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.

BRAFV600E mutation impinges on gut microbial markers defining novel biomarkers for serrated colorectal cancer effective therapies.

BACKGROUND: Colorectal cancer (CRC) harboring BRAFV600E mutation exhibits low response to conventional therapy and poorest prognosis. Due to the emerging correlation between gut microbiota and CRC carcinogenesis, we investigated in serrated BRAFV600E cases the existence of a peculiar fecal microbial fingerprint and specific bacterial markers, which might represent a tool for the development of more effective clinical strategies. METHODS: By injecting human CRC stem-like cells isolated from BRAFV600E patients in immunocompromised mice, we described a new xenogeneic model of this subtype of CRC. By performing bacterial 16S rRNA sequencing, the fecal microbiota profile was then investigated either in CRC-carrying mice or in a cohort of human CRC subjects. The microbial communities' functional profile was also predicted. Data were compared with Mann-Whitney U, Welch's t-test for unequal variances and Kruskal-Wallis test with Benjamini-Hochberg false discovery rate (FDR) correction, extracted as potential BRAF class biomarkers and selected as model features. The obtained mean test prediction scores were subjected to Receiver Operating characteristic (ROC) analysis. To discriminate the BRAF status, a Random Forest classifier (RF) was employed. RESULTS: A specific microbial signature distinctive for BRAF status emerged, being the BRAF-mutated cases closer to healthy controls than BRAF wild-type counterpart. In agreement, a considerable score of correlation was also pointed out between bacteria abundance from BRAF-mutated cases and the level of markers distinctive of BRAFV600E pathway, including those involved in inflammation, innate immune response and epithelial-mesenchymal transition. We provide evidence that two candidate bacterial markers, Prevotella enoeca and Ruthenibacterium lactatiformans, more abundant in BRAFV600E and BRAF wild-type subjects respectively, emerged as single factors with the best performance in distinguishing BRAF status (AUROC = 0.72 and 0.74, respectively, 95% confidence interval). Furthermore, the combination of the 10 differentially represented microorganisms between the two groups improved performance in discriminating serrated CRC driven by BRAF mutation from BRAF wild-type CRC cases (AUROC = 0.85, 95% confidence interval, 0.69-1.01). CONCLUSION: Overall, our results suggest that BRAFV600E mutation itself drives a distinctive gut microbiota signature and provide predictive CRC-associated bacterial biomarkers able to discriminate BRAF status in CRC patients and, thus, useful to devise non-invasive patient-selective diagnostic strategies and patient-tailored optimized therapies.