Gender-specific changes of the gut microbiome correlate with tumor development in murine models of pancreatic cancer

From Genes to Environment: Elucidating Pancreatic Carcinogenesis Through Genetically Engineered and Risk Factor-Integrated Mouse Models

Pancreatic cancer is characterized by late diagnosis, therapy resistance, and poor prognosis, necessitating the exploration of early carcinogenesis and prevention methods. Preclinical mouse models have evolved from cell line-based to human tumor tissue- or organoid-derived xenografts, now to humanized mouse models and genetically engineered mouse models (GEMMs). GEMMs, primarily driven by oncogenic Kras mutations and tumor suppressor gene alterations, offer a realistic platform for investigating pancreatic cancer initiation, progression, and metastasis. The incorporation of inducible somatic mutations and CRISPR-Cas9 screening methods has expanded their utility. To better recapitulate tumor initiation triggered by inflammatory cues, common pancreatic risk factors are being integrated into model designs. This approach aims to decipher the role of environmental factors as secondary or parallel triggers of tumor initiation alongside oncogenic burdens. Emerging models exploring pancreatitis, obesity, diabetes, and other risk factors offer significant translational potential. This review describes current mouse models for studying pancreatic carcinogenesis, their combination with inflammatory factors, and their utility in evaluating pathogenesis, providing guidance for selecting the most suitable models for pancreatic cancer research.

Sex bias in tumor immunity: insights from immune cells

Significant sex disparities have been observed in cancer incidence, treatment response to immunotherapy, and susceptibility to adverse effects, affecting both reproductive and non-reproductive organ cancers. While lifestyle factors, carcinogenic exposure, and healthcare access contribute to these disparities, they do not fully explain the observed male-female variation in anti-tumor immunity. Despite the preferential expression of sex hormone receptors in immune cells, X chromosome also contains numerous genes involved in immune function, and its incomplete inactivation may enhance anti-tumor immune responses in females. In contrast, loss or downregulation of Y-linked genes in males has been associated with an increased cancer risk. Additionally, estrogen, progesterone and androgen signaling pathways influence both innate and adaptive immune responses, contributing to sex-specific outcomes in cancer progression and therapy. Sex-biased differences are also evident in the epigenetic regulation of gene expression, cellular senescence, microbiota composition, metabolism, and DNA damage response, all of which impact anti-tumor immunity and immunotherapy treatment efficacy. In general, the combination of sex chromosomes, sex hormones, and hormone receptors orchestrates the phenotype and function of various immune cells involved in tumor immunity. However, sex disparity in each specific immune cell are context and environment dependent, considering the preferential expression of hormone receptor in immune cell and sex hormone levels fluctuate significantly across different life stages. This review aims to outline the molecular, cellular, and epigenetic changes in T cells, B cells, NK cells, DCs, neutrophils, and macrophages driven by sex chromosomes and sex hormone signaling. These insights may inform the design of sex-specific targeted therapies and leading to more individualized cancer treatment strategies.

Inflammatory Stimuli and Fecal Microbiota Transplantation Accelerate Pancreatic Carcinogenesis in Transgenic Mice, Accompanied by Changes in the Microbiota Composition

An association between gut microbiota and the development of pancreatic ductal adenocarcinoma (PDAC) has been previously described. To better understand the bacterial microbiota changes accompanying PDAC promotion and progression stimulated by inflammation and fecal microbiota transplantation (FMT), we investigated stool and pancreatic microbiota by 16s RNA-based metagenomic analysis in mice with inducible acinar transgenic expressions of KrasG12D, and age- and sex-matched control mice that were exposed to inflammatory stimuli and fecal microbiota obtained from mice with PDAC. Time- and inflammatory-dependent stool and pancreatic bacterial composition alterations and stool alpha microbiota diversity reduction were observed only in mice with a Kras mutation that developed advanced pancreatic changes. Stool Actinobacteriota abundance and pancreatic Actinobacteriota and Bifidobacterium abundances increased. In contrast, stool abundance of Firmicutes, Verrucomicrobiota, Spirochaetota, Desulfobacterota, Butyricicoccus, Roseburia, Lachnospiraceae A2, Lachnospiraceae unclassified, and Oscillospiraceae unclassified decreased, and pancreatic detection of Alloprevotella and Oscillospiraceae uncultured was not observed. Furthermore, FMT accelerated tumorigenesis, gradually decreased the stool alpha diversity, and changed the pancreatic and stool microbial composition in mice with a Kras mutation. Specifically, the abundance of Actinobacteriota, Bifidobacterium and Faecalibaculum increased, while the abundance of genera such as Lachnospiraceace A2 and ASF356, Desulfovibrionaceace uncultured, and Roseburia has decreased. In conclusion, pancreatic carcinogenesis in the presence of an oncogenic Kras mutation stimulated by chronic inflammation and FMT dynamically changes the stool and pancreas microbiota. In particular, a decrease in stool microbiota diversity and abundance of bacteria known to be involved in short-fatty acids production were observed. PDAC mouse model can be used for further research on microbiota-PDAC interactions and towards more personalized and effective cancer therapies.

OncoSexome: the landscape of sex-based differences in oncologic diseases

The NIH policy on sex as biological variable (SABV) emphasized the importance of sex-based differences in precision oncology. Over 50% of clinically actionable oncology genes are sex-biased, indicating differences in drug efficacy. Research has identified sex differences in non-reproductive cancers, highlighting the need for comprehensive sex-based cancer data. We therefore developed OncoSexome, a multidimensional knowledge base describing sex-based differences in cancer (https://idrblab.org/OncoSexome/) across four key topics: antineoplastic drugs and responses (SDR), oncology-related biomarkers (SBM), risk factors (SRF) and microbial landscape (SML). SDR covers sex-based differences in 2051 anticancer drugs; SBM describes 12 551 sex-differential biomarkers; SRF illustrates 350 sex-dependent risk factors; SML demonstrates 1386 microbes with sex-differential abundances associated with cancer development. OncoSexome is unique in illuminating multifaceted influences of biological sex on cancer, providing both external and endogenous contributors to cancer development and describing sex-based differences for the broadest oncological classes. Given the increasing global research interest in sex-based differences, OncoSexome is expected to impact future precision oncology practices significantly.

Gut metatranscriptomics based de novo assembly reveals microbial signatures predicting immunotherapy outcomes in non-small cell lung cancer

BACKGROUND

Advanced-stage non-small cell lung cancer (NSCLC) poses treatment challenges, with immune checkpoint inhibitors (ICIs) as the main therapy. Emerging evidence suggests the gut microbiome significantly influences ICI efficacy. This study explores the link between the gut microbiome and ICI outcomes in NSCLC patients, using metatranscriptomic (MTR) signatures.

METHODS

We utilized a de novo assembly-based MTR analysis on fecal samples from 29 NSCLC patients undergoing ICI therapy, segmented according to progression-free survival (PFS) into long (> 6 months) and short (≤ 6 months) PFS groups. Through RNA sequencing, we employed the Trinity pipeline for assembly, MMSeqs2 for taxonomic classification, DESeq2 for differential expression (DE) analysis. We constructed Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) machine learning (ML) algorithms and comprehensive microbial profiles.

RESULTS

We detected no significant differences concerning alpha-diversity, but we revealed a biologically relevant separation between the two patient groups in beta-diversity. Actinomycetota was significantly overrepresented in patients with short PFS (vs long PFS, 36.7% vs. 5.4%, p < 0.001), as was Euryarchaeota (1.3% vs. 0.002%, p = 0.009), while Bacillota showed higher prevalence in the long PFS group (66.2% vs. 42.3%, p = 0.007), when comparing the abundance of corresponding RNA reads. Among the 120 significant DEGs identified, cluster analysis clearly separated a large set of genes more active in patients with short PFS and a smaller set of genes more active in long PFS patients. Protein Domain Families (PFAMs) were analyzed to identify pathways enriched in patient groups. Pathways related to DNA synthesis and Translesion were more enriched in short PFS patients, while metabolism-related pathways were more enriched in long PFS patients. E. coli-derived PFAMs dominated in patients with long PFS. RF, SVM and XGBoost ML models all confirmed the predictive power of our selected RNA-based microbial signature, with ROC AUCs all greater than 0.84. Multivariate Cox regression tested with clinical confounders PD-L1 expression and chemotherapy history underscored the influence of n = 6 key RNA biomarkers on PFS.

CONCLUSION

According to ML models specific gut microbiome MTR signatures' associate with ICI treated NSCLC outcomes. Specific gene clusters and taxa MTR gene expression might differentiate long vs short PFS.

Predicting the potential deterioration of Barrett's esophagus based on gut microbiota: a Mendelian randomization analysis

Esophageal adenocarcinoma (EAC) is one of the most malignant tumors in the digestive system. To make thing worse, the scarcity of treatment options is disheartening. However, if detected early, there is a possibility of reversing the condition. Unfortunately, there is still a lack of relevant early screening methods. Considering that Barrett's esophagus (BE), a precursor lesion of EAC, has been confirmed as the only known precursor of EAC. Analyzing which BE cases will progress to EAC and understanding the processes and mechanisms involved is of great significance for early screening of such patients. Considering the significant alterations in the gut microbiota of patients with BE and its potential role in the progression to EAC, this study aims to analyze the relationship between BE, EAC, and GM to identify potential diagnostic biomarkers and therapeutic targets. This study utilized comprehensive statistical data on gut microbiota from a large-scale genome-wide association meta-analysis conducted by the MiBioGen consortium (n = 18,340). Subsequently, we selected a set of single nucleotide polymorphisms (SNPs) that fell below the genome-wide significance threshold (1 × 10-5) as instrumental variables. To investigate the causal relationship between gut microbiota and BE and EAC, we employed various MR analysis methods, including Inverse Variance Weighting (IVW), MR-Egger regression, weighted median (WM), and weighted mean. Additionally, we assessed the level of pleiotropy, heterogeneity, and stability of genetic variations through MR-Egger intercept test, MR-PRESSO, Cochran's Q test, and "leave-one-out" sensitivity analysis. Furthermore, we conducted reverse MR analysis to identify the causal relationships between gut microbiota and BE and EAC. The results from the Inverse Variance-Weighted (IVW) analysis indicate that Alistipes (P = 4.86 × 10-2), Lactobacillus (P = 2.11 × 10-2), Prevotella 7 (P = 4.28 × 10-2), and RuminococcaceaeUCG004 (P = 4.34 × 10-2) are risk factors for Barrett's esophagus (BE), while Flavonifractor (P = 8.81 × 10-3) and RuminococcaceaeUCG004 (P = 4.99 × 10-2) are risk factors for esophageal adenocarcinoma (EAC). On the other hand, certain gut microbiota genera appear to have a protective effect against both BE and EAC. These include Eubacterium (nodatum group) (P = 4.51 × 10-2), Holdemania (P = 1.22 × 10-2), and Lactococcus (P = 3.39 × 10-2) in the BE cohort, as well as Eubacterium (hallii group) (P = 4.07 × 10-2) and Actinomyces (P = 3.62 × 10-3) in the EAC cohort. According to the results of reverse MR analysis, no significant causal effects of BE and EAC on gut microbiota were observed. Furthermore, no significant heterogeneity or pleiotropy was detected in the instrumental variables. We have established a causal relationship between the gut microbiota and BE and EAC. This study holds profound significance for screening BE patients who may be at risk of deterioration, as it can provide them with timely medical interventions to reverse the condition.

Dynamics of intestinal and intratumoral microbiome signatures in genetically engineered mice and human pancreatic ductal adenocarcinoma

BACKGROUND

Emerging evidence has recently revealed a prominent role of the microbiome in pancreatic ductal adenocarcinoma (PDAC). However, while most observations were made in patients, mouse models still require a precise characterization of their disease-related microbiome to employ them for mechanistic and interventional preclinical studies.

METHODS

To investigate the fecal and tumoral microbiome of LSL-KrasG12D/+;LSL-Trp53R172H/+;Pdx-1-Cre (KPC) and control (CTRL) mice, Oxford Nanopore sequencing was applied. Feces were collected from 10 KPC mice and 10 CTRLs at 3 timepoints (6 weeks, 12 weeks, and when tumor-bearing (KPC) or 6 months (CTRL), respectively). Metagenomic sequencing was performed on feces DNA. KPC tumor and healthy pancreas DNA samples were subjected to 16S rRNA gene sequencing. Bacterial marker components were detected in KPC tumor tissue over time by fluorescence in situ hybridization (FISH) and immunohistochemistry (IHC).

RESULTS

Murine fecal samples showed a significantly different microbiome compared to age-matched healthy CTRLs regarding beta diversity (p = 0.001, R2 = 0.2-0.25 for Bray-Curtis). Adjusted human PDAC classifiers predicted disease status from feces of KPC mice achieving area under the receiver operating characteristic (AUROC) values of 80%. Furthermore, KPC tumors harbored significantly more bacterial components than healthy pancreas. Also the microbial composition differs significantly between KPC tumors and healthy pancreas tissue (p = 0.042 for Bray-Curtis). Microbiota found highly abundant in human PDAC samples were considerably more abundant in KPC tumors as compared to healthy pancreas samples (p-value <0.001).

CONCLUSION

KPC fecal samples show similarities with the microbial composition of stool samples from human PDAC patients.