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CLL Genome

The Chronic Lymphocytic Leukemia (CLL) Genome Project aims to identify genetic alterations involved in the development and progression of the CLL, which are still unknown, with the objective of generating a comprehensive catalogue of genetic alterations in 500 independent tumours. The CLL Genome Project, as a contributing member of the International Cancer Genome Consortium (ICGC), has the purposes of creating diagnostic tools, discovering therapeutic targets and developing new strategies that will allow a customized therapy for CLL in order to make it more precise and effective. This study consists of two datasets. EGAD00001000023 described in the Nature paper (2011), Whole-genome sequencing identifies recurrent mutations in chronic lymphocytic leukaemia, and the dataset EGA00001000044 & & EGAD00001000083 described in the Nature (2011) paper, Exome sequencing identifies recurrent mutations of the splicing factor SF3B1 gene in chronic lymphocytic leukemia.Additional data generated within the ICGC-CLL genome project can be found in studies EGAS00001000374 and EGAS00001001306

Click on a Dataset ID in the table below to learn more, and to find out who to contact about access to these data

Dataset ID Description Technology Samples
EGAD00001000023 Illumina Genome Analyzer IIx 11
EGAD00001000044 Illumina Genome Analyzer IIx 212
EGAD00001000083 Illumina Genome Analyzer II Illumina Genome Analyzer IIx 61
EGAD00010000238 Affymetrix GeneChip Human Genome U133 plus 2.0 64
EGAD00010000280 Affymetrix snp 6.0 4
EGAD00010000470 GPL570 20
Publications Citations
Whole-genome sequencing identifies recurrent mutations in chronic lymphocytic leukaemia.
Nature 475: 2011 101-105
Estimation of copy number alterations from exome sequencing data.
PLoS One 7: 2012 e51422
The role of the interactome in the maintenance of deleterious variability in human populations.
Mol Syst Biol 10: 2014 752
Genetic Predisposition to Chronic Lymphocytic Leukemia Is Mediated by a BMF Super-Enhancer Polymorphism.
Cell Rep 16: 2016 2061-2067
Allele balance bias identifies systematic genotyping errors and false disease associations.
Hum Mutat 40: 2019 115-126
The Identification and Interpretation of cis-Regulatory Noncoding Mutations in Cancer.
High Throughput 8: 2018 E1
Deep convolutional neural networks for accurate somatic mutation detection.
Nat Commun 10: 2019 1041
A practical guide for mutational signature analysis in hematological malignancies.
Nat Commun 10: 2019 2969
Insight into genetic predisposition to chronic lymphocytic leukemia from integrative epigenomics.
Nat Commun 10: 2019 3615
The rate and spectrum of mosaic mutations during embryogenesis revealed by RNA sequencing of 49 tissues.
Genome Med 12: 2020 49
Efficient and flexible Integration of variant characteristics in rare variant association studies using integrated nested Laplace approximation.
PLoS Comput Biol 17: 2021 e1007784
mmsig: a fitting approach to accurately identify somatic mutational signatures in hematological malignancies.
Commun Biol 4: 2021 424
The proliferative history shapes the DNA methylome of B-cell tumors and predicts clinical outcome.
Nat Cancer 1: 2020 1066-1081
PanCancer analysis of somatic mutations in repetitive regions reveals recurrent mutations in snRNA U2.
NPJ Genom Med 7: 2022 19
Molecular map of chronic lymphocytic leukemia and its impact on outcome.
Nat Genet 54: 2022 1664-1674
<i>SF3B1</i> mutation-mediated sensitization to H3B-8800 splicing inhibitor in chronic lymphocytic leukemia.
Life Sci Alliance 6: 2023 e202301955