RNA Sequencing Identifies Transcriptionally Viable Gene Fusions in Esophageal Adenocarcinomas

Blum, Andrew; Venkitachalam, Srividya; Guo, Yan; Kieber-Emmons, Ann Marie; Ravi, Lakshmeswari; Chandar, Apoorva K.; Iyer, Prasad G.; Canto, Marcia Irene; Wang, Jean S.; Shaheen, Nicholas J.; Barnholtz‐Sloan, Jill S.; Markowitz, Sanford D.; Willis, Joseph E.; Shyr, Yu; Chak, Amitabh; Varadan, Vinay; Guda, Kishore

doi:10.1158/0008-5472.can-16-0979

Cited by 28 publications

(31 citation statements)

References 14 publications

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“…The most prevalent fusions expected under neutral selection would be observed only 2 times, and we would expect to observe only 5 such fusions ( This analysis reveals new evidence that recurrent fusions are selected for in diverse tumors; RPS6KB1-VMP1, a fusion between the ribosomal protein kinase (Cai et al, 2015) and a vacuolar protein (VMP1) present in a 8 tumor types, is the most prevalent detected gene fusion, after TMPRSS2-ERG, across the entire TCGA cohort and supports findings by previous studies that these fusions have a driving role (Inaki et al, 2011, Blum et al, 2016; Figure 2C). Globally, 14% of the fusions (1,486) found by DEEPEST-Fusion are observed at higher rates than expected by chance (p < 1e-6); more than 11.9% of tumors (1,181) have recurrent fusions ( Figure 4 and Supplemental Table 1).…”

Section: Statistical Analysis Of Rare Fusions Shows a Selection For Fsupporting

confidence: 88%

Improved detection of gene fusions by applying statistical methods reveals new oncogenic RNA cancer drivers

Dehghannasiri

Freeman

Jordanski

et al. 2019

Preprint

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Short Abstract:The extent to which gene fusions function as drivers of cancer remains a critical open question. Current algorithms do not sufficiently identify false-positive fusions arising during library preparation, sequencing, and alignment. Here, we introduce a new algorithm, DEEPEST, that uses statistical modeling to minimize false-positives while increasing the sensitivity of fusion detection. In 9,946 tumor RNA-sequencing datasets from The Cancer Genome Atlas (TCGA) across 33 tumor types, DEEPEST identifies 31,007 fusions, 30% more than identified by other methods, while calling ten-fold fewer false-positive fusions in non-transformed human tissues. We leverage the increased precision of DEEPEST to discover new cancer biology. For example, 888 new candidate oncogenes are identified based on over-representation in DEEPEST-Fusion calls, and 1,078 previously unreported fusions involving long intergenic noncoding RNAs partners, demonstrating a previously unappreciated prevalence and potential for function. Specific protein domains are enriched in DEEPEST calls, demonstrating a global selection for fusion functionality: kinase domains are nearly 2-fold more enriched in DEEPEST calls than expected by chance, as are domains involved in (anaerobic) metabolism and DNA binding. DEEPEST also reveals a high enrichment for fusions involving known and novel oncogenes in diseases including ovarian cancer, which has had minimal treatment advances in recent decades, finding that more than 50% of tumors harbor gene fusions predicted to be oncogenic. The statistical algorithms, population-level analytic framework, and the biological conclusions of DEEPEST call for increased attention to gene fusions as drivers of cancer and for future research into using fusions for targeted therapy. Significance:Gene fusions are tumor-specific genomic aberrations and are among the most powerful biomarkers and drug targets in translational cancer biology. The advent of RNA-Seq 2 technologies over the past decade has provided a unique opportunity for detecting novel fusions via deploying computational algorithms on public sequencing databases. Yet, precise fusion detection algorithms are still out of reach. We develop DEEPEST, a highly specific and efficient statistical pipeline specially designed for mining massive sequencing databases, and apply it to all 33 tumor types and 10,500 samples in The Cancer Genome Atlas database. We systematically profile the landscape of detected fusions via employing classic statistical models and identify several signatures of selection for fusions in tumors. Software availabilityDEEPEST-Fusion workflow with a detailed readme file is available as a Github repository: https://github.com/salzmanlab/DEEPEST-Fusion. In addition to the main workflow, which is based on CWL, example input and batch scripts (for job submission on local clusters), and codes for building the SBT files and SBT querying are provided in the repository. All custom scripts used for systematic analysis of fusions are also available in the s...

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Section: Statistical Analysis Of Rare Fusions Shows a Selection For Fsupporting

confidence: 88%

Improved detection of gene fusions by applying statistical methods reveals new oncogenic RNA cancer drivers

Dehghannasiri

Freeman

Jordanski

et al. 2019

Preprint

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“…This locus was recently shown to be recurrently amplified in lung adenocarcinoma (Campbell et al, 2016). VMP1 encodes a vacuolar membrane protein that is functionally linked to autophagy and has been found to be recurrently rearranged in several cancer types, including breast and esophageal cancer (Blum et al, 2016; Inaki et al, 2011). Interestingly, the VMP1/MIR21 locus was also the ninth most highly ranked hotspot in the indel analysis (Figure 1C), though it did not pass the FDR threshold of 0.1 ( P = 6.4 × 10 −6 ).…”

Section: Resultsmentioning

confidence: 99%

Insertions and Deletions Target Lineage-Defining Genes in Human Cancers

Imieliński

Guo

Meyerson

2017

Cell

115

113

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SUMMARY Whole genome sequencing analysis of lung adenocarcinomas revealed noncoding somatic mutational hotspots near VMP1/MIR21 and indel hotspots in surfactant protein genes (SFTPA1, SFTPB, and SFTPC). Extrapolation to other solid cancers demonstrated highly recurrent and tumor-type-specific indel hotspots targeting the noncoding regions of highly expressed genes defining certain secretory cellular lineages: albumin (ALB) in liver carcinoma, gastric lipase (LIPF) in stomach carcinoma, and thyroglobulin (TG) in thyroid carcinoma. The sequence contexts of indels targeting lineage-defining genes were significantly enriched in the AATAATD DNA motif and specific chromatin contexts, including H3K27ac and H3K36me3. Our findings illuminate a prevalent and hitherto unrecognized mutational process linking cellular lineage and cancer.

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“…12,13 Although chromosomal rearrangements have been reported in EAC, no driver events caused by fusion genes have yet been described in this malignancy. Recently, Blum et al 14 identified ribosomal protein S6 kinase B1 (RPS6KB1)-vacuole membrane protein 1 (VMP1) as a fusion transcript that modulates autophagy in EAC. With the exception of this single study, however, there have been no comprehensive reports of fusion transcripts in EAC.…”

Section: Introductionmentioning

confidence: 99%

RNA sequencing of esophageal adenocarcinomas identifies novel fusion transcripts, including NPC1‐MELK, arising from a complex chromosomal rearrangement

Wang

Cheng

Abraham

et al. 2017

Cancer

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Background Studies of chromosomal rearrangements and fusion transcripts have elucidated mechanisms of tumorigenesis and led to targeted cancer therapies. In this study, we aimed to identify novel fusion transcripts in esophageal adenocarcinomas. Methods To identify new fusion transcripts associated with esophageal adenocarcinoma, we performed targeted RNA sequencing and PCR verification in 40 esophageal adenocarcinomas (EACs) and matched non-malignant specimens from the same patients. Genomic PCR and Sanger sequencing were performed to find the breakpoint of fusion genes. Results Five novel in-frame fusion transcripts were identified and verified in 40 EACs as well as in a validation cohort of 15 additional EACs (in total, 55 patients): FGFR2-GAB2 (2/55, or 3.6%), NPC1-MELK (2/55, or 3.6%), USP54-CAMK2G (2/55, or 3.6%), MKL1-FBLN1 (1/55, or 1.8%), and CNOT2-C12orf49 (1/55, or 1.8%). Genomic analysis indicated that NPC1-MELK arose from a complex inter-chromosomal translocation event involving chromosomes 18, 3, and 9 with three rearrangement points, consistent with chromoplexy. Conclusions These data indicate that fusion transcripts occur at a stable frequency in EAC. Furthermore, our results indicate that chromoplexy is an underlying mechanism that generates fusion transcripts in EAC. These and other fusion transcripts merit further study as diagnostic markers and potential therapeutic targets in EAC.

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RNA Sequencing Identifies Transcriptionally Viable Gene Fusions in Esophageal Adenocarcinomas

Cited by 28 publications

References 14 publications

Improved detection of gene fusions by applying statistical methods reveals new oncogenic RNA cancer drivers

Improved detection of gene fusions by applying statistical methods reveals new oncogenic RNA cancer drivers

Insertions and Deletions Target Lineage-Defining Genes in Human Cancers

RNA sequencing of esophageal adenocarcinomas identifies novel fusion transcripts, including NPC1‐MELK, arising from a complex chromosomal rearrangement

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