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Microbiome studies show altered microbiota in head and neck squamous cell carcinoma (HNSCC), both in terms of taxonomic composition and metabolic capacity. These studies utilized a traditional bioinformatics methodology, which allows for accurate taxonomic assignment down to the genus level, but cannot accurately resolve species level membership. We applied Resphera Insight, a high-resolution methodology for 16S rRNA taxonomic assignment that is able to provide species-level context in its assignments of 16S rRNA next generation sequencing (NGS) data. Resphera Insight applied to saliva samples from HNSCC patients and healthy controls led to the discovery that a subset of HNSCC saliva samples is significantly enriched with commensal species from the vaginal flora, including Lactobacillus gasseri/johnsonii (710x higher in saliva) and Lactobacillus vaginalis (52x higher in saliva). These species were not observed in normal saliva from Johns Hopkins patients, nor in 16S rRNA NGS saliva samples from the Human Microbiome Project (HMP). Interestingly, both species were only observed in saliva from Human Papilloma Virus (HPV) positive and HPV negative oropharyngeal cancer patients. We confirmed the representation of both species in HMP data obtained from mid-vagina (n=128) and vaginal introitus (n=121) samples. Resphera Insight also led to the discovery that Fusobacterium nucleatum, an oral cavity flora commensal bacterium linked to colon cancer, is enriched (600x higher) in saliva from a subset of HNSCC patients with advanced tumors stages. Together, these high-resolution analyses on 583 samples suggest a possible role for bacterial species in the therapeutic outcome of HPV positive and HPV negative HNSCC patients.

The majority of the epigenomic reports in hepatocellular carcinoma have focused on identifying novel differentially methylated drivers or passengers of the oncogenic process. Few reports have considered the technologies in place for clinical translation of newly identified biomarkers. The aim of this study was to identify epigenomic technologies that need only a small number of samples to discriminate HCC from non-HCC tissue, a basic requirement for biomarker development trials. To assess that potential, we used quantitative Methylation Specific PCR, oligonucleotide tiling arrays, and Methylation BeadChip assays. Concurrent global DNA hypomethylation, gene-specific hypermethylation, and chromatin alterations were observed as a hallmark of HCC. A global loss of promoter methylation was observed in HCC with the Illumina BeadChip assays and the Nimblegen oligonucleotide arrays. HCC samples had lower median methylation peak scores and a reduced number of significant promoter-wide methylated probes. Promoter hypermethylation of RASSF1A, SSBP2, and B4GALT1 quantified by qMSP had a sensitivity ranging from 38% to 52%, a specificity of 100%, and an AUC from 0.58 to 0.75. A panel combining these genes with HCC risk factors had a sensitivity of 87%, a specificity of 100%, and an AUC of 0.91.

MicroRNAs (miRNAs) are defined as small non-coding RNAs ~22 nt in length. They regulate gene expression at a post-transcriptional level through complementary base pairing with the target mRNA, leading to mRNA degradation and therefore blocking translation. In the last decade, the dysfunction of miRNAs has been related to the development and progression of many diseases. Currently, researchers need a method to identify precisely the miRNA targets, prior to applying experimental approaches that allow a better functional characterization of miRNAs in biological processes and can thus predict their effects. Computational prediction tools provide a rapid method to identify putative miRNA targets. However, since a large number of tools for the prediction of miRNA:mRNA interactions have been developed, all with different algorithms, the biological researcher sometimes does not know which is the best choice for his study and many times does not understand the bioinformatic basis of these tools. This review describes the biological fundamentals of these prediction tools, characterizes the main sequence-based algorithms, and offers some insights into their uses by biologists.

Molecular alterations that contribute to long-term (LT) and short-term (ST) survival in ovarian high-grade serous carcinoma (HGSC) may be used as precision medicine biomarkers. DNA promoter methylation is an early event in tumorigenesis, which can be detected in blood and urine, making it a feasible companion biomarker to somatic mutations for early detection and targeted treatment workflows. We compared the methylation profile in 12 HGSC tissue samples to 30 fallopian tube epithelium samples, using the Infinium Human Methylation 450K Array. We also used 450K methylation arrays to compare methylation among HGSCs long-term survivors (more than 5 years) and short-term survivors (less than 3 years). We verified the array results using bisulfite sequencing and methylation-specific PCR (qMSP). in another cohort of HGSC patient samples ( n = 35). Immunoblot and clonogenic assays after pharmacologic unmasking show that HIST1H2BB and MAGI2 promoter methylation downregulates mRNA expression levels in ovarian cancer cells. We then used qMSP in paired tissue, ascites, plasma/serum, vaginal swabs, and urine from a third cohort of patients with HGSC cancer ( n = 85) to test the clinical potential of HIST1H2BB and MAGI2 in precision medicine workflows. We also performed next-generation exome sequencing of 50 frequently mutated in human cancer genes, using the Ion AmpliSeqCancer Hotspot Panel, to show that the somatic mutation profile found in tissue and plasma can be quantified in paired urine samples from patients with HGSC. Our results suggest that HIST1H2BB and MAGI2 have growth-suppressing roles and can be used as HGSC precision medicine biomarkers., (©2020 American Association for Cancer Research.)

Clinically useful molecular tools to triage gastric cancer patients are not currently available. We aimed to develop a molecular tool to predict gastric cancer risk in endoscopy-driven biopsies obtained from high-risk gastric cancer clinics in low resource settings.We discovered and validated a DNA methylation biomarker panel in endoscopic samples obtained from 362 patients seen between 2004 and 2009 in three high-risk gastric cancer clinics in Lima, Perú, and validated it in 306 samples from the Cancer Genome Atlas project ("TCGA"). Global, epigenome wide and gene-specific DNA methylation analyses were used in a Phase I Biomarker Development Trial to identify a continuous biomarker panel that combines a Global DNA Methylation Index (GDMI) and promoter DNA methylation levels of IRF4, ELMO1, CLIP4 and MSC.We observed an inverse association between the GDMI and histological progression to gastric cancer, when comparing gastritis patients without metaplasia (mean = 5.74, 95% CI, 4.97-6.50), gastritis patients with metaplasia (mean = 4.81, 95% CI, 3.77-5.84), and gastric cancer cases (mean = 3.38, 95% CI, 2.82-3.94), respectively (p < 0.0001). Promoter methylation of IRF4 (p < 0.0001), ELMO1 (p < 0.0001), CLIP4 (p < 0.0001), and MSC (p < 0.0001), is also associated with increasing severity from gastritis with no metaplasia to gastritis with metaplasia and gastric cancer.Our findings suggest that IRF4, ELMO1, CLIP4 and MSC promoter methylation coupled with a GDMI>4 are useful molecular tools for gastric cancer risk stratification in endoscopic biopsies.

MicroRNAs (miRNAs) are defined as small non-coding RNAs ~22 nt in length. They regulate gene expression at a post-transcriptional level through complementary base pairing with the target mRNA, leading to mRNA degradation and therefore blocking translation. In the last decade, the dysfunction of miRNAs has been related to the development and progression of many diseases. Currently, researchers need a method to identify precisely the miRNA targets, prior to applying experimental approaches that allow a better functional characterization of miRNAs in biological processes and can thus predict their effects. Computational prediction tools provide a rapid method to identify putative miRNA targets. However, since a large number of tools for the prediction of miRNA:mRNA interactions have been developed, all with different algorithms, the biological researcher sometimes does not know which is the best choice for his study and many times does not understand the bioinformatic basis of these tools. This review describes the biological fundamentals of these prediction tools, characterizes the main sequence-based algorithms, and offers some insights into their uses by biologists., Competing Interests: The authors declare no conflict of interest.

Aim: The aim of the study was to evaluate the use of global and gene-specific DNA methylation changes as potential biomarkers for gallbladder cancer (GBC) in a cohort from Chile., Material & Methods: DNA methylation was analyzed through an ELISA-based technique and quantitative methylation-specific PCR., Results: Global DNA Methylation Index (p = 0.02) and promoter methylation of SSBP2 (p = 0.01) and ESR1 (p = 0.05) were significantly different in GBC when compared with cholecystitis. Receiver curve operator analysis revealed promoter methylation of APC, CDKN2A, ESR1, PGP9.5 and SSBP2, together with the Global DNA Methylation Index, had 71% sensitivity, 95% specificity, a 0.97 area under the curve and a positive predictive value of 90%., Conclusion: Global and gene-specific DNA methylation may be useful biomarkers for GBC clinical assessment.

Methylation alterations of CpG islands, CpG island shores and first exons are key events in the formation and progression of human cancer, and an increasing number of differentially methylated regions and genes have been identified in breast cancer. Recent studies of the breast cancer methylome using deep sequencing and microarray platforms are providing a novel insight on the different roles aberrant methylation plays in molecular subtypes of breast cancer. Accumulating evidence from a subset of studies suggests that promoter methylation of tumor-suppressor genes associated with breast cancer can be quantified in circulating DNA. However, there is a paucity of studies that examine the combined presence of genetic and epigenetic alterations associated with breast cancer using blood-based assays. Dysregulation of DNA repair capacity (DRC) is a genetic risk factor for breast cancer that has been measured in lymphocytes. We isolated plasma DNA from 340 participants in a breast cancer case control project to study promoter methylation levels of five genes previously shown to be associated with breast cancer in frozen tissue and in cell line DNA: MAL, KIF1A, FKBP4, VGF and OGDHL. Methylation of at least one gene was found in 49% of the cases compared to 20% of the controls. Three of the four genes had receiver characteristic operator curve values of ≥ 0.50: MAL (0.64), KIF1A (0.51) and OGDHL (0.53). KIF1A promoter methylation was associated with breast cancer and inversely associated with DRC. This is the first evidence of a significant association between genetic and epigenetic alterations in breast cancer using blood-based tests. The potential diagnostic utility of these biomarkers and their relevance for breast cancer risk prediction should be examined in larger cohorts.

The majority of the epigenomic reports in hepatocellular carcinoma have focused on identifying novel differentially methylated drivers or passengers of the oncogenic process. Few reports have considered the technologies in place for clinical translation of newly identified biomarkers. The aim of this study was to identify epigenomic technologies that need only a small number of samples to discriminate HCC from non-HCC tissue, a basic requirement for biomarker development trials. To assess that potential, we used quantitative Methylation Specific PCR, oligonucleotide tiling arrays, and Methylation BeadChip assays. Concurrent global DNA hypomethylation, gene-specific hypermethylation, and chromatin alterations were observed as a hallmark of HCC. A global loss of promoter methylation was observed in HCC with the Illumina BeadChip assays and the Nimblegen oligonucleotide arrays. HCC samples had lower median methylation peak scores and a reduced number of significant promoter-wide methylated probes. Promoter hypermethylation of RASSF1A, SSBP2, and B4GALT1 quantified by qMSP had a sensitivity ranging from 38% to 52%, a specificity of 100%, and an AUC from 0.58 to 0.75. A panel combining these genes with HCC risk factors had a sensitivity of 87%, a specificity of 100%, and an AUC of 0.91.