Background: Pseudomyxoma peritonei (PMP) is a rare peritoneal condition where mucus-secreting tumorous cells progressively produce a thick, gelatin-like substance. The prognosis of patients with PMP is determined by the degree of cellularity within the mucin (low-grade (LAMN) vs. high-grade (HAMN) histologic features) and by the extent of the disease. Methods: Prognostic relevance of tumor markers CA19-9 and CEA, gender, Peritoneal Cancer Index (PCI), and completeness of cytoreduction (CC) after cytoreductive surgery were evaluated on 193 consecutive PMP patients, based on a retrospective analysis of prospectively gathered data from a German tertial referral center. Results: We demonstrated that low PCI, CC0 status, low-grade histology, and female gender were independent positive prognostic factors for both overall survival (OS) and progression-free survival (PFS). Furthermore, LAMN patients with achieved CC0 status show significantly better OS and PFS compared to those with CC1 status (p = 0.0353 and p = 0.0026 respectively). In contrast, the duration and drug of hyperthermic intraperitoneal chemotherapy (HIPEC) were not prognostic in any comparison. Increased CA19-9 and CEA levels were significantly associated with HAMN cases, but also predicted recurrence in patients with low-grade histologies. Conclusion: Our study confirmed the prognostic role of tumor markers and emphasized the importance of CC status and PCI in a large cohort of PMP- and LAMN patients.

This study aims to combine computed tomography (CT)-based texture analysis (QTA) and a microbiome-based biomarker signature to predict the overall survival (OS) of immune checkpoint inhibitor (ICI)-treated non-small cell lung cancer (NSCLC) patients by analyzing their CT scans (n = 129) and fecal microbiome (n = 58). One hundred and five continuous CT parameters were obtained, where principal component analysis (PCA) identified seven major components that explained 80% of the data variation. Shotgun metagenomics (MG) and ITS analysis were performed to reveal the abundance of bacterial and fungal species. The relative abundance of Bacteroides dorei and Parabacteroides distasonis was associated with long OS (>6 mo), whereas the bacteria Clostridium perfringens and Enterococcus faecium and the fungal taxa Cortinarius davemallochii, Helotiales, Chaetosphaeriales, and Tremellomycetes were associated with short OS (≤6 mo). Hymenoscyphus immutabilis and Clavulinopsis fusiformis were more abundant in patients with high (≥50%) PD-L1-expressing tumors, whereas Thelephoraceae and Lachnospiraceae bacterium were enriched in patients with ICI-related toxicities. An artificial intelligence (AI) approach based on extreme gradient boosting evaluated the associations between the outcomes and various clinicopathological parameters. AI identified MG signatures for patients with a favorable ICI response and high PD-L1 expression, with 84% and 79% accuracy, respectively. The combination of QTA parameters and MG had a positive predictive value of 90% for both therapeutic response and OS. According to our hypothesis, the QTA parameters and gut microbiome signatures can predict OS, the response to therapy, the PD-L1 expression, and toxicity in NSCLC patients treated with ICI, and a machine learning approach can combine these variables to create a reliable predictive model, as we suggest in this research.

Background: The function and polarization of macrophages has a significant impact on the outcome of many diseases. Targeting tumor-associated macrophages (TAMs) is among the greatest challenges to solve because of the low in vitro reproducibility of the heterogeneous tumor microenvironment (TME). To create a more comprehensive model and to understand the inner workings of the macrophage and its dependence on extracellular signals driving polarization, we propose an in silico approach. Methods: A Boolean control network was built based on systematic manual curation of the scientific literature to model the early response events of macrophages by connecting extracellular signals (input) with gene transcription (output). The network consists of 106 nodes, classified as 9 input, 75 inner and 22 output nodes, that are connected by 217 edges. The direction and polarity of edges were manually verified and only included in the model if the literature plainly supported these parameters. Single or combinatory inhibitions were simulated mimicking therapeutic interventions, and output patterns were analyzed to interpret changes in polarization and cell function. Results: We show that inhibiting a single target is inadequate to modify an established polarization, and that in combination therapy, inhibiting numerous targets with individually small effects is frequently required. Our findings show the importance of JAK1, JAK3 and STAT6, and to a lesser extent STK4, Sp1 and Tyk2, in establishing an M1-like pro-inflammatory polarization, and NFAT5 in creating an anti-inflammatory M2-like phenotype. Conclusions: Here, we demonstrate a protein–protein interaction (PPI) network modeling the intracellular signalization driving macrophage polarization, offering the possibility of therapeutic repolarization and demonstrating evidence for multi-target methods.