Genetic Characterization of Waldenstrom Macroglobulinemia By Next Generation Sequencing: An Analysis of Fouteen Genes in a Series of 61 Patients

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 2971-2971 ◽  
Author(s):  
Cristina Jimenez ◽  
Isabel Prieto-Conde ◽  
María García-Álvarez ◽  
María Carmen Chillón ◽  
Aránzazu García-Mateo ◽  
...  

Abstract Background: Waldenström's macroglobulinemia (WM) is a rare immunoproliferative neoplasia with indolent characteristics that shows important variability, involving three different stages of presentation: IgM Monoclonal Gammopathy of Undetermined Significance (IgM-MGUS), asymptomatic WM (AWM), and Symptomatic WM (SWM). Whole-genome sequencing and some specific approaches have identified MYD88 L265P (90%) and CXCR4 (29%) mutations as the most recurrent somatic mutations in WM. However, other genetic abnormalities under as well as the mechanisms responsible for this clinical heterogeneity still remain to be clarified. Therefore, our aim was to analyze the genomic landscape of WM, distinguishing between the three stages of the disease, by using a targeted next generation sequencing (NGS) strategy. Methods: In this study, we performed a comprehensive mutation analysis of genes previously described as frequently involved in Waldenstrom Macroglobulinemia in a large and well characterized cohort of WM patients with the aim to dissect relationships between genotype and clinical and biological characteristics to integrate somatic mutations into a clinical/molecular prognostic model. Twelve genes of interest (ARID1A, CD79A, CD79B, TP53, MYBBP1A, TRAF2, TRAF3, RAG2, HIST1H1B, HIST1H1C, HIST1H1D, and HIST1H1E) were analyzed by high throughput sequencing (Illumina MiSeq, San Diego, CA) with a novel custom amplicon-based panel in a cohort of 61 patients (pts) diagnosed according to WHO classification as follows: 14 MGUS, 23 AWM and 24 SWM. DNA was extracted from bone marrow separated CD19+ B-cells and sequenced in a MiSeq (Illumina) using 150-bp paired-end reads and a mean depth of 2000X. Bioinformatics analysis was carried out with Illumina VariantStudio 2.2. Results were correlated with biological and clinical data of the patients. MYD88 and CXCR4 mutation status, available in all cases, was assessed by ASO-PCR and Sanger Sequencing, respectively. Results: Apart from MYD88 L265P mutations (present in 90% of cases) and CXCR4WHIM (21% of cases), 23 non-synonymous mutations were found, corresponding to 18/61 (30%) patients. Only one patient with MGUS demonstrated one additional mutation (7%), while seven of the AWM (30%), and 10 of the SWM (42%) demonstrated additional mutations (p<0.05 for linear association), suggesting an association between the clinical behavior of the disease and a higher number of mutations. Interestingly, patients with a wild MYD88 gene (n=6), showed no additional mutations in any of these studied genes. Genes most frequently mutated were CD79B (n=5, 8%), HIST1H1E (n=4, 7%), MYBBP1A (n=3, 5%), ARID1A and HIST1H1B (n=2, 3% for both). There were three patients who presented more than one gene mutated (TP53/CD79B; RAG2/ARID1A; HIST1H1B/HIST1H1E). Apart from the clinical diagnosis and the requirement of therapy, no relevant correlations between the presence of mutations and the final clinical behavior was found in any patient, although the patient with a mutated TP53 corresponded to a very high resistant form of the disease. Finally, no relevant differences in progression free and overall survival were seen in this series based on the presence or absence of somatic mutations. Conclusion: Our data reveal an increased incidence of mutations along the different steps of evolution in WM: IgM MGUS, asymptomatic WM and symptomatic WM. Thus, this would mean that in contrast to MYD88 L265P, present from the beginning of the pathogenesis, most of these mutations would be acquired during the evolution of the disease, and before therapy initiation. Finally, CD79B, which is part of the B-cell receptor pathway, was frequently mutated gene in our series, emerging as an interesting therapeutic target. This confirms the relevance of the BCR signaling pathway, reinforcing the use of biological agents blocking this pathway in the treatment of these patients. Disclosures Mateos: Celgene: Consultancy, Honoraria; Takeda: Consultancy; Onyx: Consultancy; Janssen-Cilag: Consultancy, Honoraria. Ocio:Array BioPharma: Consultancy, Research Funding; Celgene: Consultancy, Honoraria; Amgen/Onyx: Consultancy, Honoraria, Research Funding; Bristol Myers Squibb: Consultancy; Mundipharma: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; MSD: Research Funding; Pharmamar: Consultancy, Research Funding; Janssen: Honoraria. Puig:Janssen: Consultancy; The Binding Site: Consultancy.

2014 ◽  
Vol 13 (7) ◽  
pp. 1918-1928 ◽  
Author(s):  
Junfeng Xia ◽  
Peilin Jia ◽  
Katherine E. Hutchinson ◽  
Kimberly B. Dahlman ◽  
Douglas Johnson ◽  
...  

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 4359-4359
Author(s):  
Koji Sasaki ◽  
Rashmi Kanagal-Shamanna ◽  
Guillermo Montalban-Bravo ◽  
Rita Assi ◽  
Kiran Naqvi ◽  
...  

Abstract Introduction: Clearance of detected somatic mutations at complete response by next-generation sequencing is a prognostic marker for survival in patients with acute myeloid leukemia (AML). However, the impact of allelic burden and persistence of clonal hematopoiesis of indeterminate potential (CHIP)-associated mutations on survival remains unclear. The aim of this study is to evaluate the prognostic impact of allelic burden of CHIP mutations at diagnosis, and their persistence within 6 months of therapy. Methods: From February 1, 2012 to May 26, 2016, we reviewed 562 patients with newly diagnosed AML. Next-generation sequencing was performed on the bone marrow samples to detect the presence of CHIP-associated mutations defined as DNMT3A, TET2, ASXL1, JAK2 and TP53. Overall survival (OS) was defined as time period from the diagnosis of AML to the date of last follow-up or death. Univariate (UVA) and multivariate Cox proportional hazard regression (MVA) were performed to identify prognostic factors for OS with p value cutoff of 0.020 for the selection of variables for MVA. Landmark analysis at 6 months was performed for the evaluation of the impact of clearance of CHIP, FLT3-ITD, FLT3D835, and NPM1 mutations. Results: We identified 378 patients (74%) with AML with CHIP mutations; 134 patients (26%) with AML without CHIP mutations. The overall median follow-up of 23 months (range, 0.1-49.0). The median age at diagnosis was 70 years (range, 17-92) and 66 years (range, 20-87) in CHIP AML and non-CHIP AML, respectively (p =0.001). Of 371 patients and 127 patients evaluable for cytogenetic in CHIP AML and non-CHIP AML, 124 (33%) and 25 patients (20%) had complex karyotype, respectively (p= 0.004). Of 378 patients with CHIP AML, 183 patients (48%) had TET2 mutations; 113 (30%), TP53; 110 (29%), ASXL1; 109 (29%), DNMT3A; JAK2, 46 (12%). Of 378 patients, single CHIP mutations was observed in 225 patients (60%); double, 33 (9%); triple, 28 (7%); quadruple, 1 (0%). Concurrent FLT3-ITD mutations was detected in 47 patients (13%) and 12 patients (9%) in CHIP AML and non-CHIP AML, respectively (p= 0.287); FLT3-D835, 22 (6%) and 8 (6%), respectively (p= 0.932); NPM1 mutations, 62 (17%) and 13 (10%), respectively (p= 0.057). Of 183 patients with TET2-mutated AML, the median TET2 variant allele frequency (VAF) was 42.9% (range, 2.26-95.32); of 113 with TP53-mutated AML, the median TP53 VAF, 45.9% (range, 1.15-93.74); of 109 with ASXL1-mutated AML, the median ASXL1 VAF was 34.5% (range, 1.17-58.62); of 109 with DNMT3A-mutated AML, the median DNMT3A VAF was 41.8% (range, 1.02-91.66); of 46 with JAK2-mutated AML, the median JAK2 VAF was 54.4% (range, 1.49-98.52). Overall, the median OS was 12 months and 11 months in CHIP AML and non-CHIP AML, respectively (p= 0.564); 16 months and 5 months in TET2-mutated AML and non-TET2-mutated AML, respectively (p <0.001); 4 months and 13 months in TP53-mutated and non-TP53-mutated AML, respectively (p< 0.001); 17 months and 11 months in DNMT3A-mutated and non-DNMT3A-mutated AML, respectively (p= 0.072); 16 months and 11 months in ASXL1-mutated AML and non-ASXL1-mutated AML, respectively (p= 0.067); 11 months and 12 months in JAK2-murated and non-JAK2-mutated AML, respectively (p= 0.123). The presence and number of CHIP mutations were not a prognostic factor for OS by univariate analysis (p=0.565; hazard ratio [HR], 0.929; 95% confidence interval [CI], 0.722-1.194: p= 0.408; hazard ratio, 1.058; 95% confidence interval, 0.926-1.208, respectively). MVA Cox regression identified age (p< 0.001; HR, 1.036; 95% CI, 1.024-1.048), TP53 VAF (p= 0.007; HR, 1.009; 95% CI, 1.002-1.016), NPM1 VAF (p=0.006; HR, 0.980; 95% CI, 0.967-0.994), and complex karyotype (p<0.001; HR, 1.869; 95% CI, 1.332-2.622) as independent prognostic factors for OS. Of 33 patients with CHIP AML who were evaluated for the clearance of VAF by next generation sequencing , landmark analysis at 6 months showed median OS of not reached and 20.3 months in patients with and without CHIP-mutation clearance, respectively (p=0.310). Conclusion: The VAF of TP53 and NPM1 mutations by next generation sequencing can further stratify patients with newly diagnosed AML. Approximately, each increment of TP53 and NPM1 VAF by 1% is independently associated with 1% higher risk of death, and 2% lower risk of death, respectively. The presence of CHIP mutations except TP53 does not affect outcome. Disclosures Sasaki: Otsuka Pharmaceutical: Honoraria. Short:Takeda Oncology: Consultancy. Ravandi:Macrogenix: Honoraria, Research Funding; Seattle Genetics: Research Funding; Sunesis: Honoraria; Xencor: Research Funding; Jazz: Honoraria; Seattle Genetics: Research Funding; Abbvie: Research Funding; Macrogenix: Honoraria, Research Funding; Bristol-Myers Squibb: Research Funding; Orsenix: Honoraria; Abbvie: Research Funding; Jazz: Honoraria; Xencor: Research Funding; Orsenix: Honoraria; Sunesis: Honoraria; Amgen: Honoraria, Research Funding, Speakers Bureau; Bristol-Myers Squibb: Research Funding; Astellas Pharmaceuticals: Consultancy, Honoraria; Amgen: Honoraria, Research Funding, Speakers Bureau; Astellas Pharmaceuticals: Consultancy, Honoraria. Kadia:BMS: Research Funding; Abbvie: Consultancy; Takeda: Consultancy; Jazz: Consultancy, Research Funding; Takeda: Consultancy; Amgen: Consultancy, Research Funding; Celgene: Research Funding; Novartis: Consultancy; Amgen: Consultancy, Research Funding; BMS: Research Funding; Jazz: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Novartis: Consultancy; Abbvie: Consultancy; Celgene: Research Funding. DiNardo:Karyopharm: Honoraria; Agios: Consultancy; Celgene: Honoraria; Medimmune: Honoraria; Bayer: Honoraria; Abbvie: Honoraria. Cortes:Novartis: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Daiichi Sankyo: Consultancy, Research Funding; Astellas Pharma: Consultancy, Research Funding; Arog: Research Funding.


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