Factors related to tumor response rate from TCGA three omics data-variants, expression, methylation.

The Cancer Genome Atlas (TCGA) and its patient-derived multi-omics datasets have been the backbone of cancer research, and with novel approaches, it continues to shed new insight into the disease. In this study, we delved into a method of multi-omics integration of patient datasets and the association of biological pathways related to the disease. First, across thirty-three types of cancer present in TCGA, we merged genomic mutations and drug response datasets and filtered for the viable variant-drug response combinations available in TCGA, containing more than three samples for each drug response label with RNA sequencing (RNA-seq) and genomic methylation data available for each patient. We identified two distinct combinations in TCGA, one being pancreatic adenocarcinoma patients with/without rs121913529 variant in KRAS gene treated with gemcitabine, and the other low-grade glioma with/without rs121913500 variant in IDH1 gene administered with temozolomide. In these two groups, different patterns of gene expression were observed in the pathways often associated with cancer progression, such as mTOR and PDGF between the patients with complete response and progressive disease. Our result will provide yet another example of the relevance of these biological pathways in cancer drug response and a way for multi-omics integration in cancer datasets.

Comparing variants related to chronic diseases from genome-wide association study (GWAS) and the cancer genome atlas (TCGA).

BACKGROUND: Several genome-wide association studies (GWAS) have been performed to identify variants related to chronic diseases. Somatic variants in cancer tissues are associated with cancer development and prognosis. Expression quantitative trait loci (eQTL) and methylation QTL (mQTL) analyses were performed on chronic disease-related variants in TCGA dataset. METHODS: MuTect2 calling variants for 33 cancers from TCGA and 296 GWAS variants provided by LocusZoom were used. At least one mutation was found in TCGA 22 cancers and LocusZoom 23 studies. Differentially expressed genes (DEGs) and differentially methylated regions (DMRs) from the three cancers (TCGA-COAD, TCGA-STAD, and TCGA-UCEC). Variants were mapped to the world map using population locations of the 1000 Genomes Project (1GP) populations. Decision tree analysis was performed on the discovered features and survival analysis was performed according to the cluster. RESULTS: Based on the DEGs and DMRs with clinical data, the decision tree model classified seven and three nodes in TCGA-COAD and TCGA-STAD, respectively. A total of 11 variants were commonly detected from TCGA and LocusZoom, and eight variants were selected from the 1GP variants, and the distribution patterns were visualized on the world map. CONCLUSIONS: Variants related to tumors and chronic diseases were selected, and their geological regional 1GP-based proportions are presented. The variant distribution patterns could provide clues for regional clinical trial designs and personalized medicine.

Transposable Elements-Derived MicroRNA Expression Patterns in TCGA Dataset for 10 Species.

MicroRNAs (miRNAs) are a class of non-coding RNAs that act as regulators of disease. An evolutionary approach to the disease could reveal factors such as diagnosis, treatment, and prognosis prediction. The expression patterns of transposable element (TEs)-derived miRNAs could help elucidate diseases, and their evolutionary patterns are also valuable. The 34 miRNAs were compared in terms of stage survival and tumor status in 33 carcinomas from TCGA. Expression levels were compared using a t-test and presented as differentially expressed miRNAs (DEMs). For DEMs showing statistically specific expression patterns for 3 conditions (normal and cancer, early and advanced stage, and survival), interactions with related genes in 10 species, including humans, were compared. The enrichment term was discovered for the gene-miRNA interactions. In 18 out of the 33 carcinomas, at least one miRNA was retrieved with P < .05 and |fold change| >.05. A total of 128 DEMs for the 9 miRNAs were identified. Based on the TargetScan database, interactions between miRNAs and genes in 10 species, including humans, were confirmed. The evolutionarily conserved miR-130a was observed in all 10 species, whereas miR-151a was only observed in humans. GO terms of related genes were selected for the miRNAs commonly found in each species. Evolutionary analysis of TE-derived disease-associated miRNAs was performed, and the evolutionarily conserved miR-130a-related carcinomas included renal and thyroid cancers. Human and rhesus monkey-specific miR-625 is associated with various carcinomas.

New Drug Development and Clinical Trial Design by Applying Genomic Information Management.

Depending on the patients' genotype, the same drug may have different efficacies or side effects. With the cost of genomic analysis decreasing and reliability of analysis methods improving, vast amount of genomic information has been made available. Several studies in pharmacology have been based on genomic information to select the optimal drug, determine the dose, predict efficacy, and prevent side effects. This paper reviews the tissue specificity and genomic information of cancer. If the tissue specificity of cancer is low, cancer is induced in various organs based on a single gene mutation. Basket trials can be performed for carcinomas with low tissue specificity, confirming the efficacy of one drug for a single gene mutation in various carcinomas. Conversely, if the tissue specificity of cancer is high, cancer is induced in only one organ based on a single gene mutation. An umbrella trial can be performed for carcinomas with a high tissue specificity. Some drugs are effective for patients with a specific genotype. A companion diagnostic strategy that prescribes a specific drug for patients selected with a specific genotype is also reviewed. Genomic information is used in pharmacometrics to identify the relationship among pharmacokinetics, pharmacodynamics, and biomarkers of disease treatment effects. Utilizing genomic information, sophisticated clinical trials can be designed that will be better suited to the patients of specific genotypes. Genomic information also provides prospects for innovative drug development. Through proper genomic information management, factors relating to drug response and effects can be determined by selecting the appropriate data for analysis and by understanding the structure of the data. Selecting pre-processing and appropriate machine-learning libraries for use as machine-learning input features is also necessary. Professional curation of the output result is also required. Personalized medicine can be realized using a genome-based customized clinical trial design.

Effect of lipopolysaccharide and polyinosinic:polycytidylic acid in a murine model of nasal polyp.

Several factors, including bacterial and viral infections, have been associated with rhinosinusitis and nasal tissue remodelling that may result in nasal polyp formation. However, the potential role of bacterial or viral stimuli triggering polyp development is unclear. Here, we used lipopolysaccharide (LPS) and polyinosinic:polycytidylic acid [poly(I:C)] in a murine model of allergic rhinosinusitis to compare different effects of bacterial- and virus-derived stimuli in the pathogenesis of nasal polyp formation. Briefly, BALB/c mice were sensitised and challenged with ovalbumin and staphylococcal enterotoxin, with or without LPS or poly(I:C), and the consequent histopathological profiles, cytokines, and systemic humoral responses were studied. While no significant differences in polyp formations and epithelial disruptions were observed among the experimental groups, the local cell recruitment patterns slightly differed in animals that received either LPS or poly(I:C). Additionally, the local immune environments generated by LPS or poly(I:C) stimulation varied. LPS stimulation induced a marked Th1/Th17 response and predominantly neutrophilic nasal polyp formations, whereas poly(I:C) induced a Th2-skewed environment in neutrophilic nasal polyp development. Overall, our findings show that both cell recruitment patterns and local immune environments induced by these two stimuli differ, which may have implications in the physiopathology of rhinosinusitis with nasal polyp.