Driver Fusions and Their Implications in the Development and Treatment of Human Cancers

Gao, Qingsong; Liang, Wen-Wei; Foltz, Steven M.; Gnanavel, Mutharasu; Jayasinghe, Reyka G.; Cao, Song; Liao, Wen‐Wei; Reynolds, Sheila M.; Wyczalkowski, Matthew A.; Yao, Lijun; Yu, Lihua; Sun, Sam Q.; Chen, Ken; Lazar, Alexander J.; Fields, Ryan C.; Wendl, Michael C.; Tine, Brian A. Van; Vij, Ravi; Chen, Feng; Nykter, Matti; Shmulevich, Ilya; Li, Ding

doi:10.1016/j.celrep.2018.03.050

Cited by 418 publications

(383 citation statements)

References 46 publications

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“…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: 91%

“…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). When samples shared between three lists are considered, DEEPEST-Fusion detects much more fusions (29,820 fusions, compared to 23,624 fusions in (Gao et al, 2018) and 19,846 fusions by TumorFusions) and substantially fewer calls in real normal datasets (Supplemental Figure 2A,B), suggesting that the modeling employed by DEEPEST-Fusion is a better fit for real data. DEEPEST-Fusion-only fusions are enriched in cancers known to have high genomic instability (ESCA, OV, STAD, SARC) compared to fusions found only by TumorFusions and (Gao et al, 2018) (Supplemental Figure 2D).…”

Section: Deepest-fusion Improves Sensitivity and Specificity Of Fusiomentioning

confidence: 99%

“…To further evaluate the performance of the SBT-based refinement component, we extracted two groups of fusions from the fusions called by the junction nomination component: (1) likely false positives (LFP) that are fusions with SBT hits in the GTEx samples and (2) likely true positives (LTP) that contain fusions shared between the first component, (Gao et al, 2018), and TumorFusions. Note that LFP fusions are defined on the basis of GTEx data, whereas the SBT refinement step does not "touch" GTEx data, meaning that the LFP fusion set can be used as a test set for the specificity of the SBT refinement step.…”

Section: Statistical Analysis Of Fusion Prevalence In Large Data Quermentioning

confidence: 99%

“…In such scenarios, precise fusion detection must overcome a problem of multiple hypothesis testing: each algorithm is testing for fusions thousands of times, a regime known to introduce false positives. To overcome these problems, the field has turned to consensus-based approaches, where multiple algorithms are run in parallel (Gao et al, 2018), and a meta-caller allows 'voting' to produce the final list of fusions. This is also unsatisfactory, as it introduces false negatives.…”

Section: Introductionmentioning

confidence: 99%

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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: Deepest-fusion Improves Sensitivity and Specificity Of Fusiomentioning

confidence: 91%

Section: Deepest-fusion Improves Sensitivity and Specificity Of Fusiomentioning

confidence: 99%

Section: Statistical Analysis Of Fusion Prevalence In Large Data Quermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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“…Fusion genes are hallmarks of many human cancers. Recent studies suggest that up to 16% of cancers are driven by a fusion gene 1 . Some cancer types, such as prostate cancer or chronic myeloid leukemia, are characterized by a specific fusion gene (TMPRSS2-ERG and BCR-ABL1 respectively), whereas other cancer types do not show such clear associations 1,2 .…”

Section: Introductionmentioning

confidence: 99%

Partner-independent fusion gene detection by multiplexed CRISPR/Cas9 enrichment and long-read Nanopore sequencing

Stangl

Blank

Renkens

et al. 2019

Preprint

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Fusion genes are hallmarks of various cancer types and important determinants for diagnosis, prognosis and treatment possibilities. The promiscuity of fusion genes with respect to partner choice and exact breakpoint-positions restricts their detection in the diagnostic setting, even for known and recurrent fusion gene configurations. To accurately identify these gene fusions in an unbiased manner, we developed FUDGE: a FUsion gene Detection assay from Gene Enrichment. FUDGE couples target-selected and strand-specific CRISPR/Cas9 activity for enrichment and detection of fusion gene drivers (e.g. BRAF, EWSR1, KMT2A/MLL) -without prior knowledge of fusion partner or breakpoint-location -to long-read Nanopore sequencing.FUDGE encompasses a dedicated bioinformatics approach (NanoFG) to detect fusion genes from Nanopore sequencing data. Our strategy is flexible with respect to target choice and enables multiplexed enrichment for simultaneous analysis of several genes in multiple samples in a single sequencing run. We observe on average a 508 fold on-target enrichment and identify fusion breakpoints at nucleotide resolution -all within two days. We demonstrate that FUDGE effectively identifies fusion genes in cancer cell lines, tumor samples and on whole genome amplified DNA irrespective of partner gene or breakpoint-position in 100% of cases. Furthermore, we show that FUDGE is superior to routine diagnostic methods for fusion gene detection. In summary, we have developed a rapid and versatile fusion gene detection assay, providing an unparalleled opportunity for pan-cancer detection of fusion genes in routine diagnostics.

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Genomic analyses of high‐grade neuroendocrine gynecological malignancies reveal a unique mutational landscape and therapeutic vulnerabilities

et al. 2021

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List of abbreviations: BWA: Burrows-Wheeler alignment CNV: Copy number variation COMPASS: Complex proteins associated with Set1 DDR: DNA damage response FDR: False discovery rate FFPE: Formalin-fixed paraffin-embedded FIGO: International federation of gynecology and obstetrics (Fédération Internationale de Gynécologie et d'Obstétrique, French) HPV: Human papilloma virus IHC: Immunohistochemistry lincRNA: Long intergenic non-coding RNA MAF: Mutation annotation format NEC-GYN: Neuroendocrine carcinoma of gynecologic origin

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Driver Fusions and Their Implications in the Development and Treatment of Human Cancers

Cited by 418 publications

References 46 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

Partner-independent fusion gene detection by multiplexed CRISPR/Cas9 enrichment and long-read Nanopore sequencing

Genomic analyses of high‐grade neuroendocrine gynecological malignancies reveal a unique mutational landscape and therapeutic vulnerabilities

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