Plasma miRNA Biomarkers in Limited Volume Samples for Detection of Early-stage Pancreatic Cancer.

Early detection of pancreatic ductal adenocarcinoma (PDAC) is key to improving patient outcomes; however, PDAC is usually diagnosed late. Therefore, blood-based minimally invasive biomarker assays for limited volume clinical samples are urgently needed. A novel miRNA profiling platform (Abcam Fireplex-Oncology Panel) was used to investigate the feasibility of developing early detection miRNA biomarkers with 20 µL plasma from a training set (58 stage II PDAC cases and 30 controls) and two validation sets (34 stage II PDAC cases and 25 controls; 44 stage II PDAC cases and 18 controls). miR-34a-5p [AUC = 0.77; 95% confidence interval (CI), 0.66-0.87], miR-130a-3p (AUC = 0.74; 95% CI, 0.63-0.84), and miR-222-3p (AUC = 0.70; 95% CI, 0.58-0.81) were identified as significantly differentially abundant in plasma from stage II PDAC versus controls. Although none of the miRNAs individually outperformed the currently used serologic biomarker for PDAC, carbohydrate antigen 19-9 (CA19-9), combining the miRNAs with CA 19-9 improved AUCs from 0.89 (95% CI, 0.81-0.95) for CA 19-9 alone to 0.92 (95% CI, 0.86-0.97), 0.94 (95% CI, 0.89-0.98), and 0.92 (95% CI, 0.87-0.97), respectively. Gene set enrichment analyses of transcripts correlated with high and low expression of the three miRNAs in The Cancer Genome Atlas PDAC sample set. These miRNA biomarkers, assayed in limited volume plasma together with CA19-9, discriminate stage II PDAC from controls with good sensitivity and specificity. Unbiased profiling of larger cohorts should help develop an informative early detection biomarker assay for diagnostic settings. PREVENTION RELEVANCE: Development of minimally invasive biomarker assays for detection of premalignant disease and early-stage pancreatic cancer is key to improving patient survival. This study describes a limited volume plasma miRNA biomarker assay that can detect early-stage resectable pancreatic cancer in clinical samples necessary for effective prevention and clinical intervention.

Secretory microRNAs as biomarkers of cancer.

MicroRNAs (miRNAs) are small, non-coding RNAs that regulate gene expression predominantly by inhibiting transcription and/or promoting degradation of target mRNAs also in addition to being involved in non-canonical mechanisms regulating transcription, translation and cell signaling processes. Extracellular secretory miRNAs, either in complex with specific proteins or encapsulated in microvesicles called exosomes, are transported between cells as means of intercellular communication. Secretory miRNAs in circulation remain functional after delivery to recipient cells, regulating target genes and their corresponding signaling pathways. Cancer cell secreted miRNA-mediated intercellular communication affects physiological processes associated with the disease, such as, angiogenesis, metabolic reprogramming, immune modulation, metastasis, and chemo-resistance. Given the stability of miRNAs in body fluids and their well-documented roles in deregulating cancer-relevant genetic pathways, there is considerable interest in developing secretory miRNAs as liquid biopsy biomarkers for detection, diagnosis and prognostication of cancer. In this review, we discuss salient features of miRNA biogenesis, secretion and function in cancer as well as the current state of secretory miRNA isolation and profiling methods. Furthermore, we discuss the challenges and opportunities of secretory miRNA biomarker assay development, which need to be addressed for clinical applications.

Cell-free DNA copy number variations in plasma from colorectal cancer patients.

To evaluate the clinical utility of cell-free DNA (cfDNA), we performed whole-genome sequencing to systematically examine plasma cfDNA copy number variations (CNVs) in a cohort of patients with colorectal cancer (CRC, n = 80), polyps (n = 20), and healthy controls (n = 35). We initially compared cfDNA yield in 20 paired serum-plasma samples and observed significantly higher cfDNA concentration in serum (median = 81.20 ng, range 7.18-500 ng·mL-1 ) than in plasma (median = 5.09 ng, range 3.76-62.8 ng·mL-1 ) (P < 0.0001). However, tumor-derived cfDNA content was significantly lower in serum than in matched plasma samples tested. With ~10 million reads per sample, the sequencing-based copy number analysis showed common CNVs in multiple chromosomal regions, including amplifications on 1q, 8q, and 5q and deletions on 1p, 4q, 8p, 17p, 18q, and 22q. Copy number changes were also evident in genes critical to the cell cycle, DNA repair, and WNT signaling pathways. To evaluate whether cumulative copy number changes were associated with tumor stages, we calculated plasma genomic abnormality in colon cancer (PGA-C) score by summing the most significant CNVs. The PGA-C score showed predictive performance with an area under the curve from 0.54 to 0.84 for CRC stages I-IV. Locus-specific copy number analysis identified nine genomic regions where CNVs were significantly associated with survival in stage III-IV CRC patients. A multivariate model using six of nine genomic regions demonstrated a significant association of high-risk score with shorter survival (HR = 5.33, 95% CI = 6.76-94.44, P < 0.0001). Our study demonstrates the importance of using plasma (rather than serum) to test tumor-related genomic variations. Plasma cfDNA-based tests can capture tumor-specific genetic changes and may provide a measurable classifier for assessing clinical outcomes in advanced CRC patients.

Chromatin interactions and candidate genes at ten prostate cancer risk loci.

Genome-wide association studies have identified more than 100 common single nucleotide polymorphisms (SNPs) that are associated with prostate cancer risk. However, the vast majority of these SNPs lie in noncoding regions of the genome. To test whether these risk SNPs regulate their target genes through long-range chromatin interactions, we applied capture-based 3C sequencing technology to investigate possible cis-interactions at ten prostate cancer risk loci in six cell lines. We identified significant physical interactions between risk regions and their potential target genes including CAPG at 2p11.2, C2orf43 at 2p24.1, RFX6 at 6q22.1, NFASC at 1q32.1, MYC at 8q24.1 and AGAP7P at 10q11.23. Most of the interaction peaks were co-localized to regions of active histone modification and transcription factor binding sites. Expression quantitative trait locus (eQTL) analysis showed suggestive eQTL signals at rs1446669, rs699664 and rs1078004 for CAPG (p < 0.004), rs13394027 for C2orf43 (p = 2.25E-27), rs10993994 and rs4631830 for AGAP7P (p < 8.02E-5). Further analysis revealed an enhancer activity at genomic region surrounding rs4631830 which was expected to disrupt HOXB-like DNA binding affinity. This study identifies a set of candidate genes and their potential regulatory variants, and provides additional evidence showing the role of long-range chromatin interactions in prostate cancer etiology.

Plasma genetic and genomic abnormalities predict treatment response and clinical outcome in advanced prostate cancer.

Liquid biopsies, examinations of tumor components in body fluids, have shown promise for predicting clinical outcomes. To evaluate tumor-associated genomic and genetic variations in plasma cell-free DNA (cfDNA) and their associations with treatment response and overall survival, we applied whole genome and targeted sequencing to examine the plasma cfDNAs derived from 20 patients with advanced prostate cancer. Sequencing-based genomic abnormality analysis revealed locus-specific gains or losses that were common in prostate cancer, such as 8q gains, AR amplifications, PTEN losses and TMPRSS2-ERG fusions. To estimate tumor burden in cfDNA, we developed a Plasma Genomic Abnormality (PGA) score by summing the most significant copy number variations. Cox regression analysis showed that PGA scores were significantly associated with overall survival (p < 0.04). After androgen deprivation therapy or chemotherapy, targeted sequencing showed significant mutational profile changes in genes involved in androgen biosynthesis, AR activation, DNA repair, and chemotherapy resistance. These changes may reflect the dynamic evolution of heterozygous tumor populations in response to these treatments. These results strongly support the feasibility of using non-invasive liquid biopsies as potential tools to study biological mechanisms underlying therapy-specific resistance and to predict disease progression in advanced prostate cancer.

Prostate cancer risk locus at 8q24 as a regulatory hub by physical interactions with multiple genomic loci across the genome.

Chromosome 8q24 locus contains regulatory variants that modulate genetic risk to various cancers including prostate cancer (PC). However, the biological mechanism underlying this regulation is not well understood. Here, we developed a chromosome conformation capture (3C)-based multi-target sequencing technology and systematically examined three PC risk regions at the 8q24 locus and their potential regulatory targets across human genome in six cell lines. We observed frequent physical contacts of this risk locus with multiple genomic regions, in particular, inter-chromosomal interaction with CD96 at 3q13 and intra-chromosomal interaction with MYC at 8q24. We identified at least five interaction hot spots within the predicted functional regulatory elements at the 8q24 risk locus. We also found intra-chromosomal interaction genes PVT1, FAM84B and GSDMC and inter-chromosomal interaction gene CXorf36 in most of the six cell lines. Other gene regions appeared to be cell line-specific, such as RRP12 in LNCaP, USP14 in DU-145 and SMIN3 in lymphoblastoid cell line. We further found that the 8q24 functional domains more likely interacted with genomic regions containing genes enriched in critical pathways such as Wnt signaling and promoter motifs such as E2F1 and TCF3. This result suggests that the risk locus may function as a regulatory hub by physical interactions with multiple genes important for prostate carcinogenesis. Further understanding genetic effect and biological mechanism of these chromatin interactions will shed light on the newly discovered regulatory role of the risk locus in PC etiology and progression.

eRNA: a graphic user interface-based tool optimized for large data analysis from high-throughput RNA sequencing.

BACKGROUND: RNA sequencing (RNA-seq) is emerging as a critical approach in biological research. However, its high-throughput advantage is significantly limited by the capacity of bioinformatics tools. The research community urgently needs user-friendly tools to efficiently analyze the complicated data generated by high throughput sequencers. RESULTS: We developed a standalone tool with graphic user interface (GUI)-based analytic modules, known as eRNA. The capacity of performing parallel processing and sample management facilitates large data analyses by maximizing hardware usage and freeing users from tediously handling sequencing data. The module miRNA identification" includes GUIs for raw data reading, adapter removal, sequence alignment, and read counting. The module "mRNA identification" includes GUIs for reference sequences, genome mapping, transcript assembling, and differential expression. The module "Target screening" provides expression profiling analyses and graphic visualization. The module "Self-testing" offers the directory setups, sample management, and a check for third-party package dependency. Integration of other GUIs including Bowtie, miRDeep2, and miRspring extend the program's functionality. CONCLUSIONS: eRNA focuses on the common tools required for the mapping and quantification analysis of miRNA-seq and mRNA-seq data. The software package provides an additional choice for scientists who require a user-friendly computing environment and high-throughput capacity for large data analysis. eRNA is available for free download at https://sourceforge.net/projects/erna/?source=directory.

Characterization of human plasma-derived exosomal RNAs by deep sequencing.

BACKGROUND: Exosomes, endosome-derived membrane microvesicles, contain specific RNA transcripts that are thought to be involved in cell-cell communication. These RNA transcripts have great potential as disease biomarkers. To characterize exosomal RNA profiles systemically, we performed RNA sequencing analysis using three human plasma samples and evaluated the efficacies of small RNA library preparation protocols from three manufacturers. In all we evaluated 14 libraries (7 replicates). RESULTS: From the 14 size-selected sequencing libraries, we obtained a total of 101.8 million raw single-end reads, an average of about 7.27 million reads per library. Sequence analysis showed that there was a diverse collection of the exosomal RNA species among which microRNAs (miRNAs) were the most abundant, making up over 42.32% of all raw reads and 76.20% of all mappable reads. At the current read depth, 593 miRNAs were detectable. The five most common miRNAs (miR-99a-5p, miR-128, miR-124-3p, miR-22-3p, and miR-99b-5p) collectively accounted for 48.99% of all mappable miRNA sequences. MiRNA target gene enrichment analysis suggested that the highly abundant miRNAs may play an important role in biological functions such as protein phosphorylation, RNA splicing, chromosomal abnormality, and angiogenesis. From the unknown RNA sequences, we predicted 185 potential miRNA candidates. Furthermore, we detected significant fractions of other RNA species including ribosomal RNA (9.16% of all mappable counts), long non-coding RNA (3.36%), piwi-interacting RNA (1.31%), transfer RNA (1.24%), small nuclear RNA (0.18%), and small nucleolar RNA (0.01%); fragments of coding sequence (1.36%), 5' untranslated region (0.21%), and 3' untranslated region (0.54%) were also present. In addition to the RNA composition of the libraries, we found that the three tested commercial kits generated a sufficient number of DNA fragments for sequencing but each had significant bias toward capturing specific RNAs. CONCLUSIONS: This study demonstrated that a wide variety of RNA species are embedded in the circulating vesicles. To our knowledge, this is the first report that applied deep sequencing to discover and characterize profiles of plasma-derived exosomal RNAs. Further characterization of these extracellular RNAs in diverse human populations will provide reference profiles and open new doors for the development of blood-based biomarkers for human diseases.