Ayala, Rosa

Abstract BACKGROUND: The first step to the cure of Multiple myeloma (MM) is achieving a clinical complete response (CR). However, a majority of patients relapse in part due to the persistence of low levels of pathological plasma cells after treatment (minimal residual disease (MRD)). In previous work, we have shown that Next generation sequencing (NGS) of immunoglobulin (IG) rearranged genes is an effective technology to identify and quantify pathological clonal cells in MM, and also it is a good predictor of patient prognosis. However, this technology is a proprietary multiplex PCR, uses a reference plasmid, it is performed at the centralized laboratories, and it is not broadly available. AIMS: To present a new in-house simplified method for quantification of IG gene clonal rearrangements by NGS. To test the suitability of the method for MRD detection in MM patients compared with 8-color high sensitivity Flow Cytometry (8c-MFC) data on a prospective way. To determine its capability as a predictor of patient prognosis. METHOD: We use deep sequencing to prospectively analyzed DNA from bone marrow samples of 58 patients (58 diagnostic samples and 80 follow-up samples) of patients included the GEM10 clinical trial. The method employs the primers developed by BIOMED-2 for IGH and IGK, and a set of specific mathematical and bioinformatics tools. First, we identified the clonal rearrangement(s) (clonotype) in the diagnostic sample by fragment analysis and NGS. Second, we searched the NGS sequences from follow-up samples to detect and quantitate (MRD) the presence of the clonotype identified in the diagnostic sample. Overall Survival (OS) and Progression free Survival (PFS) were estimated by Kaplan-Meier survival analysis. The survival curves generated were compared with the log-rank test for trend. RESULTS: The sensitivity of the method is 10-5 for 150.000 cells. Reproducibility is >90%. The Spearman correlation coefficient between NGS and 8c-MFC data for diagnosis and follow-up samples was R=0.6903, p<0.0001. When we applied the criteria of 10-5 for NGS, discordant results were as follow: 121 cases were positive by both methods, 2 cases were positive (>10-5) by 8c-MFC but negative (<10-5) by NGS; 7 samples were negative (<10-5) by 8c-MFC but positive (>10-5) by NGS; and 7 cases were negative by both methods. Next we analyzed de correlation of OS and PFS with MRD levels determined by NGS and by 8c-MFC. For NGS data, median PFS (FigA) was 34 months for patients with values of MRD>10-5 vs not reached for patients with values of MRD<10-5 (hazard ratio [HR] = 3.69, P =0 .0109). Also, for 8c-MFC data, the patients with values of MRD<10-5 had longer PFS than those with values of MRD>10-5 (34 months versus 47; HR = 3.69, P =0 .0109). Median OS (FigB) was also significantly longer in MRD<10-5 (NGS) patients (not yet reached), compared to MRD>10-5 (NGS) patients (41 months, HR = 3.6, P =0.0495). For 8c-MFC we did not get a significant association (41 vs 50 months, HR=1.979, p=0.228). It should be noted that this comparison was made with the standard 8c-MFC and it is probable that the sensitivity will be inferior to that of the Next generation flow. There were no significant differences in terms of PFS between the data obtained by our in-house method, 8c-FCM and previous data obtained with LymphoSight technology. CONCLUSIONS: Our data confirms that analysis of MRD levels determined by deep sequencing is capable of predict survival outcomes in myeloma patients. NGS sequencing of Ig genes is an effective technology to identify and quantify pathological clonal cells in MM. This approach is a methodological and economical alternative to MFC and other methods for MRD follow-up. Figure 1. Figure 1. Disclosures Paiva: Janssen: Consultancy; Celgene: Consultancy; BD Bioscience: Consultancy; Sanofi: Consultancy; Binding Site: Consultancy; Onyx: Consultancy; Millenium: Consultancy; EngMab AG: Research Funding. Ocio:Pharmamar: Consultancy, Research Funding; MSD: Research Funding; Novartis: Consultancy, Research Funding; Mundipharma: Consultancy, Research Funding; Bristol Myers Squibb: Consultancy; Amgen/Onyx: Consultancy, Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Array BioPharma: Consultancy, Research Funding; Jassen: Honoraria. Mateos:Celgene: Consultancy, Honoraria; Takeda: Consultancy; Janssen-Cilag: Consultancy, Honoraria; Onyx: Consultancy. San Miguel:Novartis: Honoraria; Celgene: Honoraria; Janssen-Cilag: Honoraria; Bristol-Myers Squibb: Honoraria; Millennium: Honoraria; Onyx: Honoraria; Sanofi-Aventis: Honoraria.

Abstract 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.

Abstract Next-generation sequencing (NGS) has revealed new insight into the complexity of clonal and subclonal architecture of multiple myeloma (MM). The introduction of targeted studies allows the detection of mutations with very low allele frequencies at affordable price. However, there is few information about the prognostic impact of this mutational profile in series of homogeneously treated MM patients. In this study, we analyzed the most frequently mutated genes in MM from patients included in a randomized clinical trial applying the highest read depth to date. We performed a correlation study between our results and cytogenetics aberrations and clinical outcomes. We used Ampliseq Library Kit 2.0 to target 77 genes (M3Pv2.0 panel) related to critical pathophysiological pathways, associated to drug resistance or targetable with molecular drugs in MM. We sequenced DNA (15ng) from CD138+ purified plasma cells from 80 MM patients at diagnosis included in GEM2010MAS65 clinical trial. We sequenced at an average read depth of 1500X and using Ion Proton sequencer (Thermo Fisher, USA, PaloAlto, CA). We used Ion Reporter software applying in-house modifications in call variants process. We also sequenced DNA from CD138 negative population in 30% of patients in order to filter out potential artifacts and stablish conditions for excluding germline variants. We first fitted a cox-proportional hazard model to predict survival and a second approach using a penalized regression LASSO. We identified 137 gene mutations in the 80 patients analyzed, 54 mutations were predicted pathogenic by Shif and Poliphen and 65 have been described in COSMIC database. 85% of patients harbored at least 1 mutation. The most frequently mutated genes were KRAS (16%), DIS3 (15%), NRAS (13%), BRAF (6%), and TP53 (5%), accounting for the 54% of the total number of mutations. The most frequently mutated pathways were RAS and NFKB in 34% and 28% of patients respectively. Mutational allele frequency for KRAS, BRAF and TP53 was, in all cases, lower than 50% while DIS3 showed mutations in a broad range (from 2 to 85%). Only one patient presented one mutation in NRAS at 73 % of allele frequency. For patients with more than one mutation, two different scenarios have been found. Some patients showed several genes mutated in a similar allele frequency values. On the contrary, a complex subclonal structure was confirmed in 3 patients, with mutations at very different clonal frequency. CD138 negative fraction was sequenced to confirm that these mutations were exclusive of plasma cells. The first of them had two mutations in DIS3 at 8 and 53% of allele frequency, second in KRAS and DIS3 at 13 and 63% respectively and the third in NRAS and IFNGR2 at 5 and 37% respectively. We found no differences in the survival based on pathway, mutational allele frequency or number of mutations. We also investigated pairwise associations between events and we not found significant correlations (Figure 1). Cox-proportional hazard model only showed a negative impact in survival of patients with TP53 mutations. This ultra-deep targeted sequencing strategy is able to detect mutations in most of patients, at very low allele frequency and using a minimal amount of DNA. Despite of its huge capacity to detect mutations, we have not identified new prognostic factors in MM patients treated with highly efficient therapies. However, this study again confirms the complexity of the genomic landscape of MM and the great heterogeneity between patients. The role of theses mutations at relapse as well as in the subsequent treatment effectiveness remains unknown and should be preferably investigated in larger cohorts of homogeneously treated MM patients. Figure 1 Figure 1. Disclosures Martínez-López: Novartis: Honoraria, Speakers Bureau. Oriol:Janssen: 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; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees. Stewart:celgene: Consultancy.