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Background Oncopanel genomic testing, which identifies important somatic variants, is increasingly common in medical practice and especially in clinical trials. Currently, there is a paucity of reliable genomic reference samples having a suitably large number of pre-identified variants for properly assessing oncopanel assay analytical quality and performance. The FDA-led Sequencing and Quality Control Phase 2 (SEQC2) consortium analyze ten diverse cancer cell lines individually and their pool, termed Sample A, to develop a reference sample with suitably large numbers of coding positions with known (variant) positives and negatives for properly evaluating oncopanel analytical performance. Results In reference Sample A, we identify more than 40,000 variants down to 1% allele frequency with more than 25,000 variants having less than 20% allele frequency with 1653 variants in COSMIC-related genes. This is 5–100× more than existing commercially available samples. We also identify an unprecedented number of negative positions in coding regions, allowing statistical rigor in assessing limit-of-detection, sensitivity, and precision. Over 300 loci are randomly selected and independently verified via droplet digital PCR with 100% concordance. Agilent normal reference Sample B can be admixed with Sample A to create new samples with a similar number of known variants at much lower allele frequency than what exists in Sample A natively, including known variants having allele frequency of 0.02%, a range suitable for assessing liquid biopsy panels. Conclusion These new reference samples and their admixtures provide superior capability for performing oncopanel quality control, analytical accuracy, and validation for small to large oncopanels and liquid biopsy assays.

Clinical laboratories routinely use formalin-fixed paraffin-embedded (FFPE) tissue or cell block cytology samples in oncology panel sequencing to identify mutations that can predict patient response to targeted therapy. To understand the technical error due to FFPE processing, a robustly characterized normal cell line was used to create FFPE samples with four different pre-tissue processing formalin fixation times. A total of 96 FFPE sections were then distributed to different laboratories for targeted sequencing analysis by four oncopanels, and variants resulting from technical error were identified. Tissue sections that failed more frequently showed low cellularity, lower than recommended library preparation DNA input, or target sequencing depth. Importantly, sections from block surfaces were more likely to show FFPE-specific errors, akin to “edge effects” seen in histology, and the depth of formalin damage was related to fixation time. To assure reliable results, we recommend avoiding the block surface portion and restricting mutation detection to genomic regions of high confidence.

Background: In the last decade there have been significant advances in diagnosing and classifying adult acute myeloid leukemia (AML) based on genomic profiling, enabling risk-stratification and targeted therapies. In 2017 the US FDA approved the first gene mutation targeted therapies for AML with multiple additional targeted therapies since approved or in development. Given the typical acuity of AML at initial presentation however and the current turnaround time for next-generation sequencing (NGS) assays, most patients will start definitive initial therapy before all potentially targetable mutations are known. There is, therefore, a significant need for a fast molecular genotyping test to determine eligibility for personalized therapy in AML. The NCI Myeloid Assay (NMA) is a comprehensive targeted NGS assay on the Ion Torrent Genexus System, a fully automated platform that provides a rapid turnaround time from specimen receipt to clinical reporting. NMA utilizes Thermo Fisher Scientific's Oncomine Myeloid Assay GX and appears ideally suited for use in upcoming AML targeted therapy trials but has yet to be extensively tested in a cohort of AML patient diagnostic samples and compared to a standard targeted "myeloid panel" NGS assay platform (s-NGS). Methods: DNA samples (n=173) extracted from pretreatment bone marrow and/or peripheral blood of adult patients (n=112) diagnosed with de novo AML or high-risk myelodysplastic syndrome (MDS), were blindly tested in parallel using the NMA and s-NGS assays. For the NMA assay, 27.75ng of DNA was put into the Genexus System. All runs, controls, and samples were first analyzed for sequencing quality using established quality control (QC) metrics to assess pass/fail status. For all samples that passed QC metrics, variant results generated by the Ion Torrent Genexus pipeline were manually reviewed prior to being called true positive variants. For the s-NGS, using the ArcherDx Myeloid VariantPlex assay, a DNA input of 50ng was used for library preparation on a dual pre- and post-PCR separated automated liquid-handling workflow. Resulting libraries were sequencing on the Novaseq 6000 (Illumina) and the data analyzed using the Archer Analysis software and filtered as previously described (PMID: 34258102). Results from the two assays were compared for mutations with a variant allele fraction (VAF) >5% occurring in genes of interest in small molecule targeted clinical trials including: FLT3, IDH1, IDH2, JAK2, KIT, NPM1, NRAS, KRAS, and TP53. For FLT3-ITD comparison, the presence or absence of a call by the assay was used. Results: Utilizing a 5% VAF reporting threshold, a total of 171 and 174 variants were detected by NMA and s-NGS assays, respectively. A high rate of concordance was observed between the assays, with NMA detecting 96% of s-NGS variants and s-NGS detecting 95% of NMA variants. The VAF of detected single nucleotide variants was highly correlated (r=0.9848, P We investigated the discordant calls (n=15 total in 11 patients). One patient was correctly identified as having an NRAS p.Gly12 mutation by both approaches, but the resulting mutation was incorrectly annotated by the s-NGS pipeline. Samples from two patients (including one with both blood and marrow tested) were correctly identified as being FLT3 tyrosine kinase domain mutated by both sequencing approaches, although only the major of two missense variants identified by s-NGS was reported by the NMA pipeline. None of these patients, however, would be misclassified. The remaining 11 discordant calls were false negatives (including 6 variants detected by s-NGS but not by NMA). All of these "edge case" variants were detectable by lowering the VAF reporting threshold below 5% (Figure 1B). Conclusions: NMA is an automated sample-to-results workflow that can identify myeloid disorder-associated genomic variants in less than 48 hours from library preparation to clinical reporting. We show that NMA is highly concordant with a standard DNA NGS assay for detecting mutations within recurrently mutated AML genes. Accurate rapid genotyping is required for assignment to initial treatment with targeted therapy, and this technology may be a valuable tool for upcoming clinical trials for patients with myeloid malignancies. Figure 1 Figure 1. Disclosures Zhang: Thermo Fisher Scientific: Current Employment. Sedova: Thermo Fisher Scientific: Current Employment. Huang: Thermo Fisher Scientific: Current Employment. Mittal: Thermo Fisher Scientific: Current Employment. Hatch: Thermo Fisher Scientific: Current Employment. Ni: Thermo Fisher Scientific: Current Employment. Kaznadzey: Thermo Fisher Scientific: Current Employment. Sadis: Thermo Fisher Scientific: Current Employment. Smith: Thermo Fisher Scientific: Current Employment. Williams: Illumina: Other: CRADA. Hourigan: Sellas: Research Funding.

Molecular variants including single nucleotide variants (SNVs), copy number variants (CNVs) and fusions can be detected in the clinical setting using deep targeted sequencing. These assays support low limits of detection using little genomic input material. They are gaining in popularity in clinical laboratories, where sample volumes are limited, and low variant allele fractions may be present. However, data on reproducibility between laboratories is limited. Using a ring study, we evaluated the performance of 7 Ontario laboratories using targeted sequencing panels. All laboratories analysed a series of control and clinical samples for SNVs/CNVs and gene fusions. High concordance was observed across laboratories for measured CNVs and SNVs. Over 97% of SNV calls in clinical samples were detected by all laboratories. Whilst only a single CNV was detected in the clinical samples tested, all laboratories were able to reproducibly report both the variant and copy number. Concordance for information derived from RNA was lower than observed for DNA, due largely to decreased quality metrics associated with the RNA components of the assay, suggesting that the RNA portions of comprehensive NGS assays may be more vulnerable to variations in approach and workflow. Overall the results of this study support the use of the OFA for targeted sequencing for testing of clinical samples and suggest specific internal quality metrics that can be reliable indicators of assay failure. While we believe this evidence can be interpreted to support deep targeted sequencing in general, additional studies should be performed to confirm this.

Comprehensive genomic profiling (CGP) of tumor samples by next-generation sequencing is used to support clinical and translational research into the genetic variants that serve as biomarkers for diagnosis, prognosis and potential therapeutic response. However, as these assays grow in size to meet the expanding demands of users, it is challenging to maintain performance in the face of limited sample input necessitated by small sample volumes and to provide a simple and fast sample to report workflow with limited hands-on time. We, therefore, developed OCA Plus to meet user needs for a large CGP assay with excellent performance. Gene content was prioritized based on potential clinical relevance and variant prevalence in solid tumors. Over 500 genes were selected including genes in the indication statements of approved drug labels, clinical guidelines, and in the enrollment criteria of clinical trials. In addition, driver genes were selected in key pathways including DNA repair and immune checkpoint response. Amplicon design strategies were optimized accordingly for key hotspots, full coding sequences or copy number variation (CNV). The assay used Ion AmpliSeq™ technology with manual library preparation or automated templating on the Ion Chef™ System and sequencing on the Ion GeneStudio™ S5 platform. Twenty ng of purified DNA was routinely used as input. An automated tumor-only workflow for variant calling and sample quality reporting was provided within Ion Reporter™ Software. Streamlined access to reporting of variant relevance was enabled by Oncomine™ Reporter. In development studies of cancer cell line and formaldehyde-fixed, paraffin-embedded (FFPE) tumor samples, the assay displayed excellent uniformity (98% and 94%, respectively). Detection of single nucleotide variants and indels in cell lines and FFPE samples showed >95% sensitivity and PPV. Detection of CNV gain and loss in cell lines and FFPE samples showed >95% sensitivity and PPV. Assessment of tumor mutational burden (TMB) using publicly available whole-exome cancer sequencing data as well as test cell lines and FFPE samples showed high concordance with whole exome sequencing (R2 > 0.90). MSI sensitivity and specificity was >95% as tested using a diverse set of tumor samples. Targeted fusions were reported with 100% sensitivity and specificity when tested with commercially available controls. Total time from purified DNA to end of sequencing was < 2 days with < 3 hours of hands-on time and the time from post-sequencing to report generation was < 2 hours. Oncomine Comprehensive Assay (OCA Plus) was developed to support CGP and routine clinical research in oncology. The assay design and informatics workflow were optimized to support low input and rapid sample-to-report turn-around time, which will accelerate clinical and translational research. Citation Format: Vinay Mittal, Jennifer Kilzer, Dinesh Cyanam, Janice Au-Young, Santhoshi Bandla, Gary Bee, Sameh El-Difrawy, Aren Ewing, Rajesh Gottimukkala, Mohit Gupta, Nickolay Khazanov, Anelia Kraltcheva, Amir Marcovitz, Scott Myrand, Rose Putler, Yu-Ting Tseng, Warren Tom, Cristina Van Loy, James Veitch, Paul Williams, Elaine Wong-Ho, Huimin Xie, Chenchen Yang, Zheng Zang, Seth Sadis. Comprehensive genomic profiling of solid tumors for key targeted and immuno-oncology biomarkers using Ion Torrent NGS technology on the Oncomine Comprehensive Assay Plus (OCA Plus) [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 179.

Background: Tumor mutational burden (TMB) is an increasingly important biomarker for immune checkpoint inhibitors. Recent publications have described strong association between high TMB and objective response to mono- and combination immunotherapies in several cancer types. Existing methods to estimate TMB require large amount of input DNA, which may not always be available. Methods: In this study, we develop a method to estimate TMB using the Oncomine Tumor Mutation Load (TML) assay with 20 ng of DNA, and we characterize the performance of this method on various formalin-fixed, paraffin-embedded (FFPE) research samples of several cancer types. We measure the analytical performance of TML workflow through comparison with control samples with known truth, and we compare performance with an orthogonal method which uses matched normal sample to remove germline variants. We perform whole exome sequencing (WES) on a batch of FFPE samples and compare the WES TMB values with TMB estimates by the TML assay. Results: In-silico analyses demonstrated the Oncomine TML panel has sufficient genomic coverage to estimate somatic mutations with a strong correlation (r2=0.986) to WES. Further, in silico prediction using WES data from three separate cohorts and comparing with a subset of the WES overlapping with the TML panel, confirmed the ability to stratify responders and non-responders to immune checkpoint inhibitors with high statistical significance. We found the rate of somatic mutations with the TML assay on cell lines and control samples were similar to the known truth. We verified the performance of germline filtering using only a tumor sample in comparison to a matched tumor-normal experimental design to remove germline variants. We compared TMB estimates by the TML assay with that from WES on a batch of FFPE research samples and found high correlation (r2=0.83). We found biologically interesting tumorigenesis signatures on FFPE research samples of colorectal cancer (CRC), lung, and melanoma origin. Further, we assessed TMB on a cohort of FFPE research samples including lung, colon, and melanoma tumors to discover the biologically relevant range of TMB values. Conclusions: These results show that the TML assay targeting a 1.7-Mb genomic footprint can accurately predict TMB values that are comparable to the WES. The TML assay workflow incorporates a simple workflow using the Ion GeneStudio S5 System. Further, the AmpliSeq chemistry allows the use of low input DNA to estimate mutational burden from FFPE samples. This TMB assay enables scalable, robust research into immuno-oncology biomarkers with scarce samples.

Background Gene fusions caused by chromosomal rearrangements play an important role in oncogenesis, the progression of cancer and the selection of targeted therapies. Next-generation sequencing (NGS) using RNA enables sensitive, specific and precise detection of potentially clinically relevant gene fusions, especially when the fusion breakpoint is known. We have developed an NGS solution appropriate for FFPE tissues to detect fusion biomarkers in routine clinical research. Methods Fusion content was selected based on prioritization of actionability, verified fusion isoforms reported in the literature and collaborations, as well as prevalence of solid tumor fusion driver genes. The assay was designed to use Ion AmpliSeq multiplex PCR chemistry using manual or automated library preparation, automated templating on the Ion Chef, and sequencing on the Ion Torrent GeneStudioTM S5 sequencing platform. The analysis was supported by fully automated analysis software that performs sample QC, read-filtering, fusion calling and reporting. Streamlined access to decision support software was enabled by Oncomine™ Reporter. Results Over 1200 targeted isoforms from over 50 known fusion driver genes were incorporated into the assay that included prominent fusion drivers such as ALK, RET, ROS1, NTRK1/2/3 and FGFR1/2/3, and intragenic fusion events in MET, EGFR, BRAF and AR. The assay also reported non-targeted fusion events in relevant driver genes by using a novel statistically significant expression imbalance algorithm comparing 5’- and 3’- end gene expression. A preliminary development study was performed using commercially available total RNA for a Tri-Fusion control and Seraseq™ Fusion RNA Mix v3 where all of the expected fusions were detected with 100% sensitivity and specificity. Results were also concordant when characterized FFPE tumor samples with known fusion targets were tested using the assay. Conclusions A comprehensive NGS assay was developed to support clinical research in oncology for detecting relevant RNA structural alterations from solid tumor FFPEs. An update on assay performance will be presented. Legal entity responsible for the study Thermo Fisher Scientific. Funding Has not received any funding. Disclosure V.K. Mittal: Shareholder / Stockholder / Stock options, Full / Part-time employment: Thermo Fisher Scientific. S.P. Myrand: Shareholder / Stockholder / Stock options, Full / Part-time employment: Thermo Fisher Scientific. D. Cyanam: Shareholder / Stockholder / Stock options, Full / Part-time employment: Thermo Fisher Scientific. P.D. Williams: Shareholder / Stockholder / Stock options, Full / Part-time employment: Thermo Fisher Scientific. G.G. Bee: Shareholder / Stockholder / Stock options, Full / Part-time employment: Thermo Fisher Scientific. A. Marcovitz: Shareholder / Stockholder / Stock options, Full / Part-time employment: Thermo Fisher Scientific. R. Gottimukkala: Shareholder / Stockholder / Stock options, Full / Part-time employment: Thermo Fisher Scientific. F. Hyland: Shareholder / Stockholder / Stock options, Full / Part-time employment: Thermo Fisher Scientific. C. Allen: Shareholder / Stockholder / Stock options, Full / Part-time employment: Thermo Fisher Scientific. E. Wong-Ho: Shareholder / Stockholder / Stock options, Full / Part-time employment: Thermo Fisher Scientific. S. Sadis: Shareholder / Stockholder / Stock options, Full / Part-time employment: Thermo Fisher Scientific. C. Van Loy: Shareholder / Stockholder / Stock options, Full / Part-time employment: Thermo Fisher Scientific. J. Kilzer: Shareholder / Stockholder / Stock options, Full / Part-time employment: Thermo Fisher Scientific. N. Khazanov: Shareholder / Stockholder / Stock options, Full / Part-time employment: Thermo Fisher Scientific.

MicroRNAs (miRNAs) are a class of regulatory RNAs that control the expression of genes critical to cell function. Ectopic expression of miRNAs has been shown to result in genome-wide changes in patterns of gene expression. While the reasons for these global alterations in gene expression patterns have been attributed to the ability of miRNAs to target multiple genes, and/or to induce indirect effects downstream of target genes, the molecular basis of indirect effects of miRNA regulation remains poorly understood. In this study, we demonstrate the potential of miRNAs to regulate other miRNAs. Using miRNA microarray analysis, we show that over 70 different miRNAs are differentially expressed ( ≥1.4 fold, FDR ≤5%) in human ovarian cancer cells after transfection with a single miRNA (miR-7). We present evidence that a major component of miR-7 induced changes in levels of miRNAs is the indirect consequence of miR-7 mediated alterations in levels of protein coding genes ( e.g ., transcription and splicing factors) that exert trans-regulatory control on miRNAs.

Introduction: Tumor mutation load predicts durable benefit from immune checkpoint inhibitors in several cancer types. Existing methods to estimate tumor mutation load have large input DNA and extensive infrastructure requirements and are associated with delays due to shipping precious biopsy samples to central laboratories. Herein, we demonstrate the ability of a targeted panel with fast turn-around time to estimate mutation load from tumor research samples using low input. Methods: We developed a single sample analysis workflow to estimate mutation load (mutation count per megabase) from FFPE and fresh frozen tumor research samples. The assay utilizes a PCR-based target enrichment panel that covers 409 genes and 1.7 Mb of genomic regions. The workflow requires only 10 ng of input DNA, and enables a 2.5-day turn-around time from sample to the final report. The workflow enables < 60 minutes of hands-on time for automated library preparation and templating on a batch of 8 samples. Sequencing is performed on high throughput semiconductor sequencing platform to achieve sufficient depth (~500x coverage) and accuracy. Our analysis pipeline calls variants at ≥5% allelic frequency with parameters optimized for high accuracy. The workflow is tumor sample only, with no matched normal sample required, and germ-line variants, along with background noise, are removed through application of filters based on variants prevalent in population databases. Results: In-silico analysis demonstrated that the predicted mutation counts associated with the covered regions of the targeted panel could effectively stratify responders and non-responders to immune checkpoint inhibitors in 3 separate cohorts with high statistical significance. The assay was applied to colorectal specimens previously typed for microsatellite instability (MSI) and distinguished samples that were MSI-H (median mutation count 94.5) and MSS (median mutation count 17.8). Matched tumor-normal analyses on 7 colorectal tumor samples showed that the single tumor analysis workflow detected somatic mutations with strong correlation (r = 0.97) with tumor-normal analysis. To assess reproducibility, we compared somatic mutation count in 6 library replicates of breast cancer cell line HCC1143 and observed low relative variability (CV=9.57%) with 19.17 average mutation count. The analysis report characterizes substitution type and context of somatic mutations, and highlights mutation signatures consistent with UV damage and spontaneous deamination of 5-methyl-cytosine, as well as FFPE deamination. Conclusions: We developed a simple analysis workflow on the Ion Torrent sequencing platform with an AmpliSeq panel to estimate mutation load from FFPE and fresh frozen tumor research samples. This solution will advance research in immuno-oncology. Citation Format: Ruchi Chaudhary, Dinesh Cyanam, Vinay Mittal, Warren Tom, Janice Au-Young, Seth Sadis, Fiona Hyland. A method for estimating mutation load from tumor research samples using a targeted next-generation sequencing panel [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 580.

Title: Analytical performance of a novel next generation sequencing assay for myeloid cancers Nick Khazanov, Wally Zhang, Dinesh Cyanam, Scott P. Myrand, Denis Kaznadzey, Paul Williams, Vinay Mittal, Dan Mazur, Sihong Chen, Jason Wustman, Efren Ballesteros-Villagrana, Goutam Nistala, Jon Sherlock, Michael Hogan, Jim Veitch, John Bishop, Seth Sadis Introduction: Myeloid malignancies contain a diverse and heterogeneous set of genomic alterations that include recurrent somatic mutations in key driver genes as well as frequent and diagnostic chromosomal rearrangements that generate a wide array of gene fusion products. To support clinical and translational research into precision oncology strategies for myeloid cancers, a next-generation sequencing (NGS) assay was generated to detect common and relevant somatic alterations. Methods: To define gene targets that were recurrently altered in myeloid cancers and relevant for clinical and translational research, an extensive survey of investigators at hematology oncology research labs was performed. The gene targets identified by researchers were complemented by a comprehensive survey of literature and genomic databases. Clinical guidelines for myeloid diseases in the US and in Europe were reviewed to ensure representation of relevant alterations. A targeted Ion AmpliSeq panel was generated to support the detection of recurrent single-nucleotide variants, insertions/deletions, and gene fusions from blood or bone marrow samples. The panel was developed for manual or automated library preparation and sequencing on the Ion Torrent PGM or Ion S5 instruments. Results: The Oncomine™ Myeloid Research Assay gene panel included 58 genes and generated an average read depth of >2,000 reads per targeted amplicon with an average uniformity of >95%. Important GC-rich targets such as CEBPA generated sufficient balanced read depth to support variant detection. A cohort of samples positive for FLT3 alterations was analyzed and successful detection of FLT3-internal tandem repeat variants was demonstrated. Several gene fusion transcripts common to myeloid cancers were detected. Comparable results were observed on Ion Torrent PGM and Ion S5 instruments. Conclusions: A novel myeloid specific NGS assay capable of detecting relevant DNA and RNA alterations from the same sample was developed. The assay is useful for characterizing relevant alterations in a range of myeloid diseases including acute myeloid leukemia, myelodysplastic syndrome, myeloproliferative neoplasms, chronic myelogenous leukemia, chronic myelomonocytic leukemia, and juvenile myelomonocytic leukemia. A review of the analytical studies will be presented. Citation Format: Nickolay Khazanov, Wally Zhang, Dinesh Cyanam, Scott P. Myrand, Denis Kaznadzey, Paul D. Williams, Vinay Mittal, Daniel J. Mazur, Sihong Chen, Jason Wustman, Efren Ballesteros-Villagrana, Goutam Nistala, Santhoshi Bandla, Jim Veitch, Jon Sherlock, John Bishop, Seth Sadis. Analytical performance of a novel next generation sequencing assay for myeloid cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3863.

28 Background: High tumor mutation load is a biomarker which positively correlates with response to immune checkpoint inhibitors. Current methods to estimate tumor mutation load often require large amounts of DNA. Herein, we demonstrate the ability of a targeted panel to estimate mutation load from FFPE samples using low input amount of DNA. Methods: We developed a single-sample analysis workflow to estimate mutation load (somatic mutation count per Megabase (Mb)) from FFPE and fresh frozen tumor research samples. The assay utilizes a PCR-based target enrichment panel that covers ~1.7 Mb. Our workflow requires only 10 ng of input DNA, and enables a 2.5-day turn-around time from sample to the final report. The workflow enables < 60 minutes of hands-on time for automated library preparation and templating on a batch of 8 samples. Sequencing is performed using a high throughput semiconductor sequencing platform to achieve sufficient depth (~500x coverage) and accuracy. Our analysis pipeline calls variants with optimized parameters on the tumor sample only, with no matched normal sample required, and applies filters to remove germ-line. Results: An in-silico analysis using 21,000 exomes from COSMIC demonstrated the panel can achieve high sensitivity (>=93%) and specificity (>99%) necessary to stratify high and low mutation burden samples. Matched tumor-normal analyses on 14 lung and colorectal samples showed that our single tumor analysis workflow detects mutation load with strong correlation (r=0.8699) with tumor-normal analysis. Through dilution experiments on engineered control we learned that the workflow provides accurate estimates of mutation load, predicting ± 7% of expected mutation load on >=20% tumor content. To assess reproducibility, we compared mutation load in library replicates for a cohort of 8 samples (FFPE and fresh frozen tumors, engineered control, and NIST cell-lines) and observed high correlation (r=0.9906) among replicates. Conclusions: We developed a simple workflow on the Ion Torrent sequencing platform to estimate mutation load from FFPE and fresh frozen tumor research samples. This solution will advance research in immuno-oncology.

The problem of predicting non-long terminal repeats (LTR) like long interspersed nuclear elements (LINEs) and short interspersed nuclear elements (SINEs) from the DNA sequence is still an open problem in bioinformatics. To elevate the quality of annotations of LINES and SINEs an automated tool "RetroPred" was developed. The pipeline allowed rapid and thorough annotation of non-LTR retrotransposons. The non-LTR retrotransposable elements were initially predicted by Pairwise Aligner for Long Sequences (PALS) and Parsimonious Inference of a Library of Elementary Repeats (PILER). Predicted non-LTR elements were automatically classified into LINEs and SINEs using ANN based on the position specific probability matrix (PSPM) generated by Multiple EM for Motif Elicitation (MEME). The ANN model revealed a superior model (accuracy = 78.79 +/- 6.86 %, Q(pred) = 74.734 +/- 17.08 %, sensitivity = 84.48 +/- 6.73 %, specificity = 77.13 +/- 13.39 %) using four-fold cross validation. As proof of principle, we have thoroughly annotated the location of LINEs and SINEs in rice and Arabidopsis genome using the tool and is proved to be very useful with good accuracy. Our tool is accessible at http://www.juit.ac.in/RepeatPred/home.html.

Background Genomic rearrangements or structural variants (SVs) are one of the most common classes of mutations in cancer. Methods An integrated DNA sequencing and transcriptional profiling (RNA sequence and microarray gene expression data) analysis was performed on six ovarian cancer patient samples. Matched sets of control (whole blood) samples from these same patients were used to distinguish cancer SVs of germline origin from those arising somatically in the cancer cell lineage. Results We detected 10,034 ovarian cancer SVs (5518 germline derived; 4516 somatically derived) at base-pair level resolution. Only 11 % of these variants were shown to have the potential to form gene fusions and, of these, less than 20 % were detected at the transcriptional level. Conclusions Collectively our results are consistent with the view that gene fusions and other SVs can be significant factors in the onset and progression of ovarian cancer. The results further indicate that it may not only be the occurrence of these variants in cancer but their regulation that contributes to their biological and clinical significance. Electronic supplementary material The online version of this article (doi:10.1186/s12920-015-0118-9) contains supplementary material, which is available to authorized users.

e23224Background: Development of scalable and cost-effective NGS solutions to accurately assess relevant genomic alterations is necessary to support translational research and a future precision on...

Background Although humans and chimpanzees have accumulated significant differences in a number of phenotypic traits since diverging from a common ancestor about six million years ago, their genomes are more than 98.5% identical at protein-coding loci. This modest degree of nucleotide divergence is not sufficient to explain the extensive phenotypic differences between the two species. It has been hypothesized that the genetic basis of the phenotypic differences lies at the level of gene regulation and is associated with the extensive insertion and deletion (INDEL) variation between the two species. To test the hypothesis that large INDELs (80 to 12,000 bp) may have contributed significantly to differences in gene regulation between the two species, we categorized human-chimpanzee INDEL variation mapping in or around genes and determined whether this variation is significantly correlated with previously determined differences in gene expression. Results Extensive, large INDEL variation exists between the human and chimpanzee genomes. This variation is primarily attributable to retrotransposon insertions within the human lineage. There is a significant correlation between differences in gene expression and large human-chimpanzee INDEL variation mapping in genes or in proximity to them. Conclusions The results presented herein are consistent with the hypothesis that large INDELs, particularly those associated with retrotransposons, have played a significant role in human-chimpanzee regulatory evolution.

The rapid expansion in the quantity and quality of RNA-Seq data requires the development of sophisticated high-performance bioinformatics tools capable of rapidly transforming this data into meaningful information that is easily interpretable by biologists. Currently available analysis tools are often not easily installed by the general biologist and most of them lack inherent parallel processing capabilities widely recognized as an essential feature of next-generation bioinformatics tools. We present here a user-friendly and fully automated RNA-Seq analysis pipeline (R-SAP) with built-in multi-threading capability to analyze and quantitate high-throughput RNA-Seq datasets. R-SAP follows a hierarchical decision making procedure to accurately characterize various classes of transcripts and achieves a near linear decrease in data processing time as a result of increased multi-threading. In addition, RNA expression level estimates obtained using R-SAP display high concordance with levels measured by microarrays.