Evolving Real-World Treatment Patterns in Patients with Newly-Diagnosed Multiple Myeloma (NDMM) in the United States (U.S.)

Background Multiple myeloma (MM), a malignant neoplasm of plasma cells in the bone marrow, accounts for up to 1.8% of all cancers in the U.S., most frequently affecting people 65-74 years old. A variety of therapies are available to manage MM, including stem cell transplantation (SCT), immunomodulatory drugs (IMiD), proteasome inhibitors (PI), monoclonal antibodies (mAB), and alkylating agents (alk). Given the heterogeneity of MM and the rapidly evolving therapeutic landscape, MM contemporaneous real-world treatment patterns are not well described. We examined the patient characteristics and first-line (LT1) treatment patterns in NDMM patients. Methods MM patients (≥18 years), diagnosed in April 30, 2015 - April 29, 2017 (early cohort) or in April 30, 2017 - April 30, 2019 (recent cohort), were followed retrospectively from MM diagnosis to last patient activity in the Flatiron Health database - a geographically-diverse, longitudinal electronic health record spanning over 280 community and academic cancer clinics in the U.S. LT1 regimens were described as: 1) containing an IMiD (thalidomide, lenalidomide [R], or pomalidomide), PI (bortezomib [V], carfilzomib, or ixazomib), alk (melphalan, cyclophosphamide [C], bendamustine), mAB (daratumumab, elotuzumab), or combinations of these; and 2) doublet/monotherapy (doublets-) vs. triplet or greater agent (triplets+) combinations. Treatment patterns were examined by SCT status and by cytogenetic risk (high: del17p, t(4;14) and/or t(14;16); standard: ≥1 cytogenetic tests without high cytogenetic risk) and age groups (<65, 65-74, ≥75). Duration of therapy (DOT) and time to next therapy (TTNT) were estimated using Kaplan-Meier methods in the early cohort with longer follow-up. Results Of 4,070 NDMM patients, 3,433 were non-SCT (nSCT: early cohort: n=1,736; recent cohort: n=1,697) and 637 had SCT (early cohort: n=407; recent cohort: n=230). In nSCT patients, mean age at diagnosis was 70 years (SD: 10); 46% were female; 36% had stage III (699/1916, among non-missing), and 15% (392/2574, among non-missing) had high risk MM (25% had unknown cytogenetics). SCT patients were younger at diagnosis (mean [SD]: 61 years [9]); 44% were female; 25% (117/470, in non-missing) had stage III, and 19% (102/547, in non-missing) had high risk MM (14% had unknown cytogenetics). Overall, proportions with known cytogenetic risk were similar within SCT status cohorts over time but were lower in the SCT group (nSCT early vs. recent cohort: 26% vs. 24% had unknown cytogenetics; and in SCT: 15% vs. 13%, respectively). In nSCT and SCT patients, respectively, most common regimens were VRd (d: dexamethasone; 44% and 58%), Rd (16% and 7%), Vd (13% and 1%), and VCd (12% and 4%). In nSCT patients, the use of VRd increased over time (37% [early cohort] to 51% [recent cohort]), while frontline therapy with Rd/Vd doublets (19% to 14%/16% to 9%) and with VCd (13% to 11%) decreased. In the nSCT recent cohort, VRd (51%) frontline therapy dominated, with a slightly higher proportion of patients in the high-risk group vs. standard and unknown risk receiving VRd (56% vs. 53% and 46%); use of doublet therapy with Rd/Vd was lower in the high risk (12%/5%) vs. standard risk group (14%/9%). Irrespective of age, VRd was the most common frontline regimen in the nSCT recent cohort, albeit its use was lower among patients 75+ years of age (43%) vs. younger patients (54% [<65 years] and 59% [65-74 years]); 75+ year old patients had a higher use of Rd/Vd doublets (19%/15%) vs. <65 (10%/5%) or 65-74 (10%/6%) years of age. Triplets+ were more commonly used than doublets- across all cohorts: 59% vs. 41% (nSCT early cohort); 74% vs. 26% (nSCT recent cohort); and 89% vs. 11% (SCT early cohort); 95% vs. 5% (SCT recent cohort). mAB use in the recent cohort was low: 1.4% nSCT and 2.2% SCT patients. In the nSCT early cohort, the median (95% CI) LT1 DOT was 10 months (9-11) and for TTNT was 14 months (13-16). Conclusions PI/IMiD treatment combinations were most commonly observed in both nSCT and SCT patients, with an increase in use from early (40%) to recent (56%) cohort in nSCT patients. Use of triplets, generally, is on the rise from early (60%) to recent cohorts (74%). LT1 TTNT was lower than has been shown in clinical trials. These findings indicate a notable change in treatment patterns over time in nSCT NDMM patients, highlighting the changing landscape of MM management. Disclosures Ailawadhi: Celgene: Consultancy; Takeda: Consultancy; Cellectar: Research Funding; Amgen: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Pharmacyclics: Research Funding. Romanus:Takeda: Employment. Cherepanov:Takeda: Employment. Yin:Takeda: Employment. Cheng:Takeda: Employment. Hari:Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria, Research Funding; BMS: Consultancy, Research Funding; Janssen: Consultancy, Honoraria; Kite: Consultancy, Honoraria; Amgen: Research Funding; Spectrum: Consultancy, Research Funding; Sanofi: Honoraria, Research Funding; Cell Vault: Equity Ownership; AbbVie: Consultancy, Honoraria.

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Genomic Profiling of Smoldering Multiple Myeloma Classifies Molecular Groups with Distinct Pathogenic Phenotypes and Clinical Outcomes

Blood ◽

10.1182/blood-2021-150767 ◽

2021 ◽

Vol 138 (Supplement 1) ◽

pp. 723-723

Author(s):

Shankara Anand ◽

Mark Bustoros ◽

Romanos Sklavenitis-Pistofidis ◽

Robert A. Redd ◽

Eileen M Boyle ◽

...

Keyword(s):

Multiple Myeloma ◽

High Risk ◽

Board Of Directors ◽

Research Funding ◽

Molecular Classification ◽

Intermediate Risk ◽

Advisory Committees ◽

Increased Risk ◽

Risk Of Progression ◽

Over Time

Abstract Introduction: Multiple Myeloma (MM) is an incurable plasma cell malignancy commonly preceded by the asymptomatic stage smoldering multiple myeloma (SMM). MM is characterized with significant genomic heterogeneity of chromosomal gains and losses (CNVs), translocations, and point mutations (SNVs); alterations that are also observed in SMM patients. However, current SMM risk models rely solely on clinical markers and do not accurately capture progression risk. While incorporating some genomic biomarkers improves prediction, using all MM genomic features to comprehensively stratify patients may increase risk stratification precision in SMM. Methods: We obtained a total of 214 patient samples at SMM diagnosis. We performed whole-exome sequencing on 166 tumors; of these, RNA sequencing was performed on 100. Targeted capture was done on 48 additional tumors. Upon binarization of DNA features, we performed consensus non-negative matrix factorization to identify distinct molecular clusters. We then trained a random forest classifier on translocations, SNVs, and CNVs. The predicted clinical outcomes for the molecular subtypes were further validated in an independent SMM cohort of 74 patients. Results: We identified six genomic subtypes, four with hyperdiploidy (>48 chromosomes, HMC, HKR, HNT, HNF) and two with IgH translocations (FMD, CND) (Table 1). In multivariate analysis accounting for IMWG (20-2-20) clinical risk stages, high-risk (HMC, FMD, HKR) and intermediate-risk (HNT, HNF) genetic subtypes were independent predictors of progression (Hazards ratio [HR]: 3.8 and 5.5, P = 0.016 and 0.001, respectively). The low-risk, CND subtype harboring translocation (11;14) was enriched for the previously defined CD-2 MM signature defined by the B cell markers CD20 and CD79A (FDR = 0.003 ), showed upregulation of CCND1, E2F1, and E2F7 (FDR = 0.01, 0.0004, 0.08), and was enriched for G2M checkpoint, heme metabolism, and monocyte cell signature (FDR = 0.003, 0.003, 0.003, respectively). The FMD subtype with IgH translocations (4;14) and (14;16) was enriched for P53, mTORC1, unfolded protein signaling pathways and plasmacytoid dendritic cell signatures (FDR = 0.01, 0.005, 0.008, respectively). The HKR tumors were enriched for inflammatory cytokine signaling, MYC target genes, T regulatory cell signature, and the MM proliferative (PR) signatures (FDR = 0.02, 0.03, 0.007, 0.02, respectively). The APOBEC mutational signature was enriched in HMC and FMD tumors (P = 0.005), while there was no statistical difference across subtypes in the AID signature. The median follow-up for the primary cohort is 7.1 years. Median TTP for patients in HMC, FMD, and HKR was 3.8, 2.6, and 2.2 years, respectively; TTP for HNT and HNF was 4.3 and 5.2, respectively, while it was 11 years in CND patients (P = 0.007). Moreover, by analyzing the changes in MM clinical biomarkers over time, we found that patients from high-risk subgroups had higher odds of developing evolving hemoglobin and monoclonal protein levels over time (P = 0.01 and 0.002, respectively); Moreover, the absolute increase in M-protein was significantly higher in patients from the high-risk genetic subtypes at one, two, and five years from diagnosis (P = 0.001, 0.03, and 0,01, respectively). Applying the classifier to the external cohort replicated our findings where intermediate and high-risk genetic subgroups conferred increased risk of progression to MM in multivariate analysis after accounting for IMWG staging (HR: 5.5 and 9.8, P = 0.04 and 0.005, respectively). Interestingly, within the intermediate-risk clinical group in the primary cohort, patients in the high-risk genetic subgroups had increased risk of progression (HR: 5.2, 95% CI 1.5 - 17.3, P = 0.007). In the validation cohort, these patients also had an increased risk of progression to MM (HR: 6.7, 95% CI 1.2 - 38.3, P = 0.03), indicating that molecular classification improves the clinical risk-stratification models. Conclusion: We identified and validated in an independent dataset six SMM molecular subgroups with distinct DNA alterations, transcriptional profiles, dysregulated pathways, and risks of progression to active MM. Our results underscore the importance of molecular classification in addition to clinical evaluation in better identifying high-risk SMM patients. Moreover, these subgroups may be used to identify tumor vulnerabilities and target them with precision medicine efforts. Figure 1 Figure 1. Disclosures Bustoros: Janssen, Bristol Myers Squibb: Honoraria, Speakers Bureau; Takeda: Consultancy, Honoraria. Casneuf: Janssen: Current Employment. Kastritis: Amgen: Consultancy, Honoraria, Research Funding; Takeda: Honoraria; Pfizer: Consultancy, Honoraria, Research Funding; Genesis Pharma: Honoraria; Janssen: Consultancy, Honoraria, Research Funding. Walker: Bristol Myers Squibb: Research Funding; Sanofi: Speakers Bureau. Davies: Takeda: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Roche: Consultancy, Honoraria; Janssen: Consultancy, Honoraria. Dimopoulos: Amgen: Honoraria; BMS: Honoraria; Takeda: Honoraria; Beigene: Honoraria; Janssen: Honoraria. Bergsagel: Genetech: Consultancy, Honoraria; Oncopeptides: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Patents & Royalties: human CRBN mouse; GSK: Consultancy, Honoraria; Celgene: Consultancy, Honoraria. Yong: BMS: Research Funding; Autolus: Research Funding; Takeda: Honoraria; Janssen: Honoraria, Research Funding; Sanofi: Honoraria, Research Funding; GSK: Honoraria; Amgen: Honoraria. Morgan: BMS: Membership on an entity's Board of Directors or advisory committees; Jansen: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees. Getz: IBM, Pharmacyclics: Research Funding; Scorpion Therapeutics: Consultancy, Current holder of individual stocks in a privately-held company, Membership on an entity's Board of Directors or advisory committees. Ghobrial: AbbVie, Adaptive, Aptitude Health, BMS, Cellectar, Curio Science, Genetch, Janssen, Janssen Central American and Caribbean, Karyopharm, Medscape, Oncopeptides, Sanofi, Takeda, The Binding Site, GNS, GSK: Consultancy.

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Real-World Treatment Patterns and Outcomes of Patients with Chronic Lymphocytic Leukemia (CLL) Receiving First-Line (1L) Therapy in the United States (US)

Blood ◽

10.1182/blood-2021-148472 ◽

2021 ◽

Vol 138 (Supplement 1) ◽

pp. 4086-4086

Author(s):

Anthony R. Mato ◽

Arliene Ravelo ◽

Tu My To ◽

Robert Schuldt ◽

Juliana M.L. Biondo

Keyword(s):

High Risk ◽

Research Funding ◽

Treatment Patterns ◽

Publicly Traded ◽

Treatment Regimens ◽

The Past ◽

Oral Agents ◽

The Us ◽

Testing Patterns ◽

Over Time

Abstract Background: There have been many advances in CLL treatments over the past decade, with a number of novel agents targeting molecular pathways within CLL cells receiving approval from the US Food and Drug Administration. Here, we assessed the evolution of molecular testing patterns, treatment patterns, and clinical outcomes over time in patients receiving 1L CLL treatment in a real-world US database. Methods: This was a retrospective cohort study using the Flatiron Health database, a longitudinal database comprising de-identified, patient-level, structured and unstructured data, curated via technology-enabled abstraction. During the study period, the de-identified data originated from approximately 280 cancer clinics (~800 sites of care) in the US. Patients aged 18 years and older who were diagnosed with CLL and initiated 1L treatment between December 2015 and December 2020 were selected. Participants who took part in a clinical trial in any line of therapy, or who had any other primary cancer diagnosis, were excluded. Baseline characteristics, including testing patterns, at initiation of 1L treatment were assessed using descriptive statistics. Treatment patterns and outcomes, such as time to next treatment or death (TTNTD), were analyzed. Kaplan-Meier analysis was used to estimate TTNTD. Results: Among 3654 patients with treatment-naive CLL who were selected from the de-identified database, the mean age at 1L treatment initiation was 70 years (range, 29-85); 64.3% of patients were male; 72.1% were White, 8.2% Black, 3.9% Hispanic/Latino, 1.0% Asian, and 14.9% were of other ethnicity/race. Approximately one-third (34.7%) of patients had Rai stage 0-I disease, 6.9% had stage II, 6.3% stage III, 11.5% stage IV, and 40.6% had undocumented Rai stage. Testing patterns: The majority of identified patients (3202/3654; 87.6%) had undergone cytogenetic testing, fluorescence in situ hybridization, or IGHV mutation testing. Compared with 2015-2016, testing rates were higher in 2019-2020 for chromosome 17p deletion (del(17p); 36.1% vs 45.7%, respectively; p<0.001) and for IGHV mutation status (84.7% vs 89.2%, respectively; p=0.003). Overall, 11.0% of patients had del(17p). Of those tested for IGHV (1472/3654; 40.3%), 58.3% had unmutated IGHV. Treatment patterns: The 10 most commonly used 1L CLL treatments, which overall represented 91.8% of all 1L treatments, and their evolution over time, are reported in Table 1. Of the patients receiving these top 10 1L treatment regimens overall, 45.7% received regimens including novel targeted oral agents, 33.4% received chemo-immunotherapy (CIT), and 19.7% received anti-CD20 monotherapy. Evaluation of each 2-year period shows that treatment patterns for the top 10 1L treatment regimens shifted, with use of novel targeted oral agents increasing from 27.1% (2015-2016) to 63.8% (2019-2020) (p<0.001), while use of CIT and chemotherapy decreased over time (Table 2). Approximately 30.0% (1088/3654) of 1L-treated patients went on to receive second-line treatments. Outcomes: Median TTNTD was 34.4 months for all patients receiving 1L CLL treatment, and 36.5 months for patients who received the 10 most common 1L treatments across the 6-year study period (n=3360). Median TTNTD was 47.0 months for patients who received novel targeted oral agents and 41.5 months for patients who received CIT (unadjusted p=0.16). When evaluating outcomes in patients with high-risk cytogenetics, median TTNTD was 29.1 months for patients with del(17p) and 37.2 months for those with unmutated IGHV, but was longer in those patients who received treatment with novel targeted oral agents (median TTNTD of 43.9 and 46.7 months, respectively; Table 3). Conclusions: This analysis provides the current state of 1L CLL testing and treatment patterns and outcomes in the US from 2015 to 2020. As expected, the use of novel targeted oral agents increased over time, with a corresponding increase in TTNTD. Clinical outcomes were improved in patients receiving novel targeted oral agents, both overall and in high-risk subgroups. Following on from this, a comparative study of TTNTD for novel oral agents versus CIT, and analyses of outcomes of different sequencing of therapies, will be conducted. Figure 1 Figure 1. Disclosures Mato: Nurix: Research Funding; Johnson and Johnson: Consultancy, Research Funding; AbbVie: Consultancy, Research Funding; Acerta/AstraZeneca: Consultancy, Research Funding; DTRM BioPharma: Consultancy, Research Funding; Pharmacyclics LLC, an AbbVie Company: Consultancy, Research Funding; Adaptive Biotechnologies: Consultancy, Research Funding; BeiGene: Consultancy, Research Funding; MSKCC: Current Employment; Sunesis: Consultancy, Research Funding; AstraZeneca: Consultancy; TG Therapeutics: Consultancy, Other: DSMB, Research Funding; Genmab: Research Funding; LOXO: Consultancy, Research Funding; Genentech: Consultancy, Research Funding; Janssen: Consultancy, Research Funding. Ravelo: Genentech, Inc.: Current Employment; Roche Holdings: Current equity holder in publicly-traded company, Current holder of stock options in a privately-held company. To: Genentech, Inc.: Current Employment; F. Hoffmann-La Roche Ltd: Current equity holder in publicly-traded company, Divested equity in a private or publicly-traded company in the past 24 months. Schuldt: Genentech, Inc.: Current Employment; F. Hoffmann-La Roche Ltd: Current equity holder in publicly-traded company; Johnson & Johnson: Divested equity in a private or publicly-traded company in the past 24 months. Biondo: Genentech, Inc.: Current Employment; Roche: Current holder of individual stocks in a privately-held company.

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Global Expression Changes of Malignant Plasma Cells over Time Reveals the Evolutionary Development of Signatures of Aggressive Clinical Behavior

Blood ◽

10.1182/blood-2018-99-119113 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 4457-4457

Author(s):

Eileen M Boyle ◽

Adam Rosenthal ◽

Yan Wang ◽

Phil Farmer ◽

Michael W Rutherford ◽

...

Keyword(s):

Multiple Myeloma ◽

High Risk ◽

Board Of Directors ◽

Research Funding ◽

Plasma Cells ◽

Adverse Outcome ◽

Patient Specific ◽

Advisory Committees ◽

Over Time

Abstract Introduction: Clustering of gene expression signatures at diagnosis has identified a number of distinct disease groups that correlate with outcome in multiple myeloma (MM). Some of these are defined by an etiologic genetic event whereas others, such as the proliferation cluster (PR) and GEP70 risk relate to acquired clinical behaviors regardless of the underlying background. The PR cluster has a number of important features, including markers of proliferation, and has been associated with an adverse outcome. This logic led us to study how gene expression patterns change over time with the aim of gaining insight into acquired features that could be targeted therapeutically or be used to predict outcome. Methods: We followed 784 newly diagnosed MM patients from the Total Therapy trials over a median of 9.5 years for whom repeated GEP of CD138+ plasma cells using Affymetrix U133 Plus 2.0 plus arrays were obtained. Raw data were MAS5 normalized and GEP70-based high-risk (HR) scores, translocation classification (TC) and molecular cluster classification were derived, as previously reported. Results: At diagnosis, 85.9% percent of patients (666/784) were identified as low-risk (LR). Among them, 23.1% (154/666) went on to develop HR status (defined by a GEP70 score > 0.66) at least once after initial diagnosis. Among the non-PR cases, 28.5% (193/677) were seen to develop a PR phenotype at some point during follow-up. Similarly, among the PR patients (n=107), we observed that 43.1% (25/58) identified as LR by GEP70 at presentation eventually develop HR status at least once during follow-up. We further analyzed 147 patients with paired diagnosis and relapse samples. Seventeen percent of patients (25/147) were PR at diagnosis. Most patients were from favorable TC prognostic groups [80% D1-D2, 8% t(11;14), 8% t(4;14) and 4% t(14;20)]. Seventy-six percent of PR patients remained PR at relapse (19/25) whereas 23% switched cluster in accordance to their translocation group. Fifteen percent of patients (22/147) became PR at relapse. They originated from four clusters and three TC groups [77% from the D1-D2, 14% t(4;14) and 9% from the t(11;14)]. Overall-survival from the time of relapse was inferior for patients categorized as PR at relapse compared to other subgroups (p< 0.0001); among PR patients at relapse, there was no difference in outcome between patients classified as PR or non-PR at diagnosis (p= 0.74). When looking at GEP70 defined risk scores, the incidence of HR status rose from 23% to 39% between diagnosis and relapse with a significant increase in mean GEP70 scores using paired t-test (p<0.0001). Patients identified as HR by GEP70 at relapse had an inferior post-relapse outcome compared to patients identified as LR (p< 0.0001); there was no difference in the outcome of patients identified as HR at relapse depending on their risk status at diagnosis (p = 0.10). Discussion: Following the introduction of therapeutic regimens aimed at maximizing response, long term survival in MM has improved. This also led to an apparent increase in the development of more aggressive disease patterns at relapse including extra-medullary disease and plasma cell leukemia. Here we show, that HR features both in terms of PR and GEP70 risk status, develop as a variable over time. At relapse, most acquired HR cases originate from standard-risk presentation cases, suggesting selective pressure for HR features. Moreover, we show that the detection of such behaviors is associated with an adverse outcome from the time of relapse. These data also suggest that repeating GEP during follow-up adds precision to better comprehend individual risk and may help identify patient specific therapeutic strategies. Indeed, understanding how these patterns develop, which genes are implicated, and their impact on the immune microenvironment should allow us to effectively utilize a wide array of treatment approaches ranging from immune-therapies to novel cell-cycle targeting agents to specifically address this type of aggressive behavior. Conclusion: The acquisition of high risk patterns captured by GEP70 risk and PR status is an ongoing process from initial diagnosis. Such high risk prognostic features have an adverse outcome from the time of development. Repeating GEP during follow-up may therefore help better predict outcome and identify patient specific therapeutic strategies. Disclosures Boyle: Janssen: Honoraria, Other: travel grants; Takeda: Consultancy, Honoraria; Gilead: Honoraria, Other: travel grants; Abbvie: Honoraria; Celgene: Honoraria, Other: travel grants; La Fondation de Frace: Research Funding; Amgen: Honoraria, Other: travel grants. Dumontet:Janssen: Honoraria; Roche: Research Funding; Merck: Consultancy, Membership on an entity's Board of Directors or advisory committees; Sanofi: Honoraria. Facon:Celgene: Honoraria, Research Funding; Janssen: Honoraria, Research Funding. Barlogie:Celgene: Consultancy, Research Funding; Multiple Myeloma Research Foundation: Other: travel stipend; European School of Haematology- International Conference on Multiple Myeloma: Other: travel stipend; Dana Farber Cancer Institute: Other: travel stipend; Millenium: Consultancy, Research Funding; ComtecMed- World Congress on Controversies in Hematology: Other: travel stipend; Myeloma Health, LLC: Patents & Royalties: : Co-inventor of patents and patent applications related to use of GEP in cancer medicine licensed to Myeloma Health, LLC; International Workshop on Waldenström's Macroglobulinemia: Other: travel stipend. Davies:TRM Oncology: Honoraria; Janssen: Consultancy, Honoraria; ASH: Honoraria; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Abbvie: Consultancy; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; MMRF: Honoraria; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees. Morgan:Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Janssen: Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria.

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A Novel Early Relapse Prediction Score Based on Age, ISS and Disease Status at the Time of Transplant in Patients with Newly Diagnosed Multiple Myeloma. a Study of the EBMT Chronic Malignancies Working Party

Blood ◽

10.1182/blood-2021-147001 ◽

2021 ◽

Vol 138 (Supplement 1) ◽

pp. 3937-3937

Author(s):

Meral Beksac ◽

Simona Iacobelli ◽

Linda Koster ◽

Didier Blaise ◽

Jan J. Cornelissen ◽

...

Keyword(s):

Multiple Myeloma ◽

Bone Marrow ◽

High Risk ◽

Scoring System ◽

Research Funding ◽

Risk Group ◽

Disease Status ◽

Early Relapse ◽

Bone Marrow Plasma ◽

Additional Point

Abstract Rationale and Aim: In patients with Myeloma, early relapse following Autologous Haematopoietic Cell Transplantation (Auto-HCT) is a poor prognostic marker. Two groups have published scoring systems to predict early relapse. The CIBMTR score is based on cytogenetics, the bone marrow plasma cell percentage at the time of Auto-HCT and serum albumin. The GIMEMA Simplified early relapse in multiple myeloma (S-ERMM) score is a cumulative score based on a raised serum lactate dehydrogenase (LDH), t(4;14), del17p, low albumin, bone marrow plasma percentage >60%, and lambda light chain. The aim of the current study was to develop a scoring system to predict early relapse post-Auto-HSCT-1 using readily available variables. Study design and statistics: Within the EBMT database, there were 8,206 patients meeting the following eligibility criteria: First auto transplant 2014-2019, Known sex, ISS at diagnosis, cytogenetics analysis at diagnosis, disease status at Auto-HCT, Interval diagnosis-Auto-HCT > 1 month and <= 12 months, conditioning with Melphalan 200 mg/m2 and known information on relapse; tandem auto-allo patients were excluded. The analysis consisted of two steps: (1) Training: modeling based on 4,389 patients (611 events for PFS12) transplanted between 2014 and 2017, with internal validation carried out by bootstrapping; and (2) Testing: the models obtained were applied to 3,817 patients (346 events for PFS12) transplanted in 2018 and 2019 for external validation. The characteristics of the two cohorts are first reported separately and then together (Table 1). Possible adjustment factors analyzed for the prognostic model included Age at Auto-HCT, Known sex, ISS at diagnosis, disease status at Auto-HCT, and time from diagnosis to Auto-HCT. Complete cytogenetic information was not available at the time of this analysis and will be included in the later analysis. The shape of the effect of age and of time from diagnosis to Auto-HCT was investigated both by the analysis of residuals and by applying boot-strap backward selection among different alternatives. The final results were confirmed in a robustness analysis excluding patients undergoing tandem Auto-HCT. Results: Comparison of the training and validation cohorts revealed no relevant differences (Table 1). Importantly, OS and PFS of both cohorts were overlapping with the probability of PFS at 12 months being 83.3% and 86.8%, respectively. The cumulative incidence of relapse at 12 month was 15.7% and 12.1%, respectively. Among patients who relapsed early, this occurred at a median of 6.64 months (0.56-11.99) in the first cohort, and at 5.85 months (0.1- 11.99) in the second cohort. The final model included (1) disease status at Auto-HCT, (2) age at Auto-HCT, and (3) ISS at diagnosis. Considering the order of magnitude of the coefficients, the points attributed in the risk score were: 0 for CR or VGPR; 1 for PR or SD/MR; 3 for Rel/Prog; 0 / 1/ 2 respectively for ISS I / II / III and -1 for Age<=55 yrs; -2 for Age (55-75 yrs]; -3 for Age>=75 yrs. The Hazard Ratio for a +1 point is 1.52 i.e. the risk of early relapse/death increased on average by 52% for each additional point in the score. The distribution of risk scores was as follows: Score= -2 (n=757), -1 (n=1,481), 0 (n=1,358), 1 (n=647), and 2 (n=146). The score allows separation of the PFS12 curves (Figure 1), with the lowest risk group (N=757) having a PFS at 12 months of 91%, and the highest risk group (N=146) having a PFS at 12 months of 65%. Despite some minor differences in the risk factors between the training and validation cohorts, the score has a similar average effect (HR=1.48 i.e. + 48% hazard for each additional point) and worked well in separating the curves, in particular in identifying the patients at high risk of early relapse. Discussion and conclusion: The new EBMT score to predict early relapse post-Auto-HCT uses the easily available variables of age and ISS stage at diagnosis as well as the dynamic variable of response to induction. With this simple approach, we were able to clearly identify patients at high risk of early relapse. To our surprise, older age emerged as a protective factor against relapse. This may reflect a relative selection bias in that older patients with higher risk disease may not have been selected for transplant. Impact of cytogenetics will be presented at the Congress. In conclusion, this novel scoring system is robust and easy to use in routine daily practice. Figure 1 Figure 1. Disclosures Beksac: Amgen: Consultancy, Speakers Bureau; Janssen: Consultancy, Speakers Bureau; Celgene: Consultancy, Speakers Bureau; Sanofi: Consultancy, Speakers Bureau; Takeda: Consultancy, Speakers Bureau; Oncopeptides: Consultancy. Blaise: Jazz Pharmaceuticals: Honoraria. Leleu: Karyopharm Therapeutics: Honoraria; AbbVie: Honoraria; Bristol-Myers Squibb: Honoraria; Amgen: Honoraria; Merck: Honoraria; Mundipharma: Honoraria; Novartis: Honoraria; Carsgen Therapeutics Ltd: Honoraria; Oncopeptides: Honoraria; Janssen-Cilag: Honoraria; Gilead Sciences: Honoraria; Celgene: Honoraria; Pierre Fabre: Honoraria; Roche: Honoraria; Sanofi: Honoraria; Takeda: Honoraria, Other: Non-financial support. Forcade: Novartis: Consultancy, Other: Travel Support, Speakers Bureau; Gilead: Other: Travel Support, Speakers Bureau; Jazz: Other: Travel Support, Speakers Bureau; MSD: Other: Travel Support. Rabin: Janssen: Consultancy, Honoraria, Other: Travel support for meetings; BMS / Celgene: Consultancy, Honoraria, Other: Travel support for meetings; Takeda: Consultancy, Honoraria, Other: Travel support for meetings. Kobbe: Celgene: Research Funding. Sossa: Amgen: Research Funding. Hayden: Jansen, Takeda: Other: Travel, Accomodation, Expenses; Amgen: Honoraria. Schoenland: Pfizer: Honoraria; sanofi: Research Funding; janssen,Prothena,Takeda,: Consultancy, Honoraria. Yakoub-Agha: Jazz Pharmaceuticals: Honoraria.

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Unsupervised Clustering of DNA Copy Number Profiles Identifies a High-Risk Subtype of Hyperdiploid Multiple Myeloma: An Mmrf Commpass Analysis

Blood ◽

10.1182/blood-2019-132152 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 1805-1805 ◽

Cited By ~ 1

Author(s):

Austin Christofferson ◽

Sheri Skerget ◽

Jessica Aldrich ◽

Christophe Legendre ◽

Sara Nasser ◽

...

Keyword(s):

Multiple Myeloma ◽

High Risk ◽

Copy Number ◽

Research Funding ◽

Clustering Algorithm ◽

Dynamic Range ◽

Risk Group ◽

Consensus Clustering ◽

Dna Copy Number ◽

Data Set

Multiple myeloma (MM) is a malignancy of the antibody producing plasma cell, which exhibits a high degree of genetic diversity between patients. As genetic analysis technologies have improved so has our understanding of the diverse genetic phenotypes underlying the disease. The MMRF CoMMpass study (NCT01454297) is using whole genome (WGS), exome (WES), and RNA (RNAseq) sequencing to provide a precise characterization of each patient before and after therapy. However, these advanced assays are not widely available to patients today limiting the utility of many observations to a small population of patients. To expand the utility of the data set to a broader patient population we focused on DNA copy number (CN) phenotypes that can be identified by the standard FISH assays widely used in the field. To discover potential underlying phenotypes of myeloma beyond the known dichotomy of hyperdiploid (HRD) and non-hyperdiploid (NHRD) karyotypes, unsupervised consensus clustering was performed on 871 patients with CN profiles from WGS. Given the limited dynamic range of CN values, a Monte Carlo reference-based consensus clustering algorithm, M3C, was used to limit potential overfitting issues. Three independent replicates of this procedure identified an optimal solution of eight subtypes with no more than 6 patients having different class assignments between replicates. The eight CN subtypes consisted of five HRD and three NHRD subtypes and were annotated based on common CN features. The HRD classic subtype had ubiquitous CN gains, trisomies, of classic HRD chromosomes, 3, 5, 7, 9, 11, 15, and 19. The remaining HRD subtypes were annotated based on deviations from the classic HRD phenotype. The HRD, ++15 subtype phenocopies classic HRD except tetrasomy, not trisomy, is observed on chr15. Two groups of HRD patients were identified lacking CN gains of chr7 which are split into two distinct subtypes: the HRD, diploid 7 subtype, which lacked gains of chr7; and the HRD diploid 3, 7 subtype lacking trisomies of both chr3 and chr7. This suggest some relationship between chromosomes 3 and 7 where trisomy 7 is not tolerated in the absence of trisomy 3. Finally, the HRD, +1q, diploid 11, -13 subtype had gains of the classic HRD chromosomes except chr11 with gains of chr1q and loss of chr13. This subtype suggests trisomy 11 is essential for an HRD phenotype but it can be phenocopied by the combination of 1q gains and 13 loss. Within the NHRD subtypes, the diploid subtype is almost devoid of CN abnormalities less a common gain of 11q initiating at the breakpoint the t(11;14) event, which is almost universally observed in this subtype. Unlike the diploid subtype, the remaining NHRD subtypes have more complex CN profiles with the -13 subtype defined by monosomy 13, and the +1q/-13 subtype defined by gains of 1q and monosomy 13. Outcome analyses of the CN subtypes identified in CoMMpass revealed that both HRD and NHRD patients with gains of chr1q and loss of chr13 exhibited poor PFS and OS outcomes as compared to patients in other CN subtypes. Interestingly, the PFS curves split into three groups with a good risk group defined by the HRD classic and HRD ++15 subtypes. a high-risk group defined by 1q gain and monosomy 13 regardless of ploidy phenotype, and an intermediate group with all other subtypes. The distribution of HRD patients into these three outcome groups highlights the danger of assuming all HRD myeloma patients will have similar outcomes. Patients in the HRD, +1q, diploid 11, -13 subtype exhibited poor OS outcomes (median = 56 months) as compared to patients in the HRD, ++15 (p<0.01), HRD, classic (median = 65 months, p<0.05), diploid (p<0.01), and -13 (p<0.05) subtypes. Patients in the +1q, -13 subtype also exhibited poor OS outcomes (median = 57 months) as compared to patients in the diploid (p<0.01), -13 (p<0.05), HRD classic (p<0.05), and HRD, ++15 (p<0.01) subtypes. Overall, both HRD and NHRD patients with gain of 1q and loss of chr13 exhibit poor outcome as compared to patients with other genetic backgrounds (HR = 1.928, 95% CI = 1.435 - 2.59, p<0.001). Further, the observation that NHRD patients in the +1q, -13 subtype exhibit poor OS outcomes as compared to NHRD patients in the -13 subtype highlights the importance of 1q gains in determining patient prognosis. These results can easily be translated into clinics around the world by matching existing FISH data to each of these groups until more advanced testing is common practice. Disclosures Lonial: BMS: Consultancy; GSK: Consultancy; Karyopharm: Consultancy; Genentech: Consultancy; Janssen: Consultancy, Research Funding; Celgene Corporation: Consultancy, Research Funding; Takeda: Consultancy, Research Funding; Amgen: Consultancy.

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Real-World Outcomes of Ixazomib in the Treatment of Multiple Myeloma from a Single Center in China

Blood ◽

10.1182/blood-2019-126718 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 5581-5581

Author(s):

Weiwei Sui ◽

Gang An ◽

Shuhui Deng ◽

Yan Xu ◽

Mu Hao ◽

...

Keyword(s):

Multiple Myeloma ◽

High Risk ◽

Maintenance Therapy ◽

Real World ◽

Conflicts Of Interest ◽

Risk Group ◽

Continuous Treatment ◽

Control Group ◽

Single Center ◽

Frontline Therapy

OBJECTIVE:To review the role of ixazomib in the treatment of MM from a single center in China, focusing on the efficacy and safety of this oral PI at all stages of disease since ixazomib was approved by CFDA in 2018.METHODS:The data of real-world outcomes were collected from of the patients with high-risk multiple myeloma diagnosed in the blood diseases hospital,CAMS&PUMC from July 2018 to June 2019 when the patients received the first Ixazomib cycle. The patients were divided into three groups, which were salvage therapy of R/RMM, maintenance and frontline therapy of NDMM respectively.RESULTS:The first group was Ixa-based salvage treatment in R/RMM.31 patients were included and median age was 62years(47-71).The proportion of patients with cytogenetic high risk was 50%(One patient was unable to stratify risk). Extramedullary invasion was very high 40% in early relapse. Half of them received more than 2 lines of treatment, and about 10% received 4 or more lines of prior treatment. ORR was 51.6%; ≥ PR 35.5%; VGPR+CR9.7%.The median ixazomib therapy cycles were 3(1-12). 5 patients entered maintenance therapy for more than eight cycles.In all 8 patients who interruptedIxazomib treatment,six were due to disease progression,one was SD,and the remaining one will receive CART therapy.The median follow-up was 6.8 months, and the median PFS was not yet achieved.The survival curves of high-risk group and standard-risk group were obviously separated, but there was no statistical difference.However, the prognosis of extramedullary invasion group was very poor. PFS was significantly shorter than that of control group after treatment with Ixazomib(2.86m vs NA).The second group was Ixa-based maintenance therapy of NDMM. Seven patients received maintenance therapy after ASCT, and 20 transplant-ineligible(TIE) patients entered maintenance therapy.Maitenance with Ixazomib appears to have efficacy with deepen responses,the transplantation group(57.1%CR before vs 85.7%after) similar to the non-transplantation group(35.3%before vs 58.8%after).All the patients did not interrupt Ixazomib treatment.Only 1 patient(3.7%) had grade 4 adverse events, which was thrombocytopenia.The median ixazomib therapy cycles were 4(1-11).The third group was Ixa-based frontline therapy. The median age of 24 patients was 66y (42-79), CA High Risk was 25% (6/24), R-ISS III Stage was 12.5%(3/24), and no patients with EMD.The total of 9 patients were suitable for transplantation. Among them, 5 patients completed stem cell collection, 3 patients completed autotransplantation, 1 patient waited for transplantation, and 1 patient entered the continuous treatment group because of poor mobilization.The other 4 patients were still in induction stage. There were 15 patients in the TIE group and one patient dropped out of the group because of disease progression.The remaining 23 patients achieved a overall response rate of 78.3%. Nearly half of the patients achieved deep remission(VGPR+CR 43.5%).Posttransplantation patients could achieve 100% deep remission rate.As the number of treatment cycles increased, the proportion of complete remission increased in TIE patients.CONCLUSION:Ixazomib as induction/consolidation after ASCT, followed by maintenance, is well tolerated, convenient, and effective. Ixazomib is an important option for high-risk patients with real-world experience.KEYWORDS:Real-world, Ixazomib, multiple myeloma Figure Disclosures No relevant conflicts of interest to declare.

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

Blood ◽

10.1182/blood.v118.21.2881.2881 ◽

2011 ◽

Vol 118 (21) ◽

pp. 2881-2881

Author(s):

Esteban Braggio ◽

Jonathan J Keats ◽

Shaji Kumar ◽

Gregory Ahmann ◽

Jeremy Mantei ◽

...

Keyword(s):

Gene Expression ◽

Multiple Myeloma ◽

High Risk ◽

Risk Stratification ◽

Research Funding ◽

Plasma Cells ◽

Risk Group ◽

High Risk Group ◽

Proliferation Index ◽

Gene Index

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

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Multiple Myeloma Patients Undergoing Carfilzomib: Development and Validation of a Risk Score for Cardiovascular Adverse Events Prediction

Cancers ◽

10.3390/cancers13071631 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1631

Author(s):

Anna Astarita ◽

Giulia Mingrone ◽

Lorenzo Airale ◽

Fabrizio Vallelonga ◽

Michele Covella ◽

...

Keyword(s):

Blood Pressure ◽

Risk Assessment ◽

Multiple Myeloma ◽

Cardiovascular Risk ◽

High Risk ◽

Adverse Events ◽

Risk Score ◽

Risk Group ◽

High Risk Group ◽

Cardiovascular Risk Assessment

Cardiovascular adverse events (CVAEs) are linked to Carfilzomib (CFZ) therapy in multiple myeloma (MM); however, no validated protocols on cardiovascular risk assessment are available. In this prospective study, the effectiveness of the European Myeloma Network protocol (EMN) in cardiovascular risk assessment was investigated, identifying major predictors of CVAEs. From January 2015 to March 2020, 116 MM patients who had indication for CFZ therapy underwent a baseline evaluation (including blood pressure measurements, echocardiography and arterial stiffness estimation) and were prospectively followed. The median age was 64.53 ± 8.42 years old, 56% male. Five baseline independent predictors of CVAEs were identified: office systolic blood pressure, 24-h blood pressure variability, left ventricular hypertrophy, pulse wave velocity value and global longitudinal strain. The resulting ‘CVAEs risk score’ distinguished a low- and a high-risk group, obtaining a negative predicting value for the high-risk group of 90%. 52 patients (44.9%) experienced one or more CVAEs: 17 (14.7%) had major and 45 (38.7%) had hypertension-related events. In conclusion, CVAEs are frequent and a specific management protocol is crucial. The EMN protocol and the risk score proved to be useful to estimate the baseline risk for CVAEs during CFZ therapy, allowing the identification of higher-risk patients.

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Mechanisms of Adaptation to Ibrutinib in High Risk Chronic Lymphocytic Leukemia

Blood ◽

10.1182/blood-2018-99-116739 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 585-585 ◽

Cited By ~ 4

Author(s):

Valeria Spina ◽

Gabriela Forestieri ◽

Antonella Zucchetto ◽

Alessio Bruscaggin ◽

Tamara Bittolo ◽

...

Keyword(s):

High Risk ◽

Chronic Lymphocytic Leukemia ◽

Nuclear Localization ◽

Peripheral Blood ◽

Research Funding ◽

Clonal Evolution ◽

Lymphocytic Leukemia ◽

Adaptation Process ◽

Over Time ◽

Stimulation Of

Abstract Introduction. Ibrutinib inhibits the BTK molecule downstream the B-cell receptor (BCR). Though highly active in high risk chronic lymphocytic leukemia (CLL), the most typical response achievable in patients is a minimal residual disease (MRD) positive partial remission (PR) which is maintained until the development of genetically driven resistance caused by the acquisition of mutations in the BTK or PLCG2 genes. The study aims at characterizing the adaptation process allowing residual CLL cells to persist despite BTK inhibition. Methods. The IOSI-EMA-001 study (NCT02827617) is an observational study consisting in the prospective and longitudinal collection of peripheral blood samples and clinical data from high risk CLL patients treated with ibrutinib. Peripheral blood CLL cells longitudinally drawn from patients before treatment start and at fixed timepoints under ibrutinib were monitored by: i) next generation flow cytometry approaches for changes in proliferation rate, surfaceome, and pathway activation; and ii) CAPP-seq targeted deep next generation (sensitivity ~10-3) for clonal evolution. Results. The study cohort comprised 31 high risk CLL patients, including 15 treatment naïve, 16 relapsed, 80% IGHV unmutated, 42% 17p deleted and 55% TP53 mutated. Median duration of ibrutinib treatment was 45 weeks (24-72 weeks). All patients obtained a MRD positive PR that was maintained in all but one who progressed with a PLCG2 mutation (VAF 3%). Compared to baseline, under ibrutinib therapy CLL cells slowed down their proliferation, as suggested by the decreased expression of Ki-67, the reduction of the proliferating fraction (CXCR4dimCD5bright), and the increase of the resting fraction (CXCR4brightCD5dim). Compared to baseline, under ibrutinib therapy CLL cells also upregulated BCR and adhesion/homing proteins, and decreased the expression of BCR inhibitor proteins. Upon stimulation of the BCR with anti-IgM, the downstream path through pBTK and pPLCG2 was inhibited by ibrutinib, while conversely the downstream path through pAKT and pERK was still inducible throughout all the assessed timepoints. The proportion of CLL cells harboring nuclear localization of NF-kB progressively increased over time under ibrutinib. NF-kB nuclear localization was inducible throughout all the assessed timepoints by CD40L stimulation of the non-canonical NF-kB pathway, but not by anti-IgM stimulation of the BCR/canonical NF-kB pathway. Overall, 880 individual mutations were longitudinally discovered and monitored across a total of 121 sequential timepoints collected during ibrutinib treatment. Clonal evolution was observed in (67.7%) cases, a proportion rate previously documented in CLL treated with chemoimmunotherapy. Clonal evolution appeared to be heterogeneous involving different genes without a stereotypic targeting. Consistently, none of the main driver gene mutations was homogeneously selected or suppressed by ibrutinib suggesting that the biological adaptation of CLL cells under ibrutinib is not genetically driven. Clonal evolution propensity was not associated with any of the biomarkers of the disease, and it did not decrease over time under ibrutinib. Conclusions. Taken together these results suggest that residual CLL cells persisting under ibrutinib therapy adapt their phenotype by upregulating adhesion molecules, chemokine receptors and BCR molecules, and by maintaining a competence of BCR signaling through the PI3K/AKT/ERK pathway. The progressive selection of CLL cells having NF-kB in the nucleus, likely due to the BTK independent non-canonical NF-kB pathway, might explain their survival despite ibrutinib therapy. Finally, clonal evolution is not suppressed by ibrutinib chemotherapy, and despite does not seem to be directly involved in such adaptation process, may ultimately favor the acquisition of BTK and PLCG2 ibrutinib resistance mutations. Disclosures Zucca: Celltrion: Consultancy; AstraZeneca: Consultancy. Ghia:Sunesis: Honoraria, Research Funding; Novartis: Honoraria, Research Funding; AbbVie, Inc: Honoraria, Research Funding; Acerta: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Gilead: Honoraria, Research Funding; BeiGene: Honoraria, Research Funding. Montillo:Janssen: Consultancy, Honoraria; Gilead: Consultancy, Honoraria, Speakers Bureau; AbbVie: Consultancy, Honoraria, Speakers Bureau; Roche: Consultancy, Honoraria, Research Funding. Tedeschi:Janssen: Consultancy, Speakers Bureau; Gilead: Consultancy; AbbVie: Consultancy. Gaidano:AbbVie: Consultancy, Honoraria; Gilead: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Morphosys: Honoraria; Roche: Consultancy, Honoraria.

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Phase II Trial of the Combination of Ixazomib, Lenalidomide, and Dexamethasone in High-Risk Smoldering Multiple Myeloma

Blood ◽

10.1182/blood-2018-99-117871 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 804-804 ◽

Cited By ~ 7

Author(s):

Mark Bustoros ◽

Chia-jen Liu ◽

Kaitlen Reyes ◽

Kalvis Hornburg ◽

Kathleen Guimond ◽

...

Keyword(s):

Multiple Myeloma ◽

Bone Marrow ◽

High Risk ◽

Disease Progression ◽

Board Of Directors ◽

Rna Sequencing ◽

Research Funding ◽

Response Rate ◽

Advisory Committees ◽

Equity Ownership

Abstract Background. This study aimed to determine the progression-free survival and response rate using early therapeutic intervention in patients with high-risk smoldering multiple myeloma (SMM) using the combination of ixazomib, lenalidomide, and dexamethasone. Methods. Patients enrolled on study met eligibility for high-risk SMM based on the newly defined criteria proposed by Rajkumar et al., Blood 2014. The treatment plan was designed to be administered on an outpatient basis where patients receive 9 cycles of induction therapy of ixazomib (4mg) at days 1, 8, and 15, in combination with lenalidomide (25mg) at days 1-21 and Dexamethasone at days 1, 8, 15, and 22. This induction phase is followed by ixazomib (4mg) and lenalidomide (15mg) maintenance for another 15 cycles. A treatment cycle is defined as 28 consecutive days, and therapy is administered for a total of 24 cycles total. Bone marrow samples from all patients were obtained before starting therapy for baseline assessment, whole exome sequencing (WES), and RNA sequencing of plasma and bone marrow microenvironment cells. Moreover, blood samples were obtained at screening and before each cycle to isolate cell-free DNA (cfDNA) and circulating tumor cells (CTCs). Stem cell collection is planned for all eligible patients. Results. In total, 26 of the planned 56 patients were enrolled in this study from February 2017 to April 2018. The median age of the patients enrolled was 63 years (range, 41 to 73) with 12 males (46.2%). Interphase fluorescence in situ hybridization (iFISH) was successful in 18 patients. High-risk cytogenetics (defined as the presence of t(4;14), 17p deletion, and 1q gain) were found in 11 patients (61.1%). The median number of cycles completed was 8 cycles (3-15). The most common toxicities were fatigue (69.6%), followed by rash (56.5%), and neutropenia (56.5%). The most common grade 3 adverse events were hypophosphatemia (13%), leukopenia (13%), and neutropenia (8.7%). One patient had grade 4 neutropenia during treatment. Additionally, grade 4 hyperglycemia occurred in another patient. As of this abstract date, the overall response rate (partial response or better) in participants who had at least 3 cycles of treatment was 89% (23/26), with 5 Complete Responses (CR, 19.2%), 9 very good partial responses (VGPR, 34.6%), 9 partial responses (34.6%), and 3 Minimal Responses (MR, 11.5%). None of the patients have shown progression to overt MM to date. Correlative studies including WES of plasma cells and single-cell RNA sequencing of the bone microenvironment cells are ongoing to identify the genomic and transcriptomic predictors for the differential response to therapy as well as for disease evolution. Furthermore, we are analyzing the cfDNA and CTCs of the patients at different time points to investigate their use in monitoring minimal residual disease and disease progression. Conclusion. The combination of ixazomib, lenalidomide, and dexamethasone is an effective and well-tolerated intervention in high-risk smoldering myeloma. The high response rate, convenient schedule with minimal toxicity observed to date are promising in this patient population at high risk of progression to symptomatic disease. Further studies and longer follow up for disease progression are warranted. Disclosures Bustoros: Dava Oncology: Honoraria. Munshi:OncoPep: Other: Board of director. Anderson:C4 Therapeutics: Equity Ownership; Celgene: Consultancy; Bristol Myers Squibb: Consultancy; Takeda Millennium: Consultancy; Gilead: Membership on an entity's Board of Directors or advisory committees; Oncopep: Equity Ownership. Richardson: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; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding. Ghobrial:Celgene: Consultancy; Takeda: Consultancy; Janssen: Consultancy; BMS: Consultancy.

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