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

胰腺导管腺癌（PDAC）是一种常见的癌症类型，但预后糟糕，5年总生存率仅为9%。大多数患者表现为晚期疾病，需要全身化疗治疗。目前的多药化疗方案可以提高总生存率，但有相当大的副作用和毒性，往往阻止其应用于老年患者和/或表现不佳的患者。由于目前缺乏能够预测PDAC治疗效果的生物标志物，我们无法可靠地将患者分层，哪些患者将受益，哪些患者将遭受很大的毒性。近年来，肠道微生物组已被确定为预测和调节抗癌免疫疗法的疗效。目前，人们对其在实体肿瘤化疗系统治疗中的作用知之甚少。因此，在预测PDAC患者化疗关键结果的生物标志物方面存在大量未满足的医学需求，并研究微生物组特征是否与之相关，以便对微生物-癌症治疗相互作用和未来治疗干预的可药物靶点提出机制假设。在目前的研究计划中，我们的目标是对接受标准治疗化疗的局部晚期和转移性PDAC患者的多中心德国队列进行微生物组宏基因组测序。自2020年以来，我们前瞻性地招募患者（N = 131），并在诊断后收集一系列唾液和粪便生物标本，直到6个月的全身治疗。在这里，我们将研究口腔和粪便微生物组在治疗过程中的纵向变化，并研究微生物组特征-物种，基因和代谢途径-与治疗反应，生存和毒性发展的关系，作为主要的临床结果，在200名患者和800个微生物组样本的最终队列中。利用霰弹枪宏基因组测序，我们也将产生一个前所未有的数据集，关于化疗药物如何修改肠道微生物组的基因库，这可能对肠道微生物稳态和微生物-宿主相互作用产生重大影响。最后，应用机器学习驱动的预测模型，我们将探索基于微生物组的分类器，并将包括临床特征和分子肿瘤特征，总体目标是为PDAC患者的治疗结果提出生物标志物。

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.