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 High-precision and cost-efficient sequencing for real-time COVID-19 surveillance

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sung Yong Park ◽  
Gina Faraci ◽  
Pamela M. Ward ◽  
Jane F. Emerson ◽  
Ha Youn Lee
Keyword(s):  
Los Angeles ◽  
Whole Genome Sequencing ◽  
Real Time ◽  
Genome Sequencing ◽  
High Precision ◽  
High Throughput Sequencing ◽  
Whole Genome ◽  
Sequencing Data ◽  
Public Health Response ◽  
Cost Efficient

AbstractCOVID-19 global cases have climbed to more than 33 million, with over a million total deaths, as of September, 2020. Real-time massive SARS-CoV-2 whole genome sequencing is key to tracking chains of transmission and estimating the origin of disease outbreaks. Yet no methods have simultaneously achieved high precision, simple workflow, and low cost. We developed a high-precision, cost-efficient SARS-CoV-2 whole genome sequencing platform for COVID-19 genomic surveillance, CorvGenSurv (Coronavirus Genomic Surveillance). CorvGenSurv directly amplified viral RNA from COVID-19 patients’ Nasopharyngeal/Oropharyngeal (NP/OP) swab specimens and sequenced the SARS-CoV-2 whole genome in three segments by long-read, high-throughput sequencing. Sequencing of the whole genome in three segments significantly reduced sequencing data waste, thereby preventing dropouts in genome coverage. We validated the precision of our pipeline by both control genomic RNA sequencing and Sanger sequencing. We produced near full-length whole genome sequences from individuals who were COVID-19 test positive during April to June 2020 in Los Angeles County, California, USA. These sequences were highly diverse in the G clade with nine novel amino acid mutations including NSP12-M755I and ORF8-V117F. With its readily adaptable design, CorvGenSurv grants wide access to genomic surveillance, permitting immediate public health response to sudden threats.

scholarly journals Estimating sequencing error rates using families

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Kelley Paskov ◽  
Jae-Yoon Jung ◽  
Brianna Chrisman ◽  
Nate T. Stockham ◽  
Peter Washington ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Exome Sequencing ◽  
Genome Sequencing ◽  
Variant Calling ◽  
Error Rates ◽  
Sequencing Error ◽  
Whole Genome ◽  
Sequencing Data ◽  
Sequencing Platform ◽  
Whole Exome

Abstract Background As next-generation sequencing technologies make their way into the clinic, knowledge of their error rates is essential if they are to be used to guide patient care. However, sequencing platforms and variant-calling pipelines are continuously evolving, making it difficult to accurately quantify error rates for the particular combination of assay and software parameters used on each sample. Family data provide a unique opportunity for estimating sequencing error rates since it allows us to observe a fraction of sequencing errors as Mendelian errors in the family, which we can then use to produce genome-wide error estimates for each sample. Results We introduce a method that uses Mendelian errors in sequencing data to make highly granular per-sample estimates of precision and recall for any set of variant calls, regardless of sequencing platform or calling methodology. We validate the accuracy of our estimates using monozygotic twins, and we use a set of monozygotic quadruplets to show that our predictions closely match the consensus method. We demonstrate our method’s versatility by estimating sequencing error rates for whole genome sequencing, whole exome sequencing, and microarray datasets, and we highlight its sensitivity by quantifying performance increases between different versions of the GATK variant-calling pipeline. We then use our method to demonstrate that: 1) Sequencing error rates between samples in the same dataset can vary by over an order of magnitude. 2) Variant calling performance decreases substantially in low-complexity regions of the genome. 3) Variant calling performance in whole exome sequencing data decreases with distance from the nearest target region. 4) Variant calls from lymphoblastoid cell lines can be as accurate as those from whole blood. 5) Whole-genome sequencing can attain microarray-level precision and recall at disease-associated SNV sites. Conclusion Genotype datasets from families are powerful resources that can be used to make fine-grained estimates of sequencing error for any sequencing platform and variant-calling methodology.

scholarly journals Systematic dissection of biases in whole-exome and whole-genome sequencing reveals major determinants of coding sequence coverage

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yury A. Barbitoff ◽  
Dmitrii E. Polev ◽  
Andrey S. Glotov ◽  
Elena A. Serebryakova ◽  
Irina V. Shcherbakova ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Whole Genome ◽  
Sequence Coverage ◽  
Coding Sequence ◽  
Whole Exome

scholarly journals Readability of informed consent forms for whole-exome and whole-genome sequencing

2017 ◽  
Vol 9 (2) ◽  
pp. 143-151 ◽  
Author(s):  
Emilia Niemiec ◽  
Danya F. Vears ◽  
Pascal Borry ◽  
Heidi Carmen Howard
Keyword(s):  
Informed Consent ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Whole Genome ◽  
Consent Forms ◽  
Whole Exome
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