A powerful machine learning approach to identify interactions of differentially abundant gut microbial subsets in patients with metastatic and non-metastatic pancreatic cancer.

Pancreatic cancer has a dismal prognosis, as it is often diagnosed at stage IV of the disease and is characterized by metastatic spread. Gut microbiota and its metabolites have been suggested to influence the metastatic spread by modulating the host immune system or by promoting angiogenesis. To date, the gut microbial profiles of metastatic and non-metastatic patients need to be explored. Taking advantage of the 16S metagenomic sequencing and the PEnalized LOgistic Regression Analysis (PELORA) we identified clusters of bacteria with differential abundances between metastatic and non-metastatic patients. An overall increase in Gram-negative bacteria in metastatic patients compared to non-metastatic ones was identified using this method. Furthermore, to gain more insight into how gut microbes can predict metastases, a machine learning approach (iterative Random Forest) was performed. Iterative Random Forest analysis revealed which microorganisms were characterized by a different level of relative abundance between metastatic and non-metastatic patients and established a functional relationship between the relative abundance and the probability of having metastases. At the species level, the following bacteria were found to have the highest discriminatory power: Anaerostipes hadrus, Coprobacter secundus, Clostridium sp. 619, Roseburia inulinivorans, Porphyromonas and Odoribacter at the genus level, and Rhodospirillaceae, Clostridiaceae and Peptococcaceae at the family level. Finally, these data were intertwined with those from a metabolomics analysis on fecal samples of patients with or without metastasis to better understand the role of gut microbiota in the metastatic process. Artificial intelligence has been applied in different areas of the medical field. Translating its application in the field of gut microbiota analysis may help fully exploit the potential information contained in such a large amount of data aiming to open up new supportive areas of intervention in the management of cancer.

Counteracting gemcitabine+nab-paclitaxel induced dysbiosis in KRAS wild type and KRASG12D mutated pancreatic cancer in vivo model.

Pancreatic cancer (PC) has a very low survival rate mainly due to late diagnosis and refractoriness to therapies. The latter also cause adverse effects negatively affecting the patients' quality of life, often requiring dose reduction or discontinuation of scheduled treatments, compromising the chances of cure. We explored the effects of a specific probiotic blend on PC mice xenografted with KRAS wild-type or KRASG12D mutated cell lines alone or together with gemcitabine+nab-paclitaxel treatment to then assess tumor volume and clinical pathological variables. Beside a semi-quantitative histopathological evaluation of murine tumor and large intestine samples, histochemical and immunohistochemical analyses were carried out to evaluate collagen deposition, proliferation index Ki67, immunological microenvironment tumor-associated, DNA damage markers and also mucin production. Blood cellular and biochemical parameters and serum metabolomics were further analyzed. 16S sequencing was performed to analyze the composition of fecal microbiota. Gemcitabine+nab-paclitaxel treatment impaired gut microbial profile in KRAS wild-type and KRASG12D mice. Counteracting gemcitabine+nab-paclitaxel- induced dysbiosis through the administration of probiotics ameliorated chemotherapy side effects and decreased cancer-associated stromatogenesis. Milder intestinal damage and improved blood count were also observed upon probiotics treatment as well as a positive effect on fecal microbiota, yielding an increase in species richness and in short chain fatty acids producing- bacteria. Mice' serum metabolomic profiles revealed significant drops in many amino acids upon probiotics administration in KRAS wild-type mice while in animals transplanted with PANC-1 KRASG12D mutated all treated groups showed a sharp decline in serum levels of bile acids with respect to control mice. These results suggest that counteracting gemcitabine+nab-paclitaxel-induced dysbiosis ameliorates chemotherapy side effects by restoring a favorable microbiota composition. Relieving adverse effects of the chemotherapy through microbiota manipulation could be a desirable strategy in order to improve pancreatic cancer patients' quality of life and to increase the chance of cure.