Abstract 2449: Transcriptome and whole-genome sequencing analysis of a novel murine model of chronic lymphocytic leukemia

2017 ◽  
Author(s):  
Lili Wang ◽  
Jing Sun ◽  
Amaro Taylor-Weiner ◽  
Jaegil Kim ◽  
Zachary J. Cartun ◽  
...  
Keyword(s):  
Chronic Lymphocytic Leukemia ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Murine Model ◽  
Lymphocytic Leukemia ◽  
Whole Genome ◽  
Sequencing Analysis

Whole-exome and whole-genome sequencing in chronic lymphocytic leukemia: new biomarkers to target

2020 ◽  
Vol 21 (13) ◽  
pp. 957-962
Author(s):  
Charbel Hobeika ◽  
Gaelle Rached ◽  
Alain Chebly ◽  
Eliane Chouery ◽  
Hampig Raphael Kourie
Keyword(s):  
Chronic Lymphocytic Leukemia ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
High Throughput Sequencing ◽  
Gene Mutations ◽  
Lymphocytic Leukemia ◽  
Whole Genome ◽  
Mutational Status ◽  
Whole Exome ◽  
Worse Prognosis

Many biomarkers indicate prognosis in chronic lymphocytic leukemia; such as fluorescence in situ hybridization testing: 17p or 11q deletions have a worse prognosis than trisomy 12, 13q deletion or normal result, or the mutational status of the immunoglobulin heavy chain (IGHV): unmutated IGHV have a worse prognosis than mutated IGHV. Recently, many gene mutations ( TP53, NOTCH1 etc.,) have been linked to a worse prognosis. With the new era of high-throughput sequencing, it has become easier to study gene mutations and their implication in predicting prognosis. In this review, we aim to review all the studies that performed whole-exome sequencing or whole-genome sequencing on chronic lymphocytic leukemia cells and explore the implication of various genes in disease prognosis.

Monitoring chronic lymphocytic leukemia progression by whole genome sequencing reveals heterogeneous clonal evolution patterns

Blood ◽  
2012 ◽  
Vol 120 (20) ◽  
pp. 4191-4196 ◽  
Author(s):  
Anna Schuh ◽  
Jennifer Becq ◽  
Sean Humphray ◽  
Adrian Alexa ◽  
Adam Burns ◽  
...  
Keyword(s):  
Chronic Lymphocytic Leukemia ◽  
Whole Genome Sequencing ◽  
Disease Progression ◽  
Genome Sequencing ◽  
Clonal Evolution ◽  
Chemotherapy Resistance ◽  
Treatment Strategies ◽  
Lymphocytic Leukemia ◽  
Whole Genome ◽  
Founder Mutations

Abstract Chronic lymphocytic leukemia is characterized by relapse after treatment and chemotherapy resistance. Similarly, in other malignancies leukemia cells accumulate mutations during growth, forming heterogeneous cell populations that are subject to Darwinian selection and may respond differentially to treatment. There is therefore a clinical need to monitor changes in the subclonal composition of cancers during disease progression. Here, we use whole-genome sequencing to track subclonal heterogeneity in 3 chronic lymphocytic leukemia patients subjected to repeated cycles of therapy. We reveal different somatic mutation profiles in each patient and use these to establish probable hierarchical patterns of subclonal evolution, to identify subclones that decline or expand over time, and to detect founder mutations. We show that clonal evolution patterns are heterogeneous in individual patients. We conclude that genome sequencing is a powerful and sensitive approach to monitor disease progression repeatedly at the molecular level. If applied to future clinical trials, this approach might eventually influence treatment strategies as a tool to individualize and direct cancer treatment.

scholarly journals Sarek: A portable workflow for whole-genome sequencing analysis of germline and somatic variants

2018 ◽  
Author(s):  
Maxime Garcia ◽  
Szilveszter Juhos ◽  
Malin Larsson ◽  
Pall I. Olason ◽  
Marcel Martin ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Open Source ◽  
Genome Sequencing ◽  
Development Project ◽  
Whole Genome ◽  
Sequencing Analysis ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Insertion And Deletion ◽  
Sample Heterogeneity

AbstractSummaryWhole-genome sequencing (WGS) is a cornerstone of precision medicine, but portable and reproducible open-source workflows for WGS analyses of germline and somatic variants are lacking. We present Sarek, a modular, comprehensive, and easy-to-install workflow, combining a range of software for the identification and annotation of single-nucleotide variants (SNVs), insertion and deletion variants (indels), structural variants, tumor sample heterogeneity, and karyotyping from germline or paired tumor/normal samples. Sarek is implemented in a bioinformatics workflow language (Nextflow) with Docker and Singularity compatible containers, ensuring easy deployment and full reproducibility at any Linux based compute cluster or cloud computing environment. Sarek supports the human reference genomes GRCh37 and GRCh38, and can readily be used both as a core production workflow at sequencing facilities and as a powerful stand-alone tool for individual research groups.AvailabilitySource code and instructions for local installation are available at GitHub (https://github.com/SciLifeLab/Sarek) under the MIT open-source license, and we invite the research community to contribute additional functionality as a collaborative open-source development project.

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