TumorFusions: an integrative resource for cancer-associated transcript fusions

Hu, Xin; Wang, Qianghu; Tang, Ming; Barthel, Floris P.; Amin, Samir B.; Yoshihara, Kosuke; Lang, Frederick; Martinez-Ledesma, Emmanuel; Lee, Soo Hyun; Zheng, Siyuan; Verhaak, Roel G.W.

doi:10.1093/nar/gkx1018

Cited by 201 publications

(225 citation statements)

References 32 publications

Supporting

Mentioning

210

Contrasting

Order By: Relevance

“…This is in agreement with recent findings that most post‐Chernobyl PTCs in which ETV6‐NTRK3 was identified were classified as follicular variant of PTC . The isoform detected in our study, which juxtaposes exon 4 of ETV6 (ENST00000396373) and exon 14 of NTRK3 (ENST00000394480), has been reported in PTC and gastrointestinal stromal tumor . MKRN1‐BRAF has been reported in a few cases of PTC .…”

Section: Discussionsupporting

confidence: 93%

See 1 more Smart Citation

Novel TG‐FGFR1 and TRIM33‐NTRK1 transcript fusions in papillary thyroid carcinoma

Pfeifer

Rusinek

Żebracka‐Gala

et al. 2019

Genes Chromosomes & Cancer

View full text Add to dashboard Cite

Papillary thyroid carcinoma (PTC) is most common among all thyroid cancers. Multiple genomic alterations occur in PTC, and gene rearrangements are one of them. Here we screened 14 tumors for novel fusion transcripts by RNA‐Seq. Two samples harboring RET/PTC1 and RET/PTC3 rearrangements were positive controls whereas the remaining ones were negative regarding the common PTC alterations. We used Sanger sequencing to validate potential fusions. We detected 2 novel potentially oncogenic transcript fusions: TG‐FGFR1 and TRIM33‐NTRK1 . We detected 4 novel fusion transcripts of unknown significance accompanying the TRIM33‐NTRK1 fusion: ZSWIM5‐TP53BP2 , TAF4B‐WDR1 , ABI2‐MTA3 , and ARID1B‐PSMA1 . Apart from confirming the presence of RET/PTC1 and RET/PTC3 in positive control samples, we also detected known oncogenic fusion transcripts in remaining samples: TFG‐NTRK1 , ETV6‐NTRK3 , MKRN1‐BRAF , EML4‐ALK , and novel isoform of CCDC6‐RET .

show abstract

Section: Discussionsupporting

confidence: 93%

“…18,50 The longer of two isoforms detected in our study, TFG (exon 6 of ENST00000240851)-NTRK1 (exon 10 of ENST00000524377), has been already reported in PTC. 1,51 ETV6-NTRK3 was the most prevalent alteration in PTC set analyzed by us, as it was found in 3 samples.…”

Section: Known Fusion Transcriptsmentioning

confidence: 69%

Novel TG‐FGFR1 and TRIM33‐NTRK1 transcript fusions in papillary thyroid carcinoma

Pfeifer

Rusinek

Żebracka‐Gala

et al. 2019

Genes Chromosomes & Cancer

View full text Add to dashboard Cite

show abstract

“…While calling significantly fewer fusions in normal samples, DEEPEST-Fusion identifies significantly more fusions in TCGA tumor samples compared to recent surveys of the same samples (Gao et al, 2018 andHu et al, 2017), the former is based on STAR-Fusion that is more sensitive in simulated data. While some fusion algorithms might exhibit better sensitivity (at the cost of higher false positive rates) on simulated datasets, DEEPEST-Fusion is more sensitive in real cancer datasets (Supplemental Figure 2 C,D).…”

Section: Deepest-fusion Improves Sensitivity and Specificity Of Fusiomentioning

confidence: 89%

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

Dehghannasiri

Freeman

Jordanski

et al. 2019

Preprint

View full text Add to dashboard Cite

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...

show abstract

“…With the advent of high-throughput parallel RNA sequencing (RNA-seq) technology, the nomination rate of novel fusion transcripts in both hematological and solid tumor types has exploded. This is underlined with 20731 fusion transcripts being detected in 9966 cancer samples (33 cancer types) from The Cancer Genome Atlas (TCGA) consortium alone (6).…”

Section: Introductionmentioning

confidence: 99%

ScaR - A tool for sensitive detection of known fusion transcripts: Establishing prevalence of fusions in testicular germ cell tumors

Zhao

Hoff

Skotheim

2019

Preprint

View full text Add to dashboard Cite

Bioinformatics tools for fusion transcript detection from RNA-sequencing data are in general developed for identification of novel fusions, which demands a high number of supporting reads and strict filters to avoid false discoveries. As our knowledge of bona-fide fusion genes becomes more saturated, there is a need to establish their prevalence with high sensitivity. We present ScaR, a tool that uses a scaffold realignment approach for sensitive fusion detection in RNA-seq data. ScaR detects a set of 50 synthetic fusion transcripts from simulated data at a higher sensitivity compared to established fusion finders. Applied to fusion transcripts potentially involved in testicular germ cell tumors (TGCTs), ScaR detects the fusions RCC1-ABHD12B and CLEC6A-CLEC4D in 9% and 28% of 150 TGCTs, respectively. The fusions were not detected in any of 198 normal testis tissues. Thus, we demonstrate high prevalence of RCC1-ABHD12B and CLEC6A-CLEC4D in TGCTs, and their cancer specific features. Further, we find that RCC1-ABHD12B and CLEC6A-CLEC4D are predominantly expressed in the seminoma and embryonal carcinoma histological subtypes of TGCTs, respectively. In conclusion, ScaR is useful for establishing the frequency of known fusion transcripts in larger data sets and detecting clinically relevant fusion transcripts with high sensitivity.

show abstract

TumorFusions: an integrative resource for cancer-associated transcript fusions

Cited by 201 publications

References 32 publications

Novel TG‐FGFR1 and TRIM33‐NTRK1 transcript fusions in papillary thyroid carcinoma

Novel TG‐FGFR1 and TRIM33‐NTRK1 transcript fusions in papillary thyroid carcinoma

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

ScaR - A tool for sensitive detection of known fusion transcripts: Establishing prevalence of fusions in testicular germ cell tumors

Contact Info

Product

Resources

About