Patients with Multiple Myeloma and Prior COVID-19 Have Superior Antibody Responses Against Sars-Cov-2 Compared with Fully Vaccinated Myeloma Patients with the BNT162b2 Vaccine

Abstract Introduction: Recent data suggest a suboptimal antibody response to COVID-19 vaccination in patients with multiple myeloma (MM), especially under treatment. Herein, we evaluated the development of neutralizing antibodies (NAbs) against SARS-CoV-2 in non-vaccinated MM patients who were diagnosed with COVID-19 compared to MM patients after full vaccination with the mRNA BNT162b2 vaccine. Methods: The analysis was performed in the context of an ongoing large prospective study (NCT04743388) evaluating the kinetics of anti-SARS-CoV-2 antibodies after COVID-19 vaccination. We evaluated MM patients diagnosed with COVID-19, confirmed by PCR, matched for age, gender, line of treatment, type of myeloma, type of treatment and response with vaccinated MM patients during the same time period (January - May 2021). Major exclusion criteria for both COVID-19 and vaccine MM groups included the presence of: (i) autoimmune disorder under immunosuppressive therapy or other active cancer; (ii) active HIV, hepatitis B and C infection, and (iii) end-stage renal disease . Serum was collected at 4 th week post confirmed diagnosis for the COVID-19 MM group and at 4 th week post the second BNT162b2 dose for the vaccine MM group. NAbs against SARS-CoV-2 were measured using an FDA approved methodology (cPass™ SARS-CoV-2 NAbs Detection Kit, GenScript, Piscataway, NJ, USA). Results: We evaluated 35 patients with MM and COVID-19 (6 had smoldering MM and 29 symptomatic MM), along with 35 matched MM patients who received the BNT162b2 vaccine. Among COVID-19 MM patients, 13 were diagnosed with mild, 12 with moderate and 10 with severe disease; 22/35 patients were hospitalized and 10/35 were intubated. Seven (20%) patients died due to COVID-19. During the disease course 21 patients (60%) were treated with dexamethasone. Type of treatment was not different between COVID-19 positive and vaccinated MM patients. Between the two patient groups, there was no difference in terms of age [median (IQR) 65 (59) for COVID-19 positive versus 66 (74) for COVID-19 vaccinated, respectively, p=0.76], gender [males: 19/35 (54.3%) versus 16/35 (45.7%), respectively, p=0.47), BMI (median 27 versus 26kg/m 2, respectively, p=0.56), asymptomatic disease [6/35 (18.2%) in both groups, p=1], prior lines of treatment [range: 1 to 7 versus 1 to 6, respectively, p=0.99], and type of treatment (p=0.87). Among the COVID-19 MM patients, 6 (20.7%) were in sCR/CR, 6 (20.7%) in VGPR, 12 (41.4%) patients in PR, 2 (6.9%) in MR/SD and one (3.5%) in PD at the time of confirmed infection. Among the vaccinated MM group, 10 (34.5%) patientswere in sCR/CR, 4 (13.8%) in VGPR, 11 (37.9%) in PR, one (3.5%) in MR/SD and one (3.5%) in PD at the time of vaccination (p-value=0.93 for the comparison between COVID-19 and vaccinated MM groups). No differences between COVID-19 and vaccinated MM patients were also noted regarding the median lymphocyte count (1200/μl versus 1400/μl, respectively, p=0.08) and the median immunoglobulin values (IgG 732 mg/dl versus 747 mg/dl, respectively, p=0.29; IgA 9 mg/dl versus 61 mg/dl, respectively, p=0.7; IgM 26 mg/dl versus 25 mg/dl, p=0.97). The incidence of comorbidities was also similar between the two groups (cardiovascular diseases 55.2% versus 44.8%, respectively, p=0.47; diabetes mellitus 66.7% versus 33.3%, p=0.28; chronic pulmonary disease 50% each, p=1.0). Interestingly, patients with MM and COVID-19 showed a superior humoral response compared with vaccinated MM patients. The median (IQR) NAb titers were 87.6% (IQR: 71.6-94) and 58.7% (21.4-91.8) for COVID-19 and for vaccinated MM patients, respectively (p=0.01). In both groups, 27 out of 35 patients were receiving active treatment for MM at the time of NAb evaluation. The median NAb titer was 88% (IQR 71.6%-96.3%) for COVID-19 MM patients and 35.4% (IQR 17.5%-85.5%) for vaccinated MM patients who received anti-myeloma therapy (p=0.001). Importantly, there was no difference in NAb production between COVID-19 and vaccinated MM patients who did not receive any treatment (median NAb titers, 85.1% versus 91.7%, p=0.14). Conclusion: Patients with MM and COVID-19 present a superior NAb response against SARS-CoV-2 compared with fully vaccinated patients with the BNT162b2 vaccine. This finding was more pronounced among patients receiving active treatment for MM. In this context, additional booster doses may be considered for MM patients with poor humoral response after the BNT162b2 vaccine. Figure 1 Figure 1. Disclosures Gavriatopoulou: Genesis: Honoraria; Karyopharm: Honoraria; Takeda: Honoraria; Janssen: Honoraria; Sanofi: Honoraria; GSK: Honoraria; Amgen: Honoraria. Terpos: BMS: Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Sanofi: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria, Research Funding; Janssen-Cilag: Consultancy, Honoraria, Research Funding; GSK: Honoraria, Research Funding; Novartis: Honoraria; Genesis: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria, Research Funding. Kastritis: Amgen: Consultancy, Honoraria, Research Funding; Genesis Pharma: Honoraria; Janssen: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy, Honoraria, Research Funding; Takeda: Honoraria. Dimopoulos: Amgen: Honoraria; BMS: Honoraria; Janssen: Honoraria; Takeda: Honoraria; BeiGene: Honoraria.

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Patients with Multiple Myeloma on Anti-CD38 or Anti-BCMA Based Regimens and Patients with Waldenstrom's Macroglobulinemia Under Rituximab or BTK Inhibitors Have a Poor Humoral Response Following COVID-19 Vaccination

Blood ◽

10.1182/blood-2021-144856 ◽

2021 ◽

Vol 138 (Supplement 1) ◽

pp. 3791-3791

Author(s):

Evangelos Terpos ◽

Maria Gavriatopoulou ◽

Ioannis Ntanasis-Stathopoulos ◽

Alexandros Briasoulis ◽

Sentiljana Gumeni ◽

...

Keyword(s):

Antibody Response ◽

Active Treatment ◽

Neutralizing Antibodies ◽

Research Funding ◽

Hematological Malignancies ◽

Kinase Inhibitors ◽

Viral Vector ◽

Vaccine Dose ◽

Humoral Response ◽

Kinetics Of

Abstract Introduction: Recent data suggest a suboptimal antibody response to COVID-19 vaccination in patients with hematological malignancies. Herein, we evaluated the development of neutralizing antibodies (NAbs) against SARS-CoV-2 in patients with plasma cell neoplasms (PCNs) after vaccination with either the mRNA BNT162b2 or viral vector AZD1222 vaccine, up to 50 days post their first vaccine dose. Methods: This is an ongoing large prospective study (NCT04743388) evaluating the kinetics of anti-SARS-CoV-2 antibodies after COVID-19 vaccination in healthy subjects and in patients with hematological malignancies or solid tumors. Here we present the data on patients with PCNs in comparison to controls of similar age and gender, who were vaccinated during the same time period (January to March 2021) in Athens (Greece). Major exclusion criteria for both patients and controls included the presence of: (i) autoimmune disorder under immunosuppressive therapy or other active malignant disease; (ii) HIV or active hepatitis B and C infection, (iii) end-stage renal disease and (iv) prior diagnosis of COVID-19. Serum was collected on day 1 (D1; before the first vaccine dose), on day 22 (D22; before the second dose of the BNT162b2 or 3 weeks post the first AZD1222 dose) and on day 50 (D50; 4 weeks post second dose of the BNT162b2 or 7 weeks post the first AZD1222 dose). NAbs against SARS-CoV-2 were measured using an FDA approved-ELISA methodology (cPass™ SARS-CoV-2 NAbs Detection Kit, GenScript, Piscataway, NJ, USA). Results: We evaluated 382 patients with PCNs after vaccination with either the BNT162b2 or the AZD1222 vaccine. Patients with MM (n=213), WM (n=106), SMM (n=38) and MGUS (n=25) and 226 healthy controls were enrolled in the study. Of MM/SMM/MGUS patients, 215 (77.9%) were vaccinated with the BNT162b2 and 61 (22.1%) with the AZD1222 vaccine, while out of 106 WM patients 90 (84.9%) were vaccinated with the BNT162b2 and 16 (15.1%) with the AZD1222 vaccine. Vaccination with either two doses of the BNT162b2 or one dose of the AZD1222 vaccine led to lower production of NAbs against SARS-CoV-2 in patients compared with controls both on day 22 and on day 50 (P<0.001 for all comparisons). After the first dose of the vaccine, on D22, the patient group had lower NAb titers compared with controls: the median NAb inhibition titer was 27% (IQR: 15.3-42%) for MM/SMM/MGUS versus 20.5% (IQR: 10-37%) for WM patients versus 38.7% (IQR: 22-54.3%) for controls (P<0.001 for all comparisons). On D50 the median NAb inhibition titer was 62.8% (IQR: 26-88.9%) for MM/SMM/MGUS versus 36% (IQR: 18-78%) for WM patients versus 90% (IQR: 58-96.4%) for controls (P<0.001 for all comparisons). 57.3% MM/SMM/MGUS, 42% WM patients and 81% controls developed NAb titers ≥50% (p<0.001 for patients versus controls). Furthermore, MM patients showed an inferior NAb response compared with MGUS on day 22 (p=0.009) and on day 50 (p=0.003). Importantly, active treatment with either anti-CD38 monoclonal antibodies or belantamab mafodotin and lymphopenia at the time of vaccination were independent prognostic factors for suboptimal antibody response following vaccination in MM (p<0.05). Disease-related immune dysregulation and therapy-related immunosuppression were involved in the low humoral response in patients with WM. Importantly, active treatment with either rituximab or Bruton's Tyrosine Kinase inhibitors (BTKIs) was proven as an independent prognostic factor for suboptimal antibody response following vaccination in WM (p<0.05). Regarding adverse events, 33% and 31.6% patients reported mild reactions after the first and second dose of the BNT162b2 vaccine, respectively; 32.8% patients vaccinated with the first dose of AZD1222 also presented with local reactions. Conclusion: Patients with MM and WM have a low humoral response following SARS-CoV-2 vaccination, especially those who are under treatment with anti-CD38-, anti-BCMA-, anti-CD20- or BTKIs-based regimens. This result suggest that these patients have to continue the protective measures against SARS-CoV-2 as they are at high risk for COVID-19. Further studies on the kinetics of immune subpopulations following COVID-19 vaccination will elucidate the underlying immune landscape and determine the potential need for additional booster vaccine doses or protective administration of antibodies against SARS-CoV-2 in MM/WM patients with poor response after full vaccination. Disclosures Terpos: Janssen-Cilag: Consultancy, Honoraria, Research Funding; BMS: Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Genesis: Consultancy, Honoraria, Research Funding; GSK: Honoraria, Research Funding; Takeda: Consultancy, Honoraria, Research Funding; Sanofi: Consultancy, Honoraria, Research Funding; Novartis: Honoraria; Amgen: Consultancy, Honoraria, Research Funding. Gavriatopoulou: Janssen: Honoraria; Takeda: Honoraria; Sanofi: Honoraria; Karyopharm: Honoraria; Genesis: Honoraria; GSK: Honoraria; Amgen: Honoraria. Kastritis: Amgen: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Genesis: Honoraria; Takeda: Honoraria; Pfizer: Honoraria. Dimopoulos: Janssen: Honoraria; BeiGene: Honoraria; Takeda: Honoraria; Amgen: Honoraria; BMS: Honoraria.

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Titers of Neutralizing Antibodies against SARS-CoV-2 Are Independent of Symptoms of Non-Severe COVID-19 in Young Adults

Viruses ◽

10.3390/v13020284 ◽

2021 ◽

Vol 13 (2) ◽

pp. 284

Author(s):

Hulda R. Jonsdottir ◽

Michel Bielecki ◽

Denise Siegrist ◽

Thomas W. Buehrer ◽

Roland Züst ◽

...

Keyword(s):

Young Adults ◽

Neutralizing Antibodies ◽

Severe Disease ◽

Humoral Response ◽

Neutralizing Antibody ◽

Neutralization Assay ◽

Asymptomatic Infection ◽

Homogeneous Population ◽

Humoral Immune ◽

Antibody Titers

Neutralizing antibodies are an important part of the humoral immune response to SARS-CoV-2. It is currently unclear to what extent such antibodies are produced after non-severe disease or asymptomatic infection. We studied a cluster of SARS-CoV-2 infections among a homogeneous population of 332 predominantly male Swiss soldiers and determined the neutralizing antibody response with a serum neutralization assay using a recombinant SARS-CoV-2-GFP. All patients with non-severe COVID-19 showed a swift humoral response within two weeks after the onset of symptoms, which remained stable for the duration of the study. One month after the outbreak, titers in COVID-19 convalescents did not differ from the titers of asymptomatically infected individuals. Furthermore, symptoms of COVID-19 did not correlate with neutralizing antibody titers. Therefore, we conclude that asymptomatic infection can induce the same humoral immunity as non-severe COVID-19 in young adults.

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Components of Global Quality of Life In Multiple Myeloma

Blood ◽

10.1182/blood.v116.21.4735.4735 ◽

2010 ◽

Vol 116 (21) ◽

pp. 4735-4735

Author(s):

Catherine D Williams ◽

Irina Proskorovsky ◽

Philip Lewis ◽

K. Jack Ishak ◽

Krista A Payne ◽

...

Keyword(s):

Quality Of Life ◽

Multiple Myeloma ◽

Body Image ◽

Research Funding ◽

Multiple Regression Model ◽

Future Perspective ◽

P Value ◽

Disease Symptoms ◽

The Mean

Abstract Abstract 4735 Introduction: Symptoms of multiple myeloma (MM) and the adverse events (AEs) associated with MM treatment can be debilitating on many levels. A better understanding of the extent to which patients are affected and how this in turn impacts global health-related quality of life (HRQOL) can improve management of patients. Methods: A survey in 11 centers in the United Kingdom and Germany gathered, among other items, data on HRQOL, measured by the European Organization for Research and Treatment of Cancer's (EORTC) generic cancer and MM modules (QLQ-C30 and QLQ-MY20 scales), from a cross-section of patients with multiple myeloma at various phases of the disease. The QLQ-C30 is comprised of a global QOL domain, 5 functional and 3 symptom domains, and 6 AE items; the QLQ-MY20 includes scales for disease symptoms, treatment side-effects, future perspective and body image. This analysis aimed to explore the association between individual QOL scales (from QLQ-C30 and QLQ-MY20) and global QOL. Values for each scale range from 0 to 100; higher values indicate better HRQOL for the global, functional, future perspective and body image scales, and worse HRQOL for the AE items, symptom domains, disease symptoms and side-effects scales. Scoring of the QLQ-C30 and MY-20 scales was described previously by Fayers et al. [i] and Cocks et al. [ii] respectively. The distribution and correlations (Spearman) between the various scales was explored. Moreover, a multiple linear regression analysis was carried out to assess the association between individual scales and global QOL (from QLQ-C30) with the aim to identify those that independently impact global QOL. Each scale was first considered alone as a predictor of global QOL; those with a statistically significant association at a p-value ≤ 0.10 were included in a multiple regression model. This was then trimmed to exclude scales that became non-significant (p-value > 0.10). Results: The survey included 154 patients: 63.0% were male and the mean age was 66.4 (SD: 10.0). Mean time since diagnosis was 3.7 years (SD: 3.7), 51.9% were currently on treatment, and 42.9% had at least one prior line of MM therapy. The mean global QOL score was 60.1 (SD: 25.5), with the middle two quartiles of patients scoring between 41.7 and 83.3. Cognitive and emotional functioning scores had means near or above 80, suggesting that these aspects of HRQOL were less affected than role (62.9 (IQR: 33.3–100)), social (63.9(IQR: 33.3–100)) and physical functioning (68.7(IQR:53.3-93.3)). While body image scores were generally high (77.9 (IQR:66.7-100)), future perspective appeared to be relatively more affected (59.9 (33.3-77.8)). Patients’ HRQOL is most affected by pain and fatigue (based on symptom and AE scales of the QLQ-C30), with means above 30, followed by insomnia and dyspnoea with means above 20, while diarrhea and nausea/vomiting scales had the lowest mean scores (below 10). The Disease Symptom (23.3 (IQR:0-38.9)) and Side Effect scale scores (19.5 (IQR:7.4-29.6)) from the QLQ-MY20 were consistent with the AE and symptom scales from the QLQ-C30. All of the domains except diarrhea and nausea/vomiting individually showed at least moderate correlations with global QOL (Spearman correlations above 0.25 in absolute value), but also exhibited strong correlations between themselves. The final multiple regression model retained physical and social functioning, fatigue, disease symptoms (QLQ-MY20) and future perspective scales (QLQ-MY20), all of which had relatively similar strength of association with global QOL. Conclusion: This study demonstrates that the impact of MM and treatment AEs can be seen on various dimensions of patients’ HRQOL, particularly reduced physical and social functioning, future perspective and various disease symptoms such as bone pain (as captured by the disease symptoms scale of the QLQ-MY20) and fatigue. Fayers P, Aaronson N, Bjordal K, Groenvold M, Curran D, Bottomley A: The EORTC QLQ-C30 Scoring Manual. 3 Edition EORTC Quality of Life Group, Brussels 2001. [ii]Cocks K, Cohen D, Wisloff F, et al. An international field study of the reliability and validity of a disease-specific questionnaire module (the QLQ-MY20) in assessing the quality of life of patients with multiple myeloma. Eur J Cancer 2007;43:1670-1678. Disclosures: Williams: Celgene: Honoraria; Jansen Cilag: Consultancy, Honoraria. Off Label Use: Some of the patients in the study received Thalidomide for the treatment of relapsed or refractory multiple myeloma. Proskorovsky:United BioSource Corporation: Consultancy, Research Funding. Lewis:Celgene International SARL: Employment. Ishak:United BioSource Corporation: Consultancy, Research Funding. Payne:United BioSource Corporation: Consultancy, Research Funding. Lordan:United BioSource Corporation: Consultancy, Research Funding. Davies:Celgene: Honoraria, Speakers Bureau; Ortho Biotech: Honoraria, Speakers Bureau. Peters:Celgene: Consultancy.

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Gene Expression Profiling-Based Risk Stratification Scores In Multiple Myeloma Can Be Highly Sensitive Towards Sample Preparation

Blood ◽

10.1182/blood.v122.21.1865.1865 ◽

2013 ◽

Vol 122 (21) ◽

pp. 1865-1865 ◽

Cited By ~ 1

Author(s):

Qing Zhang ◽

Christoph Heuck ◽

Pingping Qu ◽

Saad Z Usmani ◽

Ryan Williams ◽

...

Keyword(s):

Gene Expression ◽

Multiple Myeloma ◽

Expression Profiling ◽

Research Funding ◽

Prognostic Scores ◽

Prognostic Models ◽

Risk Scores ◽

P Value ◽

Batch Effects ◽

Paired T Test

Abstract Background Gene expression profiling (GEP) via microarray analysis enables the measurement of expression levels for tens of thousands of genes in a single experiment, and it has been widely used in clinical practice for cancer classification, risk stratification, and treatment selection. However, results of GEP-based clinical diagnostic/prognostic tests can be highly affected by batch effects when clinical samples are processed differently (e.g. on different days, by different technicians, or using different sample protocols). Yet, this problem is rarely discussed in the literature. Understanding the role of batch effects on GEP-based conclusions is vital because GEP-based-risk treatment assignment has been used for personalized treatment to improve patients' survival: patients with low risk and favorable clinical and biological features can be treated with a less intensive, lower toxicity treatment, while patients with high risk and unfavorable features can be treated with a more aggressive, potentially more toxic therapeutic approach. Here, we investigate how sample processing discrepancies influence various GEP-based prognostic models in multiple myeloma (MM) and how to adjust for such effects during data analysis. Methods/Results In 2009, Affymetrix discontinued their One-Cycle and Two-Cycle Target Labeling and Control Reagents (hereon referred to as the 'old' kit) and replaced it with a 3' IVT Express Kit (hereon referred to as the 'new' kit). To examine the impact of the replacement kit on GEP results, we set out to process eleven CD138-enriched patient plasma cell samples using both the new and old kits side-by-side before hybridizing them separately to the Affymetrix HG-U133 Plus 2.0 arrays. Various GEP-based MM prognostic scores, including UAMS-70, UAMS-80, UAMS-17, EMC-92, IFM-15, MRC-IX-6, and MILLENNIUM-100, were calculated and compared between the matched GEP pairs with either MAS5 or RMA normalization, with and without batch effect adjustment by ComBat (Combating Batch Effects When Combining Batches of Gene Expression Microarray Data). Both the UAMS-70 and UAMS-80 scores are based on log2 ratios between unfavorable and favorable genes regarding survival, which are self-normalized. However, with MAS5 alone, the UAMS-70 score was similar between the two kits (p-value=0.37 from paired t-test) but not for UAMS-80 score, which was significantly higher under the new kit (p-value<0.001 from paired t-test, Figure 1). Furthermore, besides UAMS-17, the score values from the EMC-92, IFM-15, MRC-IX-6, and MILLENNIUM-100 models without batch effect adjustment were all affected by the kit issue. After ComBat adjustment, variation caused by batch effects markedly reduced, and as a result, correlation increased between the prognostic scores of the two different kits. For most GEP-based MM prognostic scores, kit effect was minimized by RMA plus ComBat correction, which resulted in similar risk scores between the two kits. Conclusion For GEP-based prognostic models, it is important to check for possible batch effects which may be the end result of various causes, such as differences in sample preparation and processing protocols, like the new kit/old kit issue discussed here. We found that even for self-normalized prognostic signatures, risk scores can still sometimes be significantly different because of batch effects. So, it is essential to preprocess GEP raw data carefully, minimizing variance caused by batch effects, and adjusting any GEP-based diagnostic result accordingly, e.g. cutoff values in risk-assessment models. Disclosures: Usmani: Celgene: Consultancy, Research Funding, Speakers Bureau; Onyx: Research Funding, Speakers Bureau. Barlogie:Celgene: Consultancy, Honoraria, Research Funding; Myeloma Health, LLC: Patents & Royalties.

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Efficacy and Safety of Triplet Versus Doublet Salvage Therapies Among Patients with Multiple Myeloma (MM) Experiencing Early Relapse: Meta-Analysis of Phase III Randomized Controlled Trials (RCTs)

Blood ◽

10.1182/blood.v126.23.5344.5344 ◽

2015 ◽

Vol 126 (23) ◽

pp. 5344-5344 ◽

Cited By ~ 1

Author(s):

Ajay K. Nooka ◽

Jonathan L. Kaufman ◽

Madhusmita Behera ◽

Charise Gleason ◽

Hannah Collins ◽

...

Keyword(s):

Multiple Myeloma ◽

Research Funding ◽

Mixed Model ◽

Meta Analysis ◽

Phase Iii ◽

P Value ◽

Serious Adverse Events ◽

Early Relapse ◽

The Impact ◽

Relapsed Myeloma

Abstract Introduction: Controversy exists regarding the choice of triplet versus doublet salvage therapy among patients with multiple myeloma (MM) experiencing early relapse. Triplet therapies produce deeper responses (CR, ≥VGPR, ORR) and result in prolonged progression free survival (PFS) while doublet therapies demonstrate an improved toxicity profile. We performed a meta-analysis of the RCTs comparing triplet to doublet salvage regimens in early relapsed myeloma patients (1-3 prior lines of therapy). The objective is to test the hypothesis that triplet regimens are tolerable, improve CR, ≥VGPR, ORR rates and would translate to an improved PFS. Methods: We searched Pubmed, Cochrane databases and ASH, ASCO conference proceedings from 01/2000 through 07/2015 for publications and abstracts to identify the phase III RCTs comparing triplet vs. doublet salvage therapies among patients with relapsed myeloma. A meta-analysis of 4 RCTs (PANORAMA1, MMVAR/IFM 2005-04, ASPIRE, ELOQUENT2 consisting of 2475 patients) was performed using the fixed (Mantel-Haenszel) and random (DerSimonain and Laird) models to calculate the impact of triplets versus doublets (table 1) by evaluating the CR, ≥VGPR, ORR, PFS and toxicities. Mature OS data was not available for the RCTs, hence not included in meta-analysis. The consistency of results (effect sizes) among studies was investigated by means of 2 heterogeneity tests: the χ 2-based Cochran's Q test, and the I2 Statistic. We considered that heterogeneity was present when the P-value of the Cochran's Q test was <.1 and the I2 statistic was > 50%. Results: The pooled odds ratios of ORR, ≥VGPR and CR with triplets vs. doublets were 1.935 (P <0.000; 95% CI: 1.614-2.321); 2.185 (P <0.000; 95% CI: 1.832-2.606); 2.461 (P <0.000; 95% CI: 1.888-3.207) respectively, indicating that the odds of achieving higher quality responses are improved with triplet regimens compared to the use of a doublet regimens. The pooled hazard ratio (HR) for PFS was 0.661 (95% CI 0.596-0.734; P =0.000) in favor of triplet regimens (Figure 1). The Q-statistic for PFS (P =0.725; df =3; I2 = 0.00) suggests homogeneity across studies. Though the relative risk of selected ≥grade 3 serious adverse events (G3 SAE) was higher with triplet regimens (diarrhea, fatigue, thrombocytopenia 2.288 (95% CI 1.637-3.197; P =0.000), 1.654 (95% CI 1.263-2.166; P =0.000), 2.434 (95% CI 1.934-3.063; P =0.000), respectively), the overall G3 SAE were comparable with RR 1.498 (95% CI 1.176-1.908; P =0.001) favoring doublets. Conclusion: Our mixed model meta-analysis demonstrates that triplet regimens in early relapsed myeloma patients result in improved ORR, ≥VGPR, CR and PFS compared to doublets. G3 SAEs are higher with triplet regimens, however this appears to be influenced by the regimen-related toxicity from the PANORAMA1 trial. Appropriate dose modifications or use of selective HDAC inhibitors in future may mitigate the toxicities of the regimen. The pooled estimates ofresponse and survival strongly favor triplets in the early relapsed setting. Table 1. Triplet vs. doublet regimens in RCTs Trial Triplet regimen Doublet regimen PANORAMA1 Panobinostat, bortezomib, dexamethasone Placebo, bortezomib, dexamethasone MMVAR/IFM 2005-04 Bortezomib, thalidomide, Dexamethasone Thalidomide, Dexamethasone ASPIRE Carfilzomib, lenalidomide, Dexamethasone Lenalidomide, Dexamethasone ELOQUENT 2 Elotuzumab, lenalidomide, Dexamethasone Lenalidomide, Dexamethasone Figure 1. VGPR rates and PFS with triplet vs. doublet regimens Figure 1. VGPR rates and PFS with triplet vs. doublet regimens Disclosures Nooka: Spectrum Pharmaceuticals: Consultancy; Onyx Pharmaceuticals: Consultancy. Kaufman:Onyx: Consultancy; Celgene: Consultancy; Novartis: Research Funding; Onyx: Research Funding; Merck: Research Funding; Janssen: Consultancy; Spectrum: Consultancy; Novartis: Consultancy. Gleason:Onyx: Consultancy; Novartis: Consultancy; Celgene: Consultancy. Lonial:Janssen: Consultancy, Research Funding; Onyx: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Millennium: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; Celgene: Consultancy, Research Funding.

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A 13-Glycosylation Gene Signature in Multiple Myeloma Can Predicts Survival and Identifies Candidates for Targeted Therapy (GiMM13)

Blood ◽

10.1182/blood.v128.22.4423.4423 ◽

2016 ◽

Vol 128 (22) ◽

pp. 4423-4423 ◽

Cited By ~ 2

Author(s):

Caoilfhionn Connolly ◽

Alokkumar Jha ◽

Alessandro Natoni ◽

Michael E O'Dwyer

Keyword(s):

Gene Expression ◽

Multiple Myeloma ◽

High Risk ◽

Research Funding ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

Rank Test ◽

P Value ◽

Log Rank Test ◽

Novel Approach

Abstract Introduction Advances in genomics have highlighted the potential for individualized prognostication and therapy in multiple myeloma (MM). Previously developed gene expression signatures have identified patients with high risk (Kuiper et al, Blood 2016) however, they provide few insights into underlying disease biology thereby limiting their use in informing treatment decisions. Glycosylation is deregulated in MM (Glavey et al), and potential consequences include altered cell adhesion, signaling, immune evasion and drug resistance. In this study we have utilized RNA sequencing data from the IA7 CoMMpass cohort to characterize the expression profile of genes involved in glycosylation. This represents a novel approach to identify a distinct molecular pathway related to outcome, which is potentially actionable. Methods A pathway based approach was adopted to evaluate genes implicated in glycosylation, including the generation of selectin ligands. A literature review and KEGG pathway analysis of pathways relating to O-glycans, N-glycans, sialic acid metabolism, glycolipid synthesis and metabolism was completed. RNA Cufflinks-gene level FPKM expression of 458 patients enrolled in the IA7 cohort of the Multiple Myeloma Research Foundation (MMRF) CoMMpass trial (NCT145429) were analysed as derivation cohort. We developed expression cut-offs using a novel approach of adjusted existing linear regression model to define the gene expression cut-off by applying 3rd Quartile data (q1+q2/2-qmin). The analysis of overall survival (OS) was completed using adjusted 'kpas' R-package according to our cut-off model. Association between individual transcripts and OS was analyzed with log-rank test. Genes with p-value <0.2 were used in subsequent prioritization analysis. This cut-off methodology was employed to define the nearest neighbor for a gene for Gene Set Enrichment Analysis (GSEA). As far as 4th neighbor above and below the cut off was used to have centrally driven gene selection method for prioritization. The gene signature was validated in GSE2658 (Shaughnessy et al) dataset. Results Initial analysis yielded 184 prospective genes. 147 were significant on univariate analysis. Following further prioritization of these genes, we identified thirteen genes that had significant impact upon outcomes (GiMM13). Figure 1 reveals that GiMM13 signature has a significant correlation with inferior OS (HR 4.66 p-value 0.022). The prognostic impact of stratifying GiMM13 positive (High risk) or GiMM13 negative (Low risk) by ISS stage was evaluated. In Table 1. Kaplan Meier estimates generated for GiMM13 (High) or GiMM13 (Low) stratified by ISS are compared statistically using the log rank test. The prognostic ability of GiMM13 to synthesize distinct subgroups relative to each ISS stage is shown in Figure 2. ISS1-Low is the the lowest risk group with best prognosis. Hazard ratios relative to the ISS1-Low group were 1.8, p-value 0.029 (ISS2-Low), 2.1, p-value 0.031 (ISS3-Low), 4.3, p-value 0.04 (ISS1-HR), 5.9, p-value 0.039 (ISS2-HR) and 3.1, p-value 0.001 (ISS3-HR). The GiMM13 signature enhances the prognostic ability of ISS to identify patients with inferior or superior outcomes respectively. Conclusion While the therapeutic armamentarium for MM has expanded considerably, the significant molecular heterogeneity in the disease still poses a significant challenge. Our data suggests aberrant transcription of glycosylation genes, involved predominantly in selectin ligand synthesis, is associated with inferior survival outcomes and may help identify patients likely to benefit from treatment with agents targeting aberrant glycosylation, e.g. E-selectin inhibitor. Consistent with recent findings in chemoresistant minimal residual disease (MRD) (Paiva et al, Blood 2016), it would appear that O-glycosylation, rather than N-glycosylation is most significantly implicated in this biological processes conferring inferior outcomes. In conclusion, using a novel pathway-based approach to identify a 13-gene signature (GiMM13), we have developed a robust tool that can refine patient prognosis and inform clinical decision-making. Acknowledgment These data were generated as part of the Multiple Myeloma Research Foundation Personalized Medicine Initiatives (https://research.themmrf.org and www.themmrf.org). Disclosures O'Dwyer: Glycomimetics: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding.

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Treatment of Persons with Multiple Myeloma in Underprivileged Circumstances: Real-World Data of a Prospective Study in a Single Institution

Blood ◽

10.1182/blood-2019-128972 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 5707-5707

Author(s):

Iván Murrieta-Álvarez ◽

David P. Steensma ◽

Juan Carlos Olivares-Gazca ◽

Jesús Mauricio Olivares-Gazca ◽

Andrés A. León-Peña ◽

...

Keyword(s):

Multiple Myeloma ◽

Research Funding ◽

Population Data ◽

Mantel Test ◽

Novel Agents ◽

P Value ◽

Cell Transplant ◽

Hematopoietic Cell Transplant ◽

Real World Data ◽

Term Survival

Background The treatment of patients with multiple myeloma (MM) has evolved in recent years, and the disease-associated prognosis has improved substantially. This improvement has been driven largely by the approval of novel agents, many of which are expensive and not universally available. Less expensive but effective approaches would be of value globally. Patients and methods All consecutive MM patients diagnosed in the Centro de Hematología y Medicina Interna de Puebla after 1993 were prospectively entered in this study. Patients were given oral thalidomide (T), 100 mg/day, oral dexamethasone (D) (36-40 mg/week) and aspirin 100 mg/day. Bortezomib (V) (1.75 mg subcutaneously every week) was administered to those who could afford it. After 4-6 weeks of treatment, patients were offered an outpatient-based hematopoietic cell transplant (HCT). After the recovery of granulocytes following the HCT, patients continued indefinitely on T; those who failed to tolerate were switched to lenalidomide (R) (25 mg/day). The assessment of overall survival (OS) for all groups was achieved through the Kaplan-Meier method using the Cox-Mantel test. All the statistical analyses used a p value <0.05 to considered them statistically significant. Results Among 108 patients with MM who were prospectively accrued in the study (47 females and 61 males), the median age was 57 years (range 33 to 90). IgG myeloma represented 60% of patients and 49% had International Scoring System (ISS) stage III disease. The median (OS for all patients has not been reached and is >157 months. The median OS of patients who did not receive HCT was similar to those who did, with a trend for better outcomes with HCT (A). The response rate (complete remission or very good partial remission) was 71.8% for those given TD versus 88.3% for those given VTD before HCT, but OS was not different (B, C and D). As post-HCT maintenance, 37 patients received T; 26 of those (70%) could be maintained indefinitely with T, whereas 11 were switched into R after a median of 7 months; median OS of patients maintained after HCT with T or R was not different. Comparing the current population data with those obtained between 1983 and 1993 in the same institution employing only MP, the prognosis of MM patients was noted to have improved substantially. In our previous experience in the same institution, the median OS of patients treated solely with MP was 33 months, with a 72-month survival of 30%, whereas in this study of patients given IMiDs +/- HCT, median OS has not been reached and the 72-month OS is 60%, twice that obtained with MP. When analyzing the OS of patients included in this study and separated by 5-year intervals, survival continued to improve since 1993. Conclusions In this series, a regimen incorporating low cost novel agents and outpatient HCT was associated with excellent long-term survival in the treatment of persons with MM. This approach may be a model for treatment of MM in middle-income countries. Figure Disclosures Steensma: Aprea: Research Funding; Arrowhead: Equity Ownership; Summer Road: Consultancy; Astex: Consultancy; Onconova: Consultancy; H3 Biosciences: Other: Research funding to institution, not investigator.; Stemline: Consultancy; Pfizer: Consultancy.

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Patient, Disease and Treatment Characteristics of Long-Term Survivors of Multiple Myeloma

Blood ◽

10.1182/blood.v124.21.2019.2019 ◽

2014 ◽

Vol 124 (21) ◽

pp. 2019-2019

Author(s):

Michael Kennah ◽

Nastaran Noroozi ◽

Esther Masih-Khan ◽

Tony Panzarella ◽

Donna E Reece ◽

...

Keyword(s):

Multiple Myeloma ◽

Stem Cell ◽

Research Funding ◽

Novel Agents ◽

P Value ◽

Prolonged Therapy ◽

Disease Characteristics ◽

Treatment Characteristics ◽

Long Term Survivors

Abstract Introduction With routine use of autologous stem cell transplantation (ASCT) and novel agents, survival of patients with multiple myeloma (MM) has improved in recent years. Yet, MM remains incurable and long-term survivors (LTS) of ≥10 years from diagnosis remain uncommon. This study aims to identify patient, disease and treatment characteristics of MM LTS, with particular interest in the effect of novel therapies. Methods A retrospective analysis was conducted of MM patients diagnosed between 1998 and 2002 and treated at Princess Margaret Cancer Centre, a tertiary care institution. LTS were identified by survival of ≥10 years from diagnosis and were compared with patients diagnosed and followed contemporaneously at our institution with survival <10 years from diagnosis. Candidate predictor variables were identified using univariate and multivariate logistic regression analysis; a p value <0.05 was considered statistically significant. Results Seventy-five patients were identified as LTS, with a control group of 119 patients with survival <10 years. The median survival for all patients was 7.3 years (range 0.6-14.5 years). Comparison of patient, disease and treatment characteristics between groups are detailed in Table 1. Patient and disease characteristics: At diagnosis, LTS were younger (p = 0.0005) and at earlier ISS stage (p = 0.02) than non-LTS. At diagnosis, LTS had a higher baseline mean hemoglobin level (p = 0.02) and platelet count (p = 0.003), and less frequently had lytic bone lesions (p = 0.03), consistent with earlier stage at diagnosis. There were no significant differences in baseline mean leukocyte count, serum calcium and creatinine. Cytogenetics were not routinely performed during this time period. Treatment characteristics: Of the LTS, 95% received an ASCT, as compared to 86% of non-LTS (p = 0.77). Median age at transplant was younger in the LTS (p = 0.003). LTS experienced a longer time from transplant to disease progression (TTP) than non-LTS (p < 0.0001) despite achieving similar rates of complete response (CR) and very good partial response (VGPR). Exposure to novel agents was common in both the LTS and control groups (73% vs. 82%, p = 0.24). Length of exposure to thalidomide (p = 0.01) and lenalidomide (p = 0.002) was greater in LTS, leading to higher quality responses and longer TTP with both agents (p < 0.0001 and p = 0.002, respectively). Similarly, bortezomib exposure was longer in the LTS (p = 0.02) with a longer TTP over that achieved in non-LTS (p = 0.008), although the quality of response was not significantly different. In a multivariate analysis, a longer TTP after ASCT (OR = 1.004; 95% CI 1.002-1.006, p = 0.0008), thalidomide (OR = 34; 95% CI 1.7-690.6; p = 0.023) and bortezomib (OR = 28.2; 95% CI 3.5-228; p = 0.002) treatment, though not after lenalidomide, were independently predictive of LTS. Table 1. Comparison of characteristics between LTS and non-LTS Disease characteristics LTS (n=75) Non-LTS (n=119) p -value Age (y) 53.2 59.1 0.0005 ISS stage I 66 43 0.02 II 20 33 III 14 24 Hemoglobin (g/L) 109 102 0.03 Leukocytes (x 109/L) 5.97 6.29 0.45 Platelets (x 109/L) 255 218 0.003 Calcium (mmol/L) 2.41 2.46 0.39 Creatinine (umol/L) 107.5 148.6 0.16 Presence of lytic lesions (%) 55 70 0.03 Treatment characteristics Autologous stem cell transplant Age (median, years) 53.3 59.4 0.003 Response (CR or VGPR, %) 47 44 0.62 TTP (median, months) 59 19.9 0.001 Thalidomide Age (median, years) 11.4 8.2 0.01 Response (CR or VGPR, %) 40 23 0.02 TTP (median, months) 32.4 9.6 <0.0001 Lenalidomide Age (median, years) 23.2 8.1 0.002 Response (CR or VGPR, %) 56 35 0.04 TTP (median, months) 24 10.8 0.002 Bortezomib Age (median, years) 6.8 3.3 0.02 Response (CR or VGPR, %) 40 28 0.24 TTP (median, months) 18 6 0.008 Conclusion LTS with MM received prolonged therapy and achieved higher quality responses to both transplant and novel agents. Our analysis suggests that LTS have baseline characteristics (age, early-stage disease, greater marrow reserve) that may enable them to tolerate more intensive or prolonged therapy. However, it is possible that LTS have disease more indolent or sensitive to therapeutic interventions. The retrospective nature of the study limits our ability to further characterize this. Regardless, these data suggest that the practice of continued exposure to novel agents may contribute to long-term survival in MM. Disclosures Reece: Otsuka: Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Millennium: Honoraria, Research Funding; Merck: Research Funding; BMS: Research Funding; Novartis: Honoraria, Research Funding; Amgen: Honoraria. Trudel:Celgene: Honoraria; Novartis: Honoraria; Glaxo Smith Kline: Honoraria, Research Funding; Oncoethix: Research Funding. Kukreti:Celgene: Consultancy, Honoraria. Tiedemann:Janssen: Honoraria. Chen:Celgene: Honoraria; Janssen: Honoraria.

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Associations between Microrna Expression, Disease Progression and Ex Vivo Drug Response in Multiple Myeloma

Blood ◽

10.1182/blood-2019-128181 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 3069-3069

Author(s):

Romika Kumari ◽

Ashwini Kumar ◽

Alun Parsons ◽

Minna Suvela ◽

Juha Lievonen ◽

...

Keyword(s):

Multiple Myeloma ◽

Disease Progression ◽

Mirna Expression ◽

Research Funding ◽

Drug Response ◽

Drug Sensitivity ◽

Mtor Inhibitors ◽

P Value ◽

Diagnosis And Prognosis

Targeted drug treatment strategies have significantly prolonged the overall survival rate among multiple myeloma (MM) patients. However, high relapse rates and multiple drug resistance still pose major challenges. Although, the underlying molecular features of the disease have been explored both at the genomic and transcriptomic levels, the functional role of microRNAs (miRNA) in MM disease progression and prognosis is yet to be investigated at a personalized level. In earlier studies, microRNAs have been implicated to regulate gene expression and were determined to play crucial roles in the biology of MM by acting as oncogenes or tumor suppressors. Nevertheless, considering the heterogeneity of MM, little is known about the roles of miRNAs in controlling MM disease progression and drug response at an individualized systems level. We collected bone marrow aspirates from MM patients at diagnosis (n=20) and relapse (n=25) after informed consent and following approved protocols in accordance with the Declaration of Helsinki. CD138+ plasma cells were enriched from the bone marrow samples and used for miRNA-sequencing and drug sensitivity and resistance testing (DSRT). The miRNA was prepared from the CD138+ cells and subjected to sequencing using Illumina compatible technologies. DSRT was performed and responses to 83 clinically approved drugs and investigational compounds were measured as drug sensitivity scores (DSS) as described previously (Majumder et al., Oncotarget 2017). The pairwise comparative analysis of miRNA expression and drug responses was performed using Spearman's rank-order correlations, to elucidate significant associations of miRNA expression with drug sensitivity and resistance. Additionally, using DEseq2 the differential miRNA expression was determined for the newly diagnosed and relapse samples to deconvolute the role of miRNAs in MM disease progression. The comparative analysis of the miRNA expression and drug sensitivity scores revealed statistically significant associations between miRNA expression and drug sensitivity measures with the Spearman coefficient (r) ranging from -0.71 to 0.64 (adjusted p-value ≤ 0.05) (Figure 1A). Negative associations were more prevalent, with 40 miRNAs negatively associated with ≥1 drug response from the total of 30 predicted drugs. miR-486, which is known to be an effective biomarker in diagnosis and prognosis of multiple cancer types (Jiang et al., Oncotarget 2018), was found to have significant negative correlation (r= -0.71 to -0.52, p-value ≤ 0.01) with the responses of 14 drugs. Similarly, negative correlation was observed for miR-144 with 12 drugs and miR-584 with 9 drugs. We observed that PI3K/mTOR inhibitors and HDAC inhibitors were common amongst all the significant negative correlations predicted. Specifically, the PI3K/mTOR inhibitors apitosilib, omipalisib and buparlisib were found to be negatively associated with the expression of 18, 14 and 7 miRNAs respectively. These observations can lead to the understanding of miRNA mediated regulation of molecular pathways involved in drug resistance. Differential miRNA expression analysis between newly diagnosed and relapse MM samples revealed the involvement of miRNAs in disease progression. The analysis resulted in total of 31 significant differentially expressed miRNAs with fold change ≥2 and adjusted p-value ≤ 0.1 (Figure 1B). Several miRNAs known to play crucial roles in cancer diagnosis and prognosis were found to be significantly upregulated in the relapse samples. In particular, 25 miRNAs were upregulated, including following miR-17/92 cluster members: miR-18b, miR-20a, miR-92b and miR-106a, which are known to have an oncogenic role in various cancer types (Mogilyansky & Rigoutsos, Cell Death and Differentiation 2013). Interestingly, 12/31 differentially regulated miRNAs were located on chromosome X. Although cytogenetic alteration data predicted that chromosome 1q gain is significantly prominent in the relapse samples (p-value = 0.009), only 3/31 differentially regulated miRNAs were located on chromosome 1. These results demonstrate the role of miRNAs in regulating drug response and disease progression in multiple myeloma. Monitoring miRNA expression profiles in MM patients can facilitate the assessment of treatment outcome and prognosis, and miRNAs could potentially be useful prognostic and treatment biomarkers for MM. Disclosures Silvennoinen: Amgen: Research Funding; Bristol-Myers Squibb (BMS): Research Funding; Takeda: Research Funding; Celgene: Research Funding. Heckman:Celgene: Research Funding; Novartis: Research Funding; Oncopeptides: Research Funding; Orion Pharma: Research Funding.

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Single-Cell Pathway Enrichment and Regulatory Profiling of Multiple Myeloma across Disease Stages

Blood ◽

10.1182/blood-2019-131361 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 364-364

Author(s):

Tianjiao Wang ◽

Hua Sun ◽

Daniel Cui Zhou ◽

Ruiyang Liu ◽

Lijun Yao ◽

...

Keyword(s):

Cell Cycle ◽

Multiple Myeloma ◽

Board Of Directors ◽

Research Funding ◽

Plasma Cells ◽

Cell Types ◽

Differentially Expressed ◽

P Value ◽

Advisory Committees ◽

Pathway Enrichment

Multiple myeloma (MM) is a hematological malignancy, defined by aberrant monoclonal proliferation of plasma cells in the bone marrow, that to date remains an incurable disease despite advances in treatment. Key genetic and epigenetic alterations that drive MM pathogenesis have been identified, but a comprehensive profile of affected cellular pathways has yet to be fully characterized. In this study, we integrate whole-genome and whole-exome sequencing data with single-cell RNA sequencing (scRNA-seq) data from 13 patients across multiple treatment stages to 1) assess differential pathway enrichment between tumor subpopulations, 2) trace the clonal evolution of dominant disease mechanisms, and 3) investigate signaling interactions between surrounding cell types. We also analyzed bulk genomic and transcriptomic data from 662 additional Multiple Myeloma Research Foundation (MMRF) tumor samples as a large reference cohort for highly prevalent pathway disturbances. To assess whether tumor subpopulations rely on different oncogenic programs for proliferation, we analyzed the differential expression of key genes (FDR-adjusted p-value <0.05) in 12 canonical oncogenic pathways. Cell cycle, Hippo, RTK/RAS, and NFkB pathways contain the highest numbers of differentially expressed genes, with certain subclusters upregulating as many as 25% of annotated cell cycle genes and over 90% of annotated Hippo genes, whereas p53, Notch, Nrf2, and DNA repair genes tend to be uniformly expressed across subpopulations. Next, we evaluated changes in pathway enrichment across different disease timepoints, with the goal of capturing the reorganization of functional profiles through successive treatment and relapse cycles. We assessed statistical enrichment of pathways containing differentially expressed genes (DEGs) unique to Smoldering Multiple Myeloma (SMM), primary, and relapse stages using the KEGG pathway database (n = 2, 17, and 7 pathways, respectively; FDR-adjusted p-value of enrichment < 0.05). SMM is the only stage where hematopoietic differentiation and the PI3K-Akt pathways are variably expressed between plasma cell subpopulations, suggesting that these pathways may play a role in initiating events. Only primary tumor samples show significant intra-tumor variability of p53 regulation, which is lost in the relapsed tumor and may reflect selection due to treatment. Relative to SMM, primary and relapse samples are enriched for changes in the MAPK, NFkB, RAP1, and cell cycle pathways, indicating potential sources of tumor resistance. We then analyzed pathway enrichment within the tumor microenvironment to enhance our understanding of tumor development in the context of surrounding tissues. We see frequent changes in many immune cell types in TLR signaling as the disease progresses, driven by differential expression of NFkB1A, JUN, and FOS, all of which are key upstream regulators of the AP-1 pathway and responders to the MAPK and PI3K signaling cascades. These microenvironment changes may be complementary to the PI3K and MAPK dysregulation observed in tumor plasma cells. Proteasome and ubiquitin genes, which affect secretion, autophagy, and apoptosis pathways that may be relevant to MM pathogenesis are also frequently differentially expressed in immune cells between disease stages. Finally, we integrate bulk whole-exome and whole-genome sequencing analysis (from both the 13-patient cohort and MMRF) to obtain a more complete understanding of how pathways become dysregulated in MM. Our findings advance the understanding of how MM tumor subpopulations differentially regulate cellular pathways and interact within the tumor microenvironment. Disclosures O'Neal: Wugen: Patents & Royalties: Patent Pending; WashU: Patents & Royalties: Patent Pending. Rettig:WashU: Patents & Royalties: Patent Application 16/401,950. Oh:Incyte: Membership on an entity's Board of Directors or advisory committees; Blueprint Medicines: Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy. Vij:Bristol-Myers Squibb: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Genentech: Honoraria; Janssen: Honoraria; Karyopharm: Honoraria; Sanofi: Honoraria; Takeda: Honoraria, Research Funding. DiPersio:Amphivena Therapeutics: Consultancy, Research Funding; Magenta Therapeutics: Equity Ownership; Karyopharm Therapeutics: Consultancy; Incyte: Consultancy, Research Funding; RiverVest Venture Partners Arch Oncology: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy; Bioline Rx: Research Funding, Speakers Bureau; Macrogenics: Research Funding, Speakers Bureau; WUGEN: Equity Ownership, Patents & Royalties, Research Funding; NeoImmune Tech: Research Funding; Cellworks Group, Inc.: Membership on an entity's Board of Directors or advisory committees.

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