Alkylresorcinols as New Modulators of the Metabolic Activity of the Gut Microbiota.

Alkylresorcinols (ARs) are polyphenolic compounds with a wide spectrum of biological activities and are potentially involved in the regulation of host metabolism. The present study aims to establish whether ARs can be produced by the human gut microbiota and to evaluate alterations in content in stool samples as well as metabolic activity of the gut microbiota of C57BL, db/db, and LDLR (-/-) mice according to diet specifications and olivetol (5-n-pentylresorcinol) supplementation to estimate the regulatory potential of ARs. Gas chromatography with mass spectrometric detection was used to quantitatively analyse AR levels in mouse stool samples; faecal microbiota transplantation (FMT) from human donors to germ-free mice was performed to determine whether the intestinal microbiota could produce AR molecules; metagenome sequencing analysis of the mouse gut microbiota followed by reconstruction of its metabolic activity was performed to investigate olivetol's regulatory potential. A significant increase in the amounts of individual members of AR homologues in stool samples was revealed 14 days after FMT. Supplementation of 5-n-Pentylresorcinol to a regular diet influences the amounts of several ARs in the stool of C57BL/6 and LDLR (-/-) but not db/db mice, and caused a significant change in the predicted metabolic activity of the intestinal microbiota of C57BL/6 and LDLR (-/-) but not db/db mice. For the first time, we have shown that several ARs can be produced by the intestinal microbiota. Taking into account the dependence of AR levels in the gut on olivetol supplementation and microbiota metabolic activity, AR can be assumed to be potential quorum-sensing molecules, which also influence gut microbiota composition and host metabolism.

Identification of new candidate genes and signalling pathways associated with the development of neuroendocrine pancreatic tumours based on next generation sequencing data.

Despite advances in classification, treatment, and imaging, neuroendocrine tumours remain a clinically complex subject. In this work, we studied the genetic profile of well-differentiated pancreatic neuroendocrine tumours (PanNETs) in a cohort of Caucasian patients and analysed the signalling pathways and candidate genes potentially associated with the development of this oncological disease. Twenty-four formalin-fixed paraffin-embedded (FFPE) samples of well-differentiated PanNETs were subjected to massive parallel sequencing using the targeted gene panel (409 genes) of the Illumina NextSeq 550 platform (San Diego, USA). In 24 patients, 119 variants were identified in 54 genes. The median mutation rate per patient was 5 (2.8-7). The detected genetic changes were dominated by missense mutations (67%) and nonsense mutations (29%). 18% of the mutations were activating, 35% of the variants led to a loss of function of the encoded protein, and 52% were not classified. Twenty-six variants were described as new. Functionally significant changes in the tertiary structure and activity of the protein molecules in an in silico assay were predicted for 5 new genetic variants. The 5 highest priority candidate genes were selected: CREB1, TCF12, PRKAR1A, BCL11A, and BUB1B. Genes carrying the identified mutations participate in signalling pathways known to be involved in PanNETs; in addition, 38% of the cases showed genetic changes in the regulation of the SMAD2/3 signalling pathway. Well-differentiated PanNETs in a Russian cohort demonstrate various molecular genetic features, including new genetic variations and potential driver genes. The highlighted molecular genetic changes in the SMAD2/3 signalling pathway suggest new prospects for targeted therapy.

EGFR mutations and tumor metastases in patients with nonsmall cell lung cancer in the South of Russia.

PURPOSE: To assess the frequencies of somatic EGFR mutations in the tumor tissues of patients with non-small cell lung cancer (NSCLC) residing in the South of Russia (SR), and to define the relationship between genetic subtypes of NSCLC and the emergence of different types of metastases. METHODS: DNA was extracted from formalin-fixed parrafin embedded (FFPE) samples of 721 patients. A total of 29 somatic EGFR mutations were detected using commercial Therascreen EGFR RGQ PCR Kit. RESULTS: EGFR mutations were significantly more frequent in females and non-smokers even when considering the combination of both factors. The frequency of activating EGFR mutations across three age groups (<51, 51-61, >61 years) of women with NSCLC was significantly different (x2=10.94, p=0.004) and became higher with increasing age. Both activating and resistance mutations of EGFR were not associated with the frequency of regional or distant metastases. The frequencies of both regional and distant metastases were associated with higher disease stage (odds ratio/OR)=16.71; 95% confidence interval (CI): 9.5-29.38; p<0.0001, and OR=2.94; 95% CI: 2.22-3.88; p<0.0001, respectively) and adenocarcinona histology (OR=6.52; 95% CI: 2.03-20.92; p=0.002, and OR=1.99; 95% CI: 0.91-4.34; p=0.083, respectively) even when adjusted for age, gender, and smoking status. The risk for regional metastases development was associated with poor tumor differentiation (OR=2.91; 95% CI: 1.21-7.02; p=0.017). CONCLUSION: EGFR mutations were not associated with the frequency of regional or distant metastases in SR patients with NSCLC.

Machine Learning Meets Cancer.

The role of machine learning (a part of artificial intelligence-AI) in the diagnosis and treatment of various types of oncology is steadily increasing. It is expected that the use of AI in oncology will speed up both diagnostic and treatment planning processes. This review describes recent applications of machine learning in oncology, including medical image analysis, treatment planning, patient survival prognosis, and the synthesis of drugs at the point of care. The fast and reliable analysis of medical images is of great importance in the case of fast-flowing forms of cancer. The introduction of ML for the analysis of constantly growing volumes of big data makes it possible to improve the quality of prescribed treatment and patient care. Thus, ML is expected to become an essential technology for medical specialists. The ML model has already improved prognostic prediction for patients compared to traditional staging algorithms. The direct synthesis of the necessary medical substances (small molecule mixtures) at the point of care could also seriously benefit from the application of ML. We further review the main trends in the use of artificial intelligence-based technologies in modern oncology. This review demonstrates the future prospects of using ML tools to make progress in cancer research, as well as in other areas of medicine. Despite growing interest in the use of modern computer technologies in medical practice, a number of unresolved ethical and legal problems remain. In this review, we also discuss the most relevant issues among them.

Synthesis, Characterization and Biodistribution of GdF3:Tb3+@RB Nanocomposites.

Herein we report the development of a nanocomposite for X-ray-induced photodynamic therapy (X-PDT) and computed tomography (CT) based on PEG-capped GdF3:Tb3+ scintillating nanoparticles conjugated with Rose Bengal photosensitizer via electrostatic interactions. Scintillating GdF3:Tb3+ nanoparticles were synthesized by a facile and cost-effective wet chemical precipitation method. All synthesized nanoparticles had an elongated "spindle-like" clustered morphology with an orthorhombic structure. The structure, particle size, and morphology were determined by transmission electron microscopy (TEM), X-ray diffraction (XRD), and dynamic light scattering (DLS) analysis. The presence of a polyethylene glycol (PEG) coating and Rose Bengal conjugates was proved by Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TG), and ultraviolet-visible (UV-vis) analysis. Upon X-ray irradiation of the colloidal PEG-capped GdF3:Tb3+-Rose Bengal nanocomposite solution, an efficient fluorescent resonant energy transfer between scintillating nanoparticles and Rose Bengal was detected. The biodistribution of the synthesized nanoparticles in mice after intravenous administration was studied by in vivo CT imaging.

Musashi-2 (MSI2) regulates epidermal growth factor receptor (EGFR) expression and response to EGFR inhibitors in EGFR-mutated non-small cell lung cancer (NSCLC).

Non-small cell lung cancer (NSCLC) has limited treatment options. Expression of the RNA-binding protein (RBP) Musashi-2 (MSI2) is elevated in a subset of non-small cell lung cancer (NSCLC) tumors upon progression, and drives NSCLC metastasis. We evaluated the mechanism of MSI2 action in NSCLC to gain therapeutically useful insights. Reverse phase protein array (RPPA) analysis of MSI2-depleted versus control KrasLA1/+; Trp53R172HΔG/+ NSCLC cell lines identified EGFR as a MSI2-regulated protein. MSI2 control of EGFR expression and activity in an NSCLC cell line panel was studied using RT-PCR, Western blots, and RNA immunoprecipitation. Functional consequences of MSI2 depletion were explored for cell growth and response to EGFR-targeting drugs, in vitro and in vivo. Expression relationships were validated using human tissue microarrays. MSI2 depletion significantly reduced EGFR protein expression, phosphorylation, or both. Comparison of protein and mRNA expression indicated a post-transcriptional activity of MSI2 in control of steady state levels of EGFR. RNA immunoprecipitation analysis demonstrated that MSI2 directly binds to EGFR mRNA, and sequence analysis predicted MSI2 binding sites in the murine and human EGFR mRNAs. MSI2 depletion selectively impaired cell proliferation in NSCLC cell lines with activating mutations of EGFR (EGFRmut). Further, depletion of MSI2 in combination with EGFR inhibitors such as erlotinib, afatinib, and osimertinib selectively reduced the growth of EGFRmut NSCLC cells and xenografts. EGFR and MSI2 were significantly co-expressed in EGFRmut human NSCLCs. These results define MSI2 as a direct regulator of EGFR protein expression, and suggest inhibition of MSI2 could be of clinical value in EGFRmut NSCLC.

A Proteomics Analysis Reveals 9 Up-Regulated Proteins Associated with Altered Cell Signaling in Colon Cancer Patients.

Colorectal cancer is the second most common cancer in women and third most common cancer in men. Cell signaling alterations in colon cancer, especially in aggressive metastatic tumors, require further investigations. The present study aims to compare the expression pattern of proteins associated with cell signaling in paired tumor and non-tumor samples of patients with colon cancer, as well as to define the cluster of proteins to differentiate patients with non-metastatic (Dukes' grade B) and metastatic (Dukes' grade C&D) colon cancer. Frozen tumor and non-tumor samples were collected after tumor resection from 19 patients with colon cancer. The Panorama™ Antibody Microarray-Cell Signaling kits were used for the analyses. The expression ratios of paired tumor/non-tumor samples were calculated for the each protein. We employed R packages 'samr', 'gplots', 'supclust' (pelora, wilma algorithms), 'glmnet' for the differential expression analysis, supervised clustering and penalized logistic regression. Significance analysis of microarrays revealed 9 significantly up-regulated proteins, including protein kinase C gamma, c-Myc, MDM2, pan cytokeratin, and 1 significantly down-regulated protein (GAP1) in tumoral mucosa. Pan-cytokeratin and APP were up-regulated in tumor versus non-tumor tissue, and were selected in the predictive cluster to discriminate colon cancer type. Higher levels of S-100b and phospho-Tau-pSer199/202 were confirmed as the predictors of non-metastatic colon cancer by all employed regression/clustering methods. Deregulated proteins in colon cancer are involved in oncogenic signal transduction, cell cycle control, and regulation of cytoskeleton/transport. Further studies are needed to validate potential protein markers of colon cancer development and metastatic progression.