A complementary role of multiparameter flow cytometry and high-throughput sequencing for minimal residual disease detection in chronic lymphocytic leukemia: an European Research Initiative on CLL study.

Abstract

In chronic lymphocytic leukemia (CLL) the level of minimal residual disease (MRD) after therapy is an independent predictor of outcome. Given the increasing number of new agents being explored for CLL therapy, using MRD as a surrogate could greatly reduce the time necessary to assess their efficacy. In this European Research Initiative on CLL (ERIC) project we have identified and validated a flow-cytometric approach to reliably quantitate CLL cells to the level of 0.0010% (10(-5)). The assay comprises a core panel of six markers (i.e. CD19, CD20, CD5, CD43, CD79b and CD81) with a component specification independent of instrument and reagents, which can be locally re-validated using normal peripheral blood. This method is directly comparable to previous ERIC-designed assays and also provides a backbone for investigation of new markers. A parallel analysis of high-throughput sequencing using the ClonoSEQ assay showed good concordance with flow cytometry results at the 0.010% (10(-4)) level, the MRD threshold defined in the 2008 International Workshop on CLL guidelines, but it also provides good linearity to a detection limit of 1 in a million (10(-6)). The combination of both technologies would permit a highly sensitive approach to MRD detection while providing a reproducible and broadly accessible method to quantify residual disease and optimize treatment in CLL.

https://www.ncbi.nlm.nih.gov/pubmed/26639181

New Computer Program Can Help Uncover Hidden Genomic Alterations that Drive Cancers

Cancer is rarely the result of a single mutation in a single gene. Rather, tumors arise from the complex interplay between any number of mutually exclusive abnormal changes in the genome, the combinations of which can be unique to each individual patient. To better characterize the functional context of genomic variations in cancer, researchers at University of California San Diego School of Medicine and the Broad Institute developed a new computer algorithm they call REVEALER.

To continue reading click here: Program to uncover hidden genomic alteration