List of the tumor specific mutations identified by whole-genome sequencing and validated in the 10 pairs of MPM tumor and germline DNA samples

Whole genome sequencing data analysis of 10 MPM tumor and matched DNA generated by Complete Genomics platform

List of the Tumor types having mutations in both MYH9 and RHOA genes in the cBioPortal for Cancer Genomics database

Schematic representation of the SNVs and/or chromosomal aberrations identified in BAP1, NF2, TP53, MYH9, RHOA, MYH6, MYH10, PIK3C2A, TNFRSF1A, and 22q and 9p in a panel of 147 MPM tumors.

List of the 51 canonical pathways significantly enriched (p<0.05) for the mutated genes identify by Ingenuity Pathway Analysis

Expression levels of candidate genes in 151 patients by gender and histology

Expression levels for BAP1, NF2, TP53, MYH9, MYH6, MYH10, PIK3C2A, RHOA, and TNFRSF1A detected by Affymetrix® Human Gene 1.1 ST Array in 151 MPM

List of SNVs identified in the analyzed genes

BACKGROUND:MicroRNAs are small, noncoding RNAs that regulate the expression of posttranslational genes. The presence of some specific microRNAs has been associated with increased risk of both local recurrence and metastasis and worse survival in patients with osteosarcoma. Pathologic fractures in osteosarcoma are considered to be more the manifestation of a neoplasm with a more aggressive biological behavior than the cause itself of worse prognosis. However, this has not been proved at the biological or molecular level. Currently, there has not been a microRNA profiling study of patients who have osteosarcoma with and without pathologic fractures that has described differences in terms of microRNA profiling between these two groups and their correlation with biologic behavior.QUESTIONS/PURPOSES:(1) In patients with osteosarcoma of the extremities, how do the microRNA profiles of those with and without pathologic fractures compare? (2) What relationship do microRNAs have with local recurrence, risk of metastasis, disease-specific survival, and overall survival in osteosarcoma patients with pathologic fractures?METHODS:Between 1994 and 2013, 217 patients were diagnosed and treated at our institution for osteosarcoma of the extremities. Patients were excluded if (1) they underwent oncologic resection of the osteosarcoma at an outside institution (two patients) or (2) they were diagnosed with an extraskeletal osteosarcoma (29 patients) or (3) they had less than 1 year of clinical follow-up and no oncologic outcome (local recurrence, metastasis, or death) (four patients). A total of 182 patients were eligible. Of those, 143 were high-grade osteosarcomas. After evaluation of tumor samples before chemotherapy treatment, a total of 80 consecutive samples were selected for sequencing. Demographic and clinical comparison between the sequenced and non-sequenced patients did not demonstrate any differences, confirming that both groups were comparable. Diagnostic samples from the extremities of 80 patients with high-grade extremity osteosarcomas who had not yet received chemotherapy underwent microRNA sequencing for an ongoing large-scale osteosarcoma genome profiling project at our institution. Six samples were removed after a second look by a musculoskeletal pathologist who verified cellularity and quality of samples to be sequenced, leaving a total of 74 patients. Of these, two samples were removed as they were confirmed to be pelvic tumors in a second check after sequencing. The final study sample was 72 patients (11 patients with pathologic fractures and 61 without). Sequencing data were correlated with fractures and local recurrence, risk of metastasis, disease-specific survival, and overall survival through Kaplan-Meier analyses.RESULTS:Several microRNAs were expressed differently between the two groups. Among the markers with the highest differential expression (edgeR and DESeq algorithms), Hsa-mIR 656-3p, hsa-miR 493-5p, and hsa-miR 381-3p were upregulated in patients with pathologic fractures, whereas hsa-miR 363, hsa-miR 885-5p, and has-miR 20b-5p were downregulated. The highest differential expression fracture and nonfracture-associated microRNA markers also distinguished groups of patients with different metastasis risk, a well as different disease-specific and overall survival. Furthermore, the profile of pathologic fractures demonstrated a higher differential expression for microRNA markers that were previously associated with a higher risk of metastasis and lower survival rates in patients with osteosarcoma.CONCLUSIONS:In patients who have osteosarcoma, the microRNA profiles of those with pathologic fractures are different than of patients without pathologic fractures. The highest differential expression mircroRNA molecules in patients with pathologic fractures predict also higher risk of metastatic disease as well as worse disease-specific survival and overall survival. Furthermore, we found higher differential expression of microRNAs in the pathologic fracture group previously associated with poor prognosis. The higher risk of metastasis and poorer overall survival in patients with pathologic fractures is inherent to tumor aggressive biologic behavior. It is plausible that the fracture itself is not the direct cause of worse prognosis but another manifestation of tumor biologic aggressiveness. Identification of these molecules through liquid biopsies may help to determine which patients may benefit from surgery before fractures occur. The same technology can be applied to identify patterns of response to conventional chemotherapy, assisting in more specific and accurate systemic therapy.LEVEL OF EVIDENCE LEVEL:III, prognostic study.

This protocol describes a library preparation method for sequencing small RNA.The method uses degenerate adapters to alleviate the problem of bias in the ligation steps during small RNA library preparation, and it optimizes several other parameters to make it appropriate for use speci cally with plasma RNA.

RNA-seq is increasingly employed for quantitative profiling of small RNAs (e.g., microRNAs, piRNAs, snoRNAs) in diverse sample types including isolated cells, tissues and cell-free biofluids. The accuracy and reproducibility of the multiple small RNA-seq library preparation methods in use, however, have not been systematically assessed. We report systematic results obtained by a consortium of nine labs that independently sequenced reference, ‘ground truth’, samples of synthetic small RNAs and human plasma-derived RNA. Three commercially available library preparation methods employing adapters of defined sequence and six methods using adapters with degenerate bases were assessed. Both protocol- and sequence-specific biases were identified, including biases that reduce the ability of small RNA-seq to accurately measure adenosine-to-inosine editing in microRNAs. We report 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 found to be accurate and reproducible across laboratories and methods.

Nat. Biotechnol. 10.1038/nbt.4183; corrected online 31 July 2018 In the version of this article initially published online, the text "Beth Israel Deaconess Medical Center/Dana Farber Cancer Institute (BIDMC/DFCI)" was inserted into the last sentence in the right-hand column of p.10, beginning "It isworth noting.

Small RNA-seq is increasingly being used for profiling of small RNAs. Quantitative characteristics of long RNA-seq have been extensively described, but small RNA-seq involves fundamentally different methods for library preparation, with distinct protocols and technical variations that have not been fully and systematically studied. We report here the results of a study using common references (synthetic RNA pools of defined composition, as well as plasma-derived RNA) to evaluate the accuracy, reproducibility and bias of small RNA-seq library preparation for five distinct protocols and across nine different laboratories. We observed protocol-specific and sequence-specific bias, which was ameliorated using adapters for ligation with randomized end-nucleotides, and computational correction factors. Despite this technical bias, relative quantification using small RNA-seq was remarkably accurate and reproducible, even across multiple laboratories using different methods. These results provide strong evidence for the feasibility of reproducible cross-laboratory small RNA-seq studies, even those involving analysis of data generated using different protocols.

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.

Abstract Malignant pleural mesothelioma (MPM) is an aggressive cancer that occurs more frequently in men, but is associated with longer survival in women. Insight into the survival advantage of female patients may advance the molecular understanding of MPM and identify therapeutic interventions that will improve the prognosis for all MPM patients. In this study, we performed whole-genome sequencing of tumor specimens from 10 MPM patients and matched control samples to identify potential driver mutations underlying MPM. We identified molecular differences associated with gender and histology. Specifically, single-nucleotide variants of BAP1 were observed in 21% of cases, with lower mutation rates observed in sarcomatoid MPM (P < 0.001). Chromosome 22q loss was more frequently associated with the epithelioid than that nonepitheliod histology (P = 0.037), whereas CDKN2A deletions occurred more frequently in nonepithelioid subtypes among men (P = 0.021) and were correlated with shorter overall survival for the entire cohort (P = 0.002) and for men (P = 0.012). Furthermore, women were more likely to harbor TP53 mutations (P = 0.004). Novel mutations were found in genes associated with the integrin-linked kinase pathway, including MYH9 and RHOA. Moreover, expression levels of BAP1, MYH9, and RHOA were significantly higher in nonepithelioid tumors, and were associated with significant reduction in survival of the entire cohort and across gender subgroups. Collectively, our findings indicate that diverse mechanisms highly related to gender and histology appear to drive MPM. Cancer Res; 76(2); 319–28. ©2015 AACR.

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.

Quantitative validation of gene regulatory networks (GRNs) inferred from observational expression data is a difficult task usually involving time intensive and costly laboratory experiments. We were able to show that gene knock-down experiments can be used to quantitatively assess the quality of large-scale GRNs via a purely data-driven approach (Olsen et al. 2014). Our new validation framework also enables the statistical comparison of multiple network inference techniques, which was a long-standing challenge in the field. In this Data in Brief we detail the contents and quality controls for the gene expression data (available from NCBI Gene Expression Omnibus repository with accession number GSE53091) associated with our study published in Genomics (Olsen et al. 2014). We also provide R code to access the data and reproduce the analysis presented in this article.

Although many methods have been developed for inference of biological networks, the validation of the resulting models has largely remained an unsolved problem. Here we present a framework for quantitative assessment of inferred gene interaction networks using knock-down data from cell line experiments. Using this framework we are able to show that network inference based on integration of prior knowledge derived from the biomedical literature with genomic data significantly improves the quality of inferred networks relative to other approaches. Our results also suggest that cell line experiments can be used to quantitatively assess the quality of networks inferred from tumor samples.