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The presence and relative stability of extracellular RNAs (exRNAs) in biofluids has led to an emerging recognition of their promise as 'liquid biopsies' for diseases. Most prior studies on discovery of exRNAs as disease-specific biomarkers have focused on microRNAs (miRNAs) using technologies such as qRT-PCR and microarrays. The recent application of next-generation sequencing to discovery of exRNA biomarkers has revealed the presence of potential novel miRNAs as well as other RNA species such as tRNAs, snoRNAs, piRNAs and lncRNAs in biofluids. At the same time, the use of RNA sequencing for biofluids poses unique challenges, including low amounts of input RNAs, the presence of exRNAs in different compartments with varying degrees of vulnerability to isolation techniques, and the high abundance of specific RNA species (thereby limiting the sensitivity of detection of less abundant species). Moreover, discovery in human diseases often relies on archival biospecimens of varying age and limiting amounts of samples. In this study, we have tested RNA isolation methods to optimize profiling exRNAs by RNA sequencing in individuals without any known diseases. Our findings are consistent with other recent studies that detect microRNAs and ribosomal RNAs as the major exRNA species in plasma. Similar to other recent studies, we found that the landscape of biofluid microRNA transcriptome is dominated by several abundant microRNAs that appear to comprise conserved extracellular miRNAs. There is reasonable correlation of sets of conserved miRNAs across biological replicates, and even across other data sets obtained at different investigative sites. Conversely, the detection of less abundant miRNAs is far more dependent on the exact methodology of RNA isolation and profiling. This study highlights the challenges in detecting and quantifying less abundant plasma miRNAs in health and disease using RNA sequencing platforms.

Ovarian cancer is the fifth leading cause of cancer death for women in the U.S. and the seventh most fatal worldwide. Although ovarian cancer is notable for its initial sensitivity to platinum-based therapies, the vast majority of patients eventually develop recurrent cancer and succumb to increasingly platinum-resistant disease. Modern, targeted cancer drugs intervene in cell signaling, and identifying key disease mechanisms and pathways would greatly advance our treatment abilities. In order to shed light on the molecular diversity of ovarian cancer, we performed comprehensive transcriptional profiling on 129 advanced stage, high grade serous ovarian cancers. We implemented a, re-sampling based version of the ISIS class discovery algorithm (rISIS: robust ISIS) and applied it to the entire set of ovarian cancer transcriptional profiles. rISIS identified a previously undescribed patient stratification, further supported by micro-RNA expression profiles, and gene set enrichment analysis found strong biological support for the stratification by extracellular matrix, cell adhesion, and angiogenesis genes. The corresponding \"angiogenesis signature\" was validated in ten published independent ovarian cancer gene expression datasets and is significantly associated with overall survival. The subtypes we have defined are of potential translational interest as they may be relevant for identifying patients who may benefit from the addition of anti-angiogenic therapies that are now being tested in clinical trials.

We have developed a gene expression assay (Whole-Genome DASL), capable of generating whole-genome gene expression profiles from degraded samples such as formalin-fixed, paraffin-embedded (FFPE) specimens. We demonstrated a similar level of sensitivity in gene detection between matched fresh-frozen (FF) and FFPE samples, with the number and overlap of probes detected in the FFPE samples being approximately 88% and 95% of that in the corresponding FF samples, respectively; 74% of the differentially expressed probes overlapped between the FF and FFPE pairs. The WG-DASL assay is also able to detect 1.3-1.5 and 1.5-2 -fold changes in intact and FFPE samples, respectively. The dynamic range for the assay is approximately 3 logs. Comparing the WG-DASL assay with an in vitro transcription-based labeling method yielded fold-change correlations of R(2) approximately 0.83, while fold-change comparisons with quantitative RT-PCR assays yielded R(2) approximately 0.86 and R(2) approximately 0.55 for intact and FFPE samples, respectively. Additionally, the WG-DASL assay yielded high self-correlations (R(2)>0.98) with low intact RNA inputs ranging from 1 ng to 100 ng; reproducible expression profiles were also obtained with 250 pg total RNA (R(2) approximately 0.92), with approximately 71% of the probes detected in 100 ng total RNA also detected at the 250 pg level. When FFPE samples were assayed, 1 ng total RNA yielded self-correlations of R(2) approximately 0.80, while still maintaining a correlation of R(2) approximately 0.75 with standard FFPE inputs (200 ng). Taken together, these results show that WG-DASL assay provides a reliable platform for genome-wide expression profiling in archived materials. It also possesses utility within clinical settings where only limited quantities of samples may be available (e.g. microdissected material) or when minimally invasive procedures are performed (e.g. biopsied specimens).

Historical data have indicated the potential for the histologically-normal breast to harbor pre-malignant changes at the molecular level. We postulated that a histologically-normal tissue with “tumor-like” gene expression pattern might harbor substantial risk for future cancer development. Genes associated with these high-risk tissues were considered to be “malignancy-risk genes”. From a total of 90 breast cancer patients, we collected a set of 143 histologically-normal breast tissues derived from patients harboring breast cancer who underwent curative mastectomy, as well as a set of 42 invasive ductal carcinomas (IDC) of various histologic grades. All samples were assessed for global gene expression differences using microarray analysis. For the purpose of this study we defined normal breast tissue to include histologically normal and benign lesions. Here we report the discovery of a “malignancy-risk” gene signature that may portend risk of breast cancer development in benign, but molecularly-abnormal, breast tissue. Pathway analysis showed that the malignancy-risk signature had a dramatic enrichment for genes with proliferative function, but appears to be independent of ER, PR, and HER2 status. The signature was validated by RT-PCR, with a high correlation (Pearson correlation = 0.95 with P < 0.0001) with microarray data. These results suggest a predictive role for the malignancy-risk signature in normal breast tissue. Proliferative biology dominates the earliest stages of tumor development.

EBV nuclear antigen 3C (EBNA3C) is an essential transcription factor for EBV transformed lymphoblast cell line (LCL) growth. To identify EBNA3C-regulated genes in LCLs, microarrays were used to measure RNA abundances in each of three different LCLs that conditionally express EBNA3C fused to a 4-OH-Tamoxifen-dependent estrogen receptor hormone binding domain (EBNA3CHT). At least three RNAs were assayed for each EBNA3CHT LCL under nonpermissive conditions, permissive conditions, and nonpermissive conditions with wild-type EBNA3C transcomplementation. Using a two-way ANOVA model of EBNA3C levels, we identified 550 regulated genes that were at least 1.5-fold up- or down-regulated with false discovery rates < 0.01. EBNA3C-regulated genes overlapped significantly with genes regulated by EBNA2 and EBNA3A consistent with coordinated effects on cell gene transcription. Of the 550 EBNA3C-regulated genes, 106 could be placed in protein networks. A seeded Bayesian network analysis of the 80 most significant EBNA3C-regulated genes suggests that RAC1, LYN, and TNF are upstream of other EBNA3C-regulated genes. Gene set enrichment analysis found enrichment for MAP kinase signaling, cytokine-cytokine receptor interactions, JAK-STAT signaling, and cell adhesion molecules, implicating these pathways in EBNA3C effects on LCL growth or survival. EBNA3C significantly up-regulated the CXCL12 ligand and its CXCR4 receptor and increased LCL migration. CXCL12 up-regulation depended on EBNA3C's interaction with the cell transcription factor, RBPJ, which is essential for LCL growth. EBNA3C also up-regulated MYC 1.3-fold and down-regulated CDKN2A exons 2 and 3, shared by p16 and p14, 1.4-fold, with false discovery rates < 5 x 10⁻⁴.

Extracellular RNAs (exRNAs) have been identified in all tested biofluids and have been associated with a variety of extracellular vesicles, ribonucleoprotein complexes and lipoprotein complexes. Much of the interest in exRNAs lies in the fact that they may serve as signalling molecules between cells, their potential to serve as biomarkers for prediction and diagnosis of disease and the possibility that exRNAs or the extracellular particles that carry them might be used for therapeutic purposes. Among the most significant bottlenecks to progress in this field is the lack of robust and standardized methods for collection and processing of biofluids, separation of different types of exRNA-containing particles and isolation and analysis of exRNAs. The Sample and Assay Standards Working Group of the Extracellular RNA Communication Consortium is a group of laboratories funded by the U.S. National Institutes of Health to develop such methods. In our first joint endeavour, we held a series of conference calls and in-person meetings to survey the methods used among our members, placed them in the context of the current literature and used our findings to identify areas in which the identification of robust methodologies would promote rapid advancements in the exRNA field.

GIPC1 is a cytoplasmic scaffold protein that interacts with numerous receptor signaling complexes, and emerging evidence suggests that it plays a role in tumorigenesis. GIPC1 is highly expressed in a number of human malignancies, including breast, ovarian, gastric, and pancreatic cancers. Suppression of GIPC1 in human pancreatic cancer cells inhibits in vivo tumor growth in immunodeficient mice. To better understand GIPC1 function, we suppressed its expression in human breast and colorectal cancer cell lines and human mammary epithelial cells (HMECs) and assayed both gene expression and cellular phenotype. Suppression of GIPC1 promotes apoptosis in MCF-7, MDA-MD231, SKBR-3, SW480, and SW620 cells and impairs anchorage-independent colony formation of HMECs. These observations indicate GIPC1 plays an essential role in oncogenic transformation, and its expression is necessary for the survival of human breast and colorectal cancer cells. Additionally, a GIPC1 knock-down gene signature was used to interrogate publically available breast and ovarian cancer microarray datasets. This GIPC1 signature statistically correlates with a number of breast and ovarian cancer phenotypes and clinical outcomes, including patient survival. Taken together, these data indicate that GIPC1 inhibition may represent a new target for therapeutic development for the treatment of human cancers.

Background MicroRNAs (miRNAs) are nucleic acid regulators of many human mRNAs, and are associated with many tumorigenic processes. miRNA expression levels have been used in profiling studies, but some evidence suggests that expression levels do not fully capture miRNA regulatory activity. In this study we integrate multiple gene expression datasets to determine miRNA activity patterns associated with cancer phenotypes and oncogenic pathways in mesenchymal tumors – a very heterogeneous class of malignancies. Results Using a computational method, we identified differentially activated miRNAs between 77 normal tissue specimens and 135 sarcomas and we validated many of these findings with microarray interrogation of an independent, paraffin-based cohort of 18 tumors. We also showed that miRNA activity is imperfectly correlated with miRNA expression levels. Using next-generation miRNA sequencing we identified potential base sequence alterations which may explain differential activity. We then analyzed miRNA activity changes related to the RAS-pathway and found 21 miRNAs that switch from silenced to activated status in parallel with RAS activation. Importantly, nearly half of these 21 miRNAs were predicted to regulate integral parts of the miRNA processing machinery, and our gene expression analysis revealed significant reductions of these transcripts in RAS-active tumors. These results suggest an association between RAS signaling and miRNA processing in which miRNAs may attenuate their own biogenesis. Conclusions Our study represents the first gene expression-based investigation of miRNA regulatory activity in human sarcomas, and our findings indicate that miRNA activity patterns derived from integrated transcriptomic data are reproducible and biologically informative in cancer. We identified an association between RAS signaling and miRNA processing, and demonstrated sequence alterations as plausible causes for differential miRNA activity. Finally, our study highlights the value of systems level integrative miRNA/mRNA assessment with high-throughput genomic data, and the applicability of paraffin-tissue-derived RNA for validation of novel findings.

Systematic evaluation of library preparation methods for small RNA-seq identifies reproducible and accurate methods. RNA-seq is increasingly used for quantitative profiling of small RNAs (for example, microRNAs, piRNAs and snoRNAs) in diverse sample types, including isolated cells, tissues and cell-free biofluids. The accuracy and reproducibility of the currently used small RNA-seq library preparation methods have not been systematically tested. Here we report results obtained by a consortium of nine labs that independently sequenced reference, 'ground truth' samples of synthetic small RNAs and human plasma-derived RNA. We assessed three commercially available library preparation methods that use adapters of defined sequence and six methods using adapters with degenerate bases. Both protocol- and sequence-specific biases were identified, including biases that reduced the ability of small RNA-seq to accurately measure adenosine-to-inosine editing in microRNAs. We found that these biases were mitigated by library preparation methods that incorporate adapters with degenerate bases. MicroRNA relative quantification between samples using small RNA-seq was accurate and reproducible across laboratories and methods.

Combining analysis of host proteome and transcriptome perturbations induced by tumour virus proteins with ongoing genome-wide studies of cancer facilitates the prioritization of cancer genes. Tumour virus targets identified This systematic search for host targets of tumour viruses, using proteome and transcriptome analyses of viral proteins from mammalian DNA viruses with transforming or tumorigenic properties, provides an extensive catalogue of changes in genetic and protein expression that can be screened against genome-wide studies of cancer. The study focuses on human papillomavirus, Epstein–Barr virus, adenovirus and polyomavirus. The resulting list of transforming viral protein targets identifies causal genes within both somatic and Mendelian cancer-associated loci. Genotypic differences greatly influence susceptibility and resistance to disease. Understanding genotype–phenotype relationships requires that phenotypes be viewed as manifestations of network properties, rather than simply as the result of individual genomic variations 1 . Genome sequencing efforts have identified numerous germline mutations, and large numbers of somatic genomic alterations, associated with a predisposition to cancer 2 . However, it remains difficult to distinguish background, or ‘passenger’, cancer mutations from causal, or ‘driver’, mutations in these data sets. Human viruses intrinsically depend on their host cell during the course of infection and can elicit pathological phenotypes similar to those arising from mutations 3 . Here we test the hypothesis that genomic variations and tumour viruses may cause cancer through related mechanisms, by systematically examining host interactome and transcriptome network perturbations caused by DNA tumour virus proteins. The resulting integrated viral perturbation data reflects rewiring of the host cell networks, and highlights pathways, such as Notch signalling and apoptosis, that go awry in cancer. We show that systematic analyses of host targets of viral proteins can identify cancer genes with a success rate on a par with their identification through functional genomics and large-scale cataloguing of tumour mutations. Together, these complementary approaches increase the specificity of cancer gene identification. Combining systems-level studies of pathogen-encoded gene products with genomic approaches will facilitate the prioritization of cancer-causing driver genes to advance the understanding of the genetic basis of human cancer.