ALBERT

Description: Background. The TT approach has significantly improved the outcome of multiple myeloma (MM) by combining new drugs with a regimen that comprises induction, tandem autologous stem cell transplantation (ASCT), consolidation and maintenance. However, a group of 15% of patients with high risk multiple myeloma (HRMM) have derived little benefit despite similar response rates to induction chemotherapy and ASCT when compared to low risk MM. The poor outcome of HRMM is explained by early relapse post ASCT resulting in a short progression free survival (PFS) with only 15-20% of patients surviving long-term. Daratumumab (Dara) is a human IgG1k anti-CD38 monoclonal antibody that has shown favorable results in early single-arm studies and more recently in phase III studies for relapsed/refractory and newly diagnosed MM. In TT7, we introduced Dara during all phases of therapy, including immune consolidation early post ASCT, to improve responses rate and PFS in HRMM. Methods. Patients had newly diagnosed HRMM as defined by high risk cytogenetic abnormalities, presence of extramedullary disease, 〉3 focal lesions on CT-PET, elevated LDH due to MM, or ISS II/III with cytogenetic abnormality. Dara (16mg/kgx1) was added to induction with KTD-PACE (carfilzomib, thalidomide, dexamethasone; and four-day continuous infusions of cisplatin, doxorubicin, cyclophosphamide, etoposide). Conditioning for tandem autologous stem cell transplantation (ASCT) was with fractionated melphalan (50mg/m2x4) (fMEL) based on prior observations that patients with adverse cytogenetics fare better with fMEL rather than single high dose MEL200mg/m2.In the inter tandem ASCT period immunological consolidation with Dara (16mg/kg) alone for 2 doses was followed by Dara (16mg/kg) on day 1 combined with K (36mg/m2) and D (20mg) weekly for 2 cycles. DaraKD was administered to avoid treatment free periods allowing for myeloma regrowth. The 2nd ASCT was followed by further immunological consolidation with Dara (16mg/k) for 2 doses, and maintenance therapy for 3 yrs with 3-months block of alternating Dara-KD (dara 16mg/kg day 1; K 36mg/m2 and dex 20mg weekly) and Dara-lenalidomide (R)D (dara 16mg/kg day 1; R 15mg day 1-21 q28 and D 20mg weekly). Results. TT7 enrolled 43 patients thus far. The median follow-up was 11 months (range: 1-22). The median age was 61 yrs (range 44-73). Sixteen patients were ≥65 yrs (37.2%). A mean of 29.4x106 CD34+ cells/kg (range: 4.6-86.4) were collected. 36 patients completed ASCT #1 (83.7%) and 18 (41.9%) ASCT #2, whilst 14 patients have proceeded to the maintenance phase. R-ISS II/III or metaphase cytogenetic abnormalities were present in 85.1 and 58.1% of patients, respectively. Elevated LDH or 〉3FL on CT-PET were noted in 30 and 41.8%. The 1-yr cumulative incidence estimates for reaching VGPR and PR were 87 and 83%, respectively. A CR or sCR was achieved in 68 and 46%. The 1-yr estimates of PFS and OS were 91.6 and 87.2%. 40 subjects are alive, whilst 5 progressed on study therapy and 3 subsequently died. 38 patients are progression free at the time of reporting. Dara was well-tolerated and no subjects discontinued therapy due to dara-related side effects. The CR and sCR rates compared favorably to the predecessor HRMM TT5 protocol where CR and sCR rates were 59 and 27%. Conclusion. The early results of TT7 point to increased response rates of HRMM to a dara-based TT regimen with especially higher rates of CR and sCR. Longer follow-up is required to determine if these early results translate into superior PFS and OS. Figure Disclosures van Rhee: Karyopharm Therapeutics: Consultancy; Kite Pharma: Consultancy; Adicet Bio: Consultancy; Takeda: Consultancy; Sanofi Genzyme: Consultancy; Castleman Disease Collaborative Network: Consultancy; EUSA: Consultancy. Walker:Celgene: Research Funding. Morgan:Amgen, Roche, Abbvie, Takeda, Celgene, Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Other: research grant, Research Funding. Davies:Amgen, Celgene, Janssen, Oncopeptides, Roche, Takeda: Membership on an entity's Board of Directors or advisory committees, Other: Consultant/Advisor; Janssen, Celgene: Other: Research Grant, Research Funding.

Description: Introduction The multiple myeloma (MM) tumor microenvironment (TME) strongly influences patient outcomes as evidenced by the success of immunomodulatory therapies. To develop precision immunotherapeutic approaches, it is essential to identify and enumerate TME cell types and understand their dynamics. Methods We estimated the population of immune and other non-tumor cell types during the course of MM treatment at a single institution using gene expression of paired CD138-selected bone marrow aspirates and whole bone marrow (WBM) core biopsies from 867 samples of 436 newly diagnosed MM patients collected at 5 time points: pre-treatment (N=354), post-induction (N=245), post-transplant (N=83), post-consolidation (N=51), and post-maintenance (N=134). Expression profiles from the aspirates were used to infer the transcriptome contribution of immune and stromal cells in the WBM array data. Unsupervised clustering of these non-tumor gene expression profiles across all time points was performed using the R package ConsensusClusterPlus with Bayesian Information Criterion (BIC) to select the number of clusters. Individual cell types in these TMEs were estimated using the DCQ algorithm and a gene expression signature matrix based on the published LM22 leukocyte matrix (Newman et al., 2015) augmented with 5 bone marrow- and myeloma-specific cell types. Results Our deconvolution approach accurately estimated percent tumor cells in the paired samples compared to estimates from microscopy and flow cytometry (PCC = 0.63, RMSE = 9.99%). TME clusters built on gene expression data from all 867 samples resulted in 5 unsupervised clusters covering 91% of samples. While the fraction of patients in each cluster changed during treatment, no new TME clusters emerged as treatment progressed. These clusters were associated with progression free survival (PFS) (p-Val = 0.020) and overall survival (OS) (p-Val = 0.067) when measured in pre-transplant samples. The most striking outcomes were represented by Cluster 5 (N = 106) characterized by a low innate to adaptive cell ratio and shortened patient survival (Figure 1, 2). This cluster had worse outcomes than others (estimated mean PFS = 58 months compared to 71+ months for other clusters, p-Val = 0.002; estimate mean OS = 105 months compared with 113+ months for other clusters, p-Val = 0.040). Compared to other immune clusters, the adaptive-skewed TME of Cluster 5 is characterized by low granulocyte populations and high antigen-presenting, CD8 T, and B cell populations. As might be expected, this cluster was also significantly enriched for ISS3 and GEP70 high risk patients, as well as Del1p, Del1q, t12;14, and t14:16. Importantly, this TME persisted even when the induction therapy significantly reduced the tumor load (Table 1). At post-induction, outcomes for the 69 / 245 patients in Cluster 5 remain significantly worse (estimate mean PFS = 56 months compared to 71+ months for other clusters, p-Val = 0.004; estimate mean OS = 100 months compared to 121+ months for other clusters, p-Val = 0.002). The analysis of on-treatment samples showed that the number of patients in Cluster 5 decreases from 30% before treatment to 12% after transplant, and of the 63 patients for whom we have both pre-treatment and post-transplant samples, 18/20 of the Cluster 5 patients moved into other immune clusters; 13 into Cluster 4. The non-5 clusters (with better PFS and OS overall) had higher amounts of granulocytes and lower amounts of CD8 T cells. Some clusters (1 and 4) had increased natural killer (NK) cells and decreased dendritic cells, while other clusters (2 and 3) had increased adipocytes and increases in M2 macrophages (Cluster 2) or NK cells (Cluster 3). Taken together, the gain of granulocytes and adipocytes was associated with improved outcome, while increases in the adaptive immune compartment was associated with poorer outcome. Conclusions We identified distinct clusters of patient TMEs from bulk transcriptome profiles by computationally estimating the CD138- fraction of TMEs. Our findings identified differential immune and stromal compositions in patient clusters with opposing clinical outcomes and tracked membership in those clusters during treatment. Adding this layer of TME to the analysis of myeloma patient baseline and on-treatment samples enables us to formulate biological hypotheses and may eventually guide therapeutic interventions to improve outcomes for patients. Disclosures Danziger: Celgene Corporation: Employment, Equity Ownership. McConnell:Celgene Corporation: Employment. Gockley:Celgene Corporation: Employment. Young:Celgene Corporation: Employment, Equity Ownership. Schmitz:Celgene Corporation: Employment, Equity Ownership. Reiss:Celgene Corporation: Employment, Equity Ownership. Davies:MMRF: Honoraria; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; TRM Oncology: Honoraria; Abbvie: Consultancy; ASH: Honoraria; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Honoraria. Copeland:Celgene Corporation: Employment, Equity Ownership. Fox:Celgene Corporation: Employment, Equity Ownership. Fitch:Celgene Corporation: Employment, Equity Ownership. Newhall:Celgene Corporation: Employment, Equity Ownership. Barlogie:Celgene: Consultancy, Research Funding; Dana Farber Cancer Institute: Other: travel stipend; Multiple Myeloma Research Foundation: Other: travel stipend; International Workshop on Waldenström's Macroglobulinemia: Other: travel stipend; Millenium: Consultancy, Research Funding; European School of Haematology- International Conference on Multiple Myeloma: Other: travel stipend; 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. Trotter:Celgene Research SL (Spain), part of Celgene Corporation: Employment, Equity Ownership. Hershberg:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Dervan:Celgene Corporation: Employment, Equity Ownership. Ratushny:Celgene Corporation: Employment, Equity Ownership. Morgan:Takeda: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Research Funding.

Description: Introduction: Invasive bone marrow sampling is used in multiple myeloma (MM) diagnosis to obtain biological material, which can then be used to generate prognostically important genetic features. Physically sampling the bone marrow can be uncomfortable for the patient. Also, spatial heterogeneity is a common feature in MM, with multiple focal lesions (FLs) occurring throughout the skeleton, meaning a single sample from the iliac crest may be insufficient to capture intrapatient heterogeneity. An alternative strategy is to extract data directly from diagnostic positron emission tomography-computed tomography (PET-CT) scans of patients. These radiomic features can be used as a proxy from which to infer molecular and clinical phenotypes. Compared to physical sampling, there are several advantages, including rapid analysis, minimalizing patient discomfort, reduced cost and widespread availability of the required scanning equipment in hospitals. Methods: A series of 439 newly diagnosed MM patients were selected, all of which had diagnostic PET-CT scans. A radiologist examined these data and identified focal lesions in the axial skeleton of 136/439 (31%) patients. Focal lesions were manually segmented from the PET portion of the original DICOM data using a density-based thresholding method in 3DSlicer version 4.9.0. Pyradiomics version 1.3 was used to resample the voxels in the PET data to 4x4x4 mm and extract radiomic features from each FL. A combination of 10 filters and 7 feature classes were used and a total of 1679 radiomic features were generated per lesion. Radiomic features were a mixture of first order characteristics such as maximum intensity, shape characteristics and gray level matrix features. Hierarchical clustering was applied to the radiomic features, using the Pearson correlation between features as the distance metric and Ward's method for clustering. Next generation sequencing (NGS) data was available for samples from 58/136 (43%) patients with FLs in whole genome (WGS), whole exome (WES) or targeted panel (TP) modalities. The NGS data was used to detect translocations, copy number aberrations and somatic mutations. Results: There were 789 FLs identified in 136 patients, with each patient containing an average of 5.8 FLs. The median FL volume was 4350 mm3, with a median maximum 3D diameter of 29 mm. Hierarchical clustering across all FLs and radiomic features separated the FLs into 5 discrete clusters associated with various clinical and molecular features. However, clustering appeared to be independent of other classification systems based on gene expression profiling (GEP), including the UAMS classification system and GEP70 risk score. Clustering was also independent of the International Staging System (ISS) status suggesting that it can add additional prognostic information. Clusters also appeared to be independent of somatic mutations in genes previously reported as significantly mutated in MM. Patients commonly had FLs occurring in multiple clusters, suggesting that this method takes into account the heterogeneity between lesions in the same patient. Larger FLs were grouped primarily into two clusters consistent with them having distinct features that can be recognized by this approach. Looking across the different clusters distinct differences in clinical outcome were seen between the groups, with significant differences in both PFS (p=0.007) and overall survival (p=0.005), with worse prognosis being led by a cluster of smaller lesions. Conclusions: Radiomics provides a novel method to extract potentially important data from PET-CT scans which can define individual clusters that have different clinical, molecular and prognostic features. This can provide a novel non-invasive method to assess FLs based on both their physical and radiomic characteristics. Larger study sizes will be needed to confirm the differences in outcomes seen between groups. Disclosures Boyle: Celgene: Honoraria, Other: travel grants; Janssen: Honoraria, Other: travel grants; La Fondation de Frace: Research Funding; Abbvie: Honoraria; Amgen: Honoraria, Other: travel grants; Gilead: Honoraria, Other: travel grants; Takeda: Consultancy, Honoraria. Morgan:Bristol-Myers Squibb: Consultancy, Honoraria; Janssen: Research Funding; Takeda: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding. Davies:TRM Oncology: Honoraria; MMRF: Honoraria; Abbvie: Consultancy; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Honoraria; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; ASH: Honoraria.

Description: 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

Description: Introduction Novel agents are incorporated into a backbone of multi-agent chemotherapy and tandem transplantation in successive Total Therapy (TT) protocols for multiple myeloma (MM) patients. The TT protocols extend the duration of remission with a significant proportion of patients achieving long-term disease control. However, a small percentage of patients have documented late relapse and we theorize that these relapses are indolent with overall good clinical outcome. While even a late relapse is seemingly an undesirable outcome, defining the clinical implications is imperative to understanding the impact on long-term prognosis and management. Methods Our MM database was interrogated to identify patients that presented with relapsing disease ≥ 10 years from diagnosis and were treated on TT protocols. All patients had myeloma markers every 2 to 3 months and bone marrows with either PET-CT or MRI at least once a year. In the group of patients with documented relapse, we obtained the pattern of relapse, clinical and laboratory markers, cytogenetics, FISH, gene expression profile (GEP), bone marrow (BM) minimal residual disease (MRD), imaging (PET-CT and MRI, use of salvage transplant, and response assessment including best response to treatment. Patterns of relapses were evaluated based on elevated serum myeloma markers, BM plasmacytosis and presence of macrofocal relapse/extramedullary disease. Clinical relapses were classified based on the International Myeloma Working Group criteria. Indolent relapse was defined as a biochemical relapse with a rising M-protein or free light chains, less than 30% bone marrow plasmacytosis involvement, absence of focal lesion on imaging and of CRAB criteria, low riskGEP70 signature at relapse or no abnormal metaphase cytogenetics. Results A total of 2055 patients were enrolled and treated on successive TT protocols (TT1 to TT7) of which 658 patients had ≥10 years follow up from initial study enrollment. Among these 658 patients, 8% (53/658) had a clinical relapse ≥ 10 years from diagnosis. The median time to clinical relapse was 11. 8 years (range, 9.8 - 17.6). Median age at the time of relapse was 67.7 years with 28% being females. Gene expression profiling showed that 45% (24/53) belonged to the molecular CD2 and LB subgroups. The most common pattern of relapse is with BM plasmacytosis in 49% (26/53) with 36% (19/53) presenting with CRAB clinical features. Focal lesions by MRI and/or CT-PET were present in 35.8% (19/53). Overall, 56.6% (30/53) of patients had indolent relapses; of these 9 and 7 belonged to the CD2 and LB molecular subgroups, respectively. BM MRD assessment by 8-color flow cytometry was available in 17 patients and MRD positivity preceded clinical relapse by a median 17.9 months (range: 0 - 60.8). Forty-one patients required treatment for disease relapse with 11 patients managed expectantly without treatment. While most patients (73.1%, 30/41) received initial treatment with either PI/IMiD/PI IMiD combinations, 8 patients later underwent salvage ASCT. A response of ≥ VGPR was attained in 70% (31/47) of the treated patients with the large majority (87%) achieving a sCR/CR. The overall survival from relapse at 5 years was 73.3% (95% CI: 55.0% to 85.1%). A total of 14/53 patients died during follow-up;7 patients died to progressive disease; 6 of these 7 presented with focal lesions detected on imaging and had an aggressive relapse. Three patients died to treatment related complications, 1 had an unrelated pneumonia, the cause of death was indeterminate in 2 and unknown in 1. Overall, in these 14 patients, median time to death from initial diagnosis was 15.4 years (range: 11.8 - 18.0) and a median time to death from relapse was 3.2 years (range: 0.3 - 6.0). Conclusions Late relapses (〉10 years) in MM are a rare event occurring in 8% of long-term survivors and accounting for 2.6% of all patients treated on the TT protocols. Intriguingly, most patients with late relapsing MM belong to the GEP subtype LB and CD2 and accounted for 51% of the indolent relapses. These findings have clinical implications and emphasizes the importance of extended and close follow-up even in patients with prolonged clinical remission. Disclosures van Rhee: Karyopharm: Consultancy; Adaptive Biotech: Consultancy; Takeda: Consultancy; EUSA: Consultancy; CDCN: Consultancy.