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 ◽  
...  

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.

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 1000-1000 ◽  
Author(s):  
Robert James Hayashi ◽  
Stuart S. Winter ◽  
Kimberly P. Dunsmore ◽  
Meenakshi Devidas ◽  
Brent Wood ◽  
...  

Abstract Background: COG AALL0434 evaluated the safety and efficacy of a multi agent chemotherapy backbone containing Capizzi based methotrexate/pegaspargase in newly diagnosed T-LL patients. High-risk patients were randomized to receive the COG augmented BFM (ABFM) regimen with or without Nelarabine. This was part of a larger trial including T-Lymphoblastic Leukemia (T-ALL) patients featuring a 2 x 2 pseudo-factorial randomization at the end of induction using the COG ABFM regimen with a randomization of Capizzi MTX/pegaspargase (C-MTX) verses high dose MTX and a randomization with or without Nelarabine (Nel). Methods: AALL0434 enrolled 277 patients with T-LL (2010-2014). Patients were assigned to two risk categories based upon the degree of bone marrow involvement at diagnosis: (≥1%, High Risk, <1% Standard Risk), and the ability to achieve at least a partial response at the end of induction. Patients with prior steroid treatment were assigned to the high risk group. Both groups were treated using the ABFM C-MTX regimen. High-risk patients were randomized to receive or not receive six, 5-day courses of Nel 650 mg/m2/day. No patients received prophylactic cranial radiation and CNS3 patients were ineligible. Response criteria included, Complete Response (CR): disappearance, Complete Response unconfirmed (CRu): >75% reduction, Partial Response (PR): >50% reduction, of all measurable disease, all without new lesions. Results: At the end of induction, 98.9% of the evaluable patients achieved at least a partial response (30.7% CR, 34.7% CRu, 33.5% PR). For all T-LL patients, the 4-year event free survival (EFS) and overall survival (OS) were 87.0 +/- 2.1% and 90.0+/-1.8%. The 4-year Disease Free Survival (DFS) from end of induction was 90.0+/- 2.1%. There was no difference in DFS observed between the high risk and standard risk groups, (p=0.25) or by treatment regimen (p=0.31). Nel did not show an advantage for high-risk T-LL patients, with 4-year DFS 85.0 +/- 5.6% with Nel (N=60) vs 89.0 +/- 4.7% without Nel (N=58) (p=0.28). Neither stage nor tumor response at the end of four weeks of induction therapy resulted in differences in EFS (p= 0.34 and p= 0.22, respectively). Minimal detectable disease (MDD) of the bone marrow at diagnosis (<0.1%, 0.1-0.99%, >1.0%), used to establish the risk assignment for this trial, failed to demonstrate thresholds at diagnosis that resulted in differences in EFS (p=0.27). Relapse involving the CNS only occurred in 4 patients (1.4%). Overall toxicity and neurotoxicity was acceptable and not significantly different than that experienced from the ALL cohort. There was one observed second malignancy and 5 deaths not from progressive disease. Conclusion: COG AALL0434 produced excellent outcomes in one of the largest trials ever conducted for patients with newly diagnosed T-LL. The COG ABFM regimen with C-MTX provides excellent disease control regardless of stage, or the degree of disease involvement of the bone marrow at diagnosis. Nelarabine did not show an improvement in the outcome, although the trial was underpowered to address this specific question. Disclosures Teachey: Amgen: Consultancy; La Roche: Consultancy. Bollard:Torque: Honoraria, Membership on an entity's Board of Directors or advisory committees; Cellectis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Neximmune: Honoraria, Membership on an entity's Board of Directors or advisory committees.


Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 2872-2872 ◽  
Author(s):  
Farheen Mir ◽  
Andrew Grigg ◽  
Michael Herold ◽  
Wolfgang Hiddemann ◽  
Robert Marcus ◽  
...  

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.


Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 3092-3092 ◽  
Author(s):  
Rowan Kuiper ◽  
Martin van Vliet ◽  
Annemiek Broyl ◽  
Yvonne de Knegt ◽  
Bronno van der Holt ◽  
...  

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.


Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 4728-4728 ◽  
Author(s):  
Arabesque Parker ◽  
Erica A. Peterson ◽  
Agnes Y. Y. Lee ◽  
Carine de Wit ◽  
Marc Carrier ◽  
...  

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.


Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 3851-3851
Author(s):  
Jorge Cortes ◽  
Hagop M. Kantarjian ◽  
Tapan M. Kadia ◽  
Guillermo Garcia-Manero ◽  
Elias Jabbour ◽  
...  

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.


Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 3517-3517
Author(s):  
Allison C. Rosenthal ◽  
Colleen Ramsower ◽  
Raphael Mwangi ◽  
Matthew J. Maurer ◽  
Diego Villa ◽  
...  

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 &lt;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 &lt;10% ( Ki67 ≥ 30% vs. Ki67 &lt; 10%, HR 0.87, 95% CI: 0.12-6.41; p=0.892, Ki67 ≥ 10%-29% vs. Ki67 &lt; 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 &lt;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 &gt;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.


Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 451-451 ◽  
Author(s):  
Marc Rodger ◽  
Michael J. Kovacs ◽  
Susan Kahn ◽  
Phil Wells ◽  
David Anderson ◽  
...  

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.


Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 3069-3069 ◽  
Author(s):  
Antonio Palumbo ◽  
Federica Cavallo ◽  
Izhar Hardan ◽  
Barbara Lupo ◽  
Valter Redoglia ◽  
...  

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.


Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 2982-2982
Author(s):  
Erming Tian ◽  
Joshua Epstein ◽  
Pingping Qu ◽  
Christoph Heuck ◽  
Frits van Rhee ◽  
...  

Abstract Introduction In multiple myeloma (MM), deletion of chromosome 17 p13 (del17p) is a poor prognostic feature. The percentage of cells carrying an abnormality has been reported to be important with thresholds of 20% being taken generally but thresholds as high as 60% being suggested more recently. We have reported previously in the Total Therapy (TT)-2 trial (NCT00083551) for newly diagnosed (ND) MM that del17p is an adverse prognostic factor (Blood 112: 4235). The TT3 trial (NCT00081939) incorporated Brtezomib into tandem Melphalan-based autotransplants with DT-PACE for induction/consolidation and Thalidomide and Dexamethasone for maintenance to treat patients with newly diagnosed MM. In more recent iterations of these trials following the introduction of novel agents in induction and during maintenance the impact of carrying del17p has not been studied. In particular we have stratified patients into low- or high-risk molecular subgroups based on the GEP-70 (TT4 protocol [NCT00734877] or TT5 protocol [NCT00869232], respectively). We have used interphase FISH (iFISH) to detect the presence of del17p in baseline bone marrow samples. Method The iFISH slides were prepared with bone marrow aspirates after removing erythrocytes. A specific TP53 probe at chromosome 17 arm p13 combined with a control probe for the ERBB3 locus (HER2, 17q12), in different colors, were hybridized to bone marrow cells. Myeloma PCs were identified by restricted Kappa or Lambda immunoglobulin light-chain staining. We investigated role of 20% cutoffs per ≥100 tumor cells for significant deletion of the TP53 probe. Kaplan-Meier analysis was used to estimate the distributions of overall survival (OS) and progression-free survival (PFS) during the follow-ups. OS was calculated from registration until the date of decease. PFS was similarly calculated, but also incorporated progressive disease as an event. Results We examined 709 baseline samples from TT3, 4, and 5 trials with the two probes at chromosome 17. Overall, 66 of 709 patients (9.3%) had deletion of TP53 locus, including 44 of the 591 (7.5%) of low-risk patients and 20 of the 118 (17.0%) high-risk patients (Table). The range of TP53-deleted cells among newly diagnosed patients is 20-99% (median=75%) overall; 35-100% (median=62%) in TT3-low-risk; 30-97% (median=80%) in TT3-high-risk; 21-99% (median=76%) in TT4; and 20-97% (median=81%) in TT5. Deletion of TP53 was associated with significant shorter OS and PFS in HR patients treated on TT3. The 3 year estimated OS of patients for TT3-HR with del17p was 33% compared with 56% for TT3-LR with del17p, and PFS of patients for TT3-HR with del17p was 25% compared with 51% for TT3-LR with del17p (Figure). The comparison of TT4 to TT5 continued showing short OS in HR patients treated on TT5. The 3 year estimated OS of patients for HRMM with del17p was 17% compared with 75% for TT5 patients without deletion (p=0.0008). But, del17p was neutral in LR patients treated on TT4 (Figure). Conclusion Since the introduction of novel agents during various stages of the disease and a focus on HRMM and LRMM defined by GEP70 we show that while TP53 deletion is an adverse prognostic factor for patients with HRMM it is no longer prognostically relevant in LRMM. Table 1. Patients with iFISH results GEP-70 riskLow ≤0.66 High >0.66 Deletion TP53 in 20-59% PCs (n/N [%]) Deletion TP53 in ≥60% PCs (n/N, [%]) Total TT3 (N=329) Low=256 9/329, [2.7%] 9/329, [2.7%] 18/329, [5.5%] High=73 3/329, [0.9%] 9/329, [2.7%] 12/329, [3.7%] TT4 (N=313) Low=313 5/313, [1.6%] 21/313, [6.7%] 26/313, [8.3%] High=0 0 0 0 TT5 (N=67) Low=22 2/67, [3.0%] 0 2/67, [3.0%] High=45 0 8/67, [11.9%] 8/67, [11.9%] Sum (N=709) Low=591 (83.4%) 14/709, [2.0%] 30/709, [4.2%] High=118 (16.6%) 3/709, [0.4%] 17/709, [2.4%] 66/709 (9.3%) Figure 1. Figure 1. Disclosures Tian: University of Arkansas for Medical Sciecnes: Employment. Epstein:University of Arkansas for Medical Sciences: Employment. Qu:Cancer Research and Biostatistics: Employment. Heuck:Millenium: Other: Advisory Board; Janssen: Other: Advisory Board; Celgene: Consultancy; Foundation Medicine: Honoraria; University of Arkansas for Medical Sciences: Employment. van Rhee:University of Arkansa for Medical Sciences: Employment. Zangari:University of Arkansas for Medical Sciences: Employment; Millennium: Research Funding; Onyx: Research Funding; Novartis: Research Funding. Hoering:Cancer Research and Biostatistics: Employment. Sawyer:University of Arkansas for Medical Sciences: Employment. Barlogie:University of Arkansas for Medical Sciences: Employment. Morgan:Weismann Institute: Honoraria; CancerNet: Honoraria; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; MMRF: Honoraria; University of Arkansas for Medical Sciences: Employment; Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees.


Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 3285-3285
Author(s):  
Alessandro Lagana ◽  
Deepak Perumal ◽  
David Melnekoff ◽  
Ben Readhead ◽  
Brian Kidd ◽  
...  

Abstract High-risk Multiple Myeloma (MM) is characterized by unresponsiveness to multiple therapies, rapid disease progression and short overall survival, and may be significantly different from relapsed MM, where aggressiveness is usually a result of drug-resistance associated to clonal selection. Several gene expression-based signatures have been proposed in the past years, however the identification of high-risk patients at diagnosis still represents a challenge. Next generation high-throughput sequencing technologies have enabled a deeper insight into cancer genomes and transcriptomes at an unprecedented level of detail. MMRF CoMMpass is a longitudinal, prospective observational study, started in 2011, that aims to collect and analyze sequencing and clinical data from >1,000 MM patients at initial diagnosis and at relapse. CoMMpass is a real world observational study and, as such, reflects the therapeutic heterogeneity seen across patient populations and provides a unique opportunity to correlate molecular profiles, genomic alterations and clinical characteristics of MM with treatment outcome. Here we present a network approach to identify high-risk myeloma patients developed using next generation sequencing data from 450 patients in the IA7 release of CoMMpass. We generated MMNet, an integrated network model of newly diagnosed myeloma based on RNA-seq, Whole-Exome (WXS) and Whole-Genome (WGS) data correlated with clinical outcomes. MMNet consisted of 37 modules of coexpressed genes, that were further characterized by functional enrichment analysis and correlation with clinical traits and genomic alterations, i.e. somatic mutations and copy number alterations inferred from WGS and WXS data. A total of 89 progression/death events have been reported for the cohort within the second year since the beginning of the study. Cox regression analysis identified a module of co-expressed genes whose over-expression was significantly correlated with early relapse (<2yr) (HR 1.75, 95%CI = 1.169-2.614, p=0.005). The module was also associated to stage III R-ISS, high clonality (>4 clones) and high mutational burden, as well as higher percentage of plasma cells in both bone marrow and peripheral blood, which are traits associated with high-risk disease. Module expression was also up regulated in patients with mutations in TP53 and MAX, 13q deletion and 1q amplification. We further narrowed down the signature to 286 genes (the MMNet-286 signature) strongly correlated with time to Event Free Survival (EFS) (r = -0.81, p = 0). This gene-set was significantly enriched for several pathways including Cell Cycle, DNA repair and Homologous Recombination (q < 0.01). Cox regression analysis showed that the two clusters induced by MMNet-286 discriminated between lower and higher risk patients with respect to EFS (HR = 2.22, 95% CI = 1.505-3.295, p = 4.007e-5) (Fig. 1). The prognostic value of MMNet-286 was confirmed on two independent datasets: Broyl-2010 (HR = 1.76, 95% CI = 1.182-2.642, p = 0.005) and Shaughnessy-2006 (HR = 2.65, 95% CI = 1.746-4.031, p = 2.03e-6) (Fig. 2 and 3). The Broyl-2010 dataset consisted of 275 samples from newly diagnosed myeloma patients included in the HOVON65/GMMG-HD4 trial (GSE19784). The Shaughnessy-2006 dataset consisted of 559 samples from newly diagnosed patients pre-TT2 and -TT3 treatments (GSE2658). Comparison of MMNet-286 with previous high risk signatures and disease classes revealed an overlap of five genes with the UAMS-70 signature, twelve genes with the EMC-92 signature and fifteen genes with the set of up-regulated genes in the UAMS PR class, for which the coexpression module was enriched. In Conclusion, our results demonstrate the advantages of employing integrated network models to identify prognostic features based on next generation sequencing data from large cohort of patients. Applications of the MMNet-286 signature include the generation of a prognostic assay (i.e. NanoString) for the identification of high-risk patients. Future work will aim at validation of the signature in larger cohorts from CoMMpass and other studies. Figure 1 Kaplan-Meier curves of event free survival in the MMRF cohort stratified by the MMNet-286 signature. Figure 1. Kaplan-Meier curves of event free survival in the MMRF cohort stratified by the MMNet-286 signature. Figure 2 Kaplan-Meier curves of overall survival in the Broyl cohort stratified by the MMNet-286 signature. Figure 2. Kaplan-Meier curves of overall survival in the Broyl cohort stratified by the MMNet-286 signature. Figure 3 Kaplan-Meier curves of overall survival in the Shaughnessy cohort stratified by the MMNet-286 signature. Figure 3. Kaplan-Meier curves of overall survival in the Shaughnessy cohort stratified by the MMNet-286 signature. Disclosures Chari: Novartis: Consultancy, Research Funding; Array Biopharma: Consultancy, Research Funding; Pharmacyclics: Research Funding; Amgen Inc.: Honoraria, Research Funding; Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Takeda: Consultancy, Research Funding. Cho:Genentech Roche: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees, Research Funding; Agenus, Inc.: Research Funding; Ludwig Institute for Cancer Research: Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Research Funding. Barlogie:Signal Genetics: Patents & Royalties. Dudley:GlaxoSmithKline: Consultancy; Janssen Pharmaceuticals, Inc.: Consultancy; Ayasdi, Inc.: Equity Ownership; Ecoeos, Inc.: Equity Ownership; NuMedii, Inc.: Equity Ownership; Ontomics, Inc.: Equity Ownership; AstraZeneca: Speakers Bureau; NuMedii, Inc.: Patents & Royalties; Personalis: Patents & Royalties.


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