Investigating the impact of ageing on the tumour microenvironment and its crosstalk with malignant cells in pancreatic cancer

Abstract

Pancreatic ductal adenocarcinoma (PDAC) is characterised by an extensive tumour microenvironment (TME), which contributes to disease progression and therapy response. PDAC primarily affects elderly individuals as patients are typically diagnosed when 70 years old or older. While ageing has been shown to impact the TME and progression of other malignancies, most pre-clinical studies of PDAC use young mouse models that may not mimic the physiological state and TME interactions of the majority of PDAC patients. Thus, ageing could affect the PDAC TME and aged mouse models may reveal therapeutic targets to improve the treatment of elderly PDAC patients. To investigate this, I established (~18-month-old) orthotopically grafted KPC (KrasLSL-G12D/+; Trp53LSL-R172H/+; Pdx1-Cre) organoid-derived mouse models of PDAC and compared them to young (~3-month-old) mouse models. Single- cell transcriptomics, graph network-based machine learning, flow cytometry and histological analyses showed that aged models have a more inflammatory TME relative to young models. Moreover, tumours from aged models showed a higher abundance of senescent cells, suggesting that senolytic therapy could be a potential therapeutic strategy against aged PDAC. Using a recently developed natural language processing and graph network-based machine learning approach, I also demonstrated that aged mouse models of PDAC better recapitulate features of old PDAC patients. This approach identified the Toll-like receptor signalling cascade and IRAK4 activation in aged mouse models as a potential age-dependent therapeutic vulnerability. Finally, while senolytic therapy failed to show beneficial results, inhibition of IRAK4 significantly and selectively reduced tumour growth in aged mouse models of PDAC. This work highlights how ageing shapes the TME and therapy response in PDAC and how aged mouse models of PDAC could be leveraged to tailor therapies for specific groups of patients.