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dbGaP phs000922 Genetic Aberrations and Subclonal Structure Impact Chronic Lymphocytic Leukemia - pht004924.v2.p1

- 18-04-24 - 4 Formulieren, 1 Itemgroep, 3 Data-elementen, 1 Taal
Itemgroep: pht004924
Principal Investigator: Catherine Wu, MD, Dana-Farber Cancer Institute, Boston, MA, USA MeSH: Leukemia, Lymphocytic, Chronic, B-Cell https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000922 Large-scale whole-exome sequencing (WES) of primary tumor samples enables the unbiased discovery of recurrent putative driver events and patterns of clonal evolution. We report the identification of 44 recurrently mutated genes and 11 recurrent CNVs through the WES of 538 chronic lymphocytic leukemia (CLL) and matched germline DNAs. These include previously unrecognized cancer drivers (e.g., RPS15, IKZF3), and collectively identify nuclear export, MYC activity and MAPK signaling as central pathways affected by somatic mutation in CLL. A clonality analysis of this large dataset further enabled the reconstruction of temporal relationships between these driver events. Several drivers were associated with shorter progression-free survival (PFS) in 280 samples that were collected prior to uniform treatment with front line chemo-immunotherapy, with mature follow up of greater than 10 years. Direct comparison between matched pretreatment and relapse CLL from 59 samples demonstrated marked clonal evolution occurring in more than 95% of these patients. Distinct patterns of clonal evolution in relationship to specific gene alteration were observed, suggesting a hierarchy of fitness amongst mutations. Thus, large WES datasets of clinically informative samples enable the discovery of novel driver genes as well as the network of relationships between the drivers and their impact on disease relapse and clinical outcome. Additionally, we performed RNA-seq for 268 CLL samples (including 26 follow-up samples) and used them to identify expression subtypes of CLL. RRBS for 30 of these samples was also generated. In an integrative analysis of genetic, transcriptomic, and epigenetic data, incorporating known and newly identified subtypes of CLL, we built new models to improve patient prognostication.

pht004925.v2.p1

1 Itemgroep 2 Data-elementen

pht004926.v2.p1

1 Itemgroep 2 Data-elementen

pht004927.v2.p1

1 Itemgroep 7 Data-elementen

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