ALBERT

Description: Background To aid in the identification of effective treatments for individual patients, ex vivo assays for detecting cell death inducible by drugs for hematological malignancies have been in development for over 20 years. We have developed a novel approach incorporating 4 key innovations; incubating drugs in whole bone marrow sample without isolating leukocytes, using flow cytometry enables identification of the malignant cells selectively, an automated flow cytometry-based platform (ExviTech) decreases errors and enables full pharmacological characterization, and analyzing the data using pharmacodynamic population models. Aim The purpose of this study is to derive the ex vivo pharmacological profiles across the AML patient population of single drugs and combination treatments as a tool for individualized treatment selection. Patients and Methods Bone-marrow samples from 160 patients diagnosed with AML were sent to Vivia from 24 hospitals across Spain within 24 hrs. The plates were incubated for 48-hours prior to analysis with ExviTech, The percentage of leukemic cell death was determined via labeling with monoclonal antibodies and AnnexinV-FITC. A survival index is computed for each drug, the lower the survival index, the more effective the drug. Dose-response curves of cytarabine, idarubicin, daunorubicine, etoposide, mitoxantrone, fludarabine, clofarabine, and 6-thioguanine were measured in 160 patient samples. The added benefit of combining these drugs into 12 combination treatments was assessed by measuring their synergy in each individual patient. In 39 patients treated with CYT IDA we had clinical data of response, and then we performed a blinded interpretation of this in vitro test by an expert hematologist, to predict the clinical response based in this test result. Results There was a large range of interpatient variability in the response to a single drug and even larger in the synergism between drugs. The Population Pharmacological Profiles for an individual patient is shown on the figure below. The relative drug potency in terms of their percentile ranking within the population is shown in the left panel from 0 (weakest) to 100 (most potent). Green lines represent the individual patient potency relative to the population ranking, with confidence intervals. Third column lists when a drug leaves a significant % of leukemic cells alive, potential resistant clones. The panel on the right side shows the synergism of the drug combinations treatments shown as box-plots at 10-25-75-90% to highlight their distribution. The synergism value for an individual patient in each combination is shown in green, with confidence interval as parallel dotted green lines. This representation of the Pharmacological Profile of an individual patient sample quickly identifies extreme values, when a drug or combination is very sensitive (rightward shift green lines, green boxes) or very resistant (leftward shift green lines, red boxes). This patient showed average sensitivities for most drugs though highly resistant to Clofarabine (red box) that leaves 45% alive. However this patient showed lack of synergism in multiple treatments (right, red boxes). CYT and IDA show average potencies but lack of synergism, suggesting CYT-DAU might be a more efficient treatment. These representations lead to clear guidelines in 〉90% samples, and based on hematologist's interpretation of these guidelines show a clinical correlation with clinical responses to CYT-IDA of 84%. Conclusion We have developed an improved a methodology to measure the pharmacological activity of drugs and drug combinations in AML patient samples as well as modeling their pharmacological behavior. This information may be useful in selecting the optimal treatment for the individual patient, especially relapse/refractory patients in need of therapeutic alternatives. By testing the drugs used in the treatment protocols for AML directly on patient samples, a pharmacological based model has been developed to infer drug resistance or sensitivity, patient by patient. Disclosures: Ballesteros: Vivia Biotech: Equity Ownership. Primo:Vivia Biotech: Employment. Hernandez-Campo:Vivia Biotech: Employment. Rojas:Vivia Biotech: Employment. Liebana:Vivia Biotech: Employment. Lopez:Vivia Biotech: Employment. Iñaki:Vivia Biotech: Consultancy. Bennett:Vivia Biotech: Employment.

Description: Abstract 2924 Background: Clonal composition and clone dynamic changes of neoplasms are a controversial issue, whose investigation is now facilitated by the development of massive parallel sequencing. Here we have analyzed the changes in the mutational spectrum associated with progression, treatment response and relapse in a multiple myeloma patient. We sequenced exomes for the primary quiescent-tumor, the progression and the relapsed samples. M&M: Patient and samples description Samples from asymptomatic, progression and relapse phases were compared by FISH and Whole exome massive parallel sequencing in a multiple myeloma patient carrying the t(4;14)(p16.3;q32) alteration. At relapse the cytogenetic study identified the presence of two major clones, 13q14 deletion and t(4;14)(p16.3;q32) in the 60% of the cells, and 17p13 deletion in the 12% of the cells. Whole exome sequencing was performed at CNAG (Barcelona, Spain) following standard protocols for high-throughput paired-end 76pb sequencing on the Illumina HiSeq2000 instruments (Illumina Inc., San Diego, CA). The variant calling was performed using an in house written software calling potential mutations showing a minimum independent multi-aligner evidence. Results: We performed whole exome sequencing on 3 tumor samples from the same patient: the first one at the time of diagnosis correspond to bone marrow infiltrated by 7% of plasma cells. The two additional samples, at progression and relapse, were done in CD138+ bone marrow cells, at this moment the percentage of infiltration was of 84% and 64% respectively. The germinal DNA from the same patient was used as reference. The mean coverage obtained for the four samples were 93x, with around 85% of bases with at least 20X coverage. After filtering, a total of 104 single nucleotide variations (SNV) were identified, some of them in more than one sample. The variations were classified into silent (25), missense (71), nonsense (6), and essential splice (2), according to their potential functional effect. In addition to t(4;14) and del13q14, progression and relapse samples shared 36 common SNVs. There were also some variants gained and/or loss in the different time points, suggesting the presence of at least five different clones, independent but related in their evolution. The two main clones were present in progression and relapse samples, but the ratio of the mutant alleles decreased in parallel to the decrement in the percentage of cells carrying on the described cytogenetic alterations Conclusions: There is a coupling between the cytogenetic and tumor sequence changes indicating that tumor at progression was composed by a dominant clone, together with multiple minor clones. Relapse after treatment was associated with multiple changes in the clone dynamics, progressive reduction of the main clone, emerging of new subclones and lost of minor clones. Dynamic changes along progression could be facilitated/induced by the therapy received. Disclosures: No relevant conflicts of interest to declare.

Description: Multiple myeloma (MM) is a heterogeneous disease. Evaluation of prognostic factors and risk stratification at diagnosis is necessary to compare outcome. Attempts have been made to apply a comorbidity score in the clinical sitting, but a standardized general approach is still lacking. We hypothesized that a comprehensive examination of every associated disease in a large cohort of patients could better highlight the prognostic impact of comorbidity in MM. All consecutive patients diagnosed in our institution, from 1993 to 2013, with symptomatic MM according to IMWG criteria were included in our population-based MM registry. Patients with plasma cell leukemia or with palliative management were excluded. Clinical variables analyzed were: age, sex, Durie-Salmon, International Scoring System (ISS), percentage of plasma cell in bone marrow by morphology (PC), serum creatinine (Cr) and estimated glomerular filtration rate according with Modification of Diet in Renal Disease (eGFR-MDRD). The following comorbodities were analysed: hypertension (HTA), diabetes (DM), obesity (OB) (body mass index 〉 30 Kg/m2), hyperlipaemia (HL), prior malignancy (PM), hepatitis B virus (HBV), hepatitis C virus (HCV), human immunodeficiency virus (HIV), peptic ulcer (PU), thromboembolism (TE), renal transplant (RT), splenectomy (S), cutaneous disease (CD), amyloidosis (AM), heart disease (HD) (arrhythmia, congestive heart failure, coronary artery disease, other), lung disease (LD) (chronic obstructive pulmonary disease, asthma, other), liver disease (HE) (cirrhosis, non-alcoholic fatty liver disease, other), neurological disorder (ND), psychiatric disorder (PD) and rheumatologic disorder (RD). Kaplan-Meier method was used to estimate OS curves. Cox regression was used to determine the prognostic impact of each comorbidity in a univariate and multivariate model. 311 patients were eligible. Median age was 66 years (12-91), 148 men (47.6 %) and 163 women. Percentage of comorbidities was: HTA 45; OB 32.5; DM 20.4; HD 20.4; LD 15.2; PU 10; HL 9.7; ND 8; PM 7.8; PD 6.5; HBV 3.9; HE 3.9; TE 3.6; RD 3,5; AM 2.3; HCV 1.9; CD 1.6; S 1; RT 0.6; HIV 0.3. 63 patients (20.4 %) showed no comorbidities. Univariate analysis (table 1) demonstrated that AM (P=0.022), HCV (0.038), HIV (0.022), PD (0.015) and ND (0.05) were significantly associated with shorter OS. The variables associated with mortality in the multivariate analysis were age (p=0.002), ISS (III vs I: p=0.01), PC (p=0.05) and Cr (p=0.02). Results will be validated in another MM series and presented during the meeting. The overall prognosis of MM depends on a variety of host and disease-related characteristics. We confirm age, ISS, PC and Cr as robust and independent prognostic factors. Adjusting for these factors, no isolated comorbidity reach statistical significance; however, comorbidity seems to have a role in MM prognosis. More studies are warranted to define the prognostic impact of comorbidities in MM. Disclosures: No relevant conflicts of interest to declare.

Description: Introduction The Wilms' tumor 1 (WT1) gene, located on chromosome 11p13, encodes a transcription factor with both oncogene and tumor suppressor functions. WT1 is reportedly overexpressed in 90% of patients with acute myeloid leukemia (AML) and thus can be used for minimal residual disease (MRD) monitoring by quantitative RT-PCR. The aim of the present study was to analyze the usefulness of WT1 as a marker for MRD in AML after chemotherapy and as a predictor of relapse and survival. Patients and Methods This retrospective and multicentric study included 114 patients with WT1-overexpressed AML (Table 1). Quantitative assessment of WT1 transcript levels was performed by quantitative RT-PCR in 283 bone marrow (BM) samples at diagnosis, post-induction and post-consolidation. WT1 gene expression was calculated by relative quantification using the normalized ratio of the target gene (WT1) related to a reference gene (GUS) and using cell line K562 as calibrator. Inter-laboratories methodological standardization was accomplished through a pilot study with 10 BM donor samples, 20 BM patient samples and commercial WT1 plasmids (ProfileQuant Kit, Ipsogen-Qiagen). Results No significant differences in WT1 gene expression (cycle threshold, Ct) were observed between different laboratories in the pilot study. The cut-off value of WT1 over-expression in BM was established in 0.55% (median+2SD values from healthy donors). Median WT1 expression in patient samples at diagnosis was 29.5% (range, 2-1220). Differential expression at diagnosis was not correlated with age, sex, leukocytes, karyotype (risk), however a higher expression in patients with AML-M1 and AML-M2 subtypes as well as patients with mutant NPM1 and/or ITD-FLT3 was observed. Most patients (88.6%; 101/114) received intensive chemotherapy as induction treatment. After induction, 80.2% (81/101) of patients had available WT1 data, of which 23.45% (19/81) were positive. In addition, 79.8% (91/114) received intensive treatment during consolidation. WT1 results were available for 75.5% (66/91), of which 22.7% (15/66) were positive. Post-induction WT1 positivity was correlated with a higher cumulative incidence of relapse (CIR; 2 years 76% vs. 28.2% p=0.002) and a lower overall survival (OS; 2 years, 44.9% vs. 78.2% p=0.022; Figure 1a,b). Similar results were obtained when patients intensified with allogeneic stem cell transplantation (allo-SCT) were excluded from the analysis: higher CIR (2 years 88.9% vs. 32.5%; p=0.005) and lower OS (40% vs. 76.2% p=0.17). Post-consolidation WT1 positivity was correlated with a trend to a higher CIR (2 years 60.3% vs. 41.4% p=0.21) and a lower OS (2 years 44.9% vs. 66% p=0.09; Figure 1c,d). Statistically significant results were obtained after consolidation when patients treated with allo-SCT were excluded from the analysis: higher CIR (2 years 100% vs. 40.1% p=0.005) and lower OS (2 years 20% vs. 66% p=0.003). Conclusions WT1 is a useful marker for MRD in AML patients undergoing chemotherapy (induction and consolidation) which allows anticipation of relapse and survival. Post-induction results were a strong risk factor of relapse and survival in all patients, including those intensified with allo-SCT. By contrast post-consolidation results are especially relevant in the group of patients not treated with allo-SCT. Intensification with allo-SCT overcomes the poor prognosis derived from positive post-consolidation WT1 results. Paper presented on behalf of the Hematological Molecular Biology Group (GBMH) of the Spanish Society of Hematology (SEHH). Disclosures: No relevant conflicts of interest to declare.