CICERO: a versatile method for detecting complex and diverse driver fusions using cancer RNA sequencing data

Tian, Liqing; Li, Yongjin; Edmonson, Michael N.; Zhou, Xin; Newman, Scott; McLeod, Clay; Thrasher, Andrew; Liu, Yu; Tang, Bo; Rusch, Michael; Easton, John; Ma, Jing; Davis, Eric M.; Trull, Austyn; Michael, J. Robert; Szlachta, Karol; Mullighan, Charles G.; Baker, Suzanne J.; Downing, James R.; Ellison, David W.; Zhang, Jinghui

doi:10.1186/s13059-020-02043-x

Cited by 89 publications

(95 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Both direct detection of DUX4 r through the identification of paired-end reads linking IGH and DUX4 [ 9 , 12 , 13 , 14 , 17 , 18 , 19 ], as well as the detection of the distinct expression profile characteristic of this subtype [ 14 , 17 , 18 , 19 ], are possible with RNA sequencing (RNA-seq) data. A variety of different fusion calling algorithms have been employed for direct detection of DUX4 and its fusion partner, including fusionCatcher [ 64 ], TopHat-fusion [ 65 ], defuse [ 66 ] and Cicero [ 67 ]. However, these algorithms do not consistently detect a fusion involving DUX4 in all cases where a DUX4 r has been indicated by GEP [ 14 , 18 , 19 ].…”

Section: Detecting Dux4 Rmentioning

confidence: 99%

DUX Hunting—Clinical Features and Diagnostic Challenges Associated with DUX4-Rearranged Leukaemia

Rehn

O'Connor

White

et al. 2020

Cancers

View full text Add to dashboard Cite

DUX4-rearrangement (DUX4r) is a recently discovered recurrent genomic lesion reported in 4–7% of childhood B cell acute lymphoblastic leukaemia (B-ALL) cases. This subtype has favourable outcomes, especially in children and adolescents treated with intensive chemotherapy. The fusion most commonly links the hypervariable IGH gene to DUX4 a gene located within the D4Z4 macrosatellite repeat on chromosome 4, with a homologous polymorphic repeat on chromosome 10. DUX4r is cryptic to most standard diagnostic techniques, and difficult to identify even with next generation sequencing assays. This review summarises the clinical features and molecular genetics of DUX4r B-ALL and proposes prospective new diagnostic methods.

show abstract

Section: Detecting Dux4 Rmentioning

confidence: 99%

DUX Hunting—Clinical Features and Diagnostic Challenges Associated with DUX4-Rearranged Leukaemia

Rehn

O'Connor

White

et al. 2020

Cancers

View full text Add to dashboard Cite

show abstract

“…Five of these eight workflows have integrated cancer genomic analysis algorithms developed using pediatric cancer data sets such as PCGP; and their performance has been iteratively improved by the growing knowledgebase of pediatric cancer. They include: 1) Rapid RNA-Seq, which predicts gene fusions using the CICERO algorithm (28) that has discovered targetable fusions in high-risk pediatric leukemia (8), high-grade glioma (5) and melanoma (29); 2) PeCanPIE (30), which classifies germline variant pathogenicity using the Medal Ceremony algorithm that was developed to assess germline susceptibility of pediatric cancer (5) and genetic risk for subsequent neoplasms among survivors of childhood cancer (31); 3) NeoeptiopePred, which predicts the immunogenicity of somatic mutations and gene fusions, has characterized the neoepitope landscape of 23 subtypes of pediatric cancer (11); 4) cis-X, which detects non-coding driver variants, and has discovered non-coding drivers in pediatric T-lineage leukemia (32); and 5) SequencErr, which measures and suppresses next-generation sequencing errors (33).…”

Section: Resultsmentioning

confidence: 99%

St. Jude Cloud—a Pediatric Cancer Genomic Data Sharing Ecosystem

McLeod

Gout

Zhou

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Effective data sharing is key to accelerating research that will improve the precision of diagnoses, efficacy of treatments and long term survival of pediatric cancer and other childhood catastrophic diseases. We present St. Jude Cloud (https://www.stjude.cloud), a cloud based data sharing ecosystem developed via collaboration between St. Jude Children's Research Hospital, DNAnexus, and Microsoft, for accessing, analyzing and visualizing genomic data from >10,000 pediatric cancer patients, long term survivors of pediatric cancer and >800 pediatric sickle cell patients. Harmonized genomic data totaling 1.25 petabyes on St. Jude Cloud include 12,104 whole genomes, 7,697 whole exomes and 2,202 transcriptomes, which are freely available to researchers worldwide. The resource is expanding rapidly with regular data uploads from St. Jude's prospective clinical genomics programs, providing public access as soon as possible rather than holding data back until publication. Three interconnected apps within the St. Jude Cloud ecosystem Genomics Platform, Pediatric Cancer Knowledgebase (PeCan) and Visualization Community provide a unique experience for simultaneously performing advanced data analysis in the cloud and enhancing the pediatric cancer knowledgebase. We demonstrate the value of the St. Jude Cloud ecosystem through use cases that classify 48 pediatric cancer subtypes by gene expression profiling and map mutational signatures across 35 subtypes of pediatric cancer.

show abstract

“…Multiple highthroughput sequencing approaches are highly recommended, as they can be utilized to accurately recognize the prognostic factors (e.g., fusion genes, gene expressiondependent subtypes, small sequence variants, and genomic duplications and deletions) and determine the strategic therapy [149]. RNA-seq is a single and comprehensive platform for BCP-ALL diagnosis and genomic classification in the laboratory and clinical settings [9,43,[150][151][152]. For example, most of the known fusion genes (e.g., BCR-ABL1, ETV6-RUNX1, and TCF3-PBX1) and new fusion genes (e.g., TCF3/4-HLF, NUTM1, DUX4, ZNF384/ ZNF362, and MEF2D fusions) in BCP-ALL can be detected by RNA-seq [149,153].…”

Section: Ikzf1 Pasn159tyr (N159y)mentioning

confidence: 99%

“…Notably, RNA-seq is also a reliable technology for simultaneously identifying the positive-fusion gene and the gene expression-dependent subtypes, including Ph-like, ETV6-RUNX1-like, ZNF384-like, and KMT2A-like. Small sequence variants and genomic deletions (e.g., IKZF1) are also detectable by RNA-seq in BCP-ALL [8,9,77,149,[151][152][153][154]. For instance, by re-analyzing the RNA-seq data from different BCP-ALL cohorts, the molecular subtypes characterized by hotspot mutations (e.g., PAX5 P80R and ZEB2 H1038R) could be identified [8].…”

Section: Ikzf1 Pasn159tyr (N159y)mentioning

confidence: 99%

Emerging molecular subtypes and therapeutic targets in B-cell precursor acute lymphoblastic leukemia

Dai

et al. 2021

Front. Med.

View full text Add to dashboard Cite

B-cell precursor acute lymphoblastic leukemia (BCP-ALL) is characterized by genetic alterations with high heterogeneity. Precise subtypes with distinct genomic and/or gene expression patterns have been recently revealed using high-throughput sequencing technology. Most of these profiles are associated with recurrent non-overlapping rearrangements or hotspot point mutations that are analogous to the established subtypes, such as DUX4 rearrangements, MEF2D rearrangements, ZNF384/ZNF362 rearrangements, NUTM1 rearrangements, BCL2/MYC and/or BCL6 rearrangements, ETV6-RUNX1-like gene expression, PAX5alt (diverse PAX5 alterations, including rearrangements, intragenic amplifications, or mutations), and hotspot mutations PAX5 (p.Pro80Arg) with biallelic PAX5 alterations, IKZF1 (p.Asn159Tyr), and ZEB2 (p.His1038Arg). These molecular subtypes could be classified by gene expression patterns with RNA-seq technology. Refined molecular classification greatly improved the treatment strategy. Multiagent therapy regimens, including target inhibitors (e.g., imatinib), immunomodulators, monoclonal antibodies, and chimeric antigen receptor T-cell (CAR-T) therapy, are transforming the clinical practice from chemotherapy drugs to personalized medicine in the field of risk-directed disease management. We provide an update on our knowledge of emerging molecular subtypes and therapeutic targets in BCP-ALL.

show abstract

CICERO: a versatile method for detecting complex and diverse driver fusions using cancer RNA sequencing data

Cited by 89 publications

References 47 publications

DUX Hunting—Clinical Features and Diagnostic Challenges Associated with DUX4-Rearranged Leukaemia

DUX Hunting—Clinical Features and Diagnostic Challenges Associated with DUX4-Rearranged Leukaemia

St. Jude Cloud—a Pediatric Cancer Genomic Data Sharing Ecosystem

Emerging molecular subtypes and therapeutic targets in B-cell precursor acute lymphoblastic leukemia

Contact Info

Product

Resources

About