Risk Stratification In Multiple Myeloma Research Articles

Simplified in-House Deep Sequencing Method of Inmunoglobulin Genes for Minimal Residual Dissease Quantification and Risk Stratification in Multiple Myeloma

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

The Frequency of Genetic Anomalies According to Clonal Plasma Cells' Phenotype in Patients with Newly Diagnosed (ND) Multiple Myeloma (MM)

Background. Genetic anomalies (GA) are primary link of pathogenesis in MM. GA lead to formation of clonal plasma cells, which has different phenotype.Aim. To estimate the incidence of GA and their correlation with clonal plasma cells' phenotype in patients with ND MM. Methods. We analysed 22 patients with ND MM (median age 57 years, range 38-80; male/female - 1:1.75). Cytogenetic analysis was performed on bone marrow samples using standard GTG-method. Metaphase FISH analysis was performed according to the manufacturer's protocol using DNA probes: LSI 13(RB1)13q14, IGH/CCND1, IGH/FGFR3, LSI TP53 (17q13.1). 8-color immunophenotypic by flow cytometry using antibody to CD45, CD38, CD138, CD56, CD19, CD20, CD27 and CD117 antigenes.Results. Translocation t(11;14) was detected in 3/14 (21.4%) patients, del(13q) - 2/14 (14.3%), t(11;14) - 3/14 (21.4%), hypodyploidy - 1/20 (5%), del(17р) - 0% patients. Clonal plasma cells' phenotype CD38+CD138+CD45- was detected in 100%. Expression CD56+ was revealed in 11/22 (50%) patients, CD19+ in 9/22 (40.9%), CD117+ in 5/22 (22.7%), CD20+ in 1/22 (4.5%), CD27+ in 1/22 (4.5%). The frequency of GA didn't depend on clonal plasma cells' phenotype and was 27.3%(3/11) in CD56+ phenotype, 23.8%(5/21) - CD20-, 23.8%(5/21) - CD27-, 23.5%(4/17) - CD117-, 23%(3/13) - CD19-, 22.2%(2/9) - CD19+, 20%(1/5) - CD117+, 18.2%(2/11) - CD56-, 0%(0/1) - CD20+, 0%(0/1) - in CD27+ phenotype. Patients of standard risk group according to mSMART 2.0 with GA had CD19-negative plasma cells' phenotype vs. CD19-positive phenotype in patients of intermediate and high-risk groups (p<0.05). 3-years overall survival in standard risk group with CD19- phenotype was 92,3%, CD19+ - 77,7% (p>0.05).Conclusion . Identification of GA, which has adverse forecast, correlates with CD19+ plasma cells phenotype. The combined definition of plasma cells phenotype and GA can improve the system of risk stratification in MM. DisclosuresNo relevant conflicts of interest to declare.

Use of Gene Expression Profiling May Predict Clinical Outcomes in Newly Diagnosed Multiple Myeloma Patients in a Standard of Care Setting

Introduction: Multiple myeloma (MM) is characterized by malignant plasma cell (PC) proliferation. Gene expression profiling (GEP) of CD138+ bone marrow PC has emerged as a new way to identify patients with worse clinical outcomes so that an individualized treatment approach can be undertaken in the clinic. The majority of data with GEP come from clinical trials. We aimed to evaluate the use of GEP in a standard clinical setting.Methods: We retrospectively searched our database of newly diagnosed MM patients with GEP completed prior to initial treatment. 35MM patients from April 2014 until June 2015 were identified and included in our analysis. GEP was performed through MyPRS® (Signal Genetics, Little Rock, AR). Fisher's exact test was used to evaluate the associations between complete response status and other categorical variables. The Wilcoxon rank sum test was used to evaluate the difference in continuous variables between patients that achieved a complete remission after 6 months of initial treatment and those who did not. Responses were assessed using IMWG criteria.Results: Median age was 60 (38-76). Patients presented with lytic lesions (60%), anemia (80%), kidney dysfunction (11%) and hypercalcemia (20%). All patients with known initial therapy were treated with bortezomib (n=29) or carfilzomib (n=1) based therapy. 10 patients had upfront autologous stem cell transplant. 18 patients had available response at 6 months. 37% (n=13) of patients were characterized as high risk by GEP, of which 46% (n=6) had the proliferation (PR) subtype. Most low risk patients had hyperdiploidy (HY) subtype (n=12, 55%). Patients with high risk GEP presented more often with complex karyotypes whereas low risk GEP patients most often had normal or hyperdiploid karyotypes. FISH abnormalities that are usually present in high risk myeloma were also present in patients classified as GEP low risk (Table 1). At 6 months after diagnosis, lower baseline total protein, serum M-spike, serum free light chain ratio and serum kappa light chain levels were significantly associated with achieving a stringent complete response (sCR) (p<0.05). High risk patients were more likely to achieve a CR (n= 6/10, 60%) than low risk patients (n=5/16, 31%) at 6 months. Despite this, high risk patients seemed more likely to lose CR and relapse or die. Within one year of diagnosis, one patient relapsed after achieving a VGPR (high risk, PR subtype) and one patient died due to MM progressive disease after achieving an sCR (high risk, CD-1 subtype) (Table 1).Conclusion: In a standard clinical setting, GEP seems to identify MM patients that are at higher risk of adverse clinical outcomes early after diagnosis. GEP may be an adjunct to cytogenetics/FISH in MM risk stratification, as high risk FISH abnormalities were also found in low risk GEP patients. Larger studies with longer follow up may help address the particular role of GEP in the individualized treatment of MM. [Display omitted] DisclosuresOrlowski:Array BioPharma: Consultancy, Research Funding; Acetylon: Membership on an entity's Board of Directors or advisory committees; Onyx Pharmaceuticals: Consultancy, Research Funding; Spectrum Pharmaceuticals: Research Funding; Celgene: Consultancy, Research Funding; Genentech: Consultancy; Millennium Pharmaceuticals: Consultancy, Research Funding; Forma Therapeutics: Consultancy; BioTheryX, Inc.: Membership on an entity's Board of Directors or advisory committees; Janssen Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Consultancy, Research Funding.

Using a latent class model to refine risk stratification in multiple myeloma.

In multiple myeloma research, the GEP70 model is known to be capable of predicting a high risk patient group for disease progression based on the expression levels of 70 selected genes measured at baseline. The model consists of a continuous gene score that is a linear combination of the 70 genes along with a cutoff, such that patients with a score greater than the cutoff are categorized as high risk and otherwise low risk for disease progression. However, the continuous gene score may be confusing at times because of its open range nature. In addition, the present two-group model is sensitive to scores falling close to its cutoff. To facilitate patients' understanding of their prognosis, it is desirable to convert the continuous score into a probability that has an easier interpretation. In this article, we employ a latent class model to address this issue, and we also propose a superior grey zone model to refine the current risk stratification associated with the GEP70 model. Lastly, we demonstrate the robustness of the grey zone model with results from a simulation study.

Rare Igh Translocations in Newly Diagnosed Multiple Myeloma (MM) Patients: Cytogenetic Characterization and Relevance on Prognosis

INTRODUCTIONCytogenetic abnormalities (CA) are one of the major factor for risk stratification in Multiple Myeloma (MM). Approximately 50% of MM patients (pts) harbour a translocation involving the Immunoglobulin Heavy chain locus (IgH). Each translocation is associated with deregulation of a D group cyclin, either directly in t(11;14)(q13;q32) and t(6;14)(p21;q32) or indirectly in t(4;14)(p16;q32) and in MAF translocations: t(14;16)(q32;q23) and t(14;20)(q32;q12). Some translocations are recurrent but rare events, occurring in less than 5% of the pts: t(6;14) is considered a standard prognostic feature, whereas t(14;16) and t(14;20) are associated with bad prognosis. As a result of their low incidence, few data support their use of these abnormalities in the risk stratification. Currently, deletion(17)(p13) and gain(1)(q21) are linked with adverse clinical outcome and, recently, also deletion(1p) has been considered adverse prognostic factor. AIM: To assess the type and frequency of these rare IgH translocations and their association with other adverse genetic lesions and with clinical characteristics, in a large series of newly diagnosed MM pts. METHODSA total of 682 pts newly diagnosed MM pts, enrolled in several prospective clinical trials of bortezomib-based induction therapy and subsequent autologous stem cell transplantation, including most recent EMN02 clinical trial, were analyzed. Probes for FISH analysis were used to detect t(4;14), t(6;14), t(11,14), t(14;16), t(14;20); deletion of 1p32, 13q14, 17p13; gain of 1q21; trisomy of chromosomes 5/9/15. RESULTSOn evaluable cases, the frequency of each IgH translocation, as detected by FISH was as follows: t(11;14) in 114/596 (19.1%), t(4;14) in 78/605 (12.9%), t(14;16) in 27/603(4.5%), t(14;20) in 9/591(1.5%) and t(6;14) in 7/587(1.2%). Every rare translocation was associated with deletions flanking the breakpoints, either as unique pattern or with classical translocation. The t(6;14) was closely associated with del(6)(p21)(6/7), del(13q)(4/7) and del(16)(q23)(3/7); additional gain 1q and deletion 17p was observed in a single case. All together, the high-risk (HR) translocations, t(14;16) and t(14;20), were detected in 36 pts: 63.9% were females and 36.1% males. The t(14;16) was frequently occurred with del(13q) (84.6%), gain(1q) (74.1%; 20/27) and del(16)(q23) (74.1%; 20/27), while t(14;20) was associated with del(13q) (88.9%; 8/9), gain(1q) (5/9; 55.6%) and del(20)(q12) (5/9; 55.6%). In selected cases, the presence of these additional abnormalities was shown also by SNPs arrays. Beyond del(13q), gain(1q) was the most frequent additional abnormality (69.4%; 25/36), occurring in 56.4% (44/78) of t(4;14) cases and in 20.2% (23/114) of t(11;14) cases (p<0.001). Del(17p) was present in 16.6% (6/36) of rare HR translocations and in 10.3% (8/78) and 7.9% (9/114) of t(4;14) and t(11;14) cases, respectively (p=0.313). Likewise, the presence of del(1p) was higher in pts with HR translocations (7/36, 19.4%) as compared to pts with t(4;14) and t(11;14) (6/77, 7.8% and 2/114, 1.8% respectively, p=0.001). Rare HR translocations never occurred as in isolation.Compared with pts without rare HR translocations, pts harbouring t(14;16) or t(14;20) showed a high probability to present with additional characteristics related to poor prognosis, including ISS stage 3 (42% vs 20%, p=0.002), Hb <10.5 g/L (58% vs 38%, p=0.016) and Ptls < 150000/mm3(31% vs 13%, p=0.003). The occurrence of lytic bone lesions was reported in 61% vs 79% of pts with and without HR translocations respectively (p=0.011). Other clinical parameters, such as age, C reactive protein and LDH were similar between the two groups. CONCLUSIONt(14;16)(q32;q23) and t(14;20)(q32;q12) are rare in newly diagnosed MM pts and are associated with an increased frequency of additional HR abnormalities, including gain(1)(q21), deletion(1)(p32) and deletion(17)(p13). Furthermore, they are related to features of aggressive disease, such as ISS stage 3, lower level of hemoglobin and lower platelet count. A longer follow-up is needed to point out their prognostic relevance of these abnormalities. DisclosuresCavallo:Celgene: Honoraria, Membership on an entity’s Board of Directors or advisory committees; Janssen-Cilag: Honoraria; Onyx: Honoraria. Petrucci:Bristol Meyer-Sqibb: Honoraria; Sanofi: Honoraria; Janssen-Cilag: Honoraria; Celgene: Honoraria. Palumbo:Amgen: Consultancy, Honoraria; Brystol-Myers Squibb: Consultancy, Honoraria; Array BioPharma: Honoraria; Genmab A/S: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Millenium Pharmaceuticals, Inc.: Consultancy, Honoraria; Janssen-Cilag: Consultancy, Honoraria; Onyx Pharmceuticals: Consultancy, Honoraria; Sanofi Aventis: Honoraria. Sonneveld:Celgene: Research Funding, Speakers Bureau; Millennium-Takeda: Research Funding; Onyx: Research Funding, Speakers Bureau; Janssen: Speakers Bureau. Cavo:Janssen: Consultancy, Honoraria, Speakers Bureau; Celgene: Consultancy, Honoraria, Speakers Bureau; Millennium Pharm.: Consultancy, Honoraria; Bristol-Myers Squib: Consultancy, Honoraria; Onyx: Honoraria.

The RAG Model: A New Paradigm for Genetic Risk Stratification in Multiple Myeloma.

Molecular studies have shown that multiple myeloma is a highly genetically heterogonous disease which may manifest itself as any number of diverse subtypes each with variable clinicopathological features and outcomes. Given this genetic heterogeneity, a universal approach to treatment of myeloma is unlikely to be successful for all patients and instead we should strive for the goal of personalised therapy using rationally informed targeted strategies. Current DNA sequencing technologies allow for whole genome and exome analysis of patient myeloma samples that yield vast amounts of genetic data and provide a mutational overview of the disease. However, the clinical utility of this information currently lags far behind the sequencing technology which is increasingly being incorporated into clinical practice. This paper attempts to address this shortcoming by proposing a novel genetically based “traffic-light” risk stratification system for myeloma, termed the RAG (Red, Amber, Green) model, which represents a simplified concept of how complex genetic data may be compressed into an aggregate risk score. The model aims to incorporate all known clinically important trisomies, translocations, and mutations in myeloma and utilise these to produce a score between 1.0 and 3.0 that can be incorporated into diagnostic, prognostic, and treatment algorithms for the patient.

Change in ploidy status from hyperdiploid to near-tetraploid in multiple myeloma associated with bortezomib/lenalidomide resistance

Ploidy is an important prognostic factor in the risk stratification of multiple myeloma (MM) patients. Patients with MM can be divided into two groups according to the modal number of chromosomes: nonhyperdiploid (NH-MM) and hyperdiploid (H-MM), which has a more favorable outcome. The two ploidy groups represent two different oncogenetic pathways determined at the premalignant stage. The ploidy subtype also persists during the course of the disease, even during progression after the therapy, with only very rare cases of ploidy conversion. The clinical significance of ploidy conversion and its relation to drug resistance have been previously discussed. Here, we describe a female MM patient with a rare change in her ploidy status from H-MM to NH-MM, detected by cytogenetic and molecular cytogenetic examinations of consecutive bone marrow aspirates. We hypothesize that ploidy conversion (from H-MM to NH-MM) is associated with disease progression and acquired resistance to bortezomib/lenalidomide therapy.

IMWG consensus on risk stratification in multiple myeloma

Multiple myeloma is characterized by underlying clinical and biological heterogeneity, which translates to variable response to treatment and outcome. With the recent increase in treatment armamentarium and the projected further increase in approved therapeutic agents in the coming years, the issue of having some mechanism to dissect this heterogeneity and rationally apply treatment is coming to the fore. A number of robustly validated prognostic markers have been identified and the use of these markers in stratifying patients into different risk groups has been proposed. In this consensus statement, the International Myeloma Working Group propose well-defined and easily applicable risk categories based on current available information and suggests the use of this set of prognostic factors as gold standards in all clinical trials and form the basis of subsequent development of more complex prognostic system or better prognostic factors. At the same time, these risk categories serve as a framework to rationalize the use of therapies.

Critical evaluation of ASO RQ-PCR for minimal residual disease evaluation in multiple myeloma. A comparative analysis with flow cytometry

We have analyzed the applicability, sensitivity and prognostic value of allele-specific oligonucleotide real-time quantitative PCR (ASO RQ-PCR) as a method for minimal residual disease (MRD) assessment in patients with multiple myeloma (MM), comparing the results with those of multiparameter flow cytometry (MFC). A total of 170 patients enrolled in three consecutive Spanish trials achieving at least partial response after treatment were included. Lack of clonality detection (n=31), unsuccessful sequencing (n=17) and suboptimal ASO performance (n=51) limited the applicability of PCR to 42% of cases. MRD was finally investigated in 103 patients (including 32 previously studied) with persistent disease identified by PCR and MFC in 54% and 46% of cases, respectively. A significant correlation in MRD quantitation by both the techniques was noted (r=0.881, P<0.001), being reflective of treatment intensity. Patients with <10(-4) residual tumor cells showed longer progression-free survival (PFS) compared with the rest (not reached (NR) vs 31 months, P=0.002), with similar results observed with MFC. Among complete responders (n=62), PCR discriminated two risk groups with different PFS (49 vs 26 months, P=0.001) and overall survival (NR vs 60 months, P=0.008). Thus, although less applicable than MFC, ASO RQ-PCR is a powerful technique to assess treatment efficacy and risk stratification in MM.

Improved risk stratification in myeloma using a microRNA‐based classifier

Multiple myeloma (MM) is a heterogeneous disease. International Staging System/fluorescence hybridization (ISS/FISH)-based model and gene expression profiles (GEP) are effective approaches to define clinical outcome, although yet to be improved. The discovery of a class of small non-coding RNAs (micro RNAs, miRNAs) has revealed a new level of biological complexity underlying the regulation of gene expression. In this work, 163 presenting samples from MM patients were analysed by global miRNA profiling, and distinct miRNA expression characteristics in molecular subgroups with prognostic relevance (4p16, MAF and 11q13 translocations) were identified. Furthermore we developed an "outcome classifier", based on the expression of two miRNAs (MIR17 and MIR886-5p), which is able to stratify patients into three risk groups (median OS 19.4, 40.6 and 65.3 months, P = 0.001). The miRNA-based classifier significantly improved the predictive power of the ISS/FISH approach (P = 0.0004), and was independent of GEP-derived prognostic signatures (P < 0.002). Through integrative genomics analysis, we outlined the potential biological relevance of the miRNAs included in the classifier and their putative roles in regulating a large number of genes involved in MM biology. This is the first report showing that miRNAs can be built into molecular diagnostic strategies for risk stratification in MM.

Generation of An Automated Tool for the Identification of Genetics Markers and Signatures in Multiple Myeloma Risk-Stratification

Abstract 2881Multiple myeloma (MM) is characterized by a remarkable heterogeneity in outcome following standard and high-dose therapies. Significant efforts have been made to identify genetic changes and signatures that can predict clinical outcome and include them in the routine clinical care. Gene expression profiling (GEP) studies have achieved a central role in the study of multiple myeloma (MM), as they become a critical component in the risk-based stratification of the disease. To molecularly stratify disease-risk groups, we performed GEP on purified plasma cells (obtained from the immunobead selection of CD138+ cells) from 489 MM samples in different stages of the disease using the Affymetrix U133Plus2.0 array. A total of 162 probes were analyzed using an in house automated script to generate a GEP report with the most used risk stratification indices and signatures, including the UAMS 70-gene, UAMS class, TC classification, proliferation and centrosome signature, and NFKB activation indices. In a subset of 57 samples, IgH translocations were analyzed using FISH and results were correlated with GEP data. A macrophage index was calculated and used as a surrogate measurement of non-plasma cell contamination. A total of 49 samples (10%) were excluded from subsequent analysis as the macrophage index indicated a significant contamination with no plasma cells, hence potentially compromising the results. The percent of high-risk disease patients identified from different signatures ranged from 26.4% by using high proliferation index to 28.8% with high centrosome signature and 31.3% with high 70-gene index. This percent of high-risk cases based on the 70-gene index is similar to what was found in Total therapy 2 (TT2) and TT3 cohorts. A third of patients (33.2%) were classified as D1 in the TC class, followed by 11q13 (19.3%), D2 (16.4%), 4p16 (13.8%), MAF (6.1%), None (4.7%), D1+D2 (4.5%) and 6p21 (1.8%). The NF-kB pathway was likely activated in 45.5% to 59.5% of cases, depending on the index used for its calculation. High proliferation index and high centrosome signature significantly correlates with 70-gene high-risk group (p<0.0001). Conversely, the activation of NF-kB pathway was not significantly different between high- and low- risk subgroups. TC subgroups D1 (p<0.0001) and 11q13 (p=0.01) were significantly more common in the 70-gene low-risk group. Similarly, TC subgroups 4p16 (p=0.0004), Maf (p=0.02) and D2 (p=0.05) were enriched in the high-risk group. Translocations t(4;14)(p16;q32), t(11;14)(q13;q32) and t(14;16)(q32;q23) were precisely predicted by the TC classification (100% correspondence). Cases with IgH translocations with unknown partner were classified in subgroups D1 (33%), D2 (25%), 6p21 (25%) and Maf (16%). Here we summarized the associations between the most significant gene expression indices and signatures relevant to MM risk-stratification. The multiple variables simultaneously analyzed in an automated way, provide a powerful and fast tool for risk-stratification, helping in the therapeutic decision-making. Disclosures:Stewart:Celgene: Consultancy, Research Funding; Millennium: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Onyx Pharmaceuticals: Consultancy, Research Funding. Fonseca:Consulting :Genzyme, Medtronic, BMS, Amgen, Otsuka, Celgene, Intellikine, Lilly Research Support: Cylene, Onyz, Celgene: Consultancy, Research Funding.

Impact of gene expression profiling-based risk stratification in patients with myeloma receiving initial therapy with lenalidomide and dexamethasone

AbstractDetection of specific chromosomal abnormalities by FISH and metaphase cytogenetics allows risk stratification in multiple myeloma; however, gene expression profiling (GEP) based signatures may enable more specific risk categorization. We examined the utility of 2 GEP-based risk stratification systems among patients undergoing initial therapy with lenalidomide in the context of a phase 3 trial. Among 45 patients studied at baseline, 7 (16%) and 10 (22%), respectively, were high-risk using the GEP70 and GEP15 signatures. The median overall survival for the GEP70 high-risk group was 19 months versus not reached for the rest (hazard ratio = 14.1). Although the medians were not reached, the GEP15 also predicted a poor outcome among the high-risk patients. The C-statistic for the GEP70, GEP15, and FISH based risk stratification systems was 0.74, 0.7, and 0.7, respectively. Here we demonstrate the prognostic value for GEP risk stratification in a group of patients primarily treated with novel agents. This trial was registered at www.clinicaltrials.gov as #NCT00098475.

Genetic Markers Used for Risk Stratification in Multiple Myeloma

While no specific genetic markers are required in the diagnosis of multiple myeloma (MM), multiple genetic abnormalities and gene signatures are used in disease prognostication and risk stratification. This is particularly important for the adequate identification of the high-risk MM group, which does not benefit from any of the current therapies, and novel approaches need to be proposed. Fluorescence in situ hybridization (FISH) has been employed for establishing risk-based stratification and still remains the most used genetic technique in the clinical routine. The incorporation of gene expression profiling (GEP) in the study of MM has shown to be a very powerful test in the patient stratification, but its incorporation in clinical routine depends on some technical and logistic resolutions. Thus, FISH still remains the gold standard test for detecting genomic abnormalities and outcome discrimination in MM.

Generation of an automated tool for querying myeloma transcriptomics for multiple gene expression signatures used in risk-stratification.

8024 Background: Multiple myeloma (MM) is characterized by a remarkable heterogeneity in outcome following standard and high-dose therapies. In MM, gene expression profiling (GEP) has a key role in establishing clinically relevant disease subgroups. Methods: To molecularly stratify disease-risk groups, we performed GEP on purified plasma cells from 469 MM cases in different stages of the disease using the Affymetrix U133Plus2.0 array. A total of 162 probes were analyzed using an in house automated script to generate a GEP report with the most used risk stratification indices and signatures, including the UAMS 70-gene, TC classification, proliferation and centrosome signature, and NFKB activation indices. In a subset of samples, IgH translocations were analyzed using FISH and results were correlated with GEP data. Results: All signatures identified around a third of patients with high-risk disease, ranging from 26% by using high proliferation index to 28% with high centrosome signature and 31% with high 70-gene index. High proliferation index and high centrosome signature significantly correlates with 70-gene high-risk group (p<0.0001). Conversely, the activation of NF-kB pathway was not significantly different between high- and low- risk subgroups. TC subgroups D1 (p<0.0001) and 11q13 (p=0.01) were significantly more common in the 70-gene low-risk group. Similarly, TC subgroups 4p16 (p=0.0004), Maf (p=0.02), D2 (p=0.05), and None (p=0.08) were enriched in the high-risk group. Translocations t(4;14)(p16;q32), t(11;14)(q13;q32) and t(14;16)(q32;q23) were precisely predicted by the TC classification (100% correspondence). Cases with IgH translocations without known partner were classified in subgroups D1 (33%), D2 (25%), 6p21 (25%) and Maf (16%). Conclusions: Here we summarized the associations between the most significant gene expression indices and signatures relevant to MM risk-stratification. The multiple variables simultaneously analyzed in an automated way, provide a powerful and fast tool for risk-stratification, helping in the therapeutic decision-making.

Consensus recommendations for risk stratification in multiple myeloma: report of the International Myeloma Workshop Consensus Panel 2

AbstractA panel of members of the 2009 International Myeloma Workshop developed guidelines for risk stratification in multiple myeloma. The purpose of risk stratification is not to decide time of therapy but to prognosticate. There is general consensus that risk stratification is applicable to newly diagnosed patients; however, some genetic abnormalities characteristic of poor outcome at diagnosis may suggest poor outcome if only detected at the time of relapse. Thus, in good-risk patients, it is necessary to evaluate for high-risk features at relapse. Although detection of any cytogenetic abnormality is considered to suggest higher-risk disease, the specific abnormalities considered as poor risk are cytogenetically detected chromosomal 13 or 13q deletion, t(4;14) and del17p, and detection by fluorescence in situ hybridization of t(4;14), t(14;16), and del17p. Detection of 13q deletion by fluorescence in situ hybridization only, in absence of other abnormalities, is not considered a high-risk feature. High serum β2-microglobulin level and International Staging System stages II and III, incorporating high β2-microglobulin and low albumin, are considered to predict higher risk disease. There was a consensus that the high-risk features will change in the future, with introduction of other new agents or possibly new combinations.

Chromosome 1p21 Deletion Defines a Subgroup of Multiple Myeloma with Poor Clinical Outcomes Independent of del (13q), del(p53), t(4;14) or 1q21 Amplification.

Amplifications involving chromosome 1q and deletions involving 1p are frequent events in multiple myeloma (MM). The pathogenesis and clinical significance of these anomalies is largely unknown but CKS1B amplification at 1q21 detected in 30–40% of MM patients is associated with disease progression. As karyotyping and SNP based mapping analysis identify a minimal common deletion region involving the 1p21 locus, we used FISH combined with cytoplasmic light chain detection (cIg-FISH) to investigate the prevalence and prognostic significance of del(1p21) in a cohort of 186 MM patients undergoing autologous stem cell transplant. CIg-FISH detected hemizygous 1p21 deletions in 18% of the cases. The median percentage of clonal plasma cells harboring del(1p21) was 55% (range 20–95%). The presence of 1p21 deletions was strongly correlated with CKS1B amplification (p=0.004), t(4;14) (p= 0.027), and del(p53) (p=0.04), but not with del(13q) or t(11;14). There was no association between del (1p21) and other biological factors including age, gender, Hb, albumin, C-reactive protein, beta-2 microglobulin level, isotype or bone marrow plasmacytosis. Patients with 1p21 deletions had significantly shorter progression-free (median 10.5 vs. 25.4 months, p=0.0001) and overall survivals (median 33.9 months vs. not reached, p=0.001) than those without such deletions. On multivariate analysis, del(1p21) was an independent risk factor for progression free (p< 0.0001) and overall survivals (p=0.0005) after adjusting for other genetic risk factors including del(13q), del(p53), t(4;14) and CKS1B amplification. Our results indicate that del(1p21) is a novel genetic risk factor and warrant inclusion of this genetic aberration in the risk-stratification of MM. Further studies are required to identify candidate tumor suppressor gene(s) at the 1p21 locus and explore their role in the molecular pathogenesis of MM.

Recommandations pour la prise en charge cytogénétique de la leucémie myéloïde chronique (LMC) établies par le Groupe Français de Cytogénétique Hématologique (GFCH)

Ploidy is an important prognostic factor in the risk stratification of multiple myeloma (MM) patients. Patients with MM can be divided into two groups according to the modal number of chromosomes: nonhyperdiploid (NH-MM) and hyperdiploid (H-MM), which has a more favorable outcome. The two ploidy groups represent two different oncogenetic pathways determined at the premalignant stage. The ploidy subtype also persists during the course of the disease, even during progression after the therapy, with only very rare cases of ploidy conversion. The clinical significance of ploidy conversion and its relation to drug resistance have been previously discussed. Here, we describe a female MM patient with a rare change in her ploidy status from H-MM to NH-MM, detected by cytogenetic and molecular cytogenetic examinations of consecutive bone marrow aspirates. We hypothesize that ploidy conversion (from H-MM to NH-MM) is associated with disease progression and acquired resistance to bortezomib/lenalidomide therapy.