Identification of prognostic parameters in CLL with no abnormalities detected by chromosome banding and FISH analyses. (18th July 2018)

• Record Type: Journal Article
• Authors: Vetro, Calogero; Haferlach, Torsten; Jeromin, Sabine; Stengel, Anna; Zenger, Melanie; Nadarajah, Niroshan; Baer, Constance; Weissmann, Sandra; Kern, Wolfgang; Meggendorfer, Manja; Haferlach, Claudia
• Is Part Of: British journal of haematology. Volume 183:Number 1(2018)
• Page Start: 47
• Page End: 59
• Publication Date: 2018-07-18
• Subjects: chronic lymphocytic leukaemia -- cytogenetics -- molecular genetics; Hematology -- Periodicals; Blood -- Diseases -- Periodicals; 616.15
• DOI: 10.1111/bjh.15498
• Languages: English
• ISSNs: 0007-1048

Abstract

Abstract: Chronic Lymphocytic Leukaemia (CLL) is a heterogeneous disease with a clinical course dependent on cytogenetic features. However, in 15–20% of cases both chromosome banding and fluorescence in situ hybridisation analyses do not show any kind of abnormality. With the aim to identify dependable molecular prognostic factors in this subgroup, we performed a comprehensive analysis on 171 patients including genomic arrays (comparative genomic hybridisation and single nucleotide polymorphism), immunoglobulin heavy chain variable region genes (IGHV) status, flow cytometry and targeted sequencing. Genomic arrays detected 73 aberrations in 39 patients (23%). Most frequently, patients had 1 aberration (25/171; 15%), while 14 patients (8%) had at least 2 aberrations. IGHV status was unmutated in 53/171 (31%) patients. SF3B1 was the most frequently mutated gene (26/171 patients; 15%), followed by NOTCH1 (15/171; 9%). At univariate analysis, an adverse impact on time to treatment (TTT) was evident for SF3B1 mutations, higher white blood cell count, higher CLL cells percentage by flow cytometry, CD38 positivity, IGHV unmutated status and at least 2 genomic array abnormalities. Of these, SF3B1 mutations, CLL cells percentage, IGHV unmutated status and number of genomic array aberrations maintained their impact in multivariate analysis. In conclusion, by integrating genomic and molecular data, we identified patients at higher risk for treatment need.