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.

Colorectal carcinoma peritoneal metastases-derived organoids: results and perspective of a model for tailoring hyperthermic intraperitoneal chemotherapy from bench-to-bedside.

BACKGROUND: Peritoneal metastases from colorectal cancer (CRCPM) are related to poor prognosis. Cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC) have been reported to improve survival, but peritoneal recurrence rates are still high and there is no consensus on the drug of choice for HIPEC. The aim of this study was to use patient derived organoids (PDO) to build a relevant CRCPM model to improve HIPEC efficacy in a comprehensive bench-to-bedside strategy. METHODS: Oxaliplatin (L-OHP), cisplatin (CDDP), mitomycin-c (MMC) and doxorubicin (DOX) were used to mimic HIPEC on twelve PDO lines derived from twelve CRCPM patients, using clinically relevant concentrations. After chemotherapeutic interventions, cell viability was assessed with a luminescent assay, and the obtained dose-response curves were used to determine the half-maximal inhibitory concentrations. Also, induction of apoptosis by different HIPEC interventions on PDOs was studied by evaluating CASPASE3 cleavage. RESULTS: Response to drug treatments varied considerably among PDOs. The two schemes with better response at clinically relevant concentrations included MMC alone or combined with CDDP. L-OHP showed relative efficacy only when administered at low concentrations over a long perfusion period. PDOs showed that the short course/high dose L-OHP scheme did not appear to be an effective choice for HIPEC in CRCPM. HIPEC administered under hyperthermia conditions enhanced the effect of chemotherapy drugs against cancer cells, affecting PDO viability and apoptosis. Finally, PDO co-cultured with cancer-associated fibroblast impacted HIPEC treatments by increasing PDO viability and reducing CASPASES activity. CONCLUSIONS: Our study suggests that PDOs could be a reliable in vitro model to evaluate HIPEC schemes at individual-patient level and to develop more effective treatment strategies for CRCPM.

Unveiling the mechanistic link between extracellular amyloid fibrils, mechano-signaling and YAP activation in cancer.

The tumor microenvironment is a complex ecosystem that plays a critical role in cancer progression and treatment response. Recently, extracellular amyloid fibrils have emerged as novel components of the tumor microenvironment; however, their function remains elusive. In this study, we establish a direct connection between the presence of amyloid fibrils in the secretome and the activation of YAP, a transcriptional co-activator involved in cancer proliferation and drug resistance. Furthermore, we uncover a shared mechano-signaling mechanism triggered by amyloid fibrils in both melanoma and pancreatic ductal adenocarcinoma cells. Our findings highlight the crucial role of the glycocalyx protein Agrin which binds to extracellular amyloid fibrils and acts as a necessary factor in driving amyloid-dependent YAP activation. Additionally, we reveal the involvement of the HIPPO pathway core kinase LATS1 in this signaling cascade. Finally, we demonstrate that extracellular amyloid fibrils enhance cancer cell migration and invasion. In conclusion, our research expands our knowledge of the tumor microenvironment by uncovering the role of extracellular amyloid fibrils in driving mechano-signaling and YAP activation. This knowledge opens up new avenues for developing innovative strategies to modulate YAP activation and mitigate its detrimental effects during cancer progression.

Correction to "An Unexpected Deuterium-Induced Metabolic Switch in Doxophylline".

[This corrects the article DOI: 10.1021/acsmedchemlett.2c00166.].