManansalaiD, Sneha HarishiD, Sara Pinto TelesiD, Priscilla S.W. ChengiD, Gianluca MuccioloiD, Wenlong LiiD, Marta ZaccariaiD, Debasmita MukherjeeiD, Rebecca J. BraisiD, Sally Mills, Paul M Johnson, Mireia VallespinósiD, Richard J. GilbertsoniD, Giulia BiffiiD
Abstract
Pancreatic ductal adenocarcinoma (PDAC) has a dismal prognosis and is characterised by an extensive pro-tumorigenic stroma. Although most PDAC cases occur in older patients, the impact of ageing on malignant-stromal interactions and therapy response remains poorly understood. Here, we established orthotopically-grafted organoid-derived PDAC models across three murine age groups to characterise changes in the PDAC stroma and malignant cells with ageing. Cross-species analyses of tumour transcriptomes using a graph-embedding approach showed that integrating mouse models of different ages better captures the diversity of human PDAC, and that aged models more faithfully recapitulate the biology of older patients with PDAC. We also demonstrated that aged PDAC models have a more inflammatory stroma than that of younger tumours, shaping the malignant cell transcriptome. Finally, graph-embedding identified IRAK4 as a candidate therapeutic vulnerability in aged, but not young, KRAS- and p53-mutant PDAC, which we validated in preclinical drug studies. These findings highlight how ageing is a critical determinant of PDAC biology and associated therapeutic vulnerabilities, which should be an important consideration when designing disease models for preclinical development of precision therapies.
- organism: mouse
- organism: human
- assay: bulk RNA-seq
- research focus: cell-cell communication
- research focus: differential expression
- research focus: drug response / drug discovery
- approach: deep learning
- approach: graph neural network
- Citations
- 2
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