ALBERT

Description: Introduction Over the last 15 years gene expression profiling (GEP) has been used to define myeloma molecular subgroups and to determine clinical prognosis. Two major molecular subgroup classifications have been used: the UAMS which determines 7 subgroups and the TC classification based on the presence of IgH translocations and expression of D group cyclins. For prognosis, although a number of different GEP signatures have been defined, the widely used GEP70 identifies 15% of patients with high risk (HR) disease who have a median PFS and OS of 1.75 and 2.83 years. An ideal classification system would identify clinically relevant subgroups with distinct etiology and biology using standardized techniques. We have examined a large group of patients characterized at multiple genetic levels to optimize the diagnostic approach of newly diagnosed patients going forward. Materials and methods Study subjects included 1349 cases enrolled in Total Therapy trials (median follow up 7.5 years). Gene expression profiling was used to determine GEP70 risk status, molecular subgroup by UAMS and TC classifications, and to devise a new and extended TC classification (TC10). Interphase FISH associated with IgH translocations and 1q+ and 17p- were used to build GEP proxies. Data from mutational analysis generated by the FoundationOne targeted sequence panel was also incorporated. Results were validated on the UK MRC MyelomaIX and Hovon65/GMMG-4 studies. Results An initial agnostic analysis of GEP data using sparse k-means clustering verified the existence of TC based groups. Six groups were identified that corresponded overwhelmingly with known TC subgroups; CCND1-t(11;14), D1-HRD, D2-HRD, MMSET, MAF/CCND2, and CCND3. Further comparisons between the molecular subgroup and TC classifier revealed that the UAMS 7 subgroups clustered strongly within one predominant TC group: CD-1 and CD-2 to t(11;14), HY to D1, LB and PR to D2, MF to t(14;16) or t(14;20), and MS to t(4;14). As the UAMS molecular subgroups are largely contained within the TC framework, we aimed to extend the TC by developing the TC10. To extend the known TC subgroups, unsupervised clustering was applied to the 3 largest subgroups [t(11;14), D1, and D2] to determine the strongest single divisor within each respective subgroup. The dominant feature within the t(11;14) cases was CD20 expression, while the D1 and D2 subgroups both split according to RRAS2. CD20 is associated with PAX5 and VPREB3 expression, and RRAS2 is associated with decreased PTP4A3 and increased TNFAIP3 and BIRC3 expression. RRAS2 activation within D1 subgroup and CD20 activation within t(11;14) cases corresponds to an increased time to response to induction therapy suggesting they constitute important biological subgroups. The TC10 combines the known etiologic subgroups of the TC with functionally relevant subdivisions to create 10 novel subgroups: t(11;14) CD20+/-, D1: RRAS2+/-, D2: RRAS2+/-, t(4;14), t(14;16), t(14;20), and t(6;14). Analysis of mutational data revealed that RRAS2 and CD20 activation within the D1, D2, and t(11;14) subgroups reduced the number of mutations in the MAPK pathway. Further mutational analysis revealed that median mutational load was highest in t(14;16) and lowest in D2: RRAS2+ subgroups. The GEP70 score identifies 15% of patients with HR disease and is specific for this purpose. In an analysis of risk assessment methods, we compared GEP detected adverse lesions [t(4;14), t(14;16), t(14;20), 17p- and 1q+] with the GEP70 and revealed that GEP70 HR identified samples have lower OS rates than cases with more than one adverse lesion (validated in external sets). GEP70 HR segregates non-uniformly across molecular subgroups as over 40% of all HR cases are found in the TC10 t(4;14), t(14;16), and t(14;20) subgroups. GEP70 HR cases also have a higher mutational load than low risk cases. Furthermore, GEP70 HR is uniquely associated with 1q+ and 17p- as cases with at least one of these adverse lesions are 4.9 times as likely to be GEP70 HR as cases with neither. Conclusion GEP profiling has a central role in simplifying and standardizing the molecular subgroup designation and risk stratifying of MM patients. The GEP70 risk score reliably identifies HR cases and outperforms FISH in risk assessment, even in validation data sets. The TC10 provides a classification system that improves upon previous methods by defining both etiological and functionally meaningful subgroups. Disclosures Stein: University of Arkansas for Medical Sciences: Employment. Davies:University of Arkansas for Medical Sciences: Employment; Celgene: Consultancy; Janssen: Consultancy; Millenium: Consultancy; Onyx: Consultancy. Heuck:University of Arkansas for Medical Sciences: Employment; Celgene: Consultancy; Janssen: Other: Advisory Board; Millenium: Other: Advisory Board; Foundation Medicine: Honoraria. Weinhold:University of Arkansas for Medical Sciences: Employment; Janssen Cilag: Other: Advisory Board. Chavan:University of Arkansas for Medical Sciences: Employment. Thanendrarajan:University of Arkansas for Medical Sciences: Employment. Epstein:University of Arkansas for Medical Sciences: Employment. Yaccoby:University of Arkansas for Medical Sciences: Employment. Zangari:University of Arkansas for Medical Sciences: Employment; Novartis: Research Funding; Onyx: Research Funding; Millennium: Research Funding. van Rhee:University of Arkansa for Medical Sciences: Employment. Kaiser:Janssen: Honoraria; Amgen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; BristolMyerSquibb: Consultancy; Chugai: Consultancy. Sonneveld:Janssen-Cilag, Celgene, Onyx, Karyopharm: Honoraria, Research Funding; novartis: Honoraria. Goldschmidt:Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Onyx: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Bristol-Myers Squibb: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen-Cilag: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Millenium: Honoraria, Research Funding, Speakers Bureau; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Chugai: Honoraria, Research Funding, Speakers Bureau; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Barlogie:University of Arkansas for Medical Sciences: Employment. Morgan:Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; MMRF: Honoraria; CancerNet: Honoraria; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; University of Arkansas for Medical Sciences: Employment; Weismann Institute: Honoraria; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees.

Description: The ataxia telangiectasia and RAD3-related (ATR) protein kinase is a component of the cellular DNA damage response pathway and promotes cell survival by signalling repair of collapsed replication forks generated by replication stress. We hypothesised that inhibition of ATR potentiates the anti-leukaemic activity of chain terminating nucleoside analogues used in the treatment of acute myeloid leukaemia (AML). We used VE-821 and its derivative VX-970 (Vertex Pharmaceuticals, Abingdon, UK) as potent and specific inhibitors of ATR kinase activity to examine the effects of ATR inhibition in AML cell lines, primary AML cells and AML xenografts. Co-treatment with 1mM VE-821 did not consistently potentiate the anti-proliferative effects of cytarabine, clofarabine or fludarabine in a panel of AML cell lines. However, there was consistent potentiation of hydroxyurea and gemcitabine in all 7 AML cell lines tested. Treatment with hydroxyurea, which induces replication stress via depletion of dNTPs, resulted in phosphorylation of CHK1, a downstream target of ATR. CHK1 phosphorylation was attenuated when 1mM VE-821 was co-administered with hydroxyurea. Exposure of cells to gemcitabine or hydroxyurea slowed transit through S phase, which was pronounced in combination with VE-821. HL-60 AML cell clones expressing either a constitutively active or inducible shRNA construct targeting ATR had reduced ATR protein expression compared to control cells and were significantly more sensitive to the anti-proliferative effects of gemcitabine and hydroxyurea, but not to cytarabine, clofarabine or fludarabine. The growth inhibitory effects of hydroxyurea and gemcitabine were also significantly potentiated by VE-821 in primary AML patient samples, which included three adult patients with de novo AML and a paediatric patient with therapy-related AML. In contrast, ATR inhibition did not potentiate the inhibitory effects of hydroxyurea or gemcitabine in primary bone marrow cells from healthy donors ex vivo. We next sought to determine whether ATR inhibition potentiated hydroxyurea and gemcitabine in an orthotopic mouse model of AML. MV4-11 AML cells engineered to express firefly luciferase (MV4-11 pSLIEW) were intrafemorally transplanted into immunodeficient Rag2-/- gc-/- mice. Bioluminescent imaging via IVIS Spectrum (PerkinElmer, Buckinghamshire, UK) demonstrated localised femoral engraftment first detectable 4-5 days post-injection, with luciferase signal developing in other parts of the body (liver, ovaries) between days 15 and 18 in untreated mice. Treatment was initiated 7 days post-injection when disease was localised to the femur and prior to emergence of disseminated luciferase signal. Single agent hydroxyurea (250mg per kg, IP days 0-4 and 7-11) conferred some early disease control compared to controls as determined by luciferase total body flux measured on day 14, but this was not statistically significant (p=0.18) and did not affect overall survival (mean 35 days for controls and 37 days for hydroxyurea, p=0.47). Monotherapy with VX-970 (60 mg per kg, orally on days 0-4 and 7-11) also conferred early disease control compared to vehicle-treated mice (p=0.18), and resulted in significantly longer overall survival (mean 40 days, p=0.017). Combination treatment with hydroxyurea and VX-970 did not result in more effective early disease control or improved overall survival compared to monotherapy with either agent. Treatment with gemcitabine monotherapy (100 mg per kg, intraperitoneal injection on days 0, 3, 7 and 10) conferred significant early disease control (p=0.002) and significantly improved overall survival compared to controls (mean survival 73 days, p

Description: Background: Maximising response in myeloma (MM) patients with effective induction regimens prior to autologous stem cell transplant (ASCT) improves progression-free and overall survival. Triplet regimens combining an immunomodulatory agent (IMiD) and/or proteasome inhibitor (PI) are standard of care, however a more personalised approach is achieved by sequential triplet combinations based on an individual's response. Alternatively, quadruplet regimens may be more effective and new generation PIs such as carfilzomib, with less off-target activity, provide the opportunity to investigate this whilst minimising the risk of increased toxicity. The UK NCRI Myeloma XI trial is a large, phase III study aiming to answer these questions in transplant eligible (TE) patients comparing the quadruplet carfilzomib, cyclophosphamide, lenalidomide and dexamethasone to the sequential strategy of triplet IMiD combinations (with thalidomide or lenalidomide) followed by additional PI triplet therapy for those with a suboptimal response (

Description: Introduction A significant proportion of myeloma patients relapse early and show short survival with current therapies. Molecular diagnostic tools are needed to identify these high risk patients at diagnosis to stratify treatment and offer the prospect of improving outcomes. Two validated molecular approaches for risk prediction are widely used: 1) molecular genetic risk profiling [e.g. del(17p), t(4;14)] 2) gene expression (GEP) risk profiling, [e.g. EMC92 (Kuiper et al., Leukemia 2012)]. We profiled patients from a large multicentric UK National trial using both approaches for integrated risk stratification. Methods A representative group of 221 newly diagnosed, transplant eligible patients (median age 64 years) treated on the UK NCRI Myeloma XI trial were molecularly profiled. DNA and RNA were extracted from immunomagnetically CD138-sorted bone marrow plasma cells. Molecular genetic profiles, including t(4;14), t(14;16), Del(17p), Gain(1q) were generated using MLPA (MRC Holland) and a TC-classification based qRT-PCR assay (Boyle EM, et al., Gen Chrom Canc 2015, Kaiser MF, et al., Leukemia 2013). GEP risk status as per EMC92 was profiled on a diagnostic Affymetrix platform using the U133plus2.0-based, CE-marked MMprofiler (SkylineDx) which generates a standardised EMC92 risk score, called 'SKY92'. Progression-free (PFS) and overall survival (OS) were measured from initial randomization and median follow-up for the analysed group was 36 months. Statistical analyses were performed using R 3.3.0 and the 'survival' package. Results were confirmed in an independent dataset, MRC Myeloma IX, for which median follow-up was 82.7 months. Results Of the 221 analysed patients, 116 were found to carry an established genetic high risk lesion [t(4;14), t(14;16), del(17p) or gain(1q)]. We and others have recently demonstrated that adverse lesions have an additive effect and that co-occurrence of ≥2 high risk lesions is specifically associated with adverse outcome (Boyd KD et al, Leukemia 2011). 39/221 patients (17.6%) were identified as genetic high risk with ≥2 risk lesions (termed HR2). By GEP, 53/221 patients (24.0%) were identified as SKY92 high risk. Genetic and GEP high risk co-occurred in 22 patients (10.0%), 31 patients (14.0%) were high risk only by GEP and 17 patients (7.7%) by genetics only. SKY92 high risk status was associated with significantly shorter PFS (median 17.1 vs. 34.3 months; P

Description: Introduction. Minimal residual disease (MRD) is a powerful predictor of outcome in multiple myeloma (MM). We have previously demonstrated, in transplant eligible patients, that the level of MRD as a continuous variable independently predicts both PFS and OS, with approximately a one year median OS benefit per log depletion (J Clin Oncol 2013; 31:2540-7 and Blood 2015; 125:1932-5). The impact of MRD also appears to be independent of therapy received. There is more limited data on the applicability of MRD assessment in transplant ineligible patients, largely as a consequence of low rates of CR historically within this patient cohort. Patients and Methods. In this analysis we have assessed the impact of MRD on PFS amongst patients treated within the non-intensive arm of the NCRI Myeloma XI trial. Patients were randomised between thalidomide (CTDa) and lenalidomide (RCDa) based induction therapies with responding patients being subsequently randomised to maintenance with lenalidomide monotherapy, or no further therapy. Bone marrow aspirates were obtained at the end of induction and this analysis represents a subset of 297 patients (median age 74 years). MRD was assessed using flow cytometry (sensitivity 10-4) with a minimum of 500,000 cells evaluated with six-colour antibody combinations including CD138/CD38/CD45/CD19 with CD56/CD27 in all cases and CD81/CD117 in additional cases as required. Results. Overall MRD-negativity was demonstrated in 41/297 (13.8%). When considered according to induction therapy received 25/154 (16.0%) of patients randomized to RCDa were MRD-negative compared to 16/143 (10.8%) of those randomized to CTDa (p=0.24; Fisher's exact test). MRD-negativity was associated with a significant outcome advantage as the median PFS was 34 months versus 18 months for MRD-positive patients (p

Description: Introduction Segmenting multiple myeloma (MM) into subgroups with distinct pathogenesis and clinical behavior is important in order to move forward with advancements in therapy and implement a targeted therapy approach. Current technologies have elucidated five major translocation groups, which have a varying effect on prognosis: t(4;14), t(6;14), t(11;14), t(14;16) and t(14;20) along with recurrent copy number changes including deletion of CDKN2C (1p32.3) and TP53 (17p13.1) as well as gain or amplification of 1q21. However, minor translocation and mutational groups are poorly described because sample numbers are limited in small datasets. The availability of multiple sets of high quality mutation data associated with clinical outcomes has provided a unique opportunity in MM whereby clustering mutational data with chromosomal aberrations in the context of gene expression we can develop a molecular classification system to segment the disease into therapeutically meaningful subgroups. The Multiple Myeloma Genome Project (MGP) is a global collaborative initiative that aims to develop a molecular segmentation strategy for MM to develop clinically relevant tests that could improve diagnosis, prognosis, and treatment of patients with MM. Materials and methods We have established a set of 2161 patients for which whole exome sequencing (WES; n=1436), Whole Genome Sequencing (WGS; n=708), targeted panel sequencing (n=993) and expression data from RNA-Seq and Gene Expression arrays (n=1497) were available. These data were derived from the Myeloma XI trial (UK), Intergroupe Francophone du Myeloma/Dana-Faber Cancer Institute (MA), The Myeloma Institute (AR) and the Multiple Myeloma Research Foundation (IA1 - IA8). We assembled all data on a secure site and analyzed it using a streamlined and consistent pipeline using state of the art tools. First, BAM were converted to FASTQ using Picard tools v2.1.1 to extract read sequences and base quality scores. Next, all reads were realigned to the human genome assembly hg19 using BWA-mem. Duplicate marking and sorting was performed using Picard tools v2.1.1. For QAQC we use FASTQC and Picard tools. We identified somatic single nucleotide variants and indels with Mutect2 using default parameters. Translocations and large chromosomal aberrations were identified using MANTA and breakdancer and inferred copy number abnormalities and homozygous deletions using Sequenza v2.1.2 and ControlFreeC. Results We have begun to integrate these diverse large genomic datasets with various correlates. Samples were stratified by RNA-seq expression values and WES/WGS to identify the main cytogenetic groups with high concordance. In addition to the main translocation groups, translocations into MAFA, t(8;14), were detected in 1.2% of samples by both RNA-seq and WES/WGS. RNA-seq also detected fusion transcripts, including the known Ig-WHSC1 transcript in t(4;14). However, a proportion of identified in-frame fusion genes involved kinase domains consistent with activation of the Ras/MAPK pathway, which may be clinical targets for therapy. The main recurrent mutations included KRAS and NRAS, and negative regulators of the NF-κB pathway. In addition we identified recurrent copy number abnormalities and examined the interaction of these with mutations. This highlighted the interaction of the recurrent changes at 1p, 13q, and 17p with mutation of genes located within these regions, specifically indicating bi-allelic inactivation of CDKN2C, RB1 and TP53. Using WGS and RNA-Seq data we identified recurrent translocations and fusion genes that can be used to instruct therapy. Based on these data and the presence of homogeneous inactivation of key tumor expressed genes we will present clinically relevant clusters of MM that can form the basis of future risk and molecular targeted trials. Interaction of mutation with expression patterns has identified distinct expression signatures associated with mutational groups. Conclusions We have established the largest repository of molecular profiling data in MM along with associated clinical outcome data. Integrated analyses of these are enabling generation of clinically meaningful disease segments associated with differing risk. The MGP intends to build a global network by expanding collaboration with leading MM centers around the world and incorporating additional datasets through current and new collaborations. Disclosures Mavrommatis: Discitis DX: Membership on an entity's Board of Directors or advisory committees; Celgene Corporation: Employment, Equity Ownership. Ashby:University of Arkansas for Medical Sciences: Employment. Ortiz:Celgene: Employment. Towfic:Celgene: Employment, Equity Ownership; Immuneering Corp: Equity Ownership. Amatangelo:Celgene: Employment, Equity Ownership. Yu:Celgene: Employment, Equity Ownership. Avet-Loiseau:celgene: Consultancy; janssen: Consultancy; sanofi: Consultancy; amgen: Consultancy. Jackson:Janssen: Consultancy, Honoraria, Speakers Bureau; Celgene: Consultancy, Honoraria, Other: Travel support, Research Funding, Speakers Bureau; MSD: Consultancy, Honoraria, Speakers Bureau; Roche: Consultancy, Honoraria, Speakers Bureau; Takeda: Consultancy, Honoraria, Other: Travel support, Research Funding, Speakers Bureau; Amgen: Consultancy, Honoraria, Speakers Bureau. Thakurta:Celgene: Employment, Equity Ownership. Munshi:Takeda: Consultancy; Amgen: Consultancy; Janssen: Consultancy; Celgene: Consultancy; Merck: Consultancy; Pfizer: Consultancy; Oncopep: Patents & Royalties. Morgan:Univ of AR for Medical Sciences: Employment; Janssen: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Bristol Meyers: Consultancy, Honoraria.

Description: Background Darwinian evolution drives multiple myeloma (MM) and leads to diversity both within and between patients. This suggests the need for combinations of agents with different mechanisms of action targeting sub-clonal populations to maximize the depth of response and improve outcomes. Approaches to maximize response pre-transplant include the use of sequential pre-transplant consolidation with a different agent in sub-optimal responders or intensifying upfront combinations whilst aiming to minimize additional toxicities. Carfilzomib is a novel irreversible inhibitor of the proteasome that has been suggested to have greater activity than bortezomib, with deeper responses and improved outcomes. The Myeloma XI phase III randomized trial for newly diagnosed MM patients compared intensified induction with the quadruplet KCRD vs a response adapted approach of sequential triplet therapies for transplant-eligible MM patients. Methods KCRD was given in 28 day cycles (carfilzomib (K) 36mg/m2 IV d1-2, 8-9,15-16 (20mg/m2 #1d1-2), cyclophosphamide (C) 500mg PO d1,8, lenalidomide (R) 25mg PO d1-21, dexamethasone (D) 40mg PO d1-4,8-9,15-16), CRD in 28 day cycles (C 500mg PO d1,8, R 25mg PO d1-21, D 40mg PO d1-4, 12-15) or CTD in 21 day cycles (C 500mg PO d1,8,15 thalidomide (T) 100-200mg PO daily, D 40mg PO d1-4,12-15). All induction regimens were continued for a minimum of 4 cycles and to maximum response. Suboptimal responders (MR/PR) to CTD/CRD were randomized between pre-transplant intensification with a proteasome inhibitor (bortezomib, CVD) containing triplet or no further therapy prior to ASCT, patients with refractory disease (SD/PD) all received CVD. For all patients a maintenance randomization 3 months post ASCT compared lenalidomide given to disease progression to observation. Cytogenetic data, centrally analyzed, was available for a representative subset of patients. High-risk was classified as presence of t(4;14), t(14;16), t(14;20), del(17p) or gain(1q) and ultra-high risk the presence of more than one lesion. 1056 patients underwent induction randomization between December 2013 and April 2016 and were allocated to CTD n=265, CRD n=265, KCRD n=526. The groups were well matched across baseline variables with median age 61 (range 33-75). The median follow up for this analysis is 34.5 months. The independent data monitoring and ethics committee recommended immediate release of the data following an interim analysis. Results Intention to treat analysis of the initial induction regimens found that KCRD was associated with a significantly longer PFS than triplet therapy (HR 0.63, 95%CI 0.51, 0.76, median PFS KCRD NR vs CTD/CRD 36.2 months, p

Description: Key Points Using the largest set of patients with newly diagnosed myeloma, we identified 63 mutated driver genes. We identified oncogenic dependencies, particularly relating to primary translocations, indicating a nonrandom accumulation of genetic hits.

Description: Key Points The level of MRD quantified by flow cytometry is more informative than a 0.01% threshold and independently predicts OS. There was approximately 1 year survival benefit per log depletion. A lower cut point for predicting improved outcome was not reached.

Description: INTRODUCTION Features of high risk myeloma (MM) have been studied in detail but patients with longer term responses to first-line therapy are less well characterised. Identification of common features of this group may support optimised management. Here we analysed clinical and genetic characteristics of long-term responders of 4,249 trial patients from the UK MRC Myeloma IX (M-IX) and NCRI Myeloma XI (M-XI) trials. PATIENTS AND METHODS In M-IX patients were randomised between alkylating therapy (CVAD or MP) and thalidomide-based induction therapy (CTD). M-XI patients were randomised between thalidomide and lenalidomide based induction (CTD vs CRD) and a response-based bortezomib (CVD) intensification. Fitter patients received HD-Mel+ASCT consolidation. Patients were then randomised to thalidomide (M-IX) or lenalidomide (M-XI) maintenance or observation. Trials included symptomatic, newly diagnosed patients based on CRAB criteria. This analysis included 1,921 My-IX and 2,328 My-XI patients with median follow-up of 73 and 61 months (m), respectively. Genetic profiling was available for 1,866 patients. Patients with a long-term response post induction (PFS≥48m) were identified and their baseline characteristics, responses and treatment compared to those with PFS