ALBERT

Description: Background. The AML is a heterogeneous disease with a high variety of subtypes stratified by cytogenetic and molecular markers. To date, the major prognosis criteria is cytogenetic, but 40-50% of de novo AML patients have normal karyotype; AML treatment is based on risk profiles depending on age and genetic molecular factor. New methods such as Next Generation Sequencing (NGS) and ex vivo sensitivity drug tests might be able to improve this classification and to individualize the treatment for AML patients. Aims. We performed targeted massively parallel sequencing on a 32 myeloid genes custom panel to identify a mutational profile of AML which predicts ex vivo pharmacological and clinical response. Methods. We analyzed bone marrow (BM) samples at diagnosis from 39 AML patients, 34 were treated with chemotherapy; 28 AML patients received one cycle of Idarubicin (Ida)/ Cytarabine(Cyt) induction treatment (3+7) and 8 received one cycle of Fludarabine/Cyt. Median age at diagnosis was 53 years (24-81), male:female ratio 19:16. We carried out targeted gene sequencing by NGS (Ion Torrent Proton System-Life Technologies) using a 32 genes custom panel (all coding regions) implicated in leukemia prognosis, including ASXL1, CBL, DNMT3A, EPOR, ETV6, EZH2, FLT3, HRAS, IDH1, IDH2, JAK2, KDM6A, KIT, KRAS, LNK, MLL, MPL, NRAS, PHF6, PRPF40B, PTEN, RUNX1, SF1, SF3A1, SF3B1, SRSF2, TET2, TP53, U2AF35, VHL, ZRSR2 and CALR. Ex vivo pharmacological studies were performed using an innovative automated flow cytometry method based platform (Ex-viTech) is able to evaluate effect of drugs used to treat AML, Cyt and Ida, using 4 parameter: Emax (Effective Maximum Response) measures potency, EC50 (Effective Concentration inducing 50% cell death) measures efficacy, and the activity was quantified by AUC (Area Under Curve) and VUS (Volume Under Superficies). Clinical response was evaluated after induction treat. Complete remission was defined according to hematologic recovery and blast count fewer than 5%. Analysis of NGS results was performed by Ion Report software. Differences measured by Exvitech were determined using t-test or Kruskal-Wallis test. Discrete variables of patients with and without gene variants were compared using the X2 test. For survival analysis we used Kaplan-Meier analysis (log rank) using SPSS 15. Two sided p values below 0.05 were considered statistically significant. Results. We found 94 non-synonymous variants (SNVs and small Indels) in coding regions. On average, 90.8% of the target sequence showed mean depth coverage ~ 1100x. See Figure 1. We analyzed results of pharmacological test by Ex-viTech plataform in function of presence of gene variants. Only significant favorable differences were detected: KRAS (Cyt-Emax p=0,000, Ida-EC50 p=0.001, VUS p=0.003, Cyt-AUC p=0.036, Ida-AUC p=0.000), KIT (VUS p=0,010) and FLT3-TKD (Cyt-Emax p=0.002, Cyt-AUC p=0.001, VUS p=0.011). Mutational positive score, found it in 40% of patients, was defined by the presence of variants in KRAS, KIT and/or FLT3-TKD; it was correlated with pharmacological results and clinical response. All cases with positive score (n=13) achieve complete remission (CR); only 11 cases without favorable score (n=21) achieve RC (p=0.003) after induction treatment (See Table 2). In addition, response obtained for Ida Emax and Ida EC50 by Ex-viTech was able to detect patients who reached CR after induction treatment (p=0.042, p=0,039 respectively). Ex-viTech results showed that patients with positive score have a significantly more potent and effective cytotoxic effect than patients without it. See table 2. No differences were observed respect Overall Survival (OS) between cases with mutational favorable score and without it. However, we observed a plateau in OS with favorable mutational score. With a median follow-up of 5.9 months (1.9-25.74), patients having positive score showed better relapse free survival (p = 0.013). See Figure 3. Conclusion. Very deep sequencing NGS identifies a mutational profile score (KRAS, KIT or FLT3-TKD) in AML patients, which predicts ex vivo pharmacological and clinical response. This favorable score was found in 40% of AML patients. Merging NGS and ex vivo drug sensibility might be able early to predict response to induction and individualize treatments. This study was funded by Instituto Carlos III (PI13/02387). Disclosures Ballesteros: Vivia Biotech: Employment.

Description: BACKGROUND: Older patients with acute myeloid leukaemia (AML) who are unsuitable for standard induction therapy have limited treatment options. While DNMT3A, TET2, IDH1/2 and TP53 mutations have been previously associated to better response to hypomethylating agents, there are no molecular biomarkers for low-dose cytarabine (LDAC)-based regimens. AIMS: To predict outcome in AML older patients at diagnosis based on mutation status in the context of FLUGAZA trial. FLUGAZA trial was focus on 〉65 years AML de novo patients comparing azacytidine vs. fludarabine and LDAC (FLUGA Scheme). METHODS: We analyzed bone marrow (BM) samples at diagnosis from 209 out of 285 AML patients treated according Flugaza trial (NCT02319135), azacytidine-arm (n=97) and FLUGA-arm (n=112). In this trial, patients were randomized to receive 3 induction cycles with fludarabine and cytarabine (FLUGA) followed by 6 consolidation cycles with reduced intensity FLUGA, vs 3 induction cycles with 5-azacytidine (AZA) followed by 6 consolidation cycles with AZA. Median age at diagnosis was 75 years (65-90). Both treatment groups were balanced for age, leucocytes count, baseline BM blasts, karyotype risk (ELN), and FLT3-internal tandem duplication and NPM1 gene mutations. Mutational profile analysis was carried out by NGS targeted gene sequencing (Ion Torrent S5XL System-Thermo Fisher Scientific) using a 43 genes custom panel implicated in leukemia prognosis. RESULTS: We detected 893 variants, 247 Indels and 646 SNVs. 206 (23.1%) of them were included as pathogenic or like-pathogenic by clinvar database. Ninety-five percent of patients (n=203) had at least one detectable mutation, and the median number of mutations was 4 (range = 0-8 mutations). The most common gene mutations were TET2 (N=55), FLT3 (n=52), SRSF2 (n=49), TP53 (n=45), DNMT3A (n=45), ASXL1 (n=45), RUNX1 (n=43), IDH2 (n=36), IDH1 (n=34), NPM1, (n=33) and NRAS (n=23). This mutational landscape is different to previous published in younger patients (Grimwade, Blood 2016), with higher number of patients with mutations in TP53 (21.5 vs 8%), SRSF2 (23.9 vs 2%), IDH1 (16.3 vs 7%) and IDH2 (17.2 vs 9%) and lower number of patients with mutation in NMP1 (15.8 vs 33%). The median OS of global series was 6 months (range 0-40). Multivariate Cox regression in the global series showed that NRAS and TP53 mutations predict reduced OS (Table 1). Distribution of mutations between both arms was not homogeneous (Figure 1) and NRAS (p=0.012) was more frequent among patients randomized to the FLUGA-arm. However, TP53 mutation frequency distribution was homogeneous: 23.7% in AZA-arm and 19.6% in FLUGA-arm (p=NS). In the AZA-arm, patient´s age was the only variable associated with not achieving composite complete remission (CR plus CR with incomplete recovery) and TET2 and EZH2 mutations were predictors to achieve composite CR. In the FLUGA-arm, TP53 and NRAS mutations were associated with not reaching composite CR (table 2). In the AZA-arm, cytogenetic was the only variable associated with risk of early death. In the FLUGA-arm, leucocyte count, TP53 and NRAS mutations were associated with risk of early death (table 3). In the AZA-arm, BCORL1 mutations (4.1%) were the only variable associated with high risk of relapse. In the FLUGA-arm, BCOR (7.1%) and TP53 (19.6%) mutations were associated with high risk of relapse (table 4). CONCLUSION The mutational profile of AML in elderly patients is different from the previously published in young patients. We have confirmed that a molecular pattern can identify patients with poor prognosis in elderly AML patients. NRAS and TP53 mutations confer a poor prognosis in LDAC (FLUGA-arm) patients, but this effect disappeared in the AZA-arm. BCOR and BCORL1 mutations were associated to a reduced DFS. These results confirm that azacytidine could be more efficacious than LDAC treatment for older patients with AML and mutations in TP53, NRAS, TET2 and EZH2. The percentage of patients who presented mutations in these genes amounted to 77% in this AML series. The study is registered at www.ClinicalTrials.gov as NCT02319135. This study was supported by the Subdirección General de Investigación Sanitaria (ISCIII, Spain) grants PI13/02387 and PI16/01530. Disclosures Salamero: Celgene: Honoraria; Novartis: Honoraria; Pfizer: Honoraria; Daichii Sankyo: Honoraria. Paiva:Amgen, Bristol-Myers Squibb, Celgene, Janssen, Merck, Novartis, Roche, and Sanofi; unrestricted grants from Celgene, EngMab, Sanofi, and Takeda; and consultancy for Celgene, Janssen, and Sanofi: Consultancy, Honoraria, Research Funding, Speakers Bureau. Fernandez:Pfizer: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Teva: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Abbvie: Membership on an entity's Board of Directors or advisory committees; Incyte: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: 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; Janssen: 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.

Description: ABL1 Kinase Domain (ABL1-KD) mutations are a common resistance mechanism to tyrosine-kinase inhibitors (TKIs) in Chronic Myeloid Leukemia (CML) and Philadelphia Positive Acute Lymphoblastic Leukemia (ALL). Different ABL1-KD mutations induce different degrees of resistance to different TKIs. The early detection of these resistant mutations helps to adjust patient's treatment. Here we present an Ultra-Deep Sequencing approach to detect and quantify acquired ABL1-KD mutations in genomic DNA (gDNA), aiming to define a robust test to detect such alterations in TKIs exposed Philadelphia-Positive Leukemia Patients with a resolution below 1E-4. Firstly, we defined an ABL1 specific next-generation sequencing (NGS) panel designed to cover all coding regions of ABL1 exons 4-10. The 9 amplicons were designed to cover full exons where possible to detect co-occurring mutations (Figure 1A). A panel was then applied to 3 biological replicates of 3 Healthy control donors (9 NGS data points each with 220ng of gDNA). The average coverage per amplicon in all samples was at least 500,000x. The NGS data was then analyzed applying the NGS-MRD algorithm described elsewhere (Onecha, E et al. Haematologica 2019) to 25 known ABL1-KD hotspots. After applying our error correcting algorithm, we obtained an average of 135,000 (22,000-503,000) refined reads for the 25 hotspots. The limit of detection (LOD) was calculated for every position in the DNA as the mean noise (Variant Read Frequency; VRF) per position in the controls ± 3SD (standard deviation); the limit of quantification (LOQ) being defined as mean ± 10SD. For all the hotspots analyzed, the LOD was below 1E-4 and the LOQ below 3E-4 (Figure 1B), except for p.F311L (c.931T〉C; LOD=2.7E-3). The high level of noise in this position, constant in the different control samples sequenced in different sequencing runs, is most likely related to the high number of homopolymers in the region. Ten Philadelphia-Positive Leukemia patients were then screened after TKI treatment (8 CML and 2 ALL). The median BCR-ABL1 defined by quantitative PCR (ratio BCR-ABL1 vs ABL1) in these follow-up samples was 0.6% (0.034% - 95%). All patients were screened in triplicates (220ng gDNA each) and the data-points ± 1SD from the mean were considered outliers (NGS false positives) and excluded from further analysis. Five patients presented a signal above the LOD for p.T315I (c.944C〉T). This position is covered by 2 different amplicons in our panel. By bioinformatically demultiplexing the signal, the detection of those five mutations in both amplicons was confirmed (Amp_4; LOD=3E-5, Amp_5; LOD=4E-5). Moreover, aiming to validate this new approach, we applied to paired RNA samples an in-house BCR-ABL1/ABL1 nested PCR + NGS approach designed to quantify those alterations in cDNA. This approach confirmed the presence of 4 out of 5 gDNA detected mutations, with a Pearson correlation of 0.92 (Pval

Description: Follicular lymphoma (FL) is considered an indolent disorder with a relatively favourable course. With modern day treatments, long remissions are often achieved both in front-line and relapsed settings. However, a subset of patients has a more aggressive course and a poorer outcome. Both, PET-CT and minimal residual disease (MRD) evaluation by PCR defines groups of patients with different prognoses. MRD measurement by NGS is being studied to predict relapse prior to diagnostic imaging in large B cell lymphoma; however, it has never been used in FL. The aim of the present study is to validate a sensitive and standardizable approach to measure liquid Biopsy MRD (LiqBio-MRD) by NGS with 〉90% applicability and 2E-4 resolution, and to analyse its prognostic impact in FL patients. Firstly, the best source to identify genetic MRD markers was determined. Genomic DNA from paraffin embedded (FFPE) lymph node biopsies and/or cell free DNA (cfDNA) from peripheral blood (PB) was obtained from 29 FL cases at diagnosis and sequenced with a short length Ampliseq Custom Panel (Thermo-Fisher). This panel was designed to cover all coding regions of 56 lymphoma specific genes in FFPE and cfDNA samples. By applying this panel with an average depth of 700X, a total of 122 somatic mutations were detected in 37 baseline samples. 15 of these lymph node samples presented 65 mutations (average of 4 mutations per patient, rank 1-9), with a mean Variant Read Frequency (VRF) of 0.33 (0.06-0.77). On the other hand, the 21 cfDNA samples presented 71 mutations (average of 3 mutations per patient, rank 0-8), with a mean VRF of 0.21 (0.02-1.0). Notably, in 3 cases the mutations were only detected in the lymph node. Paired samples were available for 13 cases. Of the 72 somatic mutations identified in these cases, only 14 were present in both samples (Figure 1A, left). Besides the higher number of mutations in the lymph nodes, a mean decrease of 0.14 VRF was observed in cfDNA (0.21; 0.03-0.53) compared to lymph nodes (0.35; 0.09-0.76) (Figure 1A, Right). From the initial 29 FL cases, 16 had PB sequential samples available. Three patients were put under observation and the rest received an anthracycline based regimen plus R-maintenance. In treated patients, PET-CT was carried out at diagnosis and after 4 and 6 cycles of treatment. During follow-up, cfDNA was available after 4 (n=10) and 6 cycles of treatment (n=10). Median follow up was 18 months. To quantify LiqBio-MRD in the 31 follow-up samples, we defined an approach involving the sequencing of 12 NGS data points per mutation identified at diagnosis. Three of these data points were tumour sample replicates. The other 9 points were obtained from healthy control donor DNA. All LiqBio-MRD samples were sequenced with at least 100.000x and analysed applying the NGS-MRD algorithm described elsewhere (Onecha, E et al. Hematologica 2019). The mean mutation rate (noise) in controls for the studied mutations was 1.4E-5 (0 - 8E-5) below the targeted sensibility of 2E-4. The LOD was defined for every follow-up sample based on the initial amount of cfDNA used in the test. On average 39.6ng (13-66 ng) were used for cfDNA MRD monitoring. All somatic mutations were considered potential MRD markers (Figure 1B), however the degree of MRD in each follow-up sample was defined by the somatic mutation with higher VRF. MRD values were significantly lower in complete response (CR) cases compared to those with active disease (p=0.001, Figure 1C, left). Notably, MRD positivity in the interim or at the end of treatment resulted in significantly inferior PFS (median 12 months vs not reached, P = 0.09, Figure 1C, right). An extension of the cohort and clinical impact of LiqBio-MRD test will be presented at the meeting. Our results demonstrate for the first time that NGS based MRD quantification is feasible in Liquid Biopsies from FL. Despite the marked spatial genetic heterogeneity of FL, which is better identified in cfDNA, the dilution of the signal in these samples suggests the use of both; lymph node biopsies and cfDNA at diagnosis to identify all potential MRD markers. The lower degree of MRD in CR evaluations (according to the 2014 Lugano response assessment) and the existence of patients in CR with positive and negative MRD suggest the potential of our LiqBio-MRD test to prospectively identify patients with different outcome. Nevertheless, more patients and a longer follow-up are necessary to draw meaningful conclusions. Figure 1 Disclosures Heredia: Altum sequencing: Current Employment. Rufian:Altum sequencing: Current Employment. Carrillo:Altum sequencing: Current Employment. Wang:Hosea Precision Medical Technology Co., Ltd: Current Employment. Canales:Janssen: Honoraria; Karyopharm: Honoraria; Celgene: Honoraria; Takeda: Speakers Bureau; Sandoz: Honoraria; Janssen: Speakers Bureau; Novartis: Honoraria; Sandoz: Speakers Bureau; Janssen: Speakers Bureau; Takeda: Speakers Bureau; Roche: Honoraria; iQone: Honoraria; Janssen: Honoraria; Sandoz: Honoraria; Novartis: Honoraria; Roche: Honoraria; Roche: Speakers Bureau; Karyopharm: Honoraria; Gilead: Honoraria; Sandoz: Speakers Bureau; Roche: Speakers Bureau. Martinez-López:Janssen, BMS, Sanofi, Novartis, Incyte, F. Hoffmann-La Roche and Amgen: Honoraria, Other: Advisory boards; Janssen, Novartis, BMS, Incyte: Consultancy; Hosea and Altum: Membership on an entity's Board of Directors or advisory committees.

Description: Introduction: Myeloid malignancies are clonal disorders of hematopoietic stem cells and include acute myeloid leukemia (AML), myelodysplastic syndrome (MDS) and myeloproliferative neoplasm (MPN). Common biological markers have been described in the molecular pathogenesis, including gene mutations in splicing factors, epigenetic modifiers, transcription factors, signal pathways and tumor suppressors. These mechanisms have been associated with MDS and MPN progression to AML. Objectives: The main objective of this study is to identify differences in the mutational landscape of myeloid malignancies and describe mutation frequencies of genes and functional pathways in each neoplasm, as well as determine their clinical impact. Methods: This study involved a retrospective analysis of 430 patients with AML (209), MDS (106) and Philadelphia negative MPN (86) diagnosed in the Hospital Universitario 12 de Octubre (Spain). They were analyzed by a next generation sequencing (NGS)- panel for myeloid malignancies. The panel include 32 genes: CALR, ASXL1, EZH2, PHF6, DNMT3A 2, TET2, IDH1, IDH2, KDM6A, KMT2A, SF1, SF3A1, SF3B1, SRSF2, U2AF1, ZRSR2, PRPF40B, EPOR, FLT3, JAK2, KIT, SH2B3, MPL, CBL, HRAS, NRAS, KRAS, ETV6, RUNX1, VHL, TP53, PTEN. In addition, there were included 29 patients diagnosed with benign pathology that were referred to rule out MPN or congenital polyglobulia. Results: In the analyzed cohort we obtained a larger number of mutations in the more aggressive malignancies, AML and MDS. Mutations in epigenetic modifiers and signal pathways were the most frequent detected (31% and 24% respectively). The epigenetic modifiers were notably affected in AML (78%) and MDS (60.4%), whereas signal pathways were mutated more frequently in MPN (70.9%). Transcription factors, tumor suppressors and splicing factors mutations were more detected in AML and MDS (40%, 32%, 44% and 22%, 13%, 32% respectively). The mutation landscape obtained by genes was: Signal pathways: FLT3, NRAS, KIT, KRAS y SH2B3 were specially detected in AML (25%, 11%, 6%, 5% and 4% respectively). JAK2, CALR and MPL in MPN (38%, 15% and 6% respectively). Transcription factors: RUNX1, ETV6, PHF6, CEBPA and WT1 mutations were regularly observed in AML (21%, 6%, 6%, 6% and 5% respectively), and GATA1 in SMD (3.8%). Tumor suppressors: TP53 was particularly affected in AML (21%) and MDS (11%). Epigenetic modifiers: TET2 was notably mutated in MDS (32%), whereas ASXL1, DNMT3A, IDH2, IDH1 and EZH2 were in AML (21%, 21%, 17% 16% and 8% respectively). Splicing factors: SF3B1 was more frequently detected in MDS (18%) than AML (7%), whereas ZRSR2 presented a similar frequency in both pathologies (around 8%). U2AF1 was most commonly mutated in MPN (9%). SRSF2 was specially mutated in AML (23%). SF3A1 was altered in around 1%, similar in all three malignancies. With regard to survival studies, the presence of mutations in splicing factors (primarily in U2AF1) and its absence in signal pathways conferred an adverse outcome for overall survival (OS) in MPN. In MDS, gene mutations in tumor suppressors (especially TP53), U2AF1 splicing factor and EZH2 epigenetic modifier were associated with poor outcome. In our series of AML, gene mutations in tumor suppressors and TP53 were related to unfavorable prognosis in OS. Conclusion: The largest number of mutations and affected genes observed in AML suggest that leukemic transformation of MDS and MPN is conditioned by acquisition of new mutations. We observed different frequencies of mutations between AML, MDS and MPN that could guide the diagnostic and identify new targets of treatment. Further, some mutations have demonstrated differential prognostic impact. An extension of this study and the design of an algorithm with mutation data to elucidate a more accurate molecular prognosis will be presented at the meeting. This work has been financed thanks to the grant PI16/01225, PI 19/01518 and PI19/00730 from the Instituto de Salud Carlos III (Ministerio de Economia, Industria y Competititvidad) and cofinanced by the European Development Fund. Figure 1. Mutations detected (%) in AML, MDS and MPN classified by function. Table 1. Median overall survival of patients with MPN, MDS and AML according to gene state (mutated or not). Figure Disclosures No relevant conflicts of interest to declare.