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Record W2988594495 · doi:10.1182/blood-2019-130052

The U1 Spliceosomal RNA: A Novel Non-Coding Hotspot Driver Mutation Independently Associated with Clinical Outcome in Chronic Lymphocytic Leukemia

2019· article· en· W2988594495 on OpenAlexaff
Ferran Nadeu, Shimin Shuai, Ander Díaz‐Navarro, Irene López, Silvia Martín, Hiromichi Suzuki, Romina Royo, Guillem Clot, Julio Delgado, Tycho Baumann, Junyan Lu, Alba Navarro, Marta Kulis, Sachin Kumar, Ana Gutiérrez‐Fernández, Miguel Alcoceba, Marcos González, Enrique Colado, Ángel Ramírez Páyer, Cristina Capdevila, Marta Aymerich, Rosó Marés, Mònica López‐Guerra, Laura Magnano, Pablo Mozas, Alfredo Rivas‐Delgado, María José Terol, Anna Enjuanes, Wolfgang Huber, Armando López‐Guillermo, Sı́lvia Beà, José I. Martín‐Subero, Thorsten Zenz, Michael D. Taylor, Dolors Colomer, Xosé S. Puente, Lincoln Stein, Elı́as Campo

Bibliographic record

VenueBlood · 2019
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of TorontoOntario Institute for Cancer Research
Fundersnot available
KeywordsIGHV@BiologyGeneticsChronic lymphocytic leukemiaSpliceosomeRNARNA splicingSmall nuclear RNAMutationEpigeneticsIntronGeneLeukemiaNon-coding RNA

Abstract

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Introduction: Genomic studies of chronic lymphocytic leukemia (CLL) have uncovered >80 potential driver mutations. The vast majority of these mutations affect coding regions, and just two potential drivers have been identified in non-coding elements. Aim: To describe the biological and clinical impact of a recurrent A>C mutation at the third base of the small nuclear RNA U1, the non-coding component of the spliceosome involved in the recognition of the 5' splice site (5'SS). Methods: Whole-genome sequencing (WGS) and RNA-seq from 318 CLL patients were used to identify and characterize a highly recurrent A>C point mutation occurring at position 3 of the U1 snRNA gene (g.3A>C mutation). The U1 wild-type and mutant forms were introduced into three CLL cell lines (JVM3, HG3, MEC1) to validate in vitro the predicted effect of this alteration. We screened two independent cohorts including a total of 1,314 CLL patients for the presence of the mutation using the rhAmp SNP genotyping assay, and integrated the U1 mutational status with well-known driver alterations, IGHV and epigenetic subgroups, and clinical parameters. Results: The U1 mutation was found in 8/78 (10.3%) CLL cases analyzed by WGS. Given its role in 5'SS recognition by base-pairing, we reasoned that this mutation was likely to alter the splicing and expression patterns of CLL. We were able to confirm widespread specific alterations in the transcriptome by comparing RNA-seq data between wild-type and g.3A>C mutated samples. Applying this knowledge to an algorithm aimed to infer the U1 mutational status from expression data, we were able to identify 4 mutated cases among 240 additional cases that had RNA-seq but no WGS. In total, 12/318 (3.8%) CLL patients analyzed by WGS and/or RNA-seq harbored this mutation. This g.3A>C U1 mutation changes the preferential A-U base-pairing between U1 and 5'SS to C-G base-pairing, creating novel splice junctions and altering the splicing pattern of 3,193 introns in 1,519 genes. In addition to altered splicing, 869 genes were differentially expressed between mutated and wild-type cases. We identified specific cancer genes (e.g. MSI2, POLD1, or CD44) and pathways (B-cell receptor signaling, promotion of apoptosis, telomere maintenance, among others) altered by the U1 mutation. To confirm a causal link between this mutation and splicing changes, we introduced exogenous U1 genes with or without the mutation into three cell lines. Subsequent RNA-seq of these cell lines recapitulated the altered splicing and expression patterns observed in CLL patients. We next screened for the presence of the U1 mutation 1,057 patients (cohort 1) using the rhAmp assay and it was found in 30 (2.8%) cases. The distribution of the mutation was similar in Binet stages and CLL vs monoclonal B-cell lymphocytosis. However, the U1 mutation was almost always found in IGHV unmutated CLL (29/30, p=9.0e-11) and within the naïve-like CLL epigenetic subgroup (p=3.7e-7). None of the U1 mutated cases had mutations in the SF3B1 splicing factor. Considering only pre-treatment CLL samples, U1 mutation was associated with a shorter time to first treatment independently of the Binet stage, IGHV mutational status, epigenetic subgroups, and mutations in the well-known CLL drivers SF3B1, NOTCH1, ATMor TP53. In cohort 2 (n=257), this mutation was found in 13 (5.1%) patients, confirming its enrichment in IGHV unmutated cases, naïve-like epigenetic subgroup, and splicing modulation. Despite the relatively small number of pre-treatment samples carrying the U1 mutation (7/178) and short follow-up of the patients (median 2.6 years), the effect of this mutation on time to first treatment in cohort 2 was compatible with the one observed in cohort 1. Finally, we screened for the U1 mutation a cohort of diffuse large B-cell lymphoma (n=108), mantle cell lymphoma (n=101), follicular lymphoma (n=87), splenic marginal zone lymphoma (n=12), acute myeloid leukemia (n=52), and myelodysplastic syndrome (n=67). The mutation was not present in any of the samples analyzed. Conclusions: Here we have reported that the third base of the small nuclear RNA U1 is recurrently mutated in CLL, proved its effect in splicing and gene expression, and shown that this mutation is independently associated with faster disease progression. The g.3A>C U1 mutation represents a novel non-coding driver alteration in CLL with potential clinical and therapeutic implications. Disclosures Ramirez Payer: GILEAD SCIENCES: Research Funding. Terol:Astra Zeneca: Consultancy; Gilead: Research Funding; Abbvie: Consultancy; Janssen: Consultancy, Research Funding; Roche: Consultancy. Lopez-Guillermo:Celgene: Consultancy, Research Funding; Janssen: Research Funding; Roche: Consultancy, Research Funding; Gilead: Consultancy, Research Funding.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.029
GPT teacher head0.325
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2019
Admission routes1
Has abstractyes

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