Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies. Despite modest improvements in survival over recent years, the 5-year survival rate is still only 13%1. In most cases, PDAC is detected at an advanced stage, often involving local invasion or distant metastasis, which complicates treatment. The disease is biologically diverse and marked by highly invasive tumor cells, a dense desmoplastic stroma, and poor vascularization2,3,4. This complex tumor microenvironment continues to hinder efforts to fully characterize the cellular and non-cellular components that influence disease progression. To address this, effective preclinical models are needed that closely mimic human disease progression and desmoplasia.
A variety of mouse models can be used to study the biology of cancer progression and evaluate the efficacy of novel therapeutics5,6. KRas-driven genetically engineered mouse model (GEMM) spontaneously develops pancreatic tumors that recapitulate many pathophysiological and molecular features of human PDAC3,7,8,9,10,11. In particular, the Kras-driven, p53-deleted KPC model (LSL-KrasG12D; p53lox/+; Pdx1-Cre) is a well-characterized PDAC GEMM7,8,9. Tumor imaging is an important component of preclinical studies. Tumor detection and measurement of tumor size enable the selection of animals for preclinical studies, longitudinal monitoring of tumor growth and disease progression, and assessment of treatment response.
Advanced in vivo imaging modalities such as ultrasound imaging (US), bioluminescence (BLI), computer tomography (CT), fluorescence imaging (FLI), positron emission tomography (PET), and magnetic resonance imaging (MRI) can be used for tumor imaging in the KPC model and similar PDAC models12,13,14,15,16. Yet, tumor imaging in the KPC model can be challenging, and each of these imaging modalities offers unique strengths and limitations. Optical imaging technologies, such as bioluminescence (BLI) and ultrasound imaging (US), are non-invasive and cost-effective. However, their limited spatial resolution, particularly for internal organs, can lead to a poor correlation with tumor volume12,15,17,18. Bioluminescence requires genetically engineered cells expressing luciferase signal, which also depends on substrate delivery, blood flow, and tissue oxygenation. A computer tomograph provides fast imaging with minimal motion artifacts but poor soft tissue contrast without contrast agents. Iodinated contrast may cause toxicity in mice. Fluorescence imaging requires contrast agents/fluorescent probes, and autofluorescence from abdominal organs reduces sensitivity.
Because of the constraints associated with other imaging methods, magnetic resonance imaging (MRI) is generally regarded as the most reliable technique for evaluating deep-seated tumors, offering superior soft tissue contrast and molecular sensitivity for internal anatomical structures13,14,15,19. However, standard preclinical MRI protocols incur high operational costs and long acquisition times and can be a barrier for many research laboratories. Multianimal MRI can overcome this access barrier by reducing pre-scan animal preparation time (anesthesia), scanning time, and post-scan animal recovery. This protocol demonstrates the technical feasibility of PDAC tumor imaging (Figure 1, Figure 2, Figure 3, and Figure 4) and the use of image findings to recruit animals for chemotherapeutic treatment with gemcitabine (Figure 5). PDAC tumors exhibit multiple inherent mechanisms that contribute to resistance against standard-of-care (SOC) chemotherapy agents. such as gemcitabine, gemcitabine/nab-paclitaxel, or FOLFIRINOX2,3,4. This protocol provides a suitable and robust platform to evaluate new therapeutic agents and SOC chemotherapy to find promising combinations20,21,22,23,24,25,26,27 that could enhance treatment response in patients.