We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

LabMedica

Download Mobile App
Recent News Expo Clinical Chem. Molecular Diagnostics Hematology Immunology Microbiology Pathology Technology Industry Focus

Genomic Sequencing Augments Diagnosis of Lymphoid Cancer

By LabMedica International staff writers
Posted on 20 Feb 2018
Lymphoid cancers include diffuse large B-cell lymphomas (DLBCL), follicular lymphoma (FL), and chronic lymphocytic leukemia (CLL). In recent years, several sequencing-based assays have been developed, but their clinical applicability and utility for patients with specific mutations still needs to be verified.

Next-generation sequencing technologies have been instrumental in accelerating discovery in cancer genomics via whole-genome sequencing (WGS), whole-transcriptome sequencing (RNA sequencing), and deep-targeted sequencing. Two methods are commonly used for such targeted approaches: capture hybridization–based sequencing and amplicon-based sequencing, each having its own advantages and disadvantages.

Image: The HiSeq X whole genome sequencing system (Photo courtesy of Illumina).
Image: The HiSeq X whole genome sequencing system (Photo courtesy of Illumina).

Scientists at the University of British Columbia (Vancouver, BC, Canada) and their colleagues established that hybrid-capture sequencing is the method of choice for sequencing “actionable” gene mutations across the most common forms of lymphoid cancer. Due to its applicability in routinely acquired formalin-fixed, paraffin-embedded tissues, this assay can be implemented by clinical laboratories into routine diagnostic workflows. A cohort of matched tumor and normal DNA specimens was assembled from 229 patients, including 30 CLL, 80 DLBCL, and 119 FL cases. Formalin-fixed paraffin-embedded (FFPE) tissue blocks and peripheral blood DNA samples for all FL and DLBCL cases were acquired from the BC Cancer Agency (Vancouver, BC, Canada) lymphoma tumor bank.

The team used various techniques to analyze the blood and tissue samples. These included for WGS, specimens for 66 of 229 patients from the original cohort were frozen and embedded in OCT compound for DNA and RNA extractions, as well as frozen sections for histological correlation; capture sequencing with the Illumina MiSeq; Amplicon and Sanger Sequencing; Whole-Genome Library Construction and Sequencing performed on an Illumina HiSeq2500 or HiSeqX instruments.

Samples were sequenced using a panel of 20 lymphoma-specific genes via capture hybridization using Sure Select and amplicon sequencing using Illumina TruSeq Custom Amplicon. The study focused on the development of a sequencing pipeline to personalize lymphoma management establishes feasibility of capture sequencing of routinely acquired FFPE tissue (DLBCL) and fresh cell preparations (CLL) in a single assay. The team demonstrated the superiority of hybrid-capture sequencing over amplicon-based method with respect to assay sensitivity in an approach intended to cover a large capture space across the coding region of 32 genes.

Christian Steidl, MD, an Associate Professor and senior investigator of the study, said, “Our developed assay harnesses the power of modern sequencing for clinical diagnostics purposes and potentially better deployment of novel treatments in lymphoid cancers. We believe our study will help establish evidence-based approaches to decision making in lymphoid cancer care.” The study was published on February 8, 2018, in The Journal of Molecular Diagnostics.

Related Links:
University of British Columbia
BC Cancer Agency


Platinum Member
COVID-19 Rapid Test
OSOM COVID-19 Antigen Rapid Test
Magnetic Bead Separation Modules
MAG and HEATMAG
POCT Fluorescent Immunoassay Analyzer
FIA Go
New
Gold Member
Magnetic Bead Separation Modules
MAG and HEATMAG

Latest Molecular Diagnostics News

Urine-Based Test Detects Head and Neck Cancer

Blood-Based Test Detects and Monitors Aggressive Small Cell Lung Cancer

Blood-Based Machine Learning Assay Noninvasively Detects Ovarian Cancer