Faculty Bibliography

Autologous hematopoietic cell transplantation (AHCT) is a standard of care in multiple myeloma (MM) patients aged <65 years. To understand age-related trends in utilisation and outcome of AHCT, we analysed 53 675 MM patients who underwent a first AHCT in 31 European countries between 1991 and 2010. The number of patients undergoing AHCT increased for all age groups (<40, 40-49, 50-59, 60-64, 65-69 and ⩾70 years) throughout the observation period. The highest increase was observed for patients aged ⩾65 years, who accounted for 3% of AHCTs in 1991-1995 and for 18.8% of AHCTs in 2006-2010. Risk factors associated with survival over the entire observation period (P<0.001) were calendar period, remission status at AHCT, gender, disease duration before AHCT and age. Survival improved considerably more in older than in younger patients in recent years. In 2006-2010, median 2- and 5-year post-transplant survival ranged from 85.9 and 61.5% in patients <40 years to 80.2 and 49.7% in those ⩾70 years. All-cause day-100 mortality decreased throughout the observation period to ⩽2.4% for all age groups in 2006-2010. The results of this study demonstrate increased utilisation and safety of AHCT with improved post-transplant survival particularly in elderly MM patients in recent years in Europe.

BACKGROUND:Gene expression profiling (GEP) via microarray analysis is a widely used tool for assessing risk and other patient diagnostics in clinical settings. However, non-biological factors such as systematic changes in sample preparation, differences in scanners, and other potential batch effects are often unavoidable in long-term studies and meta-analysis. In order to reduce the impact of batch effects on microarray data, Johnson, Rabinovic, and Li developed ComBat for use when combining batches of gene expression microarray data. We propose a modification to ComBat that centers data to the location and scale of a pre-determined, 'gold-standard' batch. This modified ComBat (M-Combat) is designed specifically in the context of meta-analysis and batch effect adjustment for use with predictive models that are validated and fixed on historical data from a 'gold-standard' batch. RESULTS:We combined data from MIRT across two batches ('Old' and 'New' Kit sample preparation) as well as external data sets from the HOVON-65/GMMG-HD4 and MRC-IX trials into a combined set, first without transformation and then with both ComBat and M-ComBat transformations. Fixed and validated gene risk signatures developed at MIRT on the Old Kit standard (GEP5, GEP70, and GEP80 risk scores) were compared across these combined data sets. Both ComBat and M-ComBat eliminated all of the differences among probes caused by systematic batch effects (over 98% of all untransformed probes were significantly different by ANOVA with 0.01 q-value threshold reduced to zero significant probes with ComBat and M-ComBat). The agreement in mean and distribution of risk scores, as well as the proportion of high-risk subjects identified, coincided with the 'gold-standard' batch more with M-ComBat than with ComBat. The performance of risk scores improved overall using either ComBat or M-Combat; however, using M-ComBat and the original, optimal risk cutoffs allowed for greater ability in our study to identify smaller cohorts of high-risk subjects. CONCLUSION/CONCLUSIONS:M-ComBat is a practical modification to an accepted method that offers greater power to control the location and scale of batch-effect adjusted data. M-ComBat allows for historical models to function as intended on future samples despite known, often unavoidable systematic changes to gene expression data.

There is a significant unmet need in effective therapy for relapsed myeloma patients once they become refractory to bortezomib and lenalidomide. While data from the front line setting suggest bendamustine is superior to melphalan, there is no information defining optimal bendamustine dose in multiply-treated patients. We report a multi-centre randomized two-stage phase 2 trial simultaneously assessing deliverability and activity of two doses of bendamustine (60 mg/m2 vs. 100 mg/m2) days 1 and 8, thalidomide (100 mg) days 1-21 and low dose dexamethasone (20 mg) days 1, 8, 15 and 22 of a 28-d cycle. Ninety-four relapsing patients were treated on trial, with a median three prior treatment lines. A pre-planned interim deliverability and activity assessment led to closure of the 100 mg/m2 arm due to excess cytopenias, and led to amendment of entry criteria for cytopenias. Non-haematological toxicities including thromboembolism and neurotoxicity were infrequent. In the 60 mg/m2 arm, treatment was deliverable in 61.1% subjects and the partial response rate was 46.3% in the study eligible population, with 7.5 months progression-free survival. This study demonstrates bendamustine at 60 mg/m2 twice per month with thalidomide and dexamethasone is deliverable for repeated cycles in heavily pre-treated myeloma patients and has substantial clinical activity.

As part of Total Therapy (TT) 3b, baseline marrow aspirates were subjected to two-color flow cytometry of nuclear DNA content and cytoplasmic immunoglobulin (DNA/CIG) as well as plasma cell gene expression profiling (GEP). DNA/CIG-derived parameters, GEP and standard clinical variables were examined for their effects on overall survival (OS) and progression-free survival (PFS). Among DNA/CIG parameters, the percentage of the light chain-restricted (LCR) cells and their cytoplasmic immunoglobulin index (CIg) were linked to poor outcome. In the absence of GEP data, low CIg <2.8, albumin <3.5 g/dl and age ⩾65 years were significantly associated with inferior OS and PFS. When GEP information was included, low CIg survived the model along with GEP70-defined high risk and low albumin. Low CIg was linked to beta-2-microglobulin >5.5 mg/l, a percentage of LCR cells exceeding 50%, C-reactive protein ⩾8 mg/l and GEP-derived high centrosome index. Further analysis revealed an association of low CIg with 12 gene probes implicated in cell cycle regulation, differentiation and drug transportation from which a risk score was developed in TT3b that held prognostic significance also in TT3a, TT2 and HOVON trials, thus validating its general applicability. Low CIg is a powerful new prognostic variable and has identified potentially drug-able targets.

Multiple myeloma is preceded by an asymptomatic phase, comprising monoclonal gammopathy of uncertain significance and smoldering myeloma. Compared to the former, smoldering myeloma has a higher and non-uniform rate of progression to clinical myeloma, reflecting a subset of patients with higher risk. We evaluated the gene expression profile of smoldering myeloma plasma cells among 105 patients enrolled in a prospective observational trial at our institution, with a view to identifying a high-risk signature. Baseline clinical, bone marrow, cytogenetic and radiologic data were evaluated for their potential to predict time to therapy for symptomatic myeloma. A gene signature derived from four genes, at an optimal binary cut-point of 9.28, identified 14 patients (13%) with a 2-year therapy risk of 85.7%. Conversely, a low four-gene score (< 9.28) combined with baseline monoclonal protein < 3 g/dL and albumin ≥ 3.5 g/dL identified 61 patients with low-risk smoldering myeloma with a 5.0% chance of progression at 2 years. The top 40 probe sets showed concordance with indices of chromosome instability. These data demonstrate high discriminatory power of a gene-based assay and suggest a role for dysregulation of mitotic checkpoints in the context of genomic instability as a hallmark of high-risk smoldering myeloma.

In the phase III MM-003 trial, pomalidomide plus low-dose dexamethasone (POM+LoDEX) improved overall survival (OS) versus high-dose dexamethasone (HiDEX) in 455 patients with relapsed and refractory multiple myeloma (RRMM) after treatment with bortezomib and lenalidomide. Here, a two-stage Weibull method was used to adjust for the crossover of patients in the HiDEX arm to pomalidomide-based therapy. The adjusted difference in median OS between patients in the POM+LoDEX and HiDEX arms was 7·0 months (12·7 vs. 5·7 months, respectively). These findings provide important evidence for understanding the clinical efficacy of pomalidomide on OS benefits seen in RRMM patients.

PURPOSE/OBJECTIVE:The clinical outcome of multiple myeloma (MM) is heterogeneous. A simple and reliable tool is needed to stratify patients with MM. We combined the International Staging System (ISS) with chromosomal abnormalities (CA) detected by interphase fluorescent in situ hybridization after CD138 plasma cell purification and serum lactate dehydrogenase (LDH) to evaluate their prognostic value in newly diagnosed MM (NDMM). PATIENTS AND METHODS/METHODS:Clinical and laboratory data from 4,445 patients with NDMM enrolled onto 11 international trials were pooled together. The K-adaptive partitioning algorithm was used to define the most appropriate subgroups with homogeneous survival. RESULTS:ISS, CA, and LDH data were simultaneously available in 3,060 of 4,445 patients. We defined the following three groups: revised ISS (R-ISS) I (n = 871), including ISS stage I (serum β2-microglobulin level < 3.5 mg/L and serum albumin level ≥ 3.5 g/dL), no high-risk CA [del(17p) and/or t(4;14) and/or t(14;16)], and normal LDH level (less than the upper limit of normal range); R-ISS III (n = 295), including ISS stage III (serum β2-microglobulin level > 5.5 mg/L) and high-risk CA or high LDH level; and R-ISS II (n = 1,894), including all the other possible combinations. At a median follow-up of 46 months, the 5-year OS rate was 82% in the R-ISS I, 62% in the R-ISS II, and 40% in the R-ISS III groups; the 5-year PFS rates were 55%, 36%, and 24%, respectively. CONCLUSION/CONCLUSIONS:The R-ISS is a simple and powerful prognostic staging system, and we recommend its use in future clinical studies to stratify patients with NDMM effectively with respect to the relative risk to their survival.

Patients 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.

Incorporation of novel agents into auto-SCT for patients with multiple myeloma has led to improvement in their outcomes. However, the effects of new drugs, either single or combined, on PBSC mobilization have not been fully evaluated, particularly in phase 3 clinical studies. We analyzed the impact of two novel agent-based induction treatments in patients enrolled in the GIMEMA MMY-3006 study comparing bortezomib, thalidomide and dexamethasone (VTD) versus thalidomide and dexamethasone (TD) in preparation for double auto-SCT. Results showed that a short-term induction therapy with VTD did not adversely affect CD34(+) cell yields as compared with TD (9.75 vs 10.76 × 10(6) CD34(+) cells/kg, P=0.220). For poor mobilizers (<4 × 10(6) CD34(+) cells/kg), 5-year rates of time to progression (TTP), progression-free survival (PFS) and overall survival (OS) were significantly shorter than for successful mobilizers (TTP:17 vs 48%, P<0.0001; PFS: 16 vs 46%, P<0.0001; OS: 50 vs 80%, P<0.0001). These differences were retained across patients randomized to the TD arm; conversely, no differences in outcomes were seen in patients treated with VTD, irrespective of the number of harvested CD34(+) cells. The number of collected PBSCs predicted better outcomes after auto-SCT and VTD overcame the negative impact of a poor stem cell mobilization.

BACKGROUND:Gene expression profiling (GEP) has significantly contributed to the elucidation of the molecular heterogeneity of multiple myeloma plasma cells (MMPC) and only recently it has been recommended for risk stratification. Prior to GEP MMPC need to be enriched resulting in an inability to immediately freeze bone marrow aspirates or use RNA stabilization reagents. As a result in multi-center MM trials sample processing delay due to shipping may be an important confounder of molecular analyses and risk stratification based on GEP data. RESULTS:We compared GEP data of 145 in-house and 246 shipped samples and detected 3301 down-regulated and 3501 up-regulated genes in shipped samples. For 3994 genes we confirmed differential expression in an independent set of 85 in-house and 97 shipped samples. Differentially expressed genes were enriched in processes like ribosome biogenesis, cell cycle, and apoptosis. Among GEP based risk predictors the IFM-15 seemed to underestimate high risk in shipped samples, whereas the GEP70 and the EMC-92 gene signatures were more robust. In order to provide a tool to assess the "shipping effect" in public repositories, we generated a 17-gene predictor for shipped samples with a 10-fold cross validation error rate of 0.06 for the training set and an error rate of 0.15 for the validation set. CONCLUSION/CONCLUSIONS:Sample processing delay significantly influences GEP of MMPC, implying it should be avoided if samples were used for risk stratification.