Comparison Of Conventional, FISH and GEP Prognostic Factors In Multiple Myeloma: Introducing a Novel Risk Stratification

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 3092-3092 ◽  
Author(s):  
Rowan Kuiper ◽  
Martin van Vliet ◽  
Annemiek Broyl ◽  
Yvonne de Knegt ◽  
Bronno van der Holt ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Prognostic Factors ◽  
High Risk ◽  
Board Of Directors ◽  
Research Funding ◽  
Low Risk ◽  
Partial Likelihood ◽  
Data Sets ◽  
Advisory Committees ◽  
Risk Patients

Abstract Introduction Multiple Myeloma (MM) is a heterogeneous disease with highly variable survival. Gene expression profiling (GEP) classifiers, such as the EMC-92, can consistently distinguish high risk patients from standard risk patients. Other prognostic factors for MM include the international staging system (ISS) and FISH. Here we present a comparison of prognostic factors and introduce a novel stratification based on EMC-92 and ISS. Methods Scores were calculated for the GEP classifiers EMC-92, UAMS-70, UAMS-17, UAMS-80 and MRC-IX-6 for the following five studies: HOVON-65/GMMG-HD4 (n=328; GSE19784), MRC-IX (n=247; GSE15695), UAMS-TT2 (n=345; GSE2658), UAMS-TT3 (n=238; E-TABM-1138 and GSE2658) and APEX (n=264; GSE9782; for details, see Kuiper R, et al. Leukemia (2012) 26: 2406–2413). FISH data were available for the HOVON-65/GMMG-HD4 trial and the MRC-IX trial. ISS values were available for all datasets except UAMS-TT2. Univariate associations between markers and overall survival (OS) were investigated in a Cox regression analysis, using Bonferroni multiple testing correction. For pair wise analysis of markers, the significance in the increase of partial likelihood was calculated. In order to find the strongest combination (defined as the highest partial likelihood) of GEP-ISS, we compared these pair-wise on the same data. Training sets of classifiers were excluded for those analyses in which that specific classifier was tested. All survival models have been stratified for study. The calculations were done in R using the package survival. Results Prognostic value of FISH, GEP and serum markers was determined in relation to overall survival (Figure 1). GEP classifiers generally performed much better than FISH markers. Of 6 FISH markers with known adverse risk, del(17p), t(4;14), t(14;20) and del(13q) demonstrated a significant association only in one of two data sets with available FISH (HOVON-65/GMMG-HD4). GEP classifiers, on the other hand, are much more robust. Classifiers EMC-92, UAMS-70 and UAMS-80 significantly identify a high-risk population in all evaluated data sets, whereas the UAMS-17 and the MRC-IX-6 classifiers predict high-risk patients in three of four datasets. As expected, ISS staging demonstrated stable and significant hazard ratios in most studies (three out of four). Indeed, when evaluating a merged data set, both ISS and all evaluated GEP classifiers are strong prognostic factors independent of each other. Markers with additive value to each other include all combinations of GEP classifiers as well as the combination of GEP classifiers together with ISS. Tested in independent sets, the EMC-92 classifier combined with ISS is the best combination, as compared to other classifier-ISS combinations tested on the same independent data sets. The strongest risk stratification in 3 groups was achieved by splitting the EMC-92 standard risk patients into low risk, based on ISS stage I, and intermediate risk, based on ISS stage II and III. This stratification retains the original EMC-92 high-risk group, and is robust in all cohorts. The proportions of patients defined as low, intermediate and high risk for this combined EMC-92-ISS classifier are 31% / 47% / 22 % (HOVON-65/GMMG-HD4), 19% / 61% / 20 % (MRC-IX), 46% / 39% / 15 % (UAMS-TT3) and 32% / 55% / 13 % (APEX). Variability in low risk proportion is caused by the variable incidence of ISS stage I. Conclusions We conclude that GEP is the strongest predictor for survival in multiple myeloma and far more robust than FISH. Adding ISS to EMC-92 results in the strongest combination of any of the GEP classifier-ISS combinations. Stratification in low risk, intermediate and high risk may result in improved treatment and ultimately in longer survival of MM patients. This research was supported by the Center for Translational Molecular Medicine (BioCHIP project) Disclosures: van Vliet: Skyline Diagnostics: Employment. Mulligan:Millennium Pharmaceuticals: Employment. Morgan:Celgene: Consultancy, Honoraria, Membership on an entity’s Board of Directors or advisory committees; Millenium: Consultancy, Honoraria, Membership on an entity’s Board of Directors or advisory committees; Novartis: Consultancy, Honoraria, Membership on an entity’s Board of Directors or advisory committees; Merck: Consultancy, Honoraria, Membership on an entity’s Board of Directors or advisory committees; Johnson and Johnson: Consultancy, Honoraria, Membership on an entity’s Board of Directors or advisory committees. Goldschmidt:Celgene: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Research Funding. Lokhorst:Genmab A/S: Consultancy, Research Funding; Celgene: Honoraria; Johnson-Cilag: Honoraria; Mudipharma: Honoraria. van Beers:Skyline Diagnostics: Employment. Sonneveld:Janssen-Cilag: Honoraria; Celgene: Honoraria; Onyx: Honoraria; Janssen-Cilag: Research Funding; Millenium: Research Funding; Onyx: Research Funding; Celgene: Research Funding.

scholarly journals A Novel Prognostic Model That Is Superior to FLIPI-1 and FLIPI-2 and Integrates Clinical and Treatment Factors to Predict Progression-Free Survival and Early Treatment Failure in Patients with Follicular Lymphoma in the GALLIUM Trial

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 2872-2872 ◽  
Author(s):  
Farheen Mir ◽  
Andrew Grigg ◽  
Michael Herold ◽  
Wolfgang Hiddemann ◽  
Robert Marcus ◽  
...  
Keyword(s):  
High Risk ◽  
Board Of Directors ◽  
Research Funding ◽  
Prognostic Model ◽  
Low Risk ◽  
Prognostic Variables ◽  
Advisory Committees ◽  
High Risk Disease ◽  
Risk Patients ◽  
Equity Ownership

Abstract Introduction: Progression of disease within 24 months of initial therapy (POD24) is associated with poor survival in patients with follicular lymphoma (FL). Existing prognostic models, such as FLIPI-1 and FLIPI-2, show poor sensitivity for POD24, and are derived from cohorts lacking bendamustine-treated patients. More accurate predictive models based on current standard therapies are needed to identify patients with high-risk disease. The Phase III GALLIUM trial (NCT01332968) compared the safety and efficacy of standard chemotherapy regimens plus rituximab (R) or obinutuzumab (G) in patients with previously untreated FL. Using GALLIUM data, we developed a novel risk stratification model to predict both PFS and POD24 in FL patients after first-line immunochemotherapy. Methods: Enrolled patients were aged ≥18 years with previously untreated FL (grades 1-3a), Stage III/IV disease (or Stage II with bulk), and ECOG PS ≤2, and required treatment by GELF criteria. Patients were randomized to receive either G- or R-based immunochemotherapy, followed by maintenance with the same antibody in responders. The chemotherapy arm (CHOP, CVP, or bendamustine) was selected by each study center. POD24 was defined as progressive disease or death due to disease within 24 months of randomization (noPOD24 = no progression or lymphoma-related death in that period). The most strongly prognostic variables, based on PFS hazard ratios, were estimated using penalized multivariate Cox regression methodology via an Elastic Net model. Selected variables were given equal weights, and a clinical score was formed by summating the number of risk factors for each patient. Low- and high-risk categories were determined using a cut-off that provided the best balance between true- and false-positives for PFS. PFS correlation and sensitivity to predict POD24 were assessed. The data used are from an updated GALLIUM efficacy analysis (data cut-off: April 2018; median follow-up: 57 months). Results: 1202 FL patients were enrolled. Based on data availability and biological plausibility (i.e. could reasonably be linked with high-risk disease), 25 potential clinical and treatment-related prognostic variables were entered into the Elastic Net model (Table). A model containing 11 factors was retained by the methodology and chosen as the best model (Table). Patients were categorized as 'low risk' if they scored between 0 and 3 (n=521/1000 patients with complete data) and as 'high risk' if they scored between 4 and 11 (n=479/1000 patients). At 2 years, the PFS rate was 84.5% in the whole FL population. Using our model, 2-year PFS for high-risk patients was 77% compared with 79.9% for FLIPI-1 and FLIPI-2. In low-risk patients, 2-year PFS was 92% compared with 87.9% for FLIPI-1 and 87.6% for FLIPI-2 (low-intermediate-risk patients). Our model increased the inter-group difference in 2-year PFS rate from 8% (FLIPI-1) and 7.7% (FLIPI-2) to 15%. At 3 years, the inter-group difference increased from 6.9% (FLIPI-1) and 9% (FLIPI-2) to 17% (Figure). Sensitivity for a high-risk score to predict POD24 was 73% using our model compared with 55% for FLIPI-1 and 52% for FLIPI-2 (based on 127 POD24 and 873 noPOD24 patients with complete data). Excluding patients who received CVP, which is now rarely used, resulted in an inter-group difference in PFS of 15% at 2 years and 16.8% at 3 years. A sensitivity analysis showed that inclusion of the 9 clinical factors only (i.e. removal of CVP and R treatment as variables) formed a more basic scoring system (low-risk patients, 1-3; high-risk patients, 4-9); the inter-group difference in PFS was 16.5% at 2 years and 17.6% at 3 years. However, sensitivity for POD24 decreased to 56%. Conclusion: Our clinical prognostic model was more accurate at discriminating patients likely to have poor PFS than either FLIPI-1 or FLIPI-2, and its prognostic value was sustained over time. Our model also identified the FL population at risk of POD24 with greater sensitivity. Variables such as age and bone marrow involvement were not retained by our model, and thus may not have a major impact in the current era of therapy. Factors such as sum of the products of lesion diameters were included, as this captures tumor burden more accurately than presence of bulk disease. Future studies will aim to improve the accuracy of the model by considering gene expression-based prognostic markers and DNA sequencing to form a combined clinico-genomic model. Disclosures Mir: F. Hoffmann-La Roche: Employment. Hiddemann:F. Hoffman-La Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bayer: Consultancy, Research Funding. Marcus:F. Hoffman-La Roche: Other: Travel support and lecture fees; Roche: Consultancy, Other: Travel support and lecture fees ; Gilead: Consultancy. Seymour:Genentech Inc: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Honoraria, Research Funding; Celgene: Consultancy; AbbVie: Consultancy, Honoraria, Research Funding; F. Hoffmann-La Roche Ltd: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees. Bolen:Roche: Other: Ownership interests PLC*. Knapp:Roche: Employment. Launonen:Launonen: Other: Ownership interests none PLC; Travel, accommodation, expenses; Novartis: Consultancy, Equity Ownership, Other: Ownership interests none PLC; Travel. accommodation, expenses; Roche: Employment, Other: Travel, accommodation, expenses. Mattiello:Roche: Employment. Nielsen:F. Hoffmann-La Roche Ltd: Employment, Other: Ownership interests PLC. Oestergaard:Roche: Employment, Other: Ownership interests PLC. Wenger:F. Hoffmann-La Roche Ltd: Employment, Equity Ownership, Other: Ownership interests PLC. Casulo:Gilead: Honoraria; Celgene: Research Funding.

Risk Stratification for the Development of Venous Thromboembolism in Hospitalized Patients with Cancer

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 4728-4728 ◽  
Author(s):  
Arabesque Parker ◽  
Erica A. Peterson ◽  
Agnes Y. Y. Lee ◽  
Carine de Wit ◽  
Marc Carrier ◽  
...  
Keyword(s):  
Venous Thromboembolism ◽  
High Risk ◽  
Risk Stratification ◽  
Board Of Directors ◽  
Cancer Patients ◽  
Major Bleeding ◽  
Research Funding ◽  
Low Risk ◽  
Advisory Committees ◽  
Risk Patients

Abstract Introduction: No method of venous thromboembolism (VTE) risk stratification exists for hospitalized cancer patients. The Khorana score is a validated tool in outpatients with cancer. The objective of this study was to assess the Khorana score for predicting development of VTE in cancer patients during admission to hospital. Methods: We conducted a retrospective analysis of data collected from healthcare records of consecutive, medically-ill cancer patients hospitalized between January and June 2010 in 3 academic medical centers in Canada. Objectively diagnosed symptomatic VTE during hospitalization, anticoagulant thromboprophylaxis (TP), and Khorana score variables were collected for every patient. Patients receiving therapeutic anticoagulation at admission, and those with incomplete data were excluded. The risk of VTE based on Khorana score category was evaluated using logistic regression. Continuous data were compared using a Student's t-test and expressed using the means and standard deviations. Categorical data were compared using the Pearson Chi-square test and were expressed as percentages. Statistical significance was defined as alpha less than 0.05. Results: 1398 patients were included. Mean age was 61.6 years, 51.2% were male, and mean BMI was 25.9 kg/m2. The most frequent tumor types were non-small cell lung carcinoma (12.7%) followed by lymphoma (10.9%). The median length of stay was 6 days (range 0-114 days). The most frequent reasons for hospitalization were chemotherapy (22.3%) followed by pain and palliation (21.4%). 34.5% received anticoagulant TP (n = 483/1398). The incidence of VTE was 2.9% (41/1398) overall, 5.4% (9/166) in high, 3.2% (26/817) in moderate, and 1.4% (6/415) in low Khorana score risk groups. High risk patients were significantly more likely than low risk patients to have VTE (p=0.016; OR 3.9, 95% CI 1.4-11.2). There was no difference in VTE incidence between patients who received anticoagulant TP and those who did not (3.5% vs 2.6%, p = 0.345). Patients with high risk Khorana score were more likely to receive anticoagulant TP than those with low risk Khorana score (46.4% vs. 23.9%, p <0.001, OR 2.8, 95% CI 1.9-4.0). Total incidence of major bleeding was 1.8% (25/1398). There was no difference in major bleeding between patients who received anticoagulant TP and those who did not (1.7% vs. 1.9%, p = 0.787). Conclusion: The Khorana score is predictive of VTE development in cancer patients who are hospitalized for medical illness and may be a useful tool for tailoring inpatient anticoagulant prophylaxis. Disclosures Lee: LEO: Consultancy, Honoraria; Bayer: Consultancy, Honoraria; Bristol Myers-Squibb: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy, Honoraria. Carrier:BMS: Research Funding; Leo Pharma: Research Funding. Wu:Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Leo Pharma: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau.

Extended Follow-up of the Multi-Center Multi-National Prospective Cohort Study That Derived the “Men Continue and HERDOO2” Clinical Decision Rule Which Identifies Low Risk Patients Who May Be Able to Discontinue Oral Anticoagulants (Oac) 5-7 Months After Treatment for Unprovoked Venous Thromboembolism (VTE).

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 451-451 ◽  
Author(s):  
Marc Rodger ◽  
Michael J. Kovacs ◽  
Susan Kahn ◽  
Phil Wells ◽  
David Anderson ◽  
...  
Keyword(s):  
High Risk ◽  
Board Of Directors ◽  
Research Funding ◽  
Low Risk ◽  
Advisory Committees ◽  
High Risk Women ◽  
Risk Patients ◽  
Recurrent Vte ◽  
Annual Risk

Abstract Abstract 451 Introduction: To continue or discontinue OAC after 6 months of therapy for VTE is one of the most important unanswered questions in VTE management. In 2007, we developed a clinical decision rule to identify low risk patients with unprovoked VTE who could safely discontinue OAC after 5-7 months of therapy. This clinical decision rule was developed from a large prospective cohort study of patients with unprovoked VTE who discontinued anticoagulants after 5-7 months of OAC and were subsequently followed for a mean of 18 months for recurrent VTE. The “MEN continue and HERDOO2” rule states that men and high risk women should continue anticoagulants indefinitely after unprovoked VTE. High risk women are women with ≥2 of the following 1)Hyperpigmentation, Edema or Redness (HER) on exam in either leg, 2)Vidas D-Dimer >250, 3)Obesity- BMI >30 or 4)Older age over 65. Given that the OAC treatment decision is a long-term treatment decision that needs to be counter-balanced with long-term bleeding risk from OAC (1-3% annual risk of major hemorrhage) it is important to determine long-term risks of recurrent VTE in unprovoked VTE patients, high risk patients and low risk patients. Objective: We sought to confirm that the risk of recurrent VTE in high risk patients remains elevated and conversely that the risk remains low in low risk women over longer term follow-up. Methods: Multi-centre prospective cohort study of first unprovoked VTE patients who had potential predictors collected while on OAC (including D-Dimer) enrolled from 2001 to March 2006. Patients were excluded if they had: 1) recurrent unprovoked VTE, 2) known high risk thrombophilia or 3) no consent. Symptomatic suspected VTE during subsequent follow-up (up to july 2009) off of OAC was investigated with reference to baseline imaging and then independently adjudicated. Results: 646 participants were enrolled in 11 centers. At enrolment, mean age of 53 (range 17-95) and 49% were female. During a mean 3.1 years (range 0.01-6.5) of follow-up, 131/512 suspected VTE were adjudicated as recurrent VTE resulting in an annual risk of recurrent VTE of 6.7% (95% CI 5.5-7.6%) in patients with unprovoked VTE. Men had a 9.9% (95% CI 8.3-11.8%) annual risk of recurrent VTE. High risk women with 2 or more HERDOO points had an annual risk of recurrent VTE of 8.3% (95%CI 5.7-11.3%). Low risk women (1 or 0 HERDOO points) had 1.3% (95% CI 0.5-2.8%) annual risk of recurrent VTE compared to 9.5% (8.1-11.0%) annual risk of recurrent VTE in high risk patients (men and high risk women). Conclusions: Men and high risk women with unprovoked VTE should be considered for long-term OAC therapy given a high risk of recurrent VTE over 3 year follow-up . Women with a low HER DOO 2 score may be able to safely discontinue anticoagulants. Disclosures: Rodger: Bayer: Research Funding; Leo Pharma: Research Funding; Pfizer: Research Funding; Boehringer Ingelheim: Membership on an entity's Board of Directors or advisory committees; Biomerieux: Research Funding; GTC Therapeutics: Research Funding. Crowther:BI: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Bayer: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Consultancy, Honoraria, Research Funding; Leo Pharma: Consultancy, Honoraria, Research Funding; Sanofi-Aventis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Artisan Pharma: Consultancy, Membership on an entity's Board of Directors or advisory committees.

Melphalan/Prednisone/Lenalidomide (MPR) Versus High-Dose Melphalan and Autologous Transplantation (MEL200) in Newly Diagnosed Multiple Myeloma (MM) Patients <65 Years: Results of a Randomized Phase III Study

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 3069-3069 ◽  
Author(s):  
Antonio Palumbo ◽  
Federica Cavallo ◽  
Izhar Hardan ◽  
Barbara Lupo ◽  
Valter Redoglia ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Stem Cell ◽  
High Risk ◽  
Board Of Directors ◽  
Research Funding ◽  
High Dose ◽  
Newly Diagnosed ◽  
Advisory Committees ◽  
Risk Patients ◽  
High Dose Melphalan

Abstract Abstract 3069FN2 Background: High-dose chemotherapy with haemopoietic stem-cell improves outcome in multiple myeloma (MM). The introduction of novel agents questions the role of autologous stem-cell transplantation (ASCT) in MM patients. Aims: In this prospective randomized study, we compared conventional melphalan-prednisone-lenalidomide (MPR) with tandem high-dose melphalan (MEL200) in newly diagnosed MM patients younger than 65 years. Methods: All patients (N=402) received four 28-day cycles of lenalidomide (25 mg, d1-21) and low-dose dexamethasone (40 mg, d1, 8, 15, 22) (Rd) as induction. As consolidation, patients were randomized to MPR (N=202) consisting of six 28-day cycles of melphalan (0.18 mg/kg d1-4), prednisone (2 mg/kg d1-4) and lenalidomide (10 mg d1-21); or tandem melphalan 200 mg/m2 MEL200 (N=200) with stem-cell support. All patients enrolled were stratified according to International Staging System (stages 1 and 2 vs. stage 3) and age (<60 vs. ≥60 years). Progression-free survival (PFS) was the primary end point. Data were analyzed in intention-to-treat. Results: Response rates were similar: at least very good partial response (≥VGPR) rate was 60% with MPR vs. 58% with MEL200 (p=.24); the complete response (CR) rate was 20% with MPR vs. 25% with MEL200 (p=.49). After a median follow-up of 26 months, the 2-year PFS was 54% in MPR and 73% in MEL200 (HR=0.51, p<.001). The 2-year overall survival (OS) was similar in the two groups: 87% with MPR and 90% with MEL200 (HR 0.68, p=.19). In a subgroup analysis, MEL200 significantly prolonged PFS in both standard-risk patients without t(4;14) or t(14;16) or del17p abnormalities (2-year PFS was 46% in the MPR group vs. 78% in the MEL200 group, HR=0.57, p=.007) and high-risk patients with t(4;14) or t(14;16) or del17p abnormalities (2-year PFS was 27% for MPR vs. 71% for MEL200, HR=0.32, p=.004). In patients who achieved CR, the 2-year PFS was 66% for MPR vs. 87% for MEL200 (HR 0.26; p<.001); in those who achieved a partial response (PR), the 2-year PFS was 56% for MPR vs. 77% for MEL200 (HR 0.45; p<.001). In the MPR and MEL200 groups, G3-4 neutropenia was 55% vs. 89% (p<.001); G3-4 infections were 0% vs. 17% (p<.001); G3-4 gastrointestinal toxicity was 0% vs. 21% (p<.001); the incidence of second tumors was 0.5% in MPR patients and 1.5% in MEL200 patients (p=.12). Deep vein thrombosis rate was 2.44% with MPR vs. 1.13% with MEL200 (p=.43). Conclusions: PFS was significantly prolonged in the MEL200 group compared to MPR. This benefit was maintained in the subgroup of patients with standard- or high-risk cytogenetic features. Toxicities were significantly higher in the MEL200 group. This is the first report showing a PFS advantage for ASCT in comparison with conventional therapies including novel agents. These data will be updated at the meeting. Disclosures: Palumbo: celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees. Cavallo:Celgene: Honoraria; Janssen-Cilag: Honoraria. Cavo:celgene: Honoraria. Ria:celgene: Consultancy. Caravita Di Toritto:Celgene: Honoraria, Research Funding. Di Raimondo:celgene: Honoraria. Boccadoro:celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding.

scholarly journals A Phase II Study of Daratumumab in Patients with High-Risk MGUS and Low-Risk Smoldering Multiple Myeloma

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 1649-1649
Author(s):  
Omar Nadeem ◽  
Robert A. Redd ◽  
Michael Z. Koontz ◽  
Jeffrey V. Matous ◽  
Andrew J. Yee ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Bone Marrow ◽  
High Risk ◽  
Board Of Directors ◽  
Research Funding ◽  
Plasma Cells ◽  
Low Risk ◽  
M Protein ◽  
Advisory Committees ◽  
Bone Marrow Plasma

Abstract Introduction : Daratumumab (Dara) is an anti-CD38 monoclonal antibody that is approved for use in patients with newly diagnosed and relapsed multiple myeloma (MM). We hypothesized that early therapeutic intervention with Dara in patients with high-risk MGUS (HR-MGUS) or low-risk SMM (LR-SMM) would lead to eradication of the tumor clone by achieving deep responses, resulting in prevention of progression to MM. We present results of our phase II, single arm study of Dara in HR-MGUS and LR-SMM. Methods : Patients enrolled on this study met eligibility for either HR-MGUS or LR-SMM. HR-MGUS is defined as &lt;10% bone marrow plasma cells and &lt;3g/dL M protein and at least 2 of the following 3 high-risk criteria: Abnormal serum free light chain ratio (SFLC) of &lt;0.26 or &gt;1.65, M protein ≥ 1.5g/dL or non-IgG M protein. LR-SMM is defined by one of the following 3 criteria: M protein ≥3g/dL, ≥10% bone marrow plasma cells, SFLC ratio &lt;0.125 or &gt;8. Dara (16mg/kg) was administered intravenously on a weekly schedule for cycles 1-2, every other week cycles 3-6, and monthly during cycles 7-20. The primary objective of this study was to determine the proportion of patients who achieve very good partial response (VGPR) or greater after 20 cycles of Dara. Secondary objectives included duration of response, safety, and rates of minimal residual disease (MRD)-negativity in VGPR or greater patients. Correlative studies included assessing changes in immune microenvironment, evaluating clonal heterogeneity using deep sequencing, and determining association of genomic aberrations correlating with either response to therapy or progression of disease. Results : At the time of data cutoff, a total of 42 patients were enrolled on this study from 2018 to 2020 with participation of 5 sites. The median age for all patients at enrolment was 60 years (range 38 to 76), with 22 males (52.4%) and 20 females (47.6%). Majority of patients enrolled were classified as LR-SMM (n = 37, 88.1%) and the remaining 5 patients had HR-MGUS (11.9%). 41 patients have started treatment and are included in toxicity assessment, and 40 patients have at least completed 16 cycles (range 6-20). Grade 3 toxicities were rare and only experienced in 5/41 patients including diarrhea (n =1/41; 2%), flu like symptoms (n = 1/41; 2%), headache (n=1/41; 2%), and hypertension (n=2/41; 5%). Most common toxicities of any grade included fatigue (n = 24/41, 51%), cough (n = 19/41, 46%), nasal congestion (n = 18/41, 44%), headache (n = 14/41, 34%), hypertension (n = 11/41, 27%), nausea (n = 13/41, 32%), and leukopenia (n = 13/41, 32%). No patients have discontinued therapy due to toxicity. Minimal response or better was observed in 82.9% of patients (34/41) and PR or better was observed in 51.2% of patients (21/41). This included overall CR (n = 4, 9.8%), VGPR (n = 1, 2.4%), PR (n = 16, 39.0%), MR (n = 13, 31.7%), and SD (n = 7, 17.1%). In the 40 patients who completed at least 16 cycles, response rates were as follows: MR or better 85% (34/40), PR or better 52.5% (21/40) and VGPR or better 12.5% (5/40). Median time to VGPR was 7 months. Median overall survival and progression-free survival have not been reached and no patients have progressed to overt multiple myeloma while on study. Conclusion : Dara is very well tolerated among patients with HR-MGUS and LR-SMM with minimal toxicities. Responses are seen in majority of patients. Early therapeutic intervention in this precursor patient population appears promising but longer follow up is required to define the role of single agent Dara in preventing progression to MM, therefore avoiding more toxic interventions in this low-risk patient population. Disclosures Nadeem: Karyopharm: Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees; BMS: Membership on an entity's Board of Directors or advisory committees. Yee: GSK: Consultancy; Oncopeptides: Consultancy; Janssen: Consultancy; Amgen: Consultancy; Sanofi: Consultancy; Bristol Myers Squibb: Consultancy; Adaptive: Consultancy; Takeda: Consultancy; Karyopharm: Consultancy. Zonder: Caelum Biosciences: Consultancy; Amgen: Consultancy; BMS: Consultancy, Research Funding; Intellia: Consultancy; Alnylam: Consultancy; Janssen: Consultancy; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Regeneron: Consultancy. Rosenblatt: Attivare Therapeutics: Consultancy; Imaging Endpoints: Consultancy; Parexel: Consultancy; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Research Funding; Wolters Kluwer Health: Consultancy, Patents & Royalties. Mo: AbbVIE: Consultancy; BMS: Membership on an entity's Board of Directors or advisory committees; Eli Lilly: Consultancy; Epizyme: Consultancy; GSK: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria; Karyopharm: Honoraria, Membership on an entity's Board of Directors or advisory committees; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees. Sperling: Adaptive: Consultancy. Richardson: Karyopharm: Consultancy, Research Funding; AstraZeneca: Consultancy; AbbVie: Consultancy; Takeda: Consultancy, Research Funding; Celgene/BMS: Consultancy, Research Funding; Janssen: Consultancy; GlaxoSmithKline: Consultancy; Protocol Intelligence: Consultancy; Secura Bio: Consultancy; Regeneron: Consultancy; Sanofi: Consultancy; Oncopeptides: Consultancy, Research Funding; Jazz Pharmaceuticals: Consultancy, Research Funding. Ghobrial: AbbVie, Adaptive, Aptitude Health, BMS, Cellectar, Curio Science, Genetch, Janssen, Janssen Central American and Caribbean, Karyopharm, Medscape, Oncopeptides, Sanofi, Takeda, The Binding Site, GNS, GSK: Consultancy.

scholarly journals New Score Prognostic System for Patients with MDS and Intermediate IPSS-R: Novel Algorithm to Distinguish Between Low and High Risk in This Subset of Patients. Results from the Spanish Group of MDS (GESMD). Gesmdi Score

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 4323-4323
Author(s):  
Alba Redondo ◽  
Mercedes Sánchez Barba ◽  
Guillermo Sanz ◽  
Teresa Bernal ◽  
Montserrat Arnan Sangerman ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
High Risk ◽  
Board Of Directors ◽  
Research Funding ◽  
Low Risk ◽  
Advisory Committees ◽  
High Risk Patients ◽  
Intermediate Group ◽  
Risk Patients ◽  
R Value

Abstract INTRODUCTION MDS are a heterogeneous group, and it is necessary an adequate prognostic stratification in order to the best management. The new revised international prognostic scoring system (IPSS-R) has improved prognostic ability for survival and AML evolution comparing with the previous prognostic indexes. But, it is not clear the prognosis of patients included in the intermediate group, 20% of MDS, patients with a median OS of 3 years according to Greenberg et al, are they in the high or in the low risk category? The aims of the present study were to describe characteristics of patients included in this intermediate group of the IPSS-R in the Spanish MDS cohort and to identify which factors could have an impact on survival. A new score prognostic system (GESMDi score) in order to a better stratification should be proposed in this subset of patients that will be useful for determine the best therapeutic approach for them. METHODS: All patients were included in the GESMD, diagnosed of Primary MSD and Intermediate IPSS-R. The Statistical analyzes were performed using SPSS version 21, Cox models and Kaplan-Meier curves were used to demonstrate clinical outcomes. Regarding the new score proposed, GESMDi score, modeling of prognostic risk was based on multivariate analysis of survival time. Cox model for survival was built to derive the relative weights within the score. RESULTS: Data from 957 patients of 69 centers of GESMD were evaluated. Their median age was 73.9 years (p25/p75 66-80), 61.6% males (N=590), and median follow-up 21,4 months (p25-p75 de 11-41). Regarding WHO 2001 classification: 31% were RAEB-1, 21% CMML, 18% RCMD, 14% RAEB-2, 3% RCMD-RS, 3.1% RARS, 2.5% RA, 2% 5q-syndrome, 2% AML, 1% unclassified. Median hemoglobin at diagnosis was 9.8 g/dL (p25/p75:8.3-11.6), median bone marrow (BM) blasts 6% (p25/p75:3-8) and median platelet count 99x109/L (p25/p75:66-180). According to IPSS, 5% of patients were classified as low risk, 78% as intermediate-1, 16% as intermediate-2 and 1% as high risk. Cytogenetic were very good in 2% of patients, good in 76%, intermediate in 17%, poor in 5% and in 1% very poor. IPSS-R score classified patients in 3 different groups, with a punctuation of≤ 3.5 (35.6%), >3.5 and ≤ 4 (35.8%) and> 4 and ≤ 4.5 (28.5%). Median OS was 30.1 months, the estimated 1-year and 2-y OS were 79.2% and 57.8%, respectively. In the univariate analysis for OS older age (>74y, p<0.001), lower Hb level (≤9.5 g/dL, p<0.001), WHO 2001 with excess of blasts classification (p=0.035), lower platelets level (≤30 x 109/L, p=0.01), PB blasts (yes, p=0.001), ferritine level (>500 ng/ml, p=0.002), and higher IPSS-R score (>3.5 and ≤ 4 and >4 and ≤ 4.5, p=0.023 and p=0.004, figure 1) had a deleterious impact on survival. In the multivariate analysis, only age, Hb level, PB blast, ferritine level and IPSS-R value retained statistical significant impact on OS (table 1a). In the multivariate analysis, Hazard ratio, a new score system (GESMDi score) was established for all patients. Patients with adverse features were added points in order to stratify the risk of death: age<74y and/or PB blasts (2 points) and Hb level ≤9.5 g/dL and/or ferritine level >500 ng/ml and/or IPSS-R of >3.5 (1 point), table 1a. The GESMDi score was performed in 685 patients with all data available and 7 groups of patients were defined with different median OS (p<0.0001, table 1b). Two final categories were established according to the definition of risk from the Spanish MDS group, low risk patients (estimated OS >30 months) and high risk patients (<30 months). Patients with scores between 0-3 (70.6% patients, me OS 41.1, 95CI 34.4-47.7) were in the low risk definition while patients with scores between 4-6 (29.3% patients, me OS 17.5 mo, 95CI 13.4-21.5) were classified as high risk patients (p< 0.0001, Figure 2). CONCLUSIONS: GESMDi score, a proposed prognostic score system from patients with intermediate IPSS-R, allow us to establish a better prognosis stratification in this heterogeneous MDS population. Treatment and management should be better established for those patients nowadays according to this novel stratification. Table 1 a) Univariate and multivariate analysis for OS among patients with Intermediate IPSS-R b) OS according to the GESMDi score proposed Table 1. a) Univariate and multivariate analysis for OS among patients with Intermediate IPSS-R b) OS according to the GESMDi score proposed Figure 1 OS according to IPSS-R value in the intermediate group (≤3.5, ≤4 and ≤4.5) Figure 1. OS according to IPSS-R value in the intermediate group (≤3.5, ≤4 and ≤4.5) Figure 2 OS according the GESMDi score proposed in the intermediate IPSS-R group: low and high risk patients (n=685) Figure 2. OS according the GESMDi score proposed in the intermediate IPSS-R group: low and high risk patients (n=685) Disclosures Del Cañizo: Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Astex: Membership on an entity's Board of Directors or advisory committees; janssen: Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Díez Campelo:Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Astex: Membership on an entity's Board of Directors or advisory committees; Janssen: Research Funding; celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau.

scholarly journals The Prognostic Power of Gene Expression Profiling with Cytogentics and Routinely Acquired Serum Markers: SKY92 Combined with Revised ISS

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 24-25
Author(s):  
Rowan Kuiper ◽  
Mark van Duin ◽  
Martin H Van Vliet ◽  
Erik H Van Beers ◽  
Berna Berna Beverloo ◽  
...  
Keyword(s):  
High Risk ◽  
Board Of Directors ◽  
Research Funding ◽  
Low Risk ◽  
Ensembl Gene ◽  
Private Company ◽  
Advisory Committees ◽  
High Risk Patients ◽  
Risk Patients ◽  
Standard Risk

Background Updating prognostic models for multiple myeloma is important in the context of changing treatment options. Previously we have described the value of the prognostic marker SKY92, which identifies high-risk multiple myeloma patients, as well as the value of the combined SKY92-ISS marker. With the introduction of revised ISS, it is of interest to evaluate the value of the updated combination of SKY92 with R-ISS. Within the HOVON87/NMSG18 trial, stratification into 3 groups was described: high-risk: 11% SKY92 high-risk (HR) + R-ISS II-III, low-risk: 15% SKY92 standard risk (SR) + R-ISS I and intermediate risk (74%, other). The 3-year PFS rates were 54% (95%CI: 38-77%), 27% (95%CI: 21-37%) and 7% (95%CI: 1-46%) for SKY-RISS I, II and III, respectively (p &lt; 0.001). The 3-yr OS rates for SKY-RISS I to III were 88%, 66% and 26% (p=6×10-7). Here we describe the validation of SKY92-RISS in the CoMMpass dataset. Methods SKY92 was determined using RNA-seq data available from the CoMMpass dataset. Briefly, the SKY92 score was obtained as a weighted summation of the expression given by the available Ensembl gene IDs, corresponding to the probe sets of the SKY92 classifier. Renormalization of the original SKY92 discovery data (HOVON65/GMMG-HD4) allowing a direct remodeling between the Affymetrix probe-set expressions (i.e. SKY92) and RNAseq Ensembl gene IDs. Only Ensembl gene IDs with an average log2 expression &gt;8 were used. Revised ISS status was determined as described. For optimal comparison to the discovery cohort of the HOVON87/NMSG18 trial, the analysis was limited to 93 patients older than 65 years in the CoMMpass data set, that did not receive transplant, and for whom RNA-Seq at diagnosis, R-ISS and follow-up data were available. Results The median follow-up is 41 months. SKY92 identified 24 high-risk patients (24/93: 26%). The 3-yr PFS and OS rates of standard-risk patients were 49% and 80% respectively, compared to 23% and 44% for high-risk, resulting in a significant log rank test (p &lt; 0.005). The R-ISS classified patients into the low-risk R-ISS I (24% of patients), intermediate-risk R-ISS II (63%) and high-risk R-ISS III (13%). The 3-yr PFS rates were 76% (RISS I), 33% (RISS II) and 33% (RISS III); for OS: 100% (RISS I), 68% (RISS II) and 33% (RISS III; PFS, p = 0.07; OS, p &lt; 0.001). SKY92 and R-ISS were independent prognostic factors in terms of OS and PFS. The SKY-RISS classification resulted in 20% low-, 61% intermediate- and 18% high-risk patients (Figure 1). The 3-yr PFS rates were 81% (95%CI: 64-100%), 42% (95%CI: 30-59%) and 12% (95%CI: 3-44%; p &lt; 0.001) and 3-yr OS rates were 100% (95%CI: 100-100%), 77% (95%CI: 66-89%) and 32% (95%CI: 16-61%; p &lt;0.001). Out of 69 patients classed as standard risk using the SKY92 classifier (80% 3-yr OS rate), 17 and 52 were classified as SKY-RISS I and II, respectively, resulting in a 3-yr survival rate of 100% and 74%, respectively. In contrast, out of 24 SKY92 HR patients (44% 3 yr OS rate), 5 were classified as SKY-RISS II (100% alive at 3 years) with the remainder true high-risk patients (32% alive at 3 years). Out of 12 RISS III patients (3-yr OS, 33%), 5 were classified as SKY-RISS II (3-yr OS: 60%) and 7 as SKY-RISS III (3-yr OS: 14%). Conclusion This study demonstrates the value of gene expression profiling - SKY92 - alongside revised ISS. They form a solid combination, improving on either marker separately. Both models combined clearly identified more high-risk patients correctly, whilst also placing low risk patients into a more appropriate risk category. This was shown in the discovery set and was subsequently applied to an independent set, confirming the validity and usability of the SKY-RISS. Disclosures Kuiper: SkylineDx: Current Employment, Current equity holder in private company. Van Vliet:SkylineDx: Current Employment, Current equity holder in private company. Van Beers:SkylineDx: Ended employment in the past 24 months. Zweegman:Celgene: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding. Broijl:Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene/BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees. Sonneveld:Sanofi: Consultancy; Bristol-Myers Squibb: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria, Research Funding; Skyline Dx: Honoraria, Research Funding; Karyopharm: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria, Research Funding.

Prediction of Molecular Response of Chronic Phase CML Patients by the EUTOS Score: Results of the Randomized CML-Study IV,

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 3762-3762
Author(s):  
Susanne Saussele ◽  
Michael Lauseker ◽  
Verena Hoffmann ◽  
Ulrike Proetel ◽  
Benjamin Hanfstein ◽  
...  
Keyword(s):  
High Risk ◽  
Board Of Directors ◽  
Research Funding ◽  
Low Risk ◽  
Molecular Response ◽  
Intermediate Risk ◽  
Advisory Committees ◽  
High Risk Patients ◽  
Risk Patients ◽  
Eutos Score

Abstract Abstract 3762 Introduction: The EUTOS Score was developed and validated as a prognostic tool for the achievement of complete cytogenetic response (CCR) at 18 months for chronic phase (CP) CML patients under imatinib therapy. The score identifies high-risk patients not reaching CCR at 18 months with a positive predictive value of 34% and a specificity of 92% using only two variables, peripheral blood basophils and spleen size at diagnosis (Hasford et al. Blood 2011). We sought to evaluate the clinical impact of the EUTOS score to predict molecular response. Therefore, we analyzed the EUTOS score with patients from the German CML-Study IV, a randomized 5-arm trial (imatinib 400 mg vs. imatinib 800 mg vs. imatinib in combination with interferon alpha vs. imatinib in combination with araC vs. imatinib after interferon failure). Results: From July 2002 to December 2010, 1,502 patients with BCR-ABL positive CML in CP were randomized. 129 patients with imatinib after interferon alpha and 36 other patients had to be excluded (14 due to incorrect randomization or withdrawal of consent, 22 with missing baseline information). 1,337 patients were evaluable for overall and progression-free survival (OS and PFS), 1,252 for molecular responses. 749 of these patients were part of the score development sample. Therefore cytogenetic analyses are not described here. By EURO score, 36% of patients (n=475) were low risk, 51% (n=681) intermediate risk, and 12% (n=167) high risk. The EUTOS score was low risk in 88% (n=1163) and high risk in 12% (n=160). The high-risk patients differed between the two scores: EUTOS high-risk patients were classified according to EURO score in 12% as low (n=19), in 45% as intermediate (n=68) and in 43% as high risk (n=73). Patients with high, intermediate, and low risk EURO score achieved MMR in 22, 16, and 13 months and CMR4 (BCR-ABL <=0.01%) in 59, 41, and 34 months. P-values for low vs. intermediate risk groups were borderline only (0.03 for MMR and 0.04 for CMR4), whereas p-values for high vs. low/intermediate risk groups were for both molecular response levels <0.001. At 12 months the proportion of patients in MMR was 38%, 46%, 54% for high, intermediate, and low risk patients, respectively. Similar results were observed with the Sokal score. Patients with high risk EUTOS score achieved deep molecular responses (MMR and CMR4) significantly later than patients with low risk EUTOS score (MMR: median 21.0 vs. 14.8 months, p<0.001, Fig. 1a; CMR4: median 60.6 vs. 37.2 months, p<0.001, Fig. 1b). The proportions of patients achieving MMR at 12 months were significantly lower in the EUTOS high-risk group than in the EUTOS low-risk group (30.8% vs. 50.6%, p<0.001). OS after 5 years was 85% for high and 91% for low risk patients (p=n.s.), PFS was 85% and 90%, respectively. Conclusions: The EUTOS score clearly separates CML patients also according to MMR and CMR4 (MR4). The new EUTOS score should be used in future trials with tyrosine kinase inhibitors in CML. Disclosures: Neubauer: Novartis: Honoraria, Research Funding; Roche: Research Funding. Kneba:Hoffmann La Roche: Honoraria. Schnittger:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Hochhaus:Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Ariad: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. German CML Study Group:Deutsche Krebshilfe: Research Funding; Novartis: Research Funding; BMBF: Research Funding; EU: Research Funding; Roche: Research Funding; Essex: Research Funding.

Validation of the EMC92/SKY92 Signature in HOVON-87/Nmsg-18: Gene Expression Based Prognostication Is Applicable in Elderly Patients with Newly Diagnosed Multiple Myeloma

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 2967-2967
Author(s):  
Mark van Duin ◽  
Rowan Kuiper ◽  
Martin van Vliet ◽  
Annemiek Broijl ◽  
Leonie de Best ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Board Of Directors ◽  
Research Funding ◽  
Univariate Analysis ◽  
Low Risk ◽  
Advisory Committees ◽  
Hazard Ratios ◽  
Risk Patients ◽  
Standard Risk

Abstract Improved prognostication is required for multiple myeloma (MM). So far, marker development has been based on clinical trials with a study population predominantly younger than 65 years. However, the median age of newly diagnosed MM patients is 66 years old. Based on gene expression profiles of the HOVON-65/GMMG-HD4 dataset, we previously developed the EMC92 prognostic signature, consisting of 92 probe sets for improved prognostication in MM. The EMC92 was validated in the MRC-IX, TT2, TT3 and APEX datasets. These studies were mostly aimed at younger patients with a median age of 57 years. The EMC92 signature was subsequently developed for clinical use as part of the MMprofiler, and termed the SKY92 signature. To assess the validity of the SKY92 signature in older MM patients, we used the HOVON-87/NMSG-18 study, in which induction therapy with melphalan, prednisone and thalidomide, followed by thalidomide maintenance, was compared with melphalan, prednisone and lenalidomide, followed by lenalidomide maintenance (MPT-T vs. MPR-R). The median age of all patients included in this trial was 73 years, with 34% of patients 76 years or older. The median follow up of the patients still alive was 39 months. Of 143 patients both gene expression profiling and clinical data were available (median age 73; 30% ≥76; n=83 MPT-T; n=60 MPR-R). The MMprofiler was used to obtain SKY92 scores, classifying a patient as high risk or standard risk (MMprofiler- CE IVD assay, performed according to the manufacturers' instructions for use at the SkylineDx reference lab, Rotterdam, The Netherlands). The association between survival and the SKY92 signature was evaluated using Cox regression analysis. Kaplan-Meier curves were constructed for visualization. Using the SKY92 signature 22/143 patients were identified as high risk (15.4%). The median overall survival (OS) for high risk patients was 21 months, compared to 53 months for standard risk patients (hazard ratio (HR): 2.9 (95% confidence interval (CI): 1.6-5.4; p=5.6 x 10-4)). The median progression free survival (PFS) in the high risk and standard risk groups were 12 months and 23 months, respectively (HR: 2.2 (95% CI: 1.4-3.7; p=1.2 x 10-3)). In this subset of 143 patients, deletion of 17p (del17p) and gain of 1q (gain1q) were also adversely associated with OS in a univariate analysis. Including SKY92, del(17p) and gain(1q) in a multivariate model demonstrated that SKY92 and del(17p) remained significantly associated with OS (subset of 143 (n=101) with all data known; Table 1). We previously developed the combination of ISS with SKY92: low risk (ISS I-SKY92 standard risk (SR)), intermediate-low (ISS II-SKY92 SR), intermediate-high (ISS III-SKY92 SR) and high risk (ISS I-III, SKY92 high risk; Kuiper et al., ASH 2014, #3358). The Cox model for this combined marker has a p-value for the likelihood ratio test of p=3 x 10-3 for OS (Figure 2) and p=0.016 for PFS. In conclusion, the SKY92 signature (MMprofiler) is a useful prognostic marker to identify a high-risk subgroup in the elderly population. Figure 1. Performance of the SKY92 signature in the HOVON-87/NMSG-18 study. Red line indicates high risk patients (n=22), blue line indicates standard risk patients (n=121). PFS (A); OS (B). Figure 1. Performance of the SKY92 signature in the HOVON-87/NMSG-18 study. Red line indicates high risk patients (n=22), blue line indicates standard risk patients (n=121). PFS (A); OS (B). Table 1. SKY92 in relation to FISH markers in the HOVON-87/NMSG-18 (Hazard ratios (HR), 95% confidence intervals (CI) and p-values (2-sided; p) for Cox proportional hazards analysis). The multivariate analysis (bottom) was performed using the markers significant in the univariate analysis (top). Bold: p<0.05, pos: positive, neg: negative and na: not available. Table 1. SKY92 in relation to FISH markers in the HOVON-87/NMSG-18 (Hazard ratios (HR), 95% confidence intervals (CI) and p-values (2-sided; p) for Cox proportional hazards analysis). The multivariate analysis (bottom) was performed using the markers significant in the univariate analysis (top). Bold: p<0.05, pos: positive, neg: negative and na: not available. Figure 2. Combining ISS with SKY92. Groups are defined in the text. Hazard ratios of the individual groups are given relative to the low risk group. Figure 2. Combining ISS with SKY92. Groups are defined in the text. Hazard ratios of the individual groups are given relative to the low risk group. Disclosures Kuiper: SkylineDx: Employment. van Vliet:SkylineDx: Employment. Broijl:Celgene: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees. de Best:SkylineDx: Employment. van Beers:SkylineDx: Employment. Bosman:SkylineDx: Employment. Dumee:SkylineDx: Employment. van den Bosch:SkylineDx: Employment. Waage:Amgen: Research Funding; Celgene: Research Funding; Janssen: Research Funding. Zweegman:Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding. 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.

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

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 5390-5390
Author(s):  
Catherine M Claussen ◽  
Tammy Chuang ◽  
Jatin Shah ◽  
Hans Lee ◽  
Nina Shah ◽  
...  
Keyword(s):  
High Risk ◽  
Board Of Directors ◽  
Clinical Outcomes ◽  
Expression Profiling ◽  
Research Funding ◽  
Complete Response ◽  
Low Risk ◽  
Newly Diagnosed ◽  
Advisory Committees ◽  
Risk Patients

Abstract 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. Figure 1. Figure 1. Disclosures Orlowski: 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.

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