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Lipopolysaccharide (LPS) is associated with chronic intestinal inflammation and promotes intestinal cancer progression in the gut. While the interplay between LPS and intestinal immune cells has been well-characterized, little is known about LPS and the intestinal epithelium interactions. In this study, we explored the differential effects of LPS on proliferation and the transcriptome in 3D enteroids/colonoids obtained from dogs with naturally occurring gastrointestinal (GI) diseases including inflammatory bowel disease (IBD) and intestinal mast cell tumor. The study objective was to analyze the LPS-induced modulation of signaling pathways involving the intestinal epithelia and contributing to colorectal cancer development in the context of an inflammatory (IBD) or a tumor microenvironment. While LPS incubation resulted in a pro-cancer gene expression pattern and stimulated proliferation of IBD enteroids and colonoids, downregulation of several cancer-associated genes such as Gpatch4 , SLC7A1 , ATP13A2 , and TEX45 was also observed in tumor enteroids. Genes participating in porphyrin metabolism ( CP ), nucleocytoplasmic transport ( EEF1A1 ), arachidonic acid, and glutathione metabolism ( GPX1 ) exhibited a similar pattern of altered expression between IBD enteroids and IBD colonoids following LPS stimulation. In contrast, genes involved in anion transport, transcription and translation, apoptotic processes, and regulation of adaptive immune responses showed the opposite expression patterns between IBD enteroids and colonoids following LPS treatment. In brief, the crosstalk between LPS/TLR4 signal transduction pathway and several metabolic pathways such as primary bile acid biosynthesis and secretion, peroxisome, renin-angiotensin system, glutathione metabolism, and arachidonic acid pathways may be important in driving chronic intestinal inflammation and intestinal carcinogenesis.

Gliomas are among the most common types of central nervous system (CNS) tumors. A prompt diagnosis of the glioma subtype is crucial to estimate the prognosis and personalize the treatment strategy. The objective of this study was to develop a radiomics pipeline based on the clinical Magnetic Resonance Imaging (MRI) scans to noninvasively predict the glioma subtype, as defined based on the tumor grade, isocitrate dehydrogenase (IDH) mutation status, and 1p/19q codeletion status. A total of 212 patients from the public retrospective The Cancer Genome Atlas Low Grade Glioma (TCGA-LGG) and The Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM) datasets were used for the experiments and analyses. Different settings in the radiomics pipeline were investigated to improve the classification, including the Z-score normalization, the feature extraction strategy, the image filter applied to the MRI images, the introduction of clinical information, ComBat harmonization, the classifier chain strategy, etc. Based on numerous experiments, we finally reached an optimal pipeline for classifying the glioma tumors. We then tested this final radiomics pipeline on the hold-out test data with 51 randomly sampled random seeds for reliable and robust conclusions. The results showed that, after tuning the radiomics pipeline, the mean AUC improved from 0.8935 (±0.0351) to 0.9319 (±0.0386), from 0.8676 (±0.0421) to 0.9283 (±0.0333), and from 0.6473 (±0.1074) to 0.8196 (±0.0702) in the test data for predicting the tumor grade, IDH mutation, and 1p/19q codeletion status, respectively. The mean accuracy for predicting the five glioma subtypes also improved from 0.5772 (±0.0816) to 0.6716 (±0.0655). Finally, we analyzed the characteristics of the radiomic features that best distinguished the glioma grade, the IDH mutation, and the 1p/19q codeletion status, respectively. Apart from the promising prediction of the glioma subtype, this study also provides a better understanding of the radiomics model development and interpretability. The results in this paper are replicable with our python codes publicly available in github.

Purpose: The objective of our study is to propose fast, cost-effective, convenient, and effective biomarkers using the perfusion parameters from dynamic contrast-enhanced ultrasound (DCE-US) for the evaluation of immune checkpoint inhibitors (ICI) early response., Methods: The retrospective cohort used in this study included 63 patients with metastatic cancer eligible for immunotherapy. DCE-US was performed at baseline, day 8 (D8), and day 21 (D21) after treatment onset. A tumor perfusion curve was modeled on these three dates, and change in the seven perfusion parameters was measured between baseline, D8, and D21. These perfusion parameters were studied to show the impact of their variation on the overall survival (OS)., Results: After the removal of missing or suboptimal DCE-US, the Baseline-D8, the Baseline-D21, and the D8-D21 groups included 37, 53, and 33 patients, respectively. A decrease of more than 45% in the area under the perfusion curve (AUC) between baseline and D21 was significantly associated with better OS ( p = 0.0114). A decrease of any amount in the AUC between D8 and D21 was also significantly associated with better OS ( p = 0.0370)., Conclusion: AUC from DCE-US looks to be a promising new biomarker for fast, effective, and convenient immunotherapy response evaluation.