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Identification of prognostic parameters in CLL with no abnormalities detected by chromosome banding and FISH analyses
Author(s) -
Vetro Calogero,
Haferlach Torsten,
Jeromin Sabine,
Stengel Anna,
Zenger Melanie,
Nadarajah Niroshan,
Baer Constance,
Weissmann Sandra,
Kern Wolfgang,
Meggendorfer Manja,
Haferlach Claudia
Publication year - 2018
Publication title -
british journal of haematology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.907
H-Index - 186
eISSN - 1365-2141
pISSN - 0007-1048
DOI - 10.1111/bjh.15498
Subject(s) - fish <actinopterygii> , identification (biology) , chromosome , genetics , biology , microbiology and biotechnology , medicine , fishery , gene , ecology
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. SF 3B1 was the most frequently mutated gene (26/171 patients; 15%), followed by NOTCH 1 (15/171; 9%). At univariate analysis, an adverse impact on time to treatment ( TTT ) was evident for SF 3B1 mutations, higher white blood cell count, higher CLL cells percentage by flow cytometry, CD 38 positivity, IGHV unmutated status and at least 2 genomic array abnormalities. Of these, SF 3B1 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.