High-throughput RNA sequencing enables quantification of transcripts (both known and novel), exon/exon junctions and fusions of exons from different genes. Discovery of gene fusions–particularly those expressed with low abundance– is a challenge with short- and medium-length sequencing reads. To address this challenge, we implemented an RNA-Seq mapping pipeline within the LifeScope software. We introduced new features including filter and junction mapping, annotation-aided pairing rescue and accurate mapping quality values. We combined this pipeline with a Suffix Array Spliced Read (SASR) aligner to detect chimeric transcripts. Performing paired-end RNA-Seq of the breast cancer cell line MCF-7 using the SOLiD system, we called 40 gene fusions among over 120,000 splicing junctions. We validated 36 of these 40 fusions with TaqMan assays, of which 25 were expressed in MCF-7 but not the Human Brain Reference. An intra-chromosomal gene fusion involving the estrogen receptor alpha gene ESR1, and another involving the RPS6KB1 (Ribosomal protein S6 kinase beta-1) were recurrently expressed in a number of breast tumor cell lines and a clinical tumor sample.

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Gene fusions, a combination of two genes, comprising their coding and/or regulatory sequences, are caused by structural rearrangements in DNA or in RNA transcripts. Many gene fusions are strong driver mutations in neoplasia, and are important in understanding basic biology, interaction with targeted therapy, and research into risk stratification and outcomes. Next-generation sequencing enables sensitive, specific and precise detection of particular fusion isoforms for defined gene pairs. Massively multiplex Ampliseq gene fusion assays enable enrichment of fusion transcripts using as little as 10 ng of RNA extracted from FFPE samples. Sequencing on Ion Torrent instruments reveals the full sequence of the gene fusion, for precise definition of the breakpoint and the expressed exons or promoter regions of both genes. We developed cloud-based software to support the design of a custom Ampliseq gene fusion panel, comprising 1 to 1,000 fusion isoform assays and any gene expression assays for normalization. We extensively mined the scientific literature on fusions and the COSMIC database to identify over 1000 fusion isoforms. We rigorously curated this data using automated and manual methods, including mapping, confirmation and correction of reported sequence to obtain genomic coordinates, identification of breakpoints, annotation of exon junctions, and selected wet lab testing. We created a database containing over 1000 high quality curated and annotated fusion isoforms, including 70 ALK, 60 RET, 26 ROS1, and 21 NTRK1 fusions. We designed Ampliseq primer pairs for each of these fusions using advanced assay design and pooling algorithms, such that all fusion and gene expression assays can be multiplexed into 1 or 2 compatible pools. Assays can be selected by gene or gene pair; detailed information about each assay selected includes isoform, genes, exon numbers, and links to COSMIC and to relevant publications. We developed cloud-based analysis software to analyze the BAM file resulting from amplification and sequencing of custom Ampliseq fusion panels on an Ion Torrent sequencer. This analysis leverages the rich annotation information from the assay design. The reads are mapped to a custom reference sequence tailored to the custom Ampliseq fusion assay, and applying an optimized algorithm to select confidently mapped reads based on read length and overlap with each gene of the gene pair based on the reference and annotated breakpoint. Gene fusions are detected based on the total number of fusion reads and optionally frequency, and on the properties of those reads. Software QC steps for total number of mapped reads, number of reads for gene expression controls, and elimination of cross-talk artifacts result in a highly sensitive and specific detection of fusions, with LOD below 1%. Fusion results for any or all samples can be viewed, annotated, filtered, and visualized, and exported. Citation Format: Fiona Hyland, Rajesh Gottimukkala, Efren Ballesteros, Heinz Breu, Yuandan Lou, Scott Myrand, Michael Hogan, Kelli Bramlett, Guoying Liu, Seth Sadis. Cloud-based informatics enables the design and analysis of massively multiplex custom gene fusion panels for next-generation sequencing on FFPE RNA samples. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5272.

Milan Radovich DECODING THE TRANSCRIPTIONAL LANDSCAPE OF TRIPLE-NEGATIVE BREAST CANCER USING NEXT GENERATION WHOLE TRANSCRIPTOME SEQUENCING Triple-negative breast cancers (TNBCs) are negative for the expression of estrogen (ER), progesterone (PR), and HER-2 receptors. TNBC accounts for 15% of all breast cancers and results in disproportionally higher mortality compared to ER & HER2positive tumours. Moreover, there is a paucity of therapies for this subtype of breast cancer resulting primarily from an inadequate understanding of the transcriptional differences that differentiate TNBC from normal breast. To this end, we embarked on a comprehensive examination of the transcriptomes of TNBCs and normal breast tissues using next-generation whole transcriptome sequencing (RNA-Seq). By comparing RNAseq data from these tissues, we report the presence of differentially expressed coding and non-coding genes, novel transcribed regions, and mutations not previously reported in breast cancer. From these data we have identified two major themes. First, BRCA1 mutations are well known to be associated with development of TNBC. From these data we have identified many genes that work in concert with BRCA1 that are dysregulated suggesting a role of BRCA1 associated genes with sporadic TNBC. In addition, we observe a mutational profile in genes also associated with BRCA1 and DNA repair that lend more evidence to its role. Second, we demonstrate that using microdissected normal epithelium maybe an optimal comparator when searching for novel therapeutic targets for TNBC. Previous studies have used other controls such as reduction mammoplasties, adjacent normal tissue, or other breast cancer subtypes, which may be sub-optimal and have lead to identifying ineffective therapeutic targets. Our data vii suggests that the comparison of microdissected ductal epithelium to TNBC can identify potential therapeutic targets that may lead to be better clinical efficacy. In summation, with these data, we provide a detailed transcriptional landscape of TNBC and normal breast that we believe will lead to a better understanding of this complex disease.