Comprehensive mutation profiling and mRNA expression analysis in atypical chronic myeloid leukemia in comparison with chronic myelomonocytic leukemia. (11th January 2019)

• Record Type: Journal Article
• Authors: Faisal, Muhammad; Stark, Helge; Büsche, Guntram; Schlue, Jerome; Teiken, Kristin; Kreipe, Hans H.; Lehmann, Ulrich; Bartels, Stephan
• Is Part Of: Cancer medicine. Volume 8:Number 2(2019:Feb.)
• Page Start: 742
• Page End: 750
• Publication Date: 2019-01-11
• Subjects: aCML -- CMML -- machine learning algorithm -- nCounter -- NGS; 616.994005
• DOI: 10.1002/cam4.1946
• Languages: English
• ISSNs: 2045-7634

Abstract

Abstract: Atypical chronic myeloid leukemia (aCML) and chronic myelomonocytic leukemia (CMML) represent two histologically and clinically overlapping myelodysplastic/myeloproliferative neoplasms. Also the mutational landscapes of both entities show congruencies. We analyzed and compared an aCML cohort (n = 26) and a CMML cohort (n = 59) by next‐generation sequencing of 25 genes and by an nCounter approach for differential expression in 107 genes. Significant differences were found with regard to the mutation frequency of TET2, SETBP1, and CSF3R . Blast content of the bone marrow revealed an inverse correlation with the mutation status of SETBP1 in aCML and TET2 in CMML, respectively. By linear discriminant analysis, a mutation‐based machine learning algorithm was generated which placed 19/26 aCML cases (73%) and 54/59 (92%) CMML cases into the correct category. After multiple correction, differential mRNA expression could be detected between both cohorts in a subset of genes ( FLT3, CSF3R, and SETBP1 showed the strongest correlation). However, due to high variances in the mRNA expression, the potential utility for the clinic is limited. We conclude that a medium‐sized NGS panel provides a valuable assistance for the correct classification of aCML and CMML. Abstract : In this study, we present the mutation profile of 25 genes and the mRNA expression pattern of 107 genes in a cohort of atypical chronic myeloid leukemia cases (aCML, n = 26) in comparison with a cohort of chronic myelomonocytic leukemia cases (CMML, n = 59). Our aim was to identify molecular markers which may contribution to the discrimination of aCML and CMML, two entities of hematopoietic neoplasms which display a combination of myelodysplastic and myeloproliferative features (MDS/MPN). Employing a machine learning classification approach using linear discriminant analysis of the mutation data, we were able to classify more than 85% of aCML and CMML cases correctly In addition, a statistically significant negative correlation of SETBP1 mutation (in aCML) and TET2 mutation (in CMML) with the presence of a blast excess in the bone marrow was found.