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

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.

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

Blood ◽

10.1182/blood-2020-139987 ◽

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 < 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 >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 < 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 < 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 < 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 <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.

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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 ◽

10.1182/blood-2018-99-114620 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 2872-2872 ◽

Cited By ~ 2

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.

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

Blood ◽

10.1182/blood.v122.21.3092.3092 ◽

2013 ◽

Vol 122 (21) ◽

pp. 3092-3092 ◽

Cited By ~ 3

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.

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

Blood ◽

10.1182/blood.v128.22.4728.4728 ◽

2016 ◽

Vol 128 (22) ◽

pp. 4728-4728 ◽

Cited By ~ 1

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.

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Value of Minimal Residual Disease (MRD) Monitoring Using Real-Time Quantitative PCR in Patients with Acute Promyelocytic Leukemia (APL) Treated with ATRA, ATO, +/- GO

Blood ◽

10.1182/blood-2019-124599 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 3851-3851

Author(s):

Jorge Cortes ◽

Hagop M. Kantarjian ◽

Tapan M. Kadia ◽

Guillermo Garcia-Manero ◽

Elias Jabbour ◽

...

Keyword(s):

High Risk ◽

Board Of Directors ◽

Real Time ◽

Quantitative Pcr ◽

Research Funding ◽

Newly Diagnosed ◽

Advisory Committees ◽

Real Time Quantitative Pcr ◽

Risk Patients ◽

Standard Risk

Background: The combination of all-trans-retinoic acid (ATRA) and arsenic trioxide (ATO) is superior to ATRA plus chemotherapy in the treatment of standard risk patients (pts) with newly diagnosed APL. MRD monitoring has been successfully utilized for the early identification of relapse. Qualitative PCR has been superseded with the more accurate real-time quantitative PCR (RQ-PCR) for MRD detection in APL. Methods: We reviewed pts with newly diagnosed APL treated at our institution on 3 consecutive prospective clinical trials, using the combination of ATRA and ATO, with or without gemtuzumab ozogamicin (GO). GO was given to High risk pts (WBC >10 × 109/L) and pts with rising WBC. Real-time quantitative RT-PCR (RQ-PCR) was used to measure PML-RARα in bone marrow (BM) and peripheral blood (PB) specimens. We sought to determine the value of MRD monitoring in patients with APL treated with this regimen. Results: A total of 223 pts with APL have been followed from July 2002 to March 2019 with a total of 2007 samples (1622 BM, 385 PB) analyzed with a median number of samples of 8 per pt (range, 1-43). Median follow up is 55.6 months (range, 1-198). MRD positivity decreased over time on therapy; 218 pts (98%) were MRD positive after induction, while only 2 pts (1%) were positive after the first cycle of consolidation. Eight pts (3.5%) had positive MRD (all ≤0.1) during consolidation or after completing treatment but became negative after repeated MRD testing and none of them relapsed. Overall, seven pts relapsed (5 with high risk disease and 2 with low risk) and The median time to relapse after achieving CR was 9.4 months (range, 7.9-79.5).The time to the first relapse was between 7.9-12.4 months except for the pt who relapsed after 79.5 months (low risk pt), Among the high risk pts, molecular relapse preceded hematological relapse by 3.7 weeks (range, 2.1-4.1). There was a correlation between quantitative PCR values on PB and BM samples obtained concomitantly (r2=0.67, p=0.048). Conclusions: MRD monitoring may be useful for early detection of relapse in pts with high risk APL within first year after completion of therapy. Late molecular relapse is very rare and does not justify universal monitoring especially in standard risk patients. These data support the lack of need for MRD monitoring after completion of consolidation in pts with standard risk APL treated with ATRA plus ATO. Table Disclosures Kantarjian: Novartis: Research Funding; Takeda: Honoraria; Agios: Honoraria, Research Funding; Ariad: Research Funding; Daiichi-Sankyo: Research Funding; Cyclacel: Research Funding; Actinium: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria, Research Funding; Immunogen: Research Funding; BMS: Research Funding; Astex: Research Funding; AbbVie: Honoraria, Research Funding; Amgen: Honoraria, Research Funding; Jazz Pharma: Research Funding. Kadia:Celgene: Research Funding; Jazz: Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Research Funding; Bioline RX: Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Consultancy, Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Membership on an entity's Board of Directors or advisory committees. Garcia-Manero:Merck: Research Funding; Amphivena: Consultancy, Research Funding; Helsinn: Research Funding; Novartis: Research Funding; AbbVie: Research Funding; Celgene: Consultancy, Research Funding; Astex: Consultancy, Research Funding; Onconova: Research Funding; H3 Biomedicine: Research Funding. Jabbour:BMS: Consultancy, Research Funding; Adaptive: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; AbbVie: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Cyclacel LTD: Research Funding; Takeda: Consultancy, Research Funding. Borthakur:Incyte: Research Funding; Merck: Research Funding; Strategia Therapeutics: Research Funding; Janssen: Research Funding; GSK: Research Funding; Agensys: Research Funding; Oncoceutics, Inc.: Research Funding; Argenx: Membership on an entity's Board of Directors or advisory committees; Novartis: Research Funding; BioTheryX: Membership on an entity's Board of Directors or advisory committees; AbbVie: Research Funding; Eli Lilly and Co.: Research Funding; BMS: Research Funding; Polaris: Research Funding; NKarta: Consultancy; FTC Therapeutics: Membership on an entity's Board of Directors or advisory committees; Xbiotech USA: Research Funding; Arvinas: Research Funding; PTC Therapeutics: Consultancy; Cantargia AB: Research Funding; Tetralogic Pharmaceuticals: Research Funding; Eisai: Research Funding; AstraZeneca: Research Funding; Cyclacel: Research Funding; BioLine Rx: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bayer Healthcare AG: Research Funding; Oncoceutics: Research Funding. Short:Takeda Oncology: Consultancy, Research Funding; AstraZeneca: Consultancy; Amgen: Honoraria. Alvarado:Jazz Pharmaceuticals: Research Funding; Abbott: Honoraria. Daver:Karyopharm: Consultancy, Research Funding; Abbvie: Consultancy, Research Funding; Genentech: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Servier: Research Funding; Daiichi Sankyo: Consultancy, Research Funding; Astellas: Consultancy; BMS: Consultancy, Research Funding; Immunogen: Consultancy, Research Funding; Forty-Seven: Consultancy; Agios: Consultancy; Hanmi Pharm Co., Ltd.: Research Funding; Celgene: Consultancy; Glycomimetics: Research Funding; Otsuka: Consultancy; NOHLA: Research Funding; Sunesis: Consultancy, Research Funding; Incyte: Consultancy, Research Funding; Jazz: Consultancy; Novartis: Consultancy, Research Funding. Cortes:Novartis: Consultancy, Honoraria, Research Funding; Merus: Consultancy, Honoraria, Research Funding; Forma Therapeutics: Consultancy, Honoraria, Research Funding; Jazz Pharmaceuticals: Consultancy, Research Funding; BiolineRx: Consultancy; Immunogen: Consultancy, Honoraria, Research Funding; Daiichi Sankyo: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy, Honoraria, Research Funding; Sun Pharma: Research Funding; Biopath Holdings: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Research Funding; Takeda: Consultancy, Research Funding; Astellas Pharma: Consultancy, Honoraria, Research Funding. Ravandi:Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Selvita: Research Funding; Xencor: Consultancy, Research Funding; Macrogenix: Consultancy, Research Funding; Menarini Ricerche: Research Funding; Cyclacel LTD: Research Funding.

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Clinical Validation of MCL35 in Mantle Cell Lymphoma Patients ≥65 Years Receiving Bendamustine-Rituximab

Blood ◽

10.1182/blood-2021-151275 ◽

2021 ◽

Vol 138 (Supplement 1) ◽

pp. 3517-3517

Author(s):

Allison C. Rosenthal ◽

Colleen Ramsower ◽

Raphael Mwangi ◽

Matthew J. Maurer ◽

Diego Villa ◽

...

Keyword(s):

Gene Expression ◽

High Risk ◽

Gene Expression Profiling ◽

Board Of Directors ◽

Expression Profiling ◽

Research Funding ◽

Advisory Board ◽

Low Risk ◽

Advisory Committees ◽

Scientific Advisory

Abstract BACKGROUND: Mantle cell lymphoma (MCL) is a B-cell non-Hodgkin lymphoma with variable clinical outcomes. Commonly used risk stratification tools (Ki67 IHC, MIPI) in newly diagnosed MCL are not frequently used when selecting therapy, resulting in treatment choice being dictated by age and co-morbidities rather than disease biology. The MCL35 risk score was developed as a more reliable measure of proliferation and has been shown to be prognostic and can risk stratify younger transplant eligible MCL patients into three groups with significantly different overall survival (OS; Scott et al. 2017; Holte et al. 2018) but has not been evaluated in older transplant ineligible patients. We report results evaluating the prognostic value of the MCL35 assay in older MCL patients (≥65) treated with frontline bendamustine/rituximab (BR). METHODS: Archived tissue samples from 119 patients age ≥65 years treated with BR from collaborating Lymphoma/Leukemia Molecular Profiling Project (LLMPP) sites and the LEO/MER cohort were collected and analyzed using the MCL35 assay and stratified into three distinct risk groups (low, standard, and high risk). Association between MCL35 proliferation scores and OS were estimated by the Kaplan-Meier method and hazard ratios were calculated. Associations between Ki67, s-MIPI, p53 IHC status, morphology and OS were also evaluated. RESULTS: The MCL35 assay was run on tissue samples from 119 patients. Median patient age was 74 (range 65-93) and 69.5% were male. Ki67 was <30% in 29 patients (24%) and ≥30% in 90 patients (76%). Simplified MIPI (s-MIPI) score was 0-3 in 21 patients (24%), 4-5 in 42 patients (48%) and ≥6 in 25 patients (28%). Thirty-one did not have sufficient data to calculate a s-MIPI score. MCL35 was low risk in 51 patients (43%), standard risk in 39 patients (33%) and high risk in 29 patients (24%). Eleven patients had blastic morphology, 7 had pleomorphic morphology and the remainder were classic morphology (n=56). Of 57 samples with p53 IHC staining 7 (12.3%) were positive. At a median follow up of 33.4 months, 82 patients were alive and 35 had died. Patients with high risk MCL35 score had inferior OS compared to low risk (HR 2.27, 95% CI: 1.03-5.00; p=0.042) while standard risk was not statistically significant compared to low risk (HR 0.87, 95% CI: 0.37-2.0; p=0.740)(Figure 1). Ki67 IHC using a cutoff of ≥ 30% and 10%-29% was not significantly associated with OS compared to Ki67 <10% ( Ki67 ≥ 30% vs. Ki67 < 10%, HR 0.87, 95% CI: 0.12-6.41; p=0.892, Ki67 ≥ 10%-29% vs. Ki67 < 10%, HR 0.32, 95% CI: 0.04-2.83; p=0.303), however high s-MIPI score (≥6) (s-MIPI ≥6 vs. s-MIPI 0-3, HR 3.86, 95% CI 1.20-12.5; p=0.024) and positive p53 IHC (HR: 9.51, 3.26-27.7; p <0.001) were both associated with poor OS. Eighteen cases were blastic/pleomorphic by morphology, 12 of which were in the high-risk group by MCL35, and this subset also had worse survival than classic MCL (p=0.0052). CONCLUSIONS: These results suggest high risk MCL35 score is a prognostic biomarker of poor OS in patients >65 with MCL treated with BR. Conversely, Ki67 was not significantly associated with OS in these patients. Additional clinical validation using a larger sample size from the E1411 study is planned. If similar results are found, the MCL35 assay in combination with s-MIPI and p53 status may have utility in stratifying patients into risk adapted treatment arms in future prospective clinical trial designs. Figure 1 Figure 1. Disclosures Maurer: BMS: Research Funding; Genentech: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding. Villa: Janssen: Honoraria; Gilead: Honoraria; AstraZeneca: Honoraria; AbbVie: Honoraria; Seattle Genetics: Honoraria; Celgene: Honoraria; Lundbeck: Honoraria; Roche: Honoraria; NanoString Technologies: Honoraria. Habermann: Seagen: Other: Data Monitoring Committee; Incyte: Other: Scientific Advisory Board; Tess Therapeutics: Other: Data Monitoring Committee; Morphosys: Other: Scientific Advisory Board; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Cohen: Janssen, Adicet, Astra Zeneca, Genentech, Aptitude Health, Cellectar, Kite/Gilead, Loxo, BeiGene, Adaptive: Consultancy; Genentech, BMS/Celgene, LAM, BioINvent, LOXO, Astra Zeneca, Novartis, M2Gen, Takeda: Research Funding. Hill: Celgene (BMS): Consultancy, Honoraria, Research Funding; Epizyme: Consultancy, Honoraria; Gentenech: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy, Honoraria; Kite, a Gilead Company: Consultancy, Honoraria, Other: Travel Support, Research Funding; Karyopharm: Consultancy, Honoraria, Research Funding; AstraZenica: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Research Funding; Beigene: Consultancy, Honoraria, Research Funding; Incyte/Morphysis: Consultancy, Honoraria, Research Funding. Raess: Scopio Labs: Research Funding. Scott: Celgene: Consultancy; NanoString Technologies: Patents & Royalties: Patent describing measuring the proliferation signature in MCL using gene expression profiling.; BC Cancer: Patents & Royalties: Patent describing assigning DLBCL COO by gene expression profiling--licensed to NanoString Technologies. Patent describing measuring the proliferation signature in MCL using gene expression profiling. ; Rich/Genentech: Research Funding; Janssen: Consultancy, Research Funding; Incyte: Consultancy; Abbvie: Consultancy; AstraZeneca: Consultancy. Rimsza: NanoString Technologies: Other: Fee-for-service contract.

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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 ◽

10.1182/blood.v114.22.451.451 ◽

2009 ◽

Vol 114 (22) ◽

pp. 451-451 ◽

Cited By ~ 3

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.

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Efficacy of Induction Thearapy with Lenalidomide, Bortezomib, and Dexamethasone (RVD) in 1000 Newly Diagnosed Multiple Myeloma (MM) Patients

Blood ◽

10.1182/blood-2018-99-119895 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 3294-3294 ◽

Cited By ~ 2

Author(s):

Nisha Joseph ◽

Vikas A. Gupta ◽

Craig C Hofmeister ◽

Charise Gleason ◽

Leonard Heffner ◽

...

Keyword(s):

High Risk ◽

Board Of Directors ◽

Induction Therapy ◽

Research Funding ◽

Response Rates ◽

Newly Diagnosed ◽

Advisory Committees ◽

Entire Cohort ◽

Risk Patients ◽

Standard Risk

Abstract Background : Lenalidomide, bortezomib and dexamethasone (RVD) has been shown to be a well-tolerated and efficacious induction regimen in newly diagnosed myeloma patients. Two large randomized phase III trials show an overall response rate (ORR) >95% (Durie et al, Attal et al) supporting this combination regimen. We have conducted a retrospective analysis utilizing our institutional data of 1000 patients treated with RVD induction therapy at the Winship Cancer Institute of Emory University. Methods: 1000 newly diagnosed MM patients were treated with RVD induction therapy [R - 25 mg/day (days 1-14), V - 1.3 mg/m2 (days 1, 4 8, 11) and D - 40 mg once/twice weekly as tolerated every 21 days] from January 1st 2005 until August 31st 2016. Dose-adjustments were made based on the treating physician's discretion and patient tolerability. Demographic and outcomes data for the patients were obtained from our IRB approved myeloma database and responses were evaluated per IMWG Uniform Response Criteria. Results: The median age of this cohort was 61 years (range 16-83). Other notable patient characteristics include: M/F 54.3%/45.6%; W/AA 56.4%/34%; ISS I and II/III 54%/17%; Isotype IgG/IgA/FLC 59.1%/19%/15.8%; standard risk/high risk 72%/28%. High risk disease was defined as the presence of t(4;14), t(14;16), del(17p), and/or complex karyotype. A total of 835 patients (83.5%) underwent autologous stem cell transplant (ASCT) upfront after attaining at least a partial response with induction therapy, and 165 patients (16.5%) were offered deferred transplant. Among the patients that opted for deferred transplant, 56 of these patients (33.9%) underwent ASCT at first relapse with a median time to transplant of 30 months (3-96). 755 (75.5%) of patients received risk-stratified maintenance therapy following transplant. Evaluation of responses to induction therapy for the entire cohort show an ORR 97.3% with ≥VGPR of 68% post-induction therapy. Response rates 100 days post-transplant show an ORR 98% with 30.7% of patients achieving a sCR. Response rates are summarized in table 1. Median PFS was 63 months for the entire cohort, and 72 months for standard risk patients (61.75-82.25) versus 37 months for the high-risk patients (30.84-43.16), p<0.001. Median OS has not been reached at median of 38 months follow up (Figure 1). Conclusions: This is the largest reported cohort of myeloma patients treated with RVD induction. These results illustrate both the activity of this induction regimen with impressive response rates and long-term outcomes in both standard and high risk patients. Disclosures Hofmeister: Adaptive biotechnologies: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees. Heffner:ADC Therapeutics: Research Funding; Kite Pharma: Research Funding; Genentech: Research Funding; Pharmacyclics: Research Funding. Boise:AstraZeneca: Honoraria; Abbvie: Consultancy. Kaufman:BMS: Consultancy; Karyopharm: Other: data monitoring committee; Abbvie: Consultancy; Janssen: Consultancy; Roche: Consultancy. Lonial:Amgen: Research Funding. Nooka:GSK: Consultancy, Membership on an entity's Board of Directors or advisory committees; Adaptive technologies: Consultancy, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees; Spectrum Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees; BMS: Consultancy, Membership on an entity's Board of Directors or advisory committees.

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Long-Term Carfilzomib for High-Risk Patients with Newly Diagnosed Multiple Myeloma: A Pooled Analysis of Two Phase 1/2 Studies

Blood ◽

10.1182/blood-2018-99-112075 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 3240-3240

Author(s):

Roberto Mina ◽

Alessandra Larocca ◽

Maria Teresa Petrucci ◽

Gianluca Gaidano ◽

Stelvio Ballanti ◽

...

Keyword(s):

High Risk ◽

Board Of Directors ◽

Research Funding ◽

Phase 1 ◽

Advisory Board ◽

Induction Phase ◽

Advisory Committees ◽

High Risk Patients ◽

Risk Patients ◽

Standard Risk

Abstract INTRODUCTION: High-risk cytogenetic abnormalities, such as del(17p), t(4;14), and/or t(14;16), are associated to an unfavorable prognosis. Several trials investigating current approved regimens have shown that high-risk multiple myeloma (MM) patients have shorter progression-free survival (PFS) and overall survival (OS) as compared to standard-risk patients. Carfilzomib, a second generation proteasome inhibitor, demonstrated to be able to improve the survival of high-risk MM patients in the relapse setting. Here we present a pooled analysis of two phase 1/2 studies to investigate the role of carfilzomib in high-risk, newly diagnosed (ND) MM patients. METHODS: Transplant ineligible patients with NDMM enrolled in the IST-CAR 561 and IST-CAR 506 studies were pooled together and analyzed. All patients received 9 28-day induction cycles of carfilzomib, either 70 mg/m2 once weekly (IST-CAR 561) or 36 mg/m2 twice weekly (IST-CAR 506), combined with weekly cyclophosphamide (300 mg/m2) and dexamethasone (40 mg) (CCyd). After the induction phase, patients proceeded to maintenance with single-agent carfilzomib until progressive disease or intolerable toxicity. The primary objective was to compare response to treatment, PFS, PFS-2 and OS in standard versus high-risk FISH, defined by the presence of del(17p), t(4;14), and/or t(14;16). A 15% cut-off point was used for detection of translocation [t(4;14) and t(14;16)] and 10% for detection of del(17p). RESULTS: 121 NDMM patients were enrolled in the IST-CAR 561 (n=63) and in the IST-CAR 506 (n=58) study. Cytogenetic data were available in 94 patients: 37 (31%) had high-risk chromosomal abnormalities by FISH, including 10% of patients with t(4;14), 3% with t(14;16) and 18% with del(17p), while 57 patients (47%) were classified as standard-risk. After the induction phase, no difference in terms of overall response rate (ORR; 86% vs. 92%; p=0.52) and at least near complete response (39% vs. 41%; p=1) was observed between standard and high-risk patients. After a median follow-up of 39 months, median PFS from enrollment was NR in standard-risk patients and 27.8 months in high-risk ones (HR: 0.76; p=0.38) (Figure 1); at 3 years, 52% and 43% of patients, respectively, were alive and free from progression. The PFS benefit for the comparison between standard and high-risk patients was more pronounced in patients who received once weekly carfilzomib at 70 mg/m2, (median: NR vs. 39.6 months; HR: 0.78, p=0.63) as compared to those treated with twice weekly carfilzomib at 36 mg/m2 (median: NR vs. 24.2 months; HR: 0.52, p=0.12). Median PFS-2 from enrollment was NR in standard-risk patients and 44.1 months in high-risk ones (HR: 0.66; p=0.26), without significant differences in the once weekly (median, NR vs. 39.6; p=0.27) and the twice weekly group (median; NR vs. 44.1; p=0.63). Median OS from enrollment was NR in standard-risk patients and 47.5 months in high-risk ones (HR:0.71; p=0.36) (Figure 1). In patients who received once weekly carfilzomib, median OS was NR and 47.5 months (HR:0.66, p=0.48) in standard and high-risk patients, respectively, while median OS in the twice weekly group was NR in standard-risk patients and 44.1 months (HR:0.73; p=0.55) in high-risk ones. CONCLUSION: In transplant ineligible patients with NDMM, carfilzomib combined with cyclophosphamide and dexamethasone as initial treatment mitigated the poor prognosis of high-risk FISH in terms of PFS, PFS-2 and OS. The median PFS of high-risk patients treated with CCyd compares favorably with those reported with current standard of care. As compared to twice weekly carfilzomib at 36 mg/m2, once weekly carfilzomib, at the dose of 70 mg/m2, confirmed to be effective in high-risk patients. These data support the use of carfilzomib for the treatment of high-risk NDMM patients. Figure 1. Figure 1. Disclosures Larocca: Janssen-Cilag: Honoraria; Celgene: Honoraria; Bristol-Myers Squibb: Honoraria; Amgen: Honoraria. Petrucci:Amgen: Honoraria, Other: Advisory Board; Takeda: Honoraria, Other: Advisory Board; Bristol-Myers Squibb: Honoraria, Other: Advisory Board; Janssen-Cilag: Honoraria, Other: Advisory Board; Celgene: Honoraria, Other: Advisory Board. Gaidano:AbbVie: Other: Advisory Board; Janssen: Other: Advisory Board, Speakers Bureau. Musto:Amgen: Honoraria; BMS: Honoraria; Takeda: Honoraria; Janssen: Honoraria; Celgene: Honoraria. Offidani:Janssen: Honoraria, Other: Advisory Board; Takeda: Honoraria, Other: Advisory Board; Amgen: Honoraria, Other: Advisory Board; Bristol-Myers Squibb: Honoraria, Other: Advisory Board; Celgene: Honoraria, Other: Advisory Board. Cavo:Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; GlaxoSmithKline: Honoraria, Membership on an entity's Board of Directors or advisory committees; AbbVie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Honoraria, Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees. Caravita di Toritto:Bristol-Myers Squibb: Honoraria, Other: Travel and Accomodation EMN; Amgen: Other: Advisory Board; Johnson & Johnson: Other: Advisory Board, Travel and Accomodation EHA; Celgene: Other: Advisory Board, Travel and Accomodation ASH, Research Funding; Takeda: Other: Advisory Board. Montefusco:Janssen: Other: Advisory Board; Amgen: Other: Advisory Board; Celgene: Other: Advisory Board. Palumbo:Takeda: Employment. Boccadoro:Bristol-Myers Squibb: Honoraria, Research Funding; AbbVie: Honoraria; Novartis: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Amgen: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Sanofi: Honoraria, Research Funding; Mundipharma: Research Funding. Bringhen:Celgene: Honoraria; Amgen: Honoraria, Other: Advisory Board; Janssen: Honoraria, Other: Advisory Board; Takeda: Consultancy; Bristol-Myers Squibb: Honoraria.

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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 ◽

10.1182/blood.v128.22.4323.4323 ◽

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.

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