| Literature DB >> 29025587 |
Sandeep Mukherjee1, Malini Sathanoori1, Zeq Ma1, Matthew Andreatta1, Patrick A Lennon2, Scott R Wheeler1, James L Prescott1, Christopher Coldren1, Terence Casey1, Heather Rietz1, Kristina Fasig1, Randall Woodford1, Taylor Hartley1, David Spence1, William Donnelan3, Jesus Berdeja3, Ian Flinn3, Natalia Kozyr4, Mark Bouzyk4, Mick Correll5, Hao Ho1, Vladimir Kravtsov1, Dana Tunnel1, Pranil Chandra1.
Abstract
Comprehensive genetic profiling is increasingly important for the clinical workup of hematologic tumors, as specific alterations are now linked to diagnostic characterization, prognostic stratification and therapy selection. To characterize relevant genetic and genomic alterations in myeloid malignancies maximally, we utilized a comprehensive strategy spanning fluorescence in situ hybridization (FISH), classical karyotyping, Chromosomal Microarray (CMA) for detection of copy number variants (CNVs) and Next generation Sequencing (NGS) analysis. In our cohort of 569 patients spanning the myeloid spectrum, NGS and CMA testing frequently identified mutations and copy number changes in the majority of genes with important clinical associations, such as TP53, TET2, RUNX1, SRSF2, APC and ATM. Most importantly, NGS and CMA uncovered medically actionable aberrations in 75.6% of cases normal by FISH/cytogenetics testing. NGS identified mutations in 65.5% of samples normal by CMA, cytogenetics and FISH, whereas CNVs were detected in 10.1% cases that were normal by all other methodologies. Finally, FISH or cytogenetics, or both, were abnormal in 14.1% of cases where NGS or CMA failed to detect any changes. Multiple mutations and CNVs were found to coexist, with potential implications for patient stratification. Thus, high throughput genomic tumor profiling through targeted DNA sequencing and CNV analysis complements conventional methods and leads to more frequent detection of actionable alterations.Entities:
Keywords: Chromosomal microarray; actionable; genomic profiling; myeloid malignancy; next generation sequencing
Mesh:
Year: 2017 PMID: 29025587 DOI: 10.1016/j.cancergen.2017.07.010
Source DB: PubMed Journal: Cancer Genet