Abstract

Tumor-derived cell lines provide important models of disease but require validation with regard to parental disease. Hairy cell leukemia (HCL) is a rare B cell leukemia with characteristic hair-like cytoplasmic projections on tumor cells and they uniquely express multiple surface immunoglobulin (sIg) isotypes. A mutation [V(600)E] in exon 15 of BRAF, a signal transduction protein kinase, has recently been identified as near universal in typical or classic HCL (HCLc), but absent in atypical variant HCL (HCLv) and HCLc expressing the rearranged IGHV4-34 gene. In a few HCLc cases lacking the [V(600)E] mutation, alternative mutations in [D(449)E] and [F(468)C] have been reported in BRAF. We previously examined 5 HCL cell lines used as relevant disease models, Hair-M, HCLL-7876, EH, Eskol, HC-1, and HCLv-07 (the latter of known HCLv origin) and found that while they recapitulated phenotypic and functional characteristics of HCLc, they lacked the definitive BRAF [V(600)E] mutation, as did HCLv-07. More recently, whole exome next-generation sequencing studies in HCLv and HCLc/IGHV4-34 disease subsets have identified recurrent mutations in the MAP2K1 gene in ∼50% of atypical cases, all lacking the BRAF [V(600)E] mutation. MAP2K1 encodes MEK1, a dual-specificity kinase that is a direct effector of BRAF. A mutant mitogen-activated protein kinase (MAPK) pathway emerges as an important driver of neoplastic transformation in HCLc and in HCLv and HCLc/IGHV4-34 disease. We examined the reported MAP2K1 mutations and alternative BRAF mutations in each of the HCL cells lines. In 5/5 lines, no MAP2K1 or alternative BRAF mutations were found, including in the HCLv cell line. These data indicate derivation of known HCL cell lines from atypical HCL disease that lack MAP2K1 mutations, and suggest caution in their experimental use to represent specific HCL subsets. They further substantiate the importance of whole genome sequencing in providing robust genetic markers to validate disease models, in addition to identifying driver mutations relevant to understanding disease origins.

Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call