Transcriptome characterization by RNA sequencing identifies a major molecular and clinical subdivision in chronic lymphocytic leukemia
Open accessAbstract
Chronic lymphocytic leukemia (CLL) has heterogeneous clinical and biological behavior. Whole-genome and -exome sequencing has contributed to the characterization of the mutational spectrum of the disease, but the underlying transcriptional profile is still poorly understood. We have performed deep RNA sequencing in different subpopulations of normal B-lymphocytes and CLL cells from a cohort of 98 patients, and characterized the CLL transcriptional landscape with unprecedented resolution. We detected thousands of transcriptional elements differentially expressed between the CLL and normal B cells, including protein-coding genes, noncoding RNAs, and pseudogenes. Transposable elements are globally derepressed in CLL cells. In addition, two thousand genes—most of which are not differentially expressed—exhibit CLL-specific splicing patterns. Genes involved in metabolic pathways showed higher expression in CLL, while genes related to spliceosome, proteasome, and ribosome were among the most down-regulated in CLL. Clustering of the CLL samples according to RNA-seq derived gene expression levels unveiled two robust molecular subgroups, C1 and C2. C1/C2 subgroups and the mutational status of the immunoglobulin heavy variable ( IGHV ) region were the only independent variables in predicting time to treatment in a multivariate analysis with main clinico-biological features. This subdivision was validated in an independent cohort of patients monitored through DNA microarrays. Further analysis shows that B-cell receptor (BCR) activation in the microenvironment of the lymph node may be at the origin of the C1/C2 differences.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
RNA-seq provides a powerful tool to decipher global gene expression patterns far beyond the limitations of microarrays.
“RNA deep-sequencing (RNA-seq) provides a powerful tool to decipher global gene expression patterns far beyond the limitations of microarrays, including an unprecedented capability to discover novel genes, alternative transcript variants, chimeric transcripts, and expressed sequence variants as well as allele-specific expression [8–12].”
From Comparison of RNA-seq and microarray-based models for clinical endpoint prediction (2015) · cites this paper as [12]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 70 · Cited by 214
- Authors
- Ferreira, P. G., Jares, P., Rico, D., Gómez-López, G., Martínez-Trillos, A., Villamor, N., Ecker, S., González-Pérez, A., Knowles, D. G., Monlong, J., Johnson, R., Quesada, V., Djebali, S., Papasaikas, P., López-Guerra, M., Colomer, D., Royo, C., Cazorla, M., Pinyol, M., Clot, G., Aymerich, M., Rozman, M., Kulis, M., Tamborero, D., Gouin, A., Blanc, J., Gut, M., Gut, I., Puente, X. S., Pisano, D. G., Martin-Subero, J. I., López-Bigas, N., López-Guillermo, A., Valencia, A., López-Otín, C., Campo, E., & Guigó, R.
- Affiliations
- Universitat Pompeu Fabra (ES)
- Hospital Clínic de Barcelona (ES)
- Spanish National Cancer Research Centre (ES)
- Centro Nacional de Análisis Genómico (ES)
- Corresponding author
- Guigó, Roderic
- ORCID
- Ferreira, Pedro G.
- Jares, Pedro
- Rico, Daniel
- Villamor, Neus
- Ecker, Simone
- González-Pérez, Abel
- Monlong, Jean
- Johnson, Rory
- Djebali, Sarah
- Papasaikas, Panagiotis
- Colomer, Dolors
- Royo, Cristina
- Clot, Guillem
- Aymerich, Marta
- Kulis, Marta
- Tamborero, David
- Blanc, Julie
- Gut, Ivo
- Pisano, David G.
- Valencia, Alfonso
- Guigó, Roderic
- Venue
- Genome Research
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2013-11-21
- Volume
- 24
- Issue
- 2
- Pages
- 212-226
- ISSN
- 1088-9051 (ISSN-L)
- PMC
- PMC3912412
- OpenAlex
- W2103385435
- Cited by
- 214
- References
- 70
- Citations by year
- 2026: 4 · 2025: 8 · 2024: 12 · 2023: 11 · 2022: 14 · 2021: 29 · 2020: 16 · 2019: 15 · 2018: 13 · 2017: 23 · 2016: 30 · 2015: 27 · 2014: 12
- Type
- posted content
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Genome Research (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Dipòsit Digital de la Universitat de Barcelona (Universitat de Barcelona) (publishedVersion)
- RECERCAT (Consorci de Serveis Universitaris de Catalunya) (publishedVersion, other-oa)
- HAL (submittedVersion)
- Consultation of the Doctoral Thesis Database (TESEO) (Ministerio de Educación, Cultura y Deporte) (submittedVersion, cc-by-nc-nd)
- UCL Discovery (University College London) (submittedVersion)
- HighWire Press Open Archive (submittedVersion)
- RECERCAT (Consorci de Serveis Universitaris de Catalunya) (submittedVersion, public-domain)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (submittedVersion, cc-by-nc)
- Funders
- Institució Catalana de Recerca i Estudis Avançats
- Ministerio de Ciencia y Tecnología
- Instituto de Salud Carlos III
- Topics
- Health Sciences › Medicine › Genetics › Chronic Lymphocytic Leukemia Research
- Health Sciences › Medicine › Pathology and Forensic Medicine › Lymphoma Diagnosis and Treatment
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Acute Lymphoblastic Leukemia research
- Keywords
- chronic lymphocytic leukemia, transcriptome, molecular subtypes, IGHV mutation status, B cell receptor activation, transposable elements, alternative splicing, non-coding RNA, pseudogenes, metabolic pathways, time to treatment, prognostic factors
- MeSH terms
- Aged, B-Lymphocytes, Base Sequence, Female, Humans, Immunoglobulin Variable Region, Male, Middle Aged, Mutation, Ribosomes, Leukemia, Lymphocytic, Chronic, B-Cell, Gene Expression Regulation, Neoplastic, Spliceosomes, Gene Expression Profiling, High-Throughput Nucleotide Sequencing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
