A multicentric translational clinical study on the role of the oro-intestinal microbiome in pancreatic cancer chemotherapy

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Pancreatic ductal adenocarcinoma (PDAC) is a frequent cancer type, but with a miserable prognosis of a 5-year overall survival of only 9%. Most patients present with advanced stage disease and require systemic therapies with chemotherapy. Current poly-chemotherapeutic regimens can improve overall survival, but are associated with considerable side effects and toxicity, often preventing their application in elderly patients and/or patients with poor performance status. As biomarkers that can predict treatment efficacy against PDAC are currently lacking, we cannot reliably stratify patients in those who will benefit vs. those that will largely suffer from toxicity. In recent years, the gut microbiome has been identified to predict and modulate the efficacy of anticancer immunotherapies. Currently, little is known about its role in chemotherapy-based systemic treatments in solid tumors. Therefore, there is large unmet medical need on biomarkers predicting critical outcomes of chemotherapy in PDAC patients, and to investigate whether microbiome features are associated with it in order to develop mechanistic hypotheses on microbe-cancer-therapy interactions and on druggable targets for future therapeutic interventions. In the present research proposal, we aim to perform microbiome metagenome sequencing from a multicentric German cohort of locally advanced and metastatic PDAC patients receiving standard-of-care chemotherapies. Since 2020, we are prospectively recruiting patients (N = 131) and collecting serial saliva and stool biospecimens after diagnosis until 6 months of systemic therapy. Here, we will study longitudinal changes of the oral and fecal microbiome over the course of therapy and investigate associations of microbiome features – species, genes and metabolic pathways – with therapy response, survival and development of toxicities as major clinical outcomes in a final cohort of 200 patients and 800 microbiome samples in total. Utilizing shotgun metagenome sequencing, we will also generate an unprecedented data set on how chemotherapeutic agents modify the gene reservoir of the gut microbiome which may have drastic effects on gut microbial homeostasis and microbe-host interactions. Finally, applying machine-learning driven prediction models, we will explore microbiome-based classifiers and will include clinical features and molecular tumor characteristics herein with the overall goal to propose a biomarker for therapy outcomes in PDAC patients.