Search Results (8)

Exposure to polycyclic aromatic hydrocarbons (PAHs), byproducts of incomplete combustion, and their effects on the development of cancer are still being evaluated. Recent studies have analyzed the relationship between PAHs and tobacco or dietary intake in the form of processed foods and smoked/well-done meats. This study aims to assess the association of a blood biomarker and metabolite of PAHs, r-1,t-2,3,c-4-tetrahydroxy-1,2,3,4-tetrahydrophenanthrene (PheT), dietary intake, selected metabolism SNPs, and pancreatic cancer. Demographics, food-frequency data, SNPs, treatment history, and levels of PheT in plasma were determined from 400 participants (202 cases and 198 controls) and evaluated based on pancreatic adenocarcinoma diagnosis. Demographic and dietary variables were selected based on previously published literature indicating association with pancreatic cancer. A multiple regression model combined the significant demographic and food items with SNPs. Final multivariate logistic regression significant factors (p-value < 0.05) associated with pancreatic cancer included: Type 2 Diabetes [OR = 6.26 (95% CI = 2.83, 14.46)], PheT [1.03 (1.02, 1.05)], very well-done red meat [0.90 (0.83, 0.96)], fruit/vegetable servings [1.35 (1.06, 1.73)], recessive (rs12203582) [4.11 (1.77, 9.91)], recessive (rs56679) [0.2 (0.06, 0.85)], overdominant (rs3784605) [3.14 (1.69, 6.01)], and overdominant (rs721430) [0.39 (0.19, 0.76)]. Of note, by design, the level of smoking did not differ between our cases and controls. This study does not provide strong evidence that PheT is a biomarker of pancreatic cancer susceptibility independent of dietary intake and select metabolism SNPs among a nonsmoking population. Full article

DNA methylation is a process that can affect gene accessibility and therefore gene expression. In this study, a machine learning pipeline is proposed for the prediction of breast cancer and the identification of significant genes that contribute to the prediction. The current study utilized breast cancer methylation data from The Cancer Genome Atlas (TCGA), specifically the TCGA-BRCA dataset. Feature engineering techniques have been utilized to reduce data volume and make deep learning scalable. A comparative analysis of the proposed approach on Illumina 27K and 450K methylation data reveals that deep learning methodologies for cancer prediction can be coupled with feature selection models to enhance prediction accuracy. Prediction using 450K methylation markers can be accomplished in less than 13 s with an accuracy of 98.75%. Of the list of 685 genes in the feature selected 27K dataset, 578 were mapped to Ensemble Gene IDs. This reduced set was significantly (FDR < 0.05) enriched in five biological processes and one molecular function. Of the list of 1572 genes in the feature selected 450K data set, 1290 were mapped to Ensemble Gene IDs. This reduced set was significantly (FDR < 0.05) enriched in 95 biological processes and 17 molecular functions. Seven oncogene/tumor suppressor genes were common between the 27K and 450K feature selected gene sets. These genes were RTN4IP1, MYO18B, ANP32A, BRF1, SETBP1, NTRK1, and IGF2R. Our bioinformatics deep learning workflow, incorporating imputation and data balancing methods, is able to identify important methylation markers related to functionally important genes in breast cancer with high accuracy compared to deep learning or statistical models alone. Full article

Approximately 25% of all uveal melanoma (UM) contain driver mutations in the gene encoding the spliceosome factor SF3B1, and whilst patients with such SF3B1 mutations generally have an intermediate risk on developing metastatic disease, a third of these patients develop early metastasis within 5 years after diagnosis. We therefore investigated whether clinical and/or genetic variables could be indicative of short progression-free survival (PFS < 60 months) or long PFS (PFS ≥ 60 months) for SF3B1-mutated (SF3B1^mut) UM patients. We collected 146 SF3B1^mut UM from our Rotterdam Ocular Melanoma Studygroup (ROMS) database and external published datasets. After stratification of all SF3B1^mut UM using short PFS vs. long PFS, only largest tumor diameter (LTD) was significantly larger (mean: 17.7 mm (±2.8 SD) in the short PFS SF3B1^mut group vs. the long PFS group (mean: 14.7 (±3.7 SD, p = 0.001). Combined ROMS and The Cancer Genome Atlas (TCGA) transcriptomic data were evaluated, and we identified SF3B1^mut-specific canonical transcripts (e.g., a low expression of ABHD6 indicative for early-onset metastatic disease) or distinct expression of SF3B1^mut UM aberrant transcripts, indicative of early- or late-onset or no metastatic SF3B1^mut UM. Full article

Illumina and nanopore sequencing technologies are powerful tools that can be used to determine the bacterial composition of complex microbial communities. In this study, we compared nasal microbiota results at genus level using both Illumina and nanopore 16S rRNA gene sequencing. We also monitored the progression of nanopore sequencing in the accurate identification of species, using pure, single species cultures, and evaluated the performance of the nanopore EPI2ME 16S data analysis pipeline. Fifty-nine nasal swabs were sequenced using Illumina MiSeq and Oxford Nanopore 16S rRNA gene sequencing technologies. In addition, five pure cultures of relevant bacterial species were sequenced with the nanopore sequencing technology. The Illumina MiSeq sequence data were processed using bioinformatics modules present in the Mothur software package. Albacore and Guppy base calling, a workflow in nanopore EPI2ME (Oxford Nanopore Technologies—ONT, Oxford, UK) and an in-house developed bioinformatics script were used to analyze the nanopore data. At genus level, similar bacterial diversity profiles were found, and five main and established genera were identified by both platforms. However, probably due to mismatching of the nanopore sequence primers, the nanopore sequencing platform identified Corynebacterium in much lower abundance compared to Illumina sequencing. Further, when using default settings in the EPI2ME workflow, almost all sequence reads that seem to belong to the bacterial genus Dolosigranulum and a considerable part to the genus Haemophilus were only identified at family level. Nanopore sequencing of single species cultures demonstrated at least 88% accurate identification of the species at genus and species level for 4/5 strains tested, including improvements in accurate sequence read identification when the basecaller Guppy and Albacore, and when flowcell versions R9.4 (Oxford Nanopore Technologies—ONT, Oxford, UK) and R9.2 (Oxford Nanopore Technologies—ONT, Oxford, UK) were compared. In conclusion, the current study shows that the nanopore sequencing platform is comparable with the Illumina platform in detection bacterial genera of the nasal microbiota, but the nanopore platform does have problems in detecting bacteria within the genus Corynebacterium. Although advances are being made, thorough validation of the nanopore platform is still recommendable. Full article

Triple-negative breast cancer (TNBC), characterized by the absence or low expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor (HER2), is the most aggressive subtype of breast cancer. TNBC accounts for about 15% of breast cancer cases in the U.S., and is known for high relapse rates and poor overall survival (OS). Chemo-resistant TNBC is a genetically diverse, highly heterogeneous, and rapidly evolving disease that challenges our ability to individualize treatment for incomplete responders and relapsed patients. Currently, the frontline standard chemotherapy, composed of anthracyclines, alkylating agents, and taxanes, is commonly used to treat high-risk and locally advanced TNBC. Several FDA-approved drugs that target programmed cell death protein-1 (Keytruda) and programmed death ligand-1 (Tecentriq), poly ADP-ribose polymerase (PARP), and/or antibody drug conjugates (Trodelvy) have shown promise in improving clinical outcomes for a subset of TNBC. These inhibitors that target key genetic mutations and specific molecular signaling pathways that drive malignant tumor growth have been used as single agents and/or in combination with standard chemotherapy regimens. Here, we review the current TNBC treatment options, unmet clinical needs, and actionable drug targets, including epidermal growth factor (EGFR), vascular endothelial growth factor (VEGF), androgen receptor (AR), estrogen receptor beta (ERβ), phosphoinositide-3 kinase (PI3K), mammalian target of rapamycin (mTOR), and protein kinase B (PKB or AKT) activation in TNBC. Supported by strong evidence in developmental, evolutionary, and cancer biology, we propose that the K-RAS/SIAH pathway activation is a major tumor driver, and SIAH is a new drug target, a therapy-responsive prognostic biomarker, and a major tumor vulnerability in TNBC. Since persistent K-RAS/SIAH/EGFR pathway activation endows TNBC tumor cells with chemo-resistance, aggressive dissemination, and early relapse, we hope to design an anti-SIAH-centered anti-K-RAS/EGFR targeted therapy as a novel therapeutic strategy to control and eradicate incurable TNBC in the future. Full article

Several challenges present themselves when discussing current approaches to the prevention or treatment of pancreatic cancer. Up to 45% of the risk of pancreatic cancer is attributed to unknown causes, making effective prevention programs difficult to design. The most common type of pancreatic cancer, pancreatic ductal adenocarcinoma (PDAC), is generally diagnosed at a late stage, leading to a poor prognosis and 5-year survival estimate. PDAC tumors are heterogeneous, leading to many identified cell subtypes within one patient’s primary tumor. This explains why there is a high frequency of tumors that are resistant to standard treatments, leading to high relapse rates. This review will discuss how epigenetic technologies and epigenome-wide association studies have been used to address some of these challenges and the future promises these approaches hold. Full article

Pancreatic cancer is one of the most fatal common cancers affecting both men and women, representing about 3% of all new cancer cases in the United States. In this study, we aimed to investigate the association of pancreatic cancer risk with alcohol consumption as well as folate intake. We performed a case-control study of 384 patients diagnosed with pancreatic cancer from May 2004 to December 2009 and 983 primary care healthy controls in a largely white population (>96%). Our findings showed no significant association between risk of pancreatic cancer and either overall alcohol consumption or type of alcohol consumed (drinks/day). Our study showed dietary folate intake had a modest effect size, but was significantly inversely associated with pancreatic cancer (odds ratio (OR) = 0.99, p < 0.0001). The current study supports the hypothesis that pancreatic cancer risk is reduced with higher food-based folate intake. Full article

Pancreatic cancer is the fourth most common cause of cancer-related deaths with a dismal average five-year survival rate of six percent. Substitutional progress has been made in understanding how pancreatic cancer develops and progresses. Evidence is mounting which demonstrates that diet and nutrition are key factors in carcinogenesis. In particular, diets low in folate and high in fruits, vegetables, red/processed meat, and saturated fat have been identified as pancreatic cancer risk factors with a proposed mechanism involving epigenetic modifications or gene regulation. We review the current literature assessing the correlation between diet, epigenetics, and pancreatic cancer. Full article