Low-risk Multiple Myeloma Research Articles

Low-Risk Multiple Myeloma By SKY92+ISS Validated in the Multiple Myeloma Genomics Initiative Study

Low-Risk Multiple Myeloma By SKY92+ISS Validated in the Multiple Myeloma Genomics Initiative Study

The Combination of SKY92 and ISS Provides a Powerful Tool to Identify Both High Risk and Low Risk Multiple Myeloma Cases, Validation in Two Independent Cohorts

IntroductionMultiple Myeloma (MM) is a heterogeneous disease with a strong need for robust markers for prognosis. Frequently occurring chromosomal abnormalities, such as t(4;14), gain(1q), and del(17p) etc. have some prognostic power, but lack robustness across different cohorts. Alternatively, gene expression profiling (GEP) studies have developed specific high risk signatures such as the SKY92 (EMC92, Kuiper et al. Leukemia 2012), which has shown to be a robust prognostic factor across five different clinical datasets. Moreover, studies comparing prognostic markers have indicated that the SKY92 signature outperforms all other markers for identifying high risk patients, both in single and multivariate analyses. Similarly, when assessing the prognostic value of combinations of various prognostic markers, the SKY92 combined with ISS was the top performer, and also enables detection of a low risk group (Kuiper et al. ASH 2014). Here, we present a further validation of the low and high risk groups identified by the SKY92 signature in combination with ISS on two additional cohorts of patients with diverse treatment backgrounds, containing newly diagnosed, previously treated, and relapsed/refractory MM patients.Materials and MethodsThe SKY92 signature was applied to two independent datasets. Firstly, the dataset from the Total Therapy 6 (TT6) trial, which is a phase 2 trial for symptomatic MM patients who have received 1 or more prior lines of treatment. The TT6 treatment regime consists of VTD-PACE induction, double transplant with Melphalan + VRD-PACE, followed by alternating VRD/VMD maintenance. Affymetrix HG-U133 Plus 2.0 chips were performed at baseline for n=55 patients, and OS was made available previously (Gene Expression Omnibus identifier: GSE57317). However, ISS was not available for this dataset. Secondly, a dataset of patients enrolled at two hospitals in the Czech Republic, and one in Slovakia (Kryukov et al. Leuk&Lymph 2013). Patients of all ages, and from first line up to seventh line of treatment were included (treatments incl Bort, Len, Dex). For n=73 patients Affymetrix Human Gene ST 1.0 array, OS (n=66), and ISS (n=58) was made available previously (ArrayExpress accession number: E-MTAB-1038). Both datasets were processed from .CEL files by MAS5 (TT6), and RMA (Czech), followed by mean variance normalization per probeset across the patients. The SKY92 was applied as previously described (Kuiper et al. Leukemia 2012), and identifies a High Risk and Standard Risk group. In conjunction with ISS, the SKY92 Standard Risk group is then further stratified into low and intermediate risk groups (Kuiper et al. ASH 2014). Kaplan-Meier plots were created, and the Cox proportional hazards model was used to calculate Hazard Ratios (HR), and associated 1-sided p-values that assess whether the SKY92 High Risk group has worse survival than SKY92 Standard Risk group (i.e. HR>1).ResultsFigure 1 shows the Kaplan Meier plots of the SKY92 High Risk and Standard Risk groups on the TT6 and Czech cohorts. On the TT6 dataset, the SKY92 signature identifies 11 out of 55 patients (20%) as High Risk. In both datasets, the SKY92 High Risk group has significantly worse overall survival, HR=10.3, p=7.4 * 10-6 (TT6), and HR=2.6, p=2.2 * 10-2 (Czech). In addition, the combination of SKY92 with ISS on the Czech dataset identifies a low risk group of 14 out of 61 patients (23%), with a five year overall survival estimate of 100% versus 28.7% in the SKY92 High Risk group (HR=inf). Robustness of the SKY92 signature is further demonstrated by the fact that it validates on both datasets, despite different microarray platforms being used.ConclusionsThe SKY92 high risk signature has been successfully validated on two independent datasets generated using different microarray platforms. In addition, on the Czech data, the low risk group (SKY92 Standard Risk combined with ISS 1) has been successfully validated. Together, this signifies the robust nature of the SKY92 signature for high and low risk prediction, across treatments, and with applicability in newly diagnosed, treated, and relapsed/refractory MM patients. [Display omitted] Disclosuresvan Vliet:SkylineDx: Employment. Ubels:SkylineDx: Employment. de Best:SkylineDx: Employment. van Beers:SkylineDx: Employment. Sonneveld:Celgene: Honoraria, Research Funding; Amgen: Honoraria, Research Funding; Karyopharm: Research Funding; SkylineDx: Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Research Funding.

Prediction of high- and low-risk multiple myeloma based on gene expression and the International Staging System

AbstractPatients with multiple myeloma have variable survival and require reliable prognostic and predictive scoring systems. Currently, clinical and biological risk markers are used independently. Here, International Staging System (ISS), fluorescence in situ hybridization (FISH) markers, and gene expression (GEP) classifiers were combined to identify novel risk classifications in a discovery/validation setting. We used the datasets of the Dutch-Belgium Hemato-Oncology Group and German-speaking Myeloma Multicenter Group (HO65/GMMG-HD4), University of Arkansas for Medical Sciences-TT2 (UAMS-TT2), UAMS-TT3, Medical Research Council-IX, Assessment of Proteasome Inhibition for Extending Remissions, and Intergroupe Francophone du Myelome (IFM-G) (total number of patients: 4750). Twenty risk markers were evaluated, including t(4;14) and deletion of 17p (FISH), EMC92, and UAMS70 (GEP classifiers), and ISS. The novel risk classifications demonstrated that ISS is a valuable partner to GEP classifiers and FISH. Ranking all novel and existing risk classifications showed that the EMC92-ISS combination is the strongest predictor for overall survival, resulting in a 4-group risk classification. The median survival was 24 months for the highest risk group, 47 and 61 months for the intermediate risk groups, and the median was not reached after 96 months for the lowest risk group. The EMC92-ISS classification is a novel prognostic tool, based on biological and clinical parameters, which is superior to current markers and offers a robust, clinically relevant 4-group model.

The Risk Classification based on Gene Expression Profiling: a Tool for Prediction of High- and Low-risk Multiple Myeloma across International Trials

behavior, and help identify novel therapeutic targets for the development of molecularly-based therapies to improve outcome in myeloma.

Renal insufficiency retains adverse prognostic implications despite renal function improvement following Total Therapy for newly diagnosed multiple myeloma.

Renal insufficiency (RI) is a frequent complication of multiple myeloma (MM) with negative consequences for patient survival. The improved clinical outcome with successive Total Therapy (TT) protocols was limited to patients without RI. We therefore performed a retrospective analysis of overall survival, progression-free survival and time to progression (TTP) of patients enrolled in TT2 and TT3 in relationship to RI present at baseline and pre-transplant. Glomerular filtration rate was graded in four renal classes (RCs), RC1–RC4 (RC1 ⩾90 ml/min/1.73 m2, RC2 60–89 ml/min/1.73 m2, RC3 30–59 ml/min/1.73 m2 and RC4 <30 ml/min/1.73 m2). RC1–3 had comparable clinical outcomes while RC4 was deleterious, even after improvement to better RC after transplant. Among the 85% of patients with gene expression profiling defined low-risk MM, Cox regression modeling of baseline and pre-transplant features, which also took into consideration RC improvement and MM complete response (CR), identified the presence of metaphase cytogenetic abnormalities and baseline RC4 as independent variables linked to inferior TTP post-transplant, while MM CR reduced the risk of progression and TTP by more than 60%. Failure to improve clinical outcomes despite RI improvement suggested MM-related causes. Although distinguishing RC4 from RC<4, 46 gene probes bore no apparent relationship to MM biology or survival.

Curing Multiple Myeloma (MM) with Total Therapy (TT)

Intra-tumoral heterogeneity is a hindrance to curing malignant disease. By employing all MM-active drugs upfront, TT was designed to overcome such obstacle by targeting all potential MM sub-clones broadly. We are reporting on long-term results of phase-2 TT1, phase-3 TT2 and phase-2 TT3a trials, with median follow-up times of 21yr, 12yr and 9yr, respectively. Five year estimates of OS, PFS and CR duration (CRD) increased from 58%, 28% and 40% with TT1 to 65%, 42% and 50% with TT2’s control arm (TT2-), to 68%, 56% and 58% with TT2’s thalidomide arm (TT2+), and to 74%, 65% and 74% with TT3a (all p<0.0001). The 5-yr estimates of time to progression (TTP)/time to relapse (TTR) from CR decreased from 59%/58% in TT1 to 43%/42% in TT2-, to 28%/34% in TT2+, and to 22%/18% in TT3a (all p<0.0001). When examined in the era of gene expression profiling (GEP) of purified plasma cells, 5-yr estimates of TTP/TTR in GEP70 low-risk MM were 47%/46% with TT2-, 26%%/36% with TT2+, and 18%/16% with TT3a (all p<0.0001); the corresponding data for the 15% with high-risk MM were 60%/50% with TT2-, 62%/64% in TT2+, and 48%/35% in TT3a (NS). Relative survival (RS) was computed per year in relationship to age- and sex-matched controls. RS ratios approached 1 at 10–15 years for TT1 and TT2-, but earlier, at 5-10 years, for TT2+ and TT3a. A parametric mixture cure model was used to estimate PFS and CR duration for each protocol, from baseline and from a 5-year landmark. The cure model fits the data well and provides cure-fraction estimates that increase with later protocols (Figure 1). Importantly, when comparing data from GEP-defined low-risk and high-risk MM, plateaus for both PFS- and CRD-based cure fractions emerged at 10 years in the former and earlier, at 5 years, in the latter (data not shown). PFS-based cure-fraction estimates increasedsignificantly with successive TT trials: 9% in TT1, 16% in TT2-, 25% in TT2+, and 33% in TT3a (p=0.04). CRD-based cure-fraction estimates were 18%, 28%, 36%, and 49%, respectively (p=0.17) (Table 1). When a 5-year landmark was applied to exclude early myeloma-related events, PFS-based cure fraction estimates were 28% in TT1, 39% in TT2-, 51% in TT2+, and 70% in TT3a (p<0.001); in this setting, CRD-based cure fraction estimates were 32%, 47%, 56%, and 75%, respectively (p=0.007). MRD flow cytometry was available in 83 of 175 TT2 PFS patients, of whom 78% were MRD-. Our TT results are consistent with curability of MM. Solely novel agent-based trials without transplant lack the long-term follow-up to determine their cure potential.As MM usurps the bone marrow micro-environment (ME) for its growth and survival, we reasoned that ME alterations reflecting an angiogenic switch may persist beyond the state of clinical CR and even MRD. We therefore performed GEP analyses of bone marrow biopsies in CR and compared findings with age- and sex-matched controls. Indeed, biopsy “normalization” was linked to superior CR duration and is being developed as an early “cure surrogate” marker to help define the length of maintenance therapy in future trials. Although about 50% of CR patients treated with TT3a can be considered cured, the median PFS in high-risk MM of 2yr has not improved with successive TT trials. Reasoning that cytokine storms emanating after myelosuppressive therapies to restore normal hematopoiesis may be growth-stimulatory to MM cells, we applied non-myelotoxic metronomic therapy (Papanikolaou, Haematologica, 2014). Six newly diagnosed transplant-ineligible high-risk patients were offered an extended 28-day course of low-dose metronomic therapy with bortezomib, thalidomide, dexamethasone and continuous infusions of doxorubicin and cisplatin, to which were added arsenic trioxide and vincristine infusions. Remarkably, 5 of 6 achieved CR status after a single course and remain disease-free without further therapy 6-8 months later. Our results attest to the curability of MM with TT and, given the short PFS of 2yr in high-risk MM, such patients should be the focus of future novel interventions that yield information in a timely manner.Table 1Cure fraction estimatesProtocolPFSCR durationNCure FractionNCure FractionFrom start of therapyTT12318.8%7917.9%TT2 -Thal34515.5%14628.2%TT2 +Thal32325.1%20035.6%TT3a30332.9%18948.8%From 5-yr landmarkTT16528.4%3332.3%TT2 -Thal14539.2%8447.4%TT2 +Thal15051.1%13455.9%TT3a19769.8%14874.7%PFSCR duration [Display omitted] Disclosuresvan Rhee:Janssen: Membership on an entity’s Board of Directors or advisory committees; Celgene: Membership on an entity’s Board of Directors or advisory committees; Millenium: Membership on an entity’s Board of Directors or advisory committees; Sanofi: Membership on an entity’s Board of Directors or advisory committees. Heuck:Celgene: Honoraria; Foundation Medicine: Honoraria; Millennium: Membership on an entity’s Board of Directors or advisory committees; Janssen: Membership on an entity’s Board of Directors or advisory committees.

Prediction of High and Low-Risk Multiple Myeloma Based on the EMC92 Gene Expression Signature and the International Staging System

Introduction: The variable survival of multiple myeloma patients requires solid prognostic markers. It is unclear how currently used markers relate to each other. Here, ISS, cytogenetics and gene expression profiling (GEP) were combined to find novel risk stratifications in a discovery/validation setting. Next, novel combinations were compared with the currently existing markers.Methods: The following datasets were used: HOVON-65/GMMG-HD4, UAMS-TT2, UAMS-TT3, MRC-IX, APEX and IFM (total number of patients: 4720). In total, 20 markers were evaluated, including cytogenetic markers (deletions of 17p (del17p) and 13q (del13q), gain of 1q (add1q) and translocations t(4;14), t(11;14), t(14;16) and t(14;20)), GEP markers (EMC92, UAMS70, UAMS17, UAMS80, MRC-IX-6, IFM15, HM19 and GPI50) and ISS. In addition, the combination of del17p, t(4;14) and ISS (Avet-Loiseau et al., Leukemia, 27:711-717; 2013) and the combination of del17p, t(4;14) and add1q (Broyl et al., Blood, 121: 624-627, 2013) were included. Reevaluation of markers was performed by Cox regression analysis stratified for cohort combined with the likelihood-ratio test. As a result, cytogenetic markers t(11;14), t(14;16) and t(14;20) were excluded. Thus, 17 markers were analyzed in the combination analysis (number of marker pairs: 136). This resulted in 24 combinations after validation. The Akaike information criterion (AIC) was used to rank the 41 markers (24 novel combinations and 17 existing markers). Importantly, to avoid training bias, training data were excluded when testing markers originally developed using those data (i.e. GEP markers and FISH/ISS).Results: Reevaluation of existing markers in relation to overall survival (OS) demonstrated solid performance of poor-risk cytogenetic markers del17p, del13q, t(4;14) and add1q, but strikingly not for t(11;14), which is thought to predict for favorable outcome. The hazard ratios and p-values were 2.3 (p<1x10-15; del17p), 1.7 (p<1­x10-15; del13q), 2.2 (p<1x10-15; t(4;14)), 2.0 (p<1x10-15; add1q) and 0.9 (p=0.5; t(11;14)). Top performing GEP markers EMC92 and UAMS17 demonstrated good performance with hazard ratios and p-values of 2.8 (p<1x10-15) and 3.0 (p<1x10-15), respectively.In the subsequent pair-wise combination analysis, ISS demonstrated to be a valuable partner to both GEP and cytogenetic markers (Figure). Ranking all combinations as well as current markers showed that the ISS-GEP combinations consistently rank at the top. The EMC92-ISS combination was among the strongest predictors for OS, resulting in a four group risk stratification. In a pooled data analysis, the median survival was 24, 47 and 61 months and median not reached, for the highest-risk to the lowest-risk group, respectively. The highest risk group amounted to 17% and the lowest risk group to 38% of patients. Other high scoring combinations, ranking 2nd-5th, respectively, were ISS-UAMS17, ISS-HM19, ISS-UAMS80 and ISS-UAMS70. The compound FISH/ISS marker was in 7th position and ISS itself ranked 22nd out of 41. In general combinations of markers tended to perform better than univariate markers. The best univariate marker was EMC92 (14th out of 41).Conclusions:Both GEP and FISH markers are solid prognostic markers, with GEP markers demonstrating better predictions in unbiased comparisons than FISH markers. Prognostic markers can be improved by combining markers, as is evident when considering both existing combinations, such as the FISH/ISS marker proposed by Avet-Loiseau et al., and novel combinations. The EMC92-ISS model is a novel combination which is an improvement compared to currently used markers, offering a robust 4-group risk stratification based on biology and clinical parameters. This model is a good candidate to be validated in a clinical trial in order to stratify patients for treatment. [Display omitted] DisclosuresVan Vliet:SkylineDX: Employment. van Beers:SkylineDX: Employment. Mulligan:Millennium Pharmaceuticals: Employment. Gregory:Novartis: Research Funding; Schering Health Care Ltd: Research Funding; Pharmion: Research Funding; Celgene: Research Funding; Ortho Biotech: Research Funding. Goldschmidt:Janssen-Cilag: Honoraria, Research Funding, Speakers Bureau; Polyphor: Research Funding; Celgene: Honoraria, Research Funding, Speakers Bureau; Novartis: Honoraria, Research Funding, Speakers Bureau; Chugai: Research Funding, Speakers Bureau; Onyx: Consultancy, Speakers Bureau; Millenium: Consultancy, Speakers Bureau. Lokhorst:Celgene: Honoraria; Johnson-Cilag: Honoraria; Mudipharma: Honoraria. Sonneveld:Celgene: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Onyx: Honoraria, Research Funding; Millenium: Honoraria, Research Funding.

Cancer and Thrombosis

Venous thromboembolism (VTE) is a common and life-threatening condition in patients with cancer. The incidence rate depends on the tumor type. Many of risk factors for development of VTE are common in patients with cancer, which include cancer related factors and treatment related factors. In this review we define VTA to include deep vein thrombosis (DVT), pulmonary embolism (PE), superficial vein thrombisis (SVT), Splanchnic vein thromboses (SPVT), Involving the splenic, portal, mesenteric, or hepatic veins. Prophylactic anticoagulation therapy recommended for patients with active cancer who do not have a contraindication are Low molecular Wight heparins, fondaparinox, and subcutaneous Unfractionated Heparin, warfarin, and aspirin only for low risk multiple myeloma. In cancer patients low-molecular-weight heparin monotherapy has been identified as a simple and efficacious regimen compared with an initial parenteral anticoagulant followed by long-term therapy with a vitamin K antagonist.

Persistent Improvement In Clinical Outcomes With Bortezomib-Thalidomide-Dexamethasone Vs Thalidomide-Dexamethasone Incorporated Into Double Autologous Transplantation For Multiple Myeloma: An Updated Analysis Of Phase 3 Gimema-MMY-3006 Study

▪Over the last years, incorporation of novel agents into autologous stem cell transplantation (ASCT) has improved markedly the outcomes of younger patients with newly diagnosed multiple myeloma (MM). Superior results with experimental treatments vs previous standards of care have been frequently reported after preliminary analyses and need to be confirmed with longer follow up. The randomized phase 3 GIMEMA-MMY-3006 study was designed to compare bortezomib-thalidomide-dexamethasone (VTD) vs thalidomide-dexamethasone (TD) as induction therapy before, and consolidation after, double ASCT. Data from the initial analysis, with a median follow up of 36 months, demonstrated that patients randomized to the VTD arm enjoyed superior complete/near complete response (CR/nCR) rates after both induction and consolidation therapy, and had a significantly longer PFS compared to those prospectively assigned to the TD arm. We performed an updated analysis of the study after a median follow up of 59 months and results are herein reported. A persistent TTP and PFS benefit with incorporation of VTD into ASCT was confirmed. On an intention-to-treat analysis of 236 patients randomized to the VTD arm, median TTP was 62 months and median PFS was 57 months. The median values for 238 patients randomly assigned to the TD arm were 45 months for TTP (HR=0.64, p=0.001) and 42 months for PFS (HR=0.66, p=0.001) (Fig. 1). With the longer follow up of this analysis, an initial divergence between OS curves could be appreciated after 4 years, although the difference was not yet statistically significant at 6 years (75% for VTD vs 69% for TD). Superiority of VTD over TD for TTP and PFS was retained across prespecified subgroups of patients with high risk and low risk disease. In particular, PFS benefit with VTD was seen for patients age >60 years (HR=0.62, p=0.013) and younger than 60 years (HR=0.70, p=0.026), with ISS stage 1 (HR=0.59, p=0.009) and ISS stage 2-3 (HR=0.69, p=0.018), and for those with t(4;14) and/or del(17p) (HR=0.43, p<0.001) and with t(4;14) alone [t(4;14) positivity but lack of del(17p)] (HR=0.41, p=0.001). In comparison with patients with t(4;14) positivity who were randomized to TD, those assigned to the VTD arm had significantly longer PFS (median: 24 vs 53 months, HR=0.41, p=0.0007) (Fig. 2) and a trend towards longer OS (4-year estimates: 66% vs 81%, p=0.052). By the opposite, similar PFS curves were seen for patients in the VTD group regardless of the presence or absence of t(4;14) (Fig. 3). On multivariate Cox regression analysis, randomization to the VTD arm was an independent factor predicting for prolonged PFS (HR=0.64, P=0.001). Additional disease- and treatment-related variables independently affecting PFS included attainment of CR/nCR after both induction (HR=0.64, p=0.010) and consolidation therapy (HR=0.57, p<0.001), β2-m >3.5 mg/L (HR=1.7, p<0.001) and presence of t(4;14) and/or del(17p) (HR=2.0, p<0.001). On multivariate analysis, β2-m, cytogenetic abnormalities and attainment of CR/nCR after consolidation therapy were independently associated with OS. With an updated median follow-up of 49 months from the landmark of starting consolidation therapy, median PFS was 50 months for patients receiving VTD consolidation and 38 months for those treated with TD (HR= 0.69, P=0.015) (Fig. 4). Superior PFS with VTD vs TD consolidation therapy was observed for patients who failed CR/nCR after the second ASCT (HR=0.48, P=0.003) and was retained in both low risk and high risk subgroups. Finally, duration of OS from relapse or progression was similar between the two treatment groups (median, 42 for VTD vs 35 months for TD, p=0.47), even when bortezomib was incorporated into salvage therapy. In conclusion, this updated analysis of the GIMEMA-MMY-3006 study demonstrated: 1) a persistent PFS benefit with VTD vs TD in the overall population, as well as in subgroups of patients with high risk and low risk MM; 2) the ability of VTD, but not of TD, incorporated into double ASCT to overcome the adverse prognosis related to t(4;14); 3) the significant contribution of VTD consolidation to improved outcomes seen for patients randomized to the VTD arm; 4) the lack of more resistant relapse after exposure to VTD as induction and consolidation therapy compared to TD. A longer follow up is required to assess the OS benefit, if any, with VTD plus double ASCT. [Display omitted] [Display omitted] [Display omitted] [Display omitted] Disclosures:Cavo:Bristol-Myers Squibb: Consultancy, Honoraria, 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; Millennium: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees. Tacchetti:Janssen and Celgene: Honoraria. Zamagni:Celgene: Honoraria; Janssen-Cilag: Honoraria. Caravita:Celgene: Honoraria, Research Funding. Brioli:Celgene: Honoraria.

High Trip13 and Low P31 Comet Cause Drug Resistance and Poor Prognosis In Multiple Myeloma

BackgroundWe have recently established that increased chromosomal instability (CIN) signature is linked to drug resistance and poor outcome in multiple myeloma (MM) and other cancers. Thyroid Hormone Receptor Interactor 13 (Trip13), one of the 56 drug-resistant genes, plays a key role in chromosomal recombination and structure development during meiosis and has been reported to be increased in some malignancies including lung cancer, prostate cancer and breast cancer. In this study, we investigated how important Trip13 is in myelomagenesis and progression. Materials and MethodsGene expression profiling (GEP) was analyzed on plasma cells from 22 healthy donors, 44 patients with monoclonal gammopathy of undetermined significance (MGUS), 351 patients with newly diagnosed multiple myeloma, and 9 human myeloma cell lines, as well as on 36 sequential samples at diagnosis, pre-1st, pre-2nd and post-2nd autologous stem cell transplantation (ASCT). Over-expression and knock-down experiments of Trip13 were performed on myeloma cell lines by lentivirus transfection. Cell viability was assessed by trypan exclusion assay. Western blots were used to detect the expression of Trip13, P31 comet, caspase-8, caspase-9, caspase-3 and PARP, and checkpoint related proteins MAD2 and CDC20 in Trip13 overexpressed or Trip13 shRNA-transfected myeloma cells. ResultsSequential GEP samples showed that Trip13 expression increased in 8 of 9 patients after chemotherapy and ASCT compared to the samples at diagnosis strongly suggesting that increased Trip13 is associated with drug resistance. Trip13 was already significantly increased in MGUS patients, newly diagnosed MM patients and MM cell lines compared with normal plasma cells. Furthermore, Trip13 was significantly higher in high-risk MMs than in low-risk MMs and increased Trip13 was linked to an inferior event-free survival (EFS) (p<0.01) and overall survival (OS) (p<0.01) in 351 newly diagnosed MMs. In contrast, the Trip13-interacting gene P31 comet was down-regulated in high-risk MMs and high expression of P31 was associated with good outcome. Interestingly, patients with high Trip13 and low P31 comet have the worst outcome compared to patients with only one of these, suggesting the interaction of Trip 13 and p31 has a synergistic effect on MM progression. Transfection of Trip13 into ARP1 and OCI-My5 cells significantly increased cell proliferation, while knock-down Trip13 in OCI-My5, H929, RPMI8226 cells inhibited cell growth and induced MM cell apoptosis with increases of cleaved caspase-8, caspase-9, caspase-3 and PARP. Mechanistic studies showed that Trip13 over-expression decreased P31comet and MAD2 expression by western blotting, but increased CDC20. ConclusionsThe association of increased Trip13 and decreased p31 is a good biomarker for MM drug resistance and poor prognosis. Our results also show Trip13 and P31 comet could be potential targets to overcome drug resistance in MM. Disclosures:No relevant conflicts of interest to declare.

Identifying the Outliers Among Gene Expression Profiling (GEP)-Defined Low-Risk Myeloma Patients Treated with Total Therapy 2 and 3 (TT2, TT3)

AbstractAbstract 195▪▪This icon denotes a clinically relevant abstractGEP analysis is a robust method to distinguish low- and high-risk multiple myeloma (MM), pertaining to 85% and 15% of newly diagnosed patients, respectively (Shaughnessy et al., Blood, 2007; 109:2276–84). As developed in TT2 and validated in TT3A and TT3B, we are now examining, similar to previous work in high-risk MM, whether we can define outliers among low-risk MM, i.e., patients not living up to the low-risk prediction model. Toward this end, we scrutinized early relapses in TT3A and TT3B within three years of protocol entry. Using logistic regression analysis, we identified baseline parameters including GEP, en route for distinguishing this high-risk subset among low-risk MM. Also examined was whether a new model could be built within low-risk disease that allowed for the identification of a high-risk subset. Our database was interrogated for patients known to have GEP-defined low-risk in the GEP-70 model. Table 1 summarizes the 3-year events among GEP-70 low-risk subjects per protocol. An optimal cut-point at +0.146 distinguished, among the combined TT2 and TT3 patients, inferior progression-free survival (PFS) and overall survival (OS) (Figure 1a). Next, we examined outcomes among all TT2 and TT3 patients with GEP data, including those with traditionally-defined high-risk (>=0.66). Here, we were able to distinguish three subgroups with distinctly different PFS and OS (Figure 1b). Utilizing logistic regression analysis, limited to traditionally-defined GEP-70 low-risk MM (=<0.66), three-year progression events during the this period were adversely dominated by the following: GEP-70 scores >0.146 (HR=2.61, p=0.0005), the presence of cytogenetic abnormalities (CA) (HR=1.93, p=0.018), B2M >5.5mg/L (HR=1.95, p=0.04) and LDH >190U/L (HR=1.93, 0.02). These are all reported in Table 2. In conclusion, we have identified, within GEP-70 low-risk patients, a new cut-point. This allows a better categorization of patients having truly low risk disease. Also, above which a prognosis intermediate to the traditional high-risk prognostic group (>=0.66) could be identified. GEP >0.146 dominated a multivariate logistic regression model. Further efforts will be presented on unique genes characterizing this intermediate risk group in relationship to low and high-risk subsets.Table 1Three-year Events Among GEP-70 Subjects Per ProtocolProtocolTotal with GEPGEP-70 low-riskGEP-70 low-risk, event within first 3 yearsTT2 - thalidomide17615655TT2 + thalidomide17514936TT3A27523539TT3B16612923Table 2Logistic Regression for 3-year Event Factors, TT2+3 GEP-70 Low-Risk (<0.66)Event in first three years on protocolVariableNWith FactorWithout FactorOR (95% CI)P - valueMultivariateB2M > 5.5 mg/L66624/69 (35%)59/328 (18%)1.95 (1.03, 3.69)0.0401LDH >= 190 U/L66833/99 (33%)50/298 (17%)1.93 (1.10, 3.40)0.0229Cytogenetic abnormalities66535/116 (30%)48/281 (17%)1.93 (1.12, 3.32)0.0182GEP-70 score > 0.14666937/104 (36%)46/293 (16%)2.61 (1.52, 4.47)0.0005OR - Odds Ratio, 95% CI - 95% Confidence Interval, P - value from Wald Chi - Square Test in Logistic Regression.NS2 - Multivariate results not statistically significant at 0.05 level. Univariate p - values reported regardless of significance.Multivariate model uses stepwise selection with entry level 0.1 and variable remains if meets the 0.05 level.A multivariate p - value greater than 0.05 indicates variable forced into model with significant variables chosen using stepwise selection. [Display omitted] Disclosures:No relevant conflicts of interest to declare.

Metaphase Cytogenetic Abnormality (CA) Subtypes in Multiple Myeloma (MM) Treated with Front Line Total Therapy (TT) Protocols TT1, TT2, TT3, TT4 and TT5

AbstractAbstract 193We have previously reported on the adverse prognostic implications of CA, independently contributing to clinical outcome prediction even in the era of gene expression profiling (GEP). The presence of hypodiploidy/deletion13 (CA13/hypo) was associated with a particular poor prognosis, whereas the absence of CA (no CA) imparted superior outcome; the remainder (other CA) had an intermediate prognosis. Here we provide an update of overall survival (OS) and progression-free survival (PFS) with TT1, TT2 control arm (TT2-), TT2 thalidomide arm (TT2+), TT3a, TT3b, TT4-light (TT4-L, fractionated melphalan + VTD), and TT4-standard (TT4-S). The backbone of protocols was tandem transplantation with melphalan 200mg/m2 (MEL-200). TT2 employed thalidomide during all phases of the protocol in a randomized fashion. TT3a introduced bortezomib and 3-yr maintenance with VTD for year 1 and TD for years 2 and 3; TT3b applied VRD with lenalidomide in all 3 years of maintenance; TT4-S used standard MEL-200 as conditioning, whilst in TT4 MEL-50 for 4 consecutive days was given. In addition, TT4L applied 1 instead of 2 cycles of induction and consolidation therapy. Both OS and PFS were consistently superior with “no CA” followed by “other CA” and “CA13/hypo” in all trials including TT4-S (Figure 1). However, there was a striking reversal of the benefit order with TT4-L: “no CA” fared worst, and both “CA13/hypo” and “other CA” had equally superior OS and PFS. The analyses were repeated for TT3 and TT4, limiting TT3 to the ∼85% of patients with GEP-defined low-risk MM, since TT4 was designed specifically for GEP70 low risk MM (Figure 2). In the “no CA” group, 2-yr OS and PFS were >90% in TT3a/b and TT4-S, compared to 75% in TT4-L (p=0.007 and 0.01 for TT3 and TT4-s, respectively). TT4-L patients had ∼90% 2-yr OS and PFS in case of “CA13/hypo” and “other CA”, contrasting with ∼60–80% 2-yr OS and PFS for the other protocols. It could be speculated that patients with “no CA” have more slowly cycling micro-environment dependent disease in which the well-recognized MEL dose response relationship is crucial. Conversely, the more genomically unstable “CA13/hypo”-type MM may benefit from the synergistic effect of repeated application of MEL and bortezomib, which could be related to the ability of bortezomib to disrupt protein metabolism required for repair of MEL-induced DNA damage. The basis for these finding is under investigation, but do support the need for further studies investigating novel conditioning regimens and their exact beneficiaries, which could further personalize myeloma care and may be related to bortezomib’s presence in the fractionated melphalan transplant regimen in TT4-L. [Display omitted] [Display omitted] Disclosures:No relevant conflicts of interest to declare.

Total Therapy 6: Initial Results of Dose-Dense Strategy for Previously Treated Multiple Myeloma

Abstract 3081 Background:We have reported extensively on applying dose-intense total therapy approach in newly diagnosed multiple myeloma (MM), yielding long CR durations in ∼80% GEP-defined low-risk myeloma patients. Herein, we present for the first time the initial results of a phase II trial employing a dose-dense approach in previously treated MM. Patients & Methods: Patients received 5 cycles of combination chemotherapy (Figure 1) followed by maintenance with bortezomib, lenalidomide and dexamethasone until relapse or disease progression by IMWG 2006 criteria. Overall survival and progression free survival were estimated using the Kaplan-Meier method. Survival distributions were compared using the log-rank test. Results: 63 patients with previously treated MM were enrolled. The median follow-up from enrollment was 15.6 months. Baseline characteristics included age >=65yr in 37%, ISS stage II/III was seen in 41%/19% of patients, cytogenetic abnormalities (CA) in 37%, and GEP-70 high risk-MM (HRMM) in 26% of patients with available gene expression data. 11 patients (17%) had >2 prior lines of therapy including regimens that contained bortezomib (91%), thalidomide(45%), lenalidomide(81%), melphalan(27%) and steroids(100%). 14 patients (22%) discontinued therapy primarily due to progression, death or toxicity. Cumulative incidence of PR/VGPR/CR at 18 months was 69.4%, 54.8% and 37.0% respectively. Of the 60 patients with at least 3 months of follow-up, 33% had stable disease as the best overall response. The time to achieving PR or better at 6 months was more rapid in HR-MM vs LRMM (63.6% vs. 50%, p=0.008). Hematologic toxicities occurred in all patients (100%), whereas the most common non-hematologic toxicities counting all toxicities (>grades 3) included metabolic/electrolyte disturbances (95%) followed by infections (31%), hepatobiliary (20%) and gastrointestinal (16%) toxicities. Overall survival (OS) and progression free survival (PFS) at 12 months was 90% and 87%, respectively (Figure 2). The OS (96% vs 60%, p=0.0002) and PFS (92% vs 53%, p<0.0001) at 12 months was markedly superior in GEP-70 defined low-risk MM (LRMM) compared with high-risk MM (HRMM). ISS staging and GEP-defined p53 deletion did not impact OS/PFS at 12 months. Conclusions: Dose-dense strategy is highly effective in previously treated LRMM. Even though time to achieving best response was quicker in HRMM, response duration was shorter thus augmenting the significance of sustaining best response rather than depth of response in HR-MM. [Display omitted] [Display omitted] Disclosures:No relevant conflicts of interest to declare.

Gene Expression Profiling (GEP) Analysis of Plasma Cells (PC) Obtained From MRI-Defined Focal Lesions (FL) Under CT-Guided Fine-Needle Aspiration Provides Better Risk Stratification in Patients with Multiple Myeloma

Abstract▪2896▪This icon denotes a clinically relevant abstract Background:Focal lesions (FL) are a well-recognized consequence of active multiple myeloma (MM), and distinguish it from its antecedent monoclonal gammopathy of undetermined significance (MGUS). We have recently reported on the superior performance of gene expression profiling (GEP) based on random trephine bone marrow aspirate sampling (RS) in distinguishing between 85% of patients with low-risk (LO) MM and 15% with high-risk (HI) MM compared to conventional prognostic variables. MM occasionally presents as macro-focal disease, in which cases RS may be inconclusive because of paucity of malignant cells in the sample. Here we report the comparison of GEP data from paired FL and RS samples from 106 untreated patients. Methods:We identified 106 newly diagnosed patients with paired samples, who were treated on Total Therapy (TT) protocols (3 in TT2, 19 in TT3, 75 in TT4, and 9 in TT5) in our multiple myeloma database. GEP risk scores, molecular subgroup classifications, overall survival (OS), and event-free survival (EFS) were compared and tested with the RS-derived 70-gene risk prediction and molecular subgroup classification models. Results:GEP defined molecular subgroups were correlated in 90 of 106 patients (85%). Looking at GEP-defined risk designation, we found a high degree of correlation between RS and FL samples with 95 of 106 samples showing the same designation (90%). For the 11 patients with divergent GEP designations, 8 (73%) were located at the boundary of the RS GEP risk score cutoff of +0.66 (range +0.43 to +0.84). For these risk designation-divergent patients, FL- but not RS-defined risk determined clinical outcome. Conclusion:Both the 70-gene risk prediction and molecular subgroup classification models can be used in FL-GEP samples. But, more importantly, for patients with RS-GEP risk score close to cutoff boundary (about 14% in our current data sets), FL-GEP provides better risk stratification, suggesting that the FL signal more adequately reflects to disease biology, progression and treatment response in MM. We therefore recommend that, for patients with borderline RS-based GEP risk scores, FL-GEP be used for staging and prognosis assessment in myeloma. Studies are in progress to determine, among multiple FL samples from the same patient, the variability in risk score compared to multiple RS samples. Disclosures:Shaughnessy:Myeloma Health, Celgene, Genzyme, Novartis: Consultancy, Employment, Equity Ownership, Honoraria, Patents & Royalties. Barlogie:Celgene: Consultancy, Honoraria, Research Funding; IMF: Consultancy, Honoraria; MMRF: Consultancy; Millennium: Consultancy, Honoraria, Research Funding; Genzyme: Consultancy; Novartis: Research Funding; NCI: Research Funding; Johnson & Johnson: Research Funding; Centocor: Research Funding; Onyx: Research Funding; Icon: Research Funding.

Multiple myeloma and the road to personalised medicine

Rapid advances in myeloma-cell genotyping and phenotyping are bringing personalised myeloma therapy ever closer. Since the introduction of thalidomide, lenalidomide, and bortezomib, treatment outcomes have improved overall, but there is still great variability between patients and little consensus about how best to use the new drugs with the older ones. 1 Kumar SK Rajkumar SV Dispenzieri A et al. Improved survival in multiple myeloma and the impact of novel therapies. Blood. 2008; 111: 2516-2520 Crossref PubMed Scopus (1834) Google Scholar The practising haematologist now has to choose between many induction, consolidation, and maintenance regimens that combine two, three, or all four active classes of drugs at varying doses, and for varying periods of time. Not surprisingly, in view of the variability in survival outcomes, and their poor correlation with the intensity of therapy or specific drug protocols, treatment philosophies differ greatly between centres and even between physicians within a centre. On the one hand is the aggressive total therapy approach, which seeks to cure the disease but has attendant toxic effects and negative quality-of-life implications. 2 Barlogie B Anaissie E van Rhee F et al. Incorporating bortezomib into upfront treatment for multiple myeloma: early results of total therapy 3. Br J Haematol. 2007; 138: 176-185 Crossref PubMed Scopus (272) Google Scholar , 3 Nair B van Rhee F Shaughnessy Jr, JD et al. Superior results of Total Therapy 3 (2003–33) in gene expression profiling-defined low-risk multiple myeloma confirmed in subsequent trial 2006–66 with VRD maintenance. Blood. 2010; 115: 4168-4173 Crossref PubMed Scopus (176) Google Scholar On the other hand is the disease control approach, in which relatively non-toxic drug regimens are used to control the disease, sequentially switching to non-cross-reactive drug regimens as and when resistance emerges. 4 Rajkumar SV Treatment of myeloma: cure vs control. Mayo Clin Proc. 2008; 83: 1142-1145 Summary Full Text Full Text PDF PubMed Scopus (63) Google Scholar

Comparing Toxicities and Survival Outcomes with Total Therapy 4 (TT4) for 70-Gene (R70)-Defined Low-Risk Multiple Myeloma (MM) to Results Obtained with Total Therapy 3 Protocols TT3A and TT3B

AbstractAbstract 368 Background:TT3, incorporating bortezomib and thalidomide with induction prior to and consolidation after melphalan 200mg/m2-based transplants and 3 year maintenance with VTD (year 1) and TD (years 2+3) in TT3A and with VRD for 3 years in TT3B resulted in a high CR rate of ∼60% and, in the 85% of patients with GEP-defined low-risk MM, 5-yr OS/EFS of 80%/78%; 5-year CR duration estimate was 88%. Patients and Methods:Phase III trial TT4 for low-risk MM randomized patients between standard (S) and light (L) arms. TT4-L applied 1 instead of 2 cycles of induction therapy with M-VTD-PACE prior to and 1 instead of 2 cycles of consolidation with dose-reduced VTD-PACE after tandem transplantation. M-VTD-PACE comprised melphalan, bortezomib, thalidomide, dexamethasone and 4-day continuous infusions of cisplatin, doxorubicin, cyclophosphamide, etoposide. TT4-S applied standard single dose melphalan 200mg/m2, while TT4-L used a 4-day fractionated schedule of melphalan 50mg/2 on days 1–4. VRD maintenance for 3 years was identical in both arms. Here we report, for both TT4 arms combined, on grade >2 mucosal toxicities, applying CTCAE version 3.0, and on efficacy (CR, EFS, OS) in relationship to TT3 in low-risk MM. At the time of analysis, median follow-up on TT4 is 10.7 months and on TT3A/B 62.3/33.4 months. To facilitate comparisons between trials with different follow-up times, TT3 data were backdated to follow-up time comparable to TT4 as of this reporting time. Results:Baseline characteristics were similar in TT3 (n=364) and TT4 (n=165) in terms of B2M both >=3.5mg/L and >5.5mg/L, and elevated levels of CRP, creatinine, and LDH. Presence of cytogenetic abnormalities (CA) overall and in terms of CA13/hypodiploidy was similar in both. Fewer TT4 patients had ISS-1 (31% v 43%, P=0.010) and more had hemoglobin <10g/dL (35% v 26%, P=0.029). While neither trial had GEP-defined high-risk in the 70-gene model (R70), the more recently validated R80 distribution showed 7% high-risk in TT4 v 3% in TT3 (P=0.031). DelTP53 was more prevalent in TT4 than TT3 (39% v 10%, P<0.001), and MY favorable subgroup designation pertained to 3% in TT4 v 12% in TT3 (P=0.002). Toxicities are reported per protocol phase. During induction (TT4, n=160; TT3, n=364), grade >2 mucosal toxicities included colitis in 0%/1% (P=0.32), esophagitis/dysphagia in 0%/1% (P=0.33), GI mucositis, NOS in 1%/1% (P=0.99) and stomatitis/pharyngitis in 0%/1% (P=0.99). With transplant-1, (TT4, n=139; TT3, n=344), grade >2 mucosal toxicities included colitis in 3%/1% (P=0.24), esophagitis/dysphagia in 1%/5% (P=0.03), gastritis in 1%/0% (P=0.29), GI mucositis, NOS in 1%/2% (P=0.73) and stomatitis/pharyngitis in 0%/5% (P=0.008); with transplant-2 (TT4, n=105; TT3, n=294), grade >2 mucosal toxicities included colitis in 4%/3% (P=0.77), esophagitis/dysphagia in 0%/2% (P=0.20), GI mucositis, NOS in 2%/3% (P=0.99) and stomatitis/pharyngitis in 0%/1% (P=0.58). With consolidation (TT4, n=85; TT3, n=280), grade >2 mucosal toxicities included colitis in 0%/3% (P=0.36) and GI mucositis, NOS in 0%/1% (P=0.99). Timing of onset and final levels of CR differed substantially between TT4 and TT3 in favor of TT4 (P=0.006); no differences were observed in OS (P=0.36), EFS (P=0.66), and CR duration (P=0.12). Conclusion:TT4 (both arms combined) provided, despite higher proportions of patients with unfavorable characteristics than in TT3, superior CR rate and comparable survival outcomes to TT3's low-risk population. GI toxicities were reduced in TT4 v TT3. Results of TT4 arms will be presented.Patient characteristics:OverallProtocol %Factorn/N (%)TT4 (n=165)TT3 (n=364)P-valueAge >= 65 yr152/529 (29%)32270.341Albumin <3.5 g/dL179/528 (34%)4429<.001B2M >= 3.5 mg/L250/526 (48%)52460.216B2M > 5.5 mg/L109/526 (21%)25190.070Hb <10 g/dL152/528 (29%)35260.029LDH >= 190 U/L103/528 (20%)15220.052ISS Stage 1205/526 (39%)31430.010ISS Stage 2212/526 (40%)44390.292ISS Stage 3109/526 (21%)25190.070Cytogenetic abnormalities140/522 (27%)22290.080GEP 80-gene high-risk22/516 (4%)730.031TP53 deletion96/521 (18%)3910<.001 Disclosures:No relevant conflicts of interest to declare.

Superior results of Total Therapy 3 (2003-33) in gene expression profiling–defined low-risk multiple myeloma confirmed in subsequent trial 2006-66 with VRD maintenance

The Total Therapy 3 trial 2003-33 enrolled 303 newly diagnosed multiple myeloma patients and was noted to provide superior clinical outcomes compared with predecessor trial Total Therapy 2, especially in gene expression profiling (GEP)-defined low-risk disease. We report here on the results of successor trial 2006-66 with 177 patients, using bortezomib, lenalidomide, and dexamethasone maintenance for 3 years versus bortezomib, thalidomide, and dexamethasone in year 1 and thalidomide/dexamethasone in years 2 and 3 in the 2003-33 protocol. Overall survival (OS) and event-free survival (EFS) plots were super-imposable for the 2 trials, as were onset of complete response and complete response duration (CRD), regardless of GEP risk. GEP-defined high-risk designation, pertinent to 17% of patients, imparted inferior OS, EFS, and CRD in both protocols and, on multivariate analysis, was the sole adverse feature affecting OS, EFS, and CRD. Mathematical modeling of CRD in low-risk myeloma predicted a 55% cure fraction (P < .001). Despite more rapid onset and higher rate of CR than in other molecular subgroups, CRD was inferior in CCND1 without CD20 myeloma, resembling outcomes in MAF/MAFB and proliferation entities. The robustness of the GEP risk model should be exploited in clinical trials aimed at improving the notoriously poor outcome in high-risk disease.

Gene expression profiling (GEP) in multiple myeloma (MM): Distinguishing relapses with high-risk transformation from those with sustained low risk.

8122 Background: GEP-defined high-risk identifies ∼15% of untreated patients with a dismal outcome. Among the 85% with low-risk MM, high-risk transformation can be observed at the time of relapse. The purpose of this investigation was to determine whether, among patients relapsing on Total Therapy 2 (TT2) and TT3, baseline features existed that could distinguish patients with sustained low-risk (LOLO) from those with high-risk progression (LOHI) versus those with HIHI. Methods: Of 351 TT2 and 510 TT3 patients with baseline (BL) GEP information, a total of 237 have relapsed with relapse GEP available in 127. We are reporting here on the frequency of LOHI transformation relevant to baseline characteristics and treatment details (TT2 control v thalidomide arm, TT3), timeliness of completion of therapy (CT), duration of disease control antedating relapse (DDC). Results: From our MM data base, we identified 60 LOLO, 26 LOHI and 34 HIHI. Distinguishing BL GEP features included higher median GEP risk score (RS) values in HIHI (1.136) v LOHI (0.289) v LOLO (-0.270) (p<0.001); delTP53 was more prevalent in HIHI (27%) v LOHI (8%) v LOLO (7%) (p=0.017); concomitancy of amp1q21/del1p was more frequent in HIHI (36%) than LOHI (17%) than LOLO (9%) (p=0.005). Among non-GEP BL parameters, CA-13/CA- hypo, high LDH (≥300 and ≥ 500 U/L), low albumin (<3.5 g/dL) and high B2M (> 5.5 mg/L) also were significantly more common in HIHI than LOHI and LOLO. Applying logistic regression, LOLO could be distinguished from LOHI by the presence metaphase cytogenetic abnormalities (CA) of the del13 variety (CA13; OR=0.37, p=0.055). Conclusions: Although HIHI is readily distinguishable from LOHI and LOLO by several baseline parameters, the absence of CA13 was the only baseline variable with near-significance in predicting, in low-risk myeloma, low- from high-risk relapse, attesting to the far- reaching power of metaphase karyotyping. No significant financial relationships to disclose.

Defining the prognostic variables in gene expression profiling (GEP)-defined high-risk multiple myeloma (MM): Distinguishing early failures (EF) from sustained control (SC).

8019 Background: With the introduction of GEP analysis in Total Therapy 2 (TT2) and validation in Total Therapy 3 (TT3), GEP-defined high-risk, present in ∼15% of patients, has emerged as the dominant adverse feature for both overall survival (OS) and event-free survival (EFS) and, despite achieving a similar rate of complete response (CR) as in low-risk MM, also for CR duration (CRD). Thus, median OS and EFS remain dismal not exceeding 3 and 2 years, respectively. Recognizing, however, that 10% to 20% of high-risk patients enjoy extended survival, we examined the presenting features and response characteristics that may distinguish this favorable subset. Methods: Our MM data base was scrutinized for TT2 and TT3 patients who had high-risk baseline GEP features. Modeling survival curves by an exponential function, 2 subsets could be defined comprising one with steep decline in OS and a second with a shallow slope/plateau. Baseline variables of these 2 groups were determined and logistic regression applied to distinguish their characteristics. Results: We identified, among the combined TT2 and TT3 populations, 123 subjects with GEP-defined high-risk features who, by modeling, could be segregated into 2 groups with early failure (EF, n=65) and sustained control (SC, n=58). According to logistic regression, the SC-distinguishing features included GEP risk score (OR=0.25; p=0.006) and delTP53 (OR=0.20; p=0.015), of which only delTP53 survived the multivariate model (OR=0.26; p=0.008). None of the other GEP-derived variables (molecular subgroup, amp1q21, del1p) or other standard variables (CA, B2M, CRP, albumin) were discriminatory, with a borderline effect of LDH ≥190U/L (OR=0.51; p=0.062). Conclusions: Among GEP-defined high- risk MM, ∼50% could be distinguished as having SC which, in contrast to EF, was characterized by lower risk score and delTP53 frequency. Data will be presented on SC versus EF discriminating genes. No significant financial relationships to disclose.

Identification of early growth response protein 1 (EGR-1) as a novel target for JUN-induced apoptosis in multiple myeloma

AbstractTumor–bone marrow microenvironment interactions in multiple myeloma (MM) are documented to play crucial roles in plasma-cell growth/survival. In vitro coculture of MM cells with osteoclasts supported cell survival and significantly down-regulated JUN expression. JUN expression in myeloma cells from late-stage and high-risk MM was significantly lower than in plasma cells from healthy donors, monoclonal gammopathy of undetermined significance, smoldering MM, and low-risk MM; patients with low-JUN–expressing MM cells had earlier disease-related deaths. JUN overexpression in MM cells induced cell death and growth inhibition and up-regulated expression of early growth response protein 1 (EGR-1), whose low expression also carried unfavorable clinical implications. EGR-1 knockdown in MM cells abrogated JUN overexpression-induced MM cell death and growth inhibition, indicating that EGR-1 acts directly downstream of JUN. JUN modulates myeloma cell apoptosis through interacting with EGR-1, which down-regulates Survivin and triggers caspase signaling. Importantly, high JUN or EGR-1 expression was associated with improved outcome in Total Therapy 3, in which bortezomib is given throughout therapy, versus Total Therapy 2, in which bortezomib is given only at relapse. Consistently, JUN or EGR-1 knockdown in cultured MM cells enhanced their resistance to bortezomib, demonstrating the crucial role of low JUN/EGR-1 expression in MM resistance to bortezomib.