Abstract: The International Staging System (ISS) and the Revised International Staging System (R-ISS) are commonly used prognostic scores in multiple myeloma (MM). These methods have significant gaps, particularly among intermediate-risk groups. The aim of this study was to improve risk stratification in newly diagnosed MM patients using data from three different trials developed by the Spanish Myeloma Group. For this, we applied an unsupervised machine learning clusterization technique on a set of clinical, biochemical and cytogenetic variables, and we identified two novel clusters of patients with significantly different survival. The prognostic precision of this clusterization was superior to those of ISS and R-ISS scores, and appeared to be particularly useful to improve risk stratification among R-ISS 2 patients. Additionally, patients assigned to the low-risk cluster in the GEM05 over 65 years trial had a significant survival benefit when treated with VMP as compared with VTD. In conclusion, we describe a simple prognostic model for newly diagnosed MM whose predictions are independent of the ISS and R-ISS scores. Notably, the model is particularly useful in order to re-classify R-ISS score 2 patients in 2 different prognostic subgroups. The combination of ISS, R-ISS and unsupervised machine learning clusterization brings a promising approximation to improve MM risk stratification.

Abstract: Next–Generation Sequencing (NGS) implementation to perform accurate diagnosis in acute myeloid leukemia (AML) represents a major challenge for molecular laboratories in terms of specialization, standardization, costs and logistical support. In this context, the PETHEMA cooperative group has established the first nationwide diagnostic network of seven reference laboratories to provide standardized NGS studies for AML patients. Cross–validation (CV) rounds are regularly performed to ensure the quality of NGS studies and to keep updated clinically relevant genes recommended for NGS study. The molecular characterization of 2856 samples (1631 derived from the NGS–AML project; NCT03311815) with standardized NGS of consensus genes (ABL1, ASXL1, BRAF, CALR, CBL, CEBPA, CSF3R, DNMT3A, ETV6, EZH2, FLT3, GATA2, HRAS, IDH1, IDH2, JAK2, KIT, KRAS, MPL, NPM1, NRAS, PTPN11, RUNX1, SETBP1, SF3B1, SRSF2, TET2, TP53, U2AF1 and WT1) showed 97% of patients having at least one mutation. The mutational profile was highly variable according to moment of disease, age and sex, and several co–occurring and exclusion relations were detected. Molecular testing based on NGS allowed accurate diagnosis and reliable prognosis stratification of 954 AML patients according to new genomic classification proposed by Tazi et al. Novel molecular subgroups, such as mutated WT1 and mutations in at least two myelodysplasia–related genes, have been associated with an adverse prognosis in our cohort. In this way, the PETHEMA cooperative group efficiently provides an extensive molecular characterization for AML diagnosis and risk stratification, ensuring technical quality and equity in access to NGS studies.

Abstract: MYD88 L265P occurs in between a normal mutated lymphopoiesis and additional genetic alterations during lymphomagenesis.

Abstract: Next-Generation Sequencing (NGS) has recently been introduced to efficiently and simultaneously detect genetic variations in acute myeloid leukemia (AML). However, its implementation in the clinical routine raises new challenges focused on the diversity of assays and variant reporting criteria. To overcome this challenge, the PETHEMA group established a nationwide network of 7 reference laboratories aimed to deliver molecular results to the clinics. We report the technical cross-validation results for NGS and clinical validation in 2960 AML samples. Three cross-validation rounds (CVR) were performed to establish consensus parameters for NGS analysis and variant reporting. In the first CVR we evaluated the starting situation of the NGS studies. In the second CVR the laboratory network established minimum quality parameters and consensus recommendations to guarantee a valid NGS assay. The third CVR strengthened the established parameters and refined the clinical variant classification. The clinical validation was performed in 2960 samples from 2703 patients: 1530 male and 1173 female with a median age of 67.5 years old. 2522 samples were collected at diagnosis, 275 at relapse and 163 at refractory AML from October 2017 to October 2019. NGS analysis was performed according to already implemented protocols and only variants accomplishing the established quality control parameters were considered. In the first CVR the error rate (ER) was 39% with a high variability in the studied genes. Then, 30 genes were agreed as key genes for AML pathogenesis: 8 were considered mandatory due to their implication in clinical guidelines, targeted therapy and risk stratification and the study of the remaining 22 was recommended based on panel availability (Table 1). In the second CVR the ER was reduced to 14.4% and the NGS quality metrics were 4032X of mean read depth and 98.3% of median uniformity. Therefore, the laboratory network established a minimum read depth of 500X and uniformity & gt;85% as quality control parameters. Due to the high variability in the detection of low VAF variants (1 CVR: ER & lt;5%: 86.1%; 2 CVR: ER & lt;5%: 28.6%), VAF≥5% was established as a cut-off for variant reporting excepting variants with strong clinical evidence. In the third CVR the error rate for VAF≥5% variants was 10.9% achieving high concordance in variant detection and clinical classification. 8043 variants were reported in the 2960 samples, with 96.5% of patients showing at least 1 mutated gene. The mean number of variants per sample was 2.71 (range 0-9), the number of mutations in ≥65 years old patients (2.9 vs. 2.5, P & lt;0.001) and in male patients (2.8 vs. 2.6, P=0.001) were significantly higher. In the global cohort, the most frequently mutated genes were FLT3 (24.9%), DNMT3A (23.9%) and NPM1 (23%). However, this genetic profile changed according to the disease stage, age and sex. NPM1 mutations were more frequent at diagnosis (P & lt;0.001), IDH1 mutations (P=0.027) RUNX1 mutations (P=0.011) and WT1 mutations (P & lt;0.001) were more frequent at relapse and mutations in KRAS (P=0.012) were more frequently detected in refractory AML (Fig 1). Younger patients ( & lt;65) had more mutations in FLT3 and NPM1 (P & lt;0.001) while mutations in ASXL1, EZH2, IDH2, JAK2, RUNX1, SRSF2, TET2, TP53, U2AF1 and SF3B1 were associated to older age (P & lt;0.05) (Fig 2). NPM1, FLT3 and DNMT3A were frequently mutated in female patients (P & lt;0.001) while male patients had more mutations in ASXL1, JAK2, EZH2, RUNX1, SRSF2 and U2AF1 (P & lt;0.01) (Fig 3). At least 1 mutation in one of the 8 clinically relevant genes was detected in the 78.7% of patients: 39.8% of patients had targetable mutations (FLT3-ITD/TKD and IDH1/2 mutations) and 35.6% of patients, variants found in ASXL1, RUNX1 and TP53 were the only clinically relevant variant detected. The study of clonal evolution through paired-sample analysis showed that mutations in FLT3, KRAS, NRAS and PTPN11 were particularly unstable at relapse or refractoriness. We show the development of the first national strategy for validation of NGS studies with centralized analysis in an AML cooperative group. We have developed a laboratory network with standardized protocols to ensure technical quality and equity in access to NGS studies. The unification of analysis and interpretation criteria represents a significant increase in the quality of diagnostic tests and translational research. N/A-NI-AML-PETHEMA-007343, PI18/01340, PI19/00730, FI19/00059 Figure 1 Figure 1. Disclosures Ayala: Incyte Corporation: Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Astellas: Honoraria; Celgene: Honoraria. Tormo: Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Jazz Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Astellas: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Perez-Simon: JANSSEN, TAKEDA, PFIZER, JAZZ, BMS, AMGEN, GILEAD: Other: honorarium or budget for research projects and/or participation in advisory boards and / or learning activities and / or conferences. Martínez-López: Janssen, BMS, Novartis, Incyte, Roche, GSK, Pfizer: Consultancy; Roche, Novartis, Incyte, Astellas, BMS: Research Funding. Montesinos: AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Astellas: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Agios: Consultancy; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Daiichi Sankyo: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Incyte: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Karyopharm: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Pfizer: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Sanofi: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Teva: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Forma Therapeutics: Consultancy; Glycomimetics: Consultancy; Tolero Pharmaceutical: Consultancy.

Abstract: Background: Clinical, morphological and genetic characteristics of chronic myelomonocytic leukemia (CMML) are heterogeneous and vary from a myelodysplastic predominant profile to a myeloproliferative one. CMML has a highly variable course, with a median overall survival (OS) of 20 months and 15-30% of progression to acute myeloid leukemia (AML). Cytogenetic abnormalities are present in only 20-40% of cases. CMML-specific cytogenetic risk classification stratifies karyotypes into three groups: low risk (normal karyotype and isolated loss of Y chromosome, -Y), poor risk (trisomy 8, monosomy 7, 7q deletion and complex karyotype) and intermediate risk (all other chromosomal abnormalities). According to this model, 65-85% of patients fall into the low risk cytogenetic category. The aim of this study was to characterize type, frequency and prognostic impact of cytogenetic alterations detected by SNP arrays (SNP-A) in a series of 128 patients with CMML and low risk cytogenetic features or no metaphases. Methods: A retrospective study was performed on 128 patients with CMML. Cases with normal karyotype (n=120), isolated -Y (n=4) and no metaphases (n=4) were selected. Median age at diagnosis was 73 years (range 39-98), there was a 2.4:1 male predominance and 22.4% (28/125) of cases progressed to AML. Median follow up of patients was 26 months (range 1-115). Morphological WHO subtypes were CMML-1 in 104 (81%) cases and CMML-2 in 24 (19%). According to the FAB criteria 82 (64%) cases were included in the myelodysplastic variant (CMML-MD) and 46 (36%) in the myeloproliferative one (CMML-MP). High density SNP-A (Cytoscan HD, Affymetrix) were performed using DNA extracted from bone marrow (n=124) or peripheral blood (n=4) samples at diagnosis. The statistical analysis was performed with SPSS. Kaplan-Meier method was used for OS and progression-free survival (PFS) analysis and log-rank test was used for comparisons between groups. Results: SNP-A revealed novel chromosomal alterations (copy number alterations, CNA, and loss of heterozygosity, LOH) in 66% (85/128) of cases. Among the abnormal cases (CNA plus LOH), 1 alteration was detected in 65% (55/85) of cases, 2 in 20% (17/85) and ≥3 in 15% (13/85). The median size of the affected genome for CNA and LOH was 759 Kb (range 0-142Mb). CNA were detected in 38% (48/128) of cases, most of them being gains and losses smaller than 10Mb. Only 7 CNA were larger than 10Mb, four of them corresponded to patients with isolated -Y and three were novel alterations that had not been detected by conventional G-banding cytogenetics. Most affected regions (detected in 5 cases) were gains in chromosomes 3q, 8p and 21q as well as losses in chromosomes 10q and 12p. LOH were detected in 39% (50/128) of patients. Interstitial LOH larger than 〉 25Mb were detected in 30% (39/128) of cases. Recurrent interstitial LOH were detected in: 4q24-4q35 region (12 cases), involving TET2 gene, which is mutated in 40-50% of CMML; and in chromosome 11 (9 cases), 7 of which included 11q13.3-11q25 region, involving CBLgene, mutated in 5-20% of CMML. Although the number of total alterations (1, 2 or ≥3) do not have a survival impact, we found a significant correlation between the size of the affected genome (≥30 Mb, including CNA and LOH) and a poorer OS (OS at 3 years 26% vs. 46%, P=0.039). Regarding type of alterations, cases with interstitial LOH had lower OS than cases without LOH (OS at 3 years 19% vs. 45%, P=0.049). Cases with high risk alterations as defined by CMML-specific cytogenetic risk classification (losses in 7q, gains in 8q and ≥3 CNA) had lower PFS than patients with other aberrations (PFS at 1 year 76% vs. 93%, P=0.036), although statistical significance was not reached for OS (OS at 3 years, 27% vs.49%, P=0.096). Conclusions: SNP-A in CMML patients with low risk cytogenetic features or no metaphases has led to the detection of chromosomal alterations in 66% of cases. Using this technique the prognosis can be better defined, and we can detect a group of patients with worse outcome who could be considered for more intensive treatment. Acknowledgments: Instituto de Salud Carlos III, Ministerio de Sanidad y Consumo, Spain (PI 11/02519; PI 11/02010); RTICC, FEDER (RD12/0036/0044; RD12/0036/0014); 2014 SGR225 (GRE) Generalitat de Catalunya; Fundació Internacional Josep Carreras, Obra Social “La Caixa” and Celgene Spain; NHRI-EX103-10003NI, Taiwan. Footnote: Francesc Solé and Lurdes Zamora contributed equally Disclosures Xicoy: Celgene: Honoraria. Sole:Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding.

Abstract: Recent evidence suggests that the prognostic impact of gene mutations in patients with chronic lymphocytic leukemia (CLL) may differ depending on the immunoglobulin heavy variable (IGHV) gene somatic hypermutation (SHM) status. In this study, we assessed the impact of nine recurrently mutated genes ( BIRC3 , EGR2 , MYD88, NFKBIE , NOTCH1 , POT1 , SF3B1, TP53 , and XPO1 ) in pre-treatment samples from 4580 patients with CLL, using time-to-first-treatment (TTFT) as the primary end-point in relation to IGHV gene SHM status. Mutations were detected in 1588 (34.7%) patients at frequencies ranging from 2.3–9.8% with mutations in NOTCH1 being the most frequent. In both univariate and multivariate analyses, mutations in all genes except MYD88 were associated with a significantly shorter TTFT. In multivariate analysis of Binet stage A patients, performed separately for IGHV-mutated (M-CLL) and unmutated CLL (U-CLL), a different spectrum of gene alterations independently predicted short TTFT within the two subgroups. While SF3B1 and XPO1 mutations were independent prognostic variables in both U-CLL and M-CLL, TP53 , BIRC3 and EGR2 aberrations were significant predictors only in U-CLL, and NOTCH1 and NFKBIE only in M-CLL. Our findings underscore the need for a compartmentalized approach to identify high-risk patients, particularly among M-CLL patients, with potential implications for stratified management.

Abstract: The mutational status of the immunoglobuin heavy variable (IGHV) genes is an undisputable strong prognostic factor that subdivides patients with chronic lymphocytic leukemia (CLL) into 2 subgroups, i.e. IGHV-unmutated CLL (U-CLL) and IGHV-mutated CLL (M-CLL). U-CLL and M-CLL have distinct landscapes of genomic aberrations as well as distinct prognosis, since U-CLL is considerably more aggressive than M-CLL. That said, there is considerable clinical heterogeneity among M-CLL patients, ranging from patients without need of treatment to patients requiring early therapeutic intervention, indicating the need to further refine prognosis in this subgroup. In recent years, it has become evident that the prognostic impact of genomic aberrations may differ depending on IGHV gene mutational status. Hence, defining genomic aberrations with prognostic impact in M-CLL patients may help identifying patients with an predicted unfavorable prognosis within this subgroup, with obvious implications for management of follow up and therapy choice. To study the clinical impact of recurrent gene mutations in relation to IGHV gene mutational status, we collected a large, multi-center cohort including 4,674 patients with CLL [median age at diagnosis, 64.5 years; male/female, n=2,962 (63%)/n=1,712 (37%); Binet stage A (n=3,369, 74%), B (n=827, 18%), and C (n=387, 8%); IGHV-mutated (M-CLL, n=2,498, 56%) and IGHV-unmutated (U-CLL, n=1,927, 44%); isolated del(13q) (n=1,868, 41%), trisomy 12 (n=571, 13%), del(11q) (n=503, 11%), and del(17p) (n=249, 5.5%); treated (n=2,745, 59%) and untreated (n=1,929, 41%)] and performed next-generation sequencing (NGS) and/or Sanger sequencing of 9 genes (BIRC3, EGR2, NFKBIE, MYD88, NOTCH1, POT1, SF3B1, TP53, and XPO1) on pre-treatment samples. Overall, pathogenic mutations in any of these genes were detected in 1720/4674 patients (36.8%, using a variant allele frequency cutoff of 5% for NGS), while the remaining patients were wildtype; 2 mutations were observed in 361 patients (7.7%) and 3 or more mutations in 58 patients (1.2%). The mutation frequency for the individual genes was: TP53 (10.4%, including TP53 mutations and/or del(17p)), NOTCH1 (10.1%, 3'UTR mutations not included), SF3B1 (9.3%), XPO1 (3.9%), POT1 (3.8%), NFKBIE (3.7%), BIRC3 (3.0%), EGR2 (2.5%) and MYD88 (2.5%; Figure 1A). Except for MYD88, gene mutations in each of the investigated genes were associated with significantly shorter time-to-first-treatment (TTFT) in univariate analysis. In multivariate analysis of Binet stage A patients (n=3,369; including all genes, IGHV gene mutational status, age at diagnosis and gender), SF3B1 (Hazard Ratio (HR) 1.79; p & lt;0.001) , BIRC3 mutations (HR 1.50; p=0.004), XPO1 (HR 1.29; p=0.020), EGR2 (HR 1.42; p=0.021) and TP53 aberrations (HR 1.21; p=0.028), along with unmutated IGHV genes (HR 4.21; p & lt;0.001) and male gender (HR 1.12; p=0.027) remained as independent factors for shorter TTFT. In a multivariate model focusing on M-CLL Binet stage A patients (n=2,049), SF3B1 (HR 2.72; p & lt;0.001), NOTCH1 (HR 1.65; p=0.006), XPO1 (HR 2.21; p=0.021) and NFKBIE mutations (HR 1.74; p=0.025) were independent markers of poor TTFT (Figure 1B), while conversely in U-CLL Binet stage A cases (n=1157), SF3B1 mutations (HR 1.49; p & lt;0.001), TP53 aberrations (HR 1.30; p=0.011), BIRC3 mutations (HR 1.49; p=0.016) and male gender (HR 1.20; p=0.012) were significant factors for shorter TTFT (Figure 1C). In summary, different spectra of genetic mutations independently predicted short TTFT in M-CLL and U-CLL, respectively, with SF3B1 mutations as the only aberration found to be significant predictor of shorter time to first treatment in both subgroups. Importantly, mutations within several genes (i.e. SF3B1, NOTCH1, XPO1 and NFKBIE) identified patients in the M-CLL subgroup with a high-risk profile; conversely, TP53 mutations did not affect TTFT in this subgroup. On these grounds, we suggest to include analysis of recurrent gene mutations to identify high-risk patients within the M-CLL subgroup. Figure 1 Figure 1. Disclosures Brieghel: AstraZeneca: Consultancy. Rossi: Janssen: Honoraria, Research Funding; AstraZeneca: Honoraria, Research Funding; Gilead: Honoraria, Research Funding; Abbvie: Honoraria, Research Funding; Verastem: Honoraria, Research Funding; Roche: Honoraria, Research Funding; Cellestia: Honoraria, Research Funding. Scarfo: Astra Zeneca: Honoraria; Abbvie: Honoraria; Janssen: Honoraria, Other: Travel grants. Mattsson: Gilead: Research Funding. Baliakas: Janssen: Honoraria; Gilead: Honoraria, Research Funding; Abbvie: Honoraria. Martinez-Lopez: Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees; Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; GSK: Honoraria, Membership on an entity's Board of Directors or advisory committees; Incyte: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees. de la Serna: AbbVie, AstraZeneca, Beigene, Gilead, GSK, Janssen, Jazzpharma, Novartis, Roche: Consultancy; ABBVIE, ASTRAZENECA,ROCHE: Research Funding; AbbVie, AstraZeneca, Roche: Speakers Bureau. Hernández Rivas: Amgen: Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene/BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees. Smedby: Jansen-Cilag: Other: part of a research collaboration between Karolinska Institutet and Janssen Pharmaceutica NV for which Karolinska Institutet has received grant support. Bullinger: Pfizer: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Gilead: Consultancy; Daiichi Sankyo: Consultancy, Honoraria; Hexal: Consultancy; Janssen: Consultancy, Honoraria; Jazz Pharmaceuticals: Consultancy, Honoraria, Research Funding; Menarini: Consultancy; Novartis: Consultancy, Honoraria; Amgen: Honoraria; Astellas: Honoraria; Sanofi: Honoraria; Seattle Genetics: Honoraria; Bayer: Research Funding. Bosch: TAKEDA: Membership on an entity's Board of Directors or advisory committees, Other: Travel; Gilead: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel; AbbVie: Membership on an entity's Board of Directors or advisory committees, Other: Travel; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Research Funding; Roche: Membership on an entity's Board of Directors or advisory committees, Other: Travel. Terol: BMS: Consultancy; Roche: Membership on an entity's Board of Directors or advisory committees, Other: Travel; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel; Janssen: Membership on an entity's Board of Directors or advisory committees, Other: Travel, Research Funding; Roche: Consultancy; Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Other: Travel; Hospital Clinico Valencia: Current Employment. Cuneo: AstraZeneca: Consultancy, Speakers Bureau; Janssen: Consultancy, Speakers Bureau; Gilead: Consultancy, Speakers Bureau; AbbVie: Consultancy, Speakers Bureau. Gaidano: Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Incyte: Membership on an entity's Board of Directors or advisory committees; Beigene: Membership on an entity's Board of Directors or advisory committees; Astrazeneca: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Niemann: Novo Nordisk Foundation: Research Funding; CSL Behring, Genmab, Takeda, Octapharma: Consultancy; Abbvie, AstraZeneca, Janssen: Consultancy, Research Funding. Ghia: Roche: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Research Funding; Sunesis: Research Funding; AbbVie: Consultancy, Honoraria, Research Funding; Acerta/AstraZeneca: Consultancy, Honoraria, Research Funding; AstraZeneca: Consultancy, Honoraria, Research Funding; ArQule/MSD: Consultancy, Honoraria; BeiGene: Consultancy, Honoraria; Celgene/Juno/BMS: Consultancy, Honoraria; Gilead: Consultancy, Research Funding. Rosenquist: Roche: Honoraria; Janssen: Honoraria; Illumina: Honoraria; AstraZeneca: Honoraria; Abbvie: Honoraria.

Abstract: Patients with 3q21q26 rearrangements seem to share similar clinicopathologic features and a common molecular mechanism, leading to MDS or AML. The overexpression of EVI1 (3q26) has been implicated in the dysplasia that characterizes this subset of myeloid neoplasias with poor prognosis. However, EVI1 overexpression had been detected in 9% of AML cases without 3q26 abnormalities; and several 3q21q26 cases with no EVI1 overexpression had been reported. Our aim was to characterize different subgroups in 3q rearrangements, and to define the gene expression profile of these groups, specially in the group with 3q21q26 rearrangements. We analyzed 44 samples (6 cell lines and 38 samples of patients) with 3q rearrangements at G-band level, 36 had AML and 8 MDS. As a control, we analyzed 6 samples of patients with 3q rearrangements and lymphoid neoplasias, a cDNA panel of normal human tissues (Clontech), and 10 normal bone marrow samples. FISH analysis was performed using six BACs: PR11-390G14, RP11-475N22 (GATA2), RP11-689D3 (RPN1) on 3q21; RP11-82C9 (EVI1), RP11-115B16 (MDS1) and RP11-196F13 (TRAIL) on 3q26; and a centromere probe. Expression of GATA2, MDS1-EVI1, and EVI1 was measured by a quantitative real-time RT-PCR (Taqman) assay (Applied Biosystems). Samples were first classified attending at the G-banding. FISH analysis showed the heterogeneity of breakpoints in these cases, and the samples were grouped as 3q21 (5 cases), 3q26 (11 cases), 3q21q26 (16 cases) and other 3q rearrangements (12 cases). Real time RT-PCR showed overexpression (OE) of EVI1 and/or GATA2 in 75% of all the cases (33/44), suggesting the important role of these genes in patients with 3q rearrangements and myeloid neoplasias. In patients with lymphoid neoplasias and 3q rearrangements no genes were found to be OE. A different expression profile was found in the groups analyzed (Table 1). In the 3q21 group, 40% of the patients had GATA2 OE, but no EVI1 or MDS1-EVI1 OE was found. Our data show that EVI1 OE could be specific of the groups 3q26 (64%) and 3q21q26 (69%). The number of cases with EVI1 OE was significantly higher in patients with 3q rearrangements than in those with complex karyotype (data not show). In the group with 3q21q26, EVI1 and/or GATA2 were overexpressed in 87.5% of the cases (14/16). In conclusion, we have detected a different expression profile of the genes analyzed among samples with different 3q rearrangements. Our results suggest a more complex mechanism in patients with 3q21q26 rearrangements, in which GATA2 overexpression may contribute with EVI1 to the leukemogenic progress. Further molecular studies are in progress to analyze the different EVI1 transcripts. Table 1: Incidence of the overexpression of EVI1, GATA2 and MDS1-EVI1 in 32 patients with 3q21 and/or 3q26 rearrangements. Frequency & Percentage EVI1 GATA2 MDS1-EVI1 3q21 0% (0/5) 40% (2/5) 0% (0/5) 3q26 64% (7/11) 73% (8/11) 36% (4/11) 3q21q26 75% (12/16) 69% (11/16) 13% (2/16)