Myeloma Survival Research Articles

Treatment evolution and improved survival in multiple myeloma in Taiwan

The incidence of multiple myeloma (MM) is increasing worldwide, but the rate of increase is greatest in Asia. Few data describe the epidemiology and treatment of MM in Asia. Building on a cohort study from 2007 to 2012 using the Taiwan National Healthcare Insurance Research database, we extended our analysis to estimate the disease burden and treatment patterns of patients with MM in Taiwan through 2015. A further 1664 patients with newly diagnosed MM from 2013 to 2015 (total 4387 patients from 2007 to 2015) were enrolled and followed up until death or end of the observation period (December 31, 2016), whichever occurred first. The age distribution of the 2013–2015 cohort was similar to that for previous years, but there were fewer men (52.1% versus 58.0%), and more patients had renal impairment at diagnosis (19.7% versus 16.4%). From 2007 to 2015, crude annual incidences per 100,000 population of newly diagnosed MM increased from 1.74 to 2.48 and age-adjusted incidences from 1.41 to 1.65. Crude all-cause mortality rates increased over time. Case fatality decreased from 25.5 to 18.3% and median survival increased from 2.10 to 3.12 years. From 2007 to 2015, the percentage of patients receiving first-line therapy with novel agents increased from 0.4 to 89.4%, autologous stem cell transplantation doubled, and chemotherapy use decreased by 81%. Comprehensive national data covering 9 years of follow-up demonstrate continuing change in the disease burden, treatment, and survival of MM in Taiwan. Despite increased use of new treatments, MM remains largely incurable.

Fractures and survival in multiple myeloma: results from a population-based study.

Multiple myeloma causes lytic bone lesions and fractures. The impact of fractures on multiple myeloma (MM) survival is unclear. The aim of this study was to evaluate the effect of fractures on survival in MM using data from MM patients diagnosed in Sweden in the years 1990-2013, identified from the Swedish Cancer Registry. Information on date of birth, MM diagnosis, fractures, and death was collected from central registries. A Cox regression model was used to compare survival in patients with and without a fracture at MM diagnosis and another Cox model was used with fracture as a time-dependent variable to assess the effect of fracture on survival after MM diagnosis. Results were adjusted for age, sex, year of diagnosis, and previous fractures. A total of 14,013 patients were diagnosed with MM during the study, of whom 1,213 (8.7%) were diagnosed with a fracture at MM diagnosis, and 3,235 (23.1%) after diagnosis. Patients with a fracture at diagnosis were at a significantly increased risk of death (hazard ratio=1.28; 95% confidence interval: 1.19-1.37). The risk of death was significantly increased in patients with a fracture after MM diagnosis (2.00; 1.90-2.10). The impact of fractures on survival did not change significantly between the two calendar periods 1990-1999 and 2000-2013 (0.98; 0.89-1.08). Our large study shows that MM patients with fractures are at a significantly increased risk of dying compared to those without fractures, which stresses the importance of preventing bone disease in MM.

Examining Facility & Provider Influence on Multiple Myeloma Survival

Examining Facility & Provider Influence on Multiple Myeloma Survival

Trends in myeloma relative survival in Queensland by treatment era, age, place of residence, and socioeconomic status

Relative survival (RS) in myeloma has improved in younger but not older patients (≥80 years) with treatment advances. Whether place of residence or socioeconomic status (SES) affect RS is unknown. We used the Queensland cancer registry to calculate the five-year RS of myeloma patients diagnosed between 1982 and 2014. This period was divided into three eras: (1) 1982–1995 chemotherapy alone; (2) 1996–2007 autologous stem cell transplantation; (3) 2008–2014 novel agents (proteasome inhibitors and IMIDs). 6025 patients were diagnosed from 1982 to 2014. RS improved across eras: (1) 30% vs. (2) 43% vs. (3) 53% (p < .001 (2) vs. (1); p < .001 (3) vs. (2)). RS improved across all age groups, including patients ≥80 years. Patients with disadvantaged SES (39% vs. affluent 46%; p < .001) and rural patients (40% vs. urban 45%; p < .001) had an inferior RS. RS has improved across all ages with treatment advances.

Versican (VCAN) Proteolysis Predicts Survival in Multiple Myeloma (MM) after High Dose Therapy and Autologous Hematopoietic Cell Transplantation (HDT/AHCT)

Background: Despite unprecedented response rates associated with "novel agents", HDT/AHCT is a therapeutic mainstay for transplant eligible patients with MM. However, relapse is universal and almost all patients diagnosed with symptomatic myeloma will die of their disease. The immunosuppressive network orchestrated by regulatory myeloid or lymphoid cells and effector dysfunction characterize relapses in MM. We have shown that myeloma-infiltrating myeloid cells produce versican (VCAN), a large matrix proteoglycan that promotes tumor sustaining inflammation and immunosuppression. VCAN proteolysis by a-disintegrin-and-metalloproteinase-with-thrombospondin-motifs(ADAMTS) proteases generates versikine, a bioactive fragment ("matrikine") that regulates Batf3-dendritic cells that control CD8+-attracting chemokine networks. We have shown that VCAN proteolysis predicts post AHCT survival in myeloma in a small series of patients. Here, we explore whether VCAN proteolysis is prognostic of outcomes in an expanded cohort. Methods: Bone marrow core biopsies from patients who underwent HDT/AHCT for MM from 2015-2018 were analyzed at day 90-100 (n=66). ADAMTS protease-mediated VCAN proteolysis was analyzed by immunohistochemistry from the biopsies through detection of neo-epitope DPEAAE as described previously. CD8+ T cells per high power field (HPF) was calculated at 400X magnification (10X ocular with a 40X objective) for available patients (N=35). Patients were divided into low (1+), moderate (2+) and high (3+) groups based on the scoring intensity. Patient characteristics were compared between the three groups using chi-square test or ANOVA as appropriate. Correlation analyses for VCAN proteolysis and CD8+ T cells/HPF were performed using ANOVA and linear regression in patients with available data (n=35). Kaplan Meier estimates were used to generate overall survival (OS) and progression-free survival (PFS). Multivariate analysis using Cox regression model were performed to correlate the association between VCAN proteolysis with PFS and OS using factors that affect outcomes: age, gender, cytogenetics, and treatment regimens. Logistic regression analyses were used to estimate predictors of 2-year OS and PFS. Data were analyzed using SPSS and p&lt;0.05 was considered statistically significant. Results: A total of 66 patients were included and VCAN proteolysis intensity was observed as low (1+) in 32 (48.5%), moderate (2+) in 23 (35%) and high (3+) in 11 (17%) patients respectively. Baseline characteristics between the three groups were similar (Table 1). VCAN proteolysis did not correlate with pre-AHCT response rates but was associated with post AHCT response (&gt;very good partial response, VGPR in low: 87.5%, moderate: 65%, high: 45.5%; p=0.016, Figure 1A). The median follow-up of survivors was 37 months (range 7-116). Higher VCAN proteolysis had worse median PFS (months) [9 (95% CI 0-24) in high vs. 29 (95% CI 11-47) in moderate vs. 53 (95% CI 43-63) in low; p=0.019, Fig 1B) but no difference was observed in OS (Figure 1C). On multivariate analysis, moderate/high VCAN proteolysis had significantly worse PFS compared to low cohort (HR 2.62, 95% CI 1.20-5.74; p=0.016), but not significant for OS (HR 1.82, 95% CI 0.78-4.20; p=0.164). High/moderate VCAN proteolysis was associated with worse 2-year PFS (OR 2.79, 95% CI 1.02-7.64; p=0.046) and 2-year OS (OR 4.26, 95% CI 1.32-13.74; p=0.015) respectively. VCAN proteolysis intensity also correlated with higher CD8+ T cells (median in low: 15.5, moderate: 26, high: 66; regression coefficient 21.9, 95% CI 11.8-32.0, p&lt;0.001). Of 25 patients with VCAN status available at diagnosis, 4 (16%) converted from low to moderate/high status, while 5 (20%) converted from moderate/high to low VCAN status post AHCT respectively. Conclusions: We demonstrate in an expanded cohort that VCAN proteolysis status is associated independently with post AHCT response status and PFS. Increasing VCAN proteolysis signal intensity was associated with progressively worse outcomes consistent with the hypothesis that VCAN proteolysis is a surrogate marker for an immunosuppressive environmental network that promotes MM survival and CD8 T cell dysfunction. VCAN accumulation and turnover maybe predictive of early risk of relapse and can form the basis of future clinical trials aimed at restoring anti-MM immunity guided by VCAN proteolysis status. Disclosures Dhakal: Amgen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Honoraria; Takeda: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees. 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.

Genetic and Transcript Changesin Cereblon in IMiD-Treated Myeloma Patients

Myeloma survival has been significantly improved by novel therapies over the past two decades. Immunomodulatory agents (IMiDs, Lenalidomide (LEN) and Pomalidomide (POM)) are a backbone of current treatment strategies. But myeloma (MM) remains incurable, because patients ultimately relapse. IMiD drug resistance mechanisms are multifactorial, and can be either dependent or independent of IMiDs binding to Cereblon (CRBN) (a component of an E3 ligase) in tumor and immune cells. A small series of relapsed patients demonstrated sub-clonal mutation rates of 12% in CRBN and 10% in other CRBN pathway genes (Kortum et al, Blood 2016), but the clinical implication of this observation is unknown. In contrast, baseline gene expression of CRBN was not associated with clinical outcome to POM-DEX therapy (Qian et al, Leukemia &amp; Lymphoma 2018). An integrated assessment of the burden of different mechanisms of CRBN function loss, the selective pressure for their survival, and their effects on response to CRBN-modulating agents, is lacking. For patients that progress on IMiD-based therapies, there is a need to develop drugs that will overcome their resistance. To appropriately target the right novel agents to the right patients, resistance mechanisms must be understood. A deeper understanding of the mechanistic basis for IMiD-specific resistance mechanisms is critical for differentiating new Cereblon modulating agents (eg CELMoDs) from the IMiDs. Here, we present the largest comprehensive analysis of the burden of CRBN mutation or transcript variants in relapsed refractory myeloma (RRMM) patients. We analysed WGS and RNASeq data from 298 MM samples from 268 patients across 4 clinical trials (CC-4047-MM-010 (N=226), CC-4047-MM-013 (N=17), CC-220-MM-001 (N=45) and CC-122-ST-001MM2 (N=10), for whom outcome data were available. All patients had been exposed to LEN-based therapy and a subset (69/268) were also exposed to POM-based therapy. The overall incidence of single nucleotide variants in CRBN was 17/298 (5.7%). The incidence of at least monoalleleic deletion at the CRBN gene locus was 21/298 (5.5%). CRBN has different transcript isoforms (Gandhi et al, BJHaem 2014). As high levels of isoform ENST00000424814.5, with deletion of exon 10, was previously correlated with poorer survival (Neri et al, Blood 2016), we assessed incidence of a high ratio (&gt;2) of exon 10-deleted CRBN transcript to full length CRBN transcript. 92 samples had sufficient purity (&gt;90% tumor cells) for this analysis. 13/92 samples (14.1%) had a high exon10-deleted transcript ratio. Overall, 43/268 (16.0%) patients had genetic or transcript variants in CRBN. In contrast, 27/514 (5.2%) newly diagnosed myeloma (NDMM) patients from the Myeloma Genome Project had genetic or transcript variants in CBRN; 2/514 (0.4%) had CRBN mutations, 11/514 (2.1%) had CRBN gene deletion and 14/514 (2.7%) had a high exon10-deleted transcript ratio. Thus, there was an increase in CRBN variants from NDMM to RRMM. In patients exposed to LEN but not POM (219/268), there were 5 CRBN mutations, 14 monoallelic CRBN deletions and 13/92 patients with high exon10-deleted transcript ratio. In 69/268 patients exposed to POM (baseline from CC-220-MM-001 (N=35), CC-122-ST-001MM2 (N=10), or follow up samples from CC-4047-MM-010 (N=24)), there were 12 CRBN mutations in 8 patients (11.6%) and 7 CRBN deletions (10.1%), approximately double the incidence seen in the whole cohort. 3 CRBN deletions were homozygous, which was not observed in non-POM-exposed individuals. Sample purity was insufficient to measure transcript ratios. In summary, 16.0% of RRMM patients that received LEN or POM have genetic or transcript variants in CRBN, a higher proportion than in NDMM. The impact of these aberrations on CRBN function, especially related to binding of CRBN-modulating drugs, remains to be ascertained. Analysis of the correlation between CRBN variation and response to therapy, clinical outcomes, and the incidence and effect of mutation or copy loss of CRBN interactors (E3 Ligase members and regulators, CRBN substrates) is underway and will be presented. Disclosures Gooding: Celgene: Research Funding. Ansari-Pour:Celgene Corporation: Consultancy. Towfic:Celgene Corporation: Employment, Equity Ownership. Ortiz:Celgene Corporation: Employment, Equity Ownership. Rozelle:Celgene Corporation: Other: Contractor for Celgene. Zadorozhny:Celgene Corporation: Other: Contractor for Celgene. Amatangelo:Celgene Corporation: Employment, Equity Ownership. Flynt:Celgene Corporation: Employment, Equity Ownership. Tsai:Celgene Corporation: Employment, Equity Ownership. Neri:Celgene, Janssen: Consultancy, Honoraria, Research Funding. Bahlis:AbbVie: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria. Vyas:Novartis: Research Funding, Speakers Bureau; Pfizer: Speakers Bureau; Daiichi Sankyo: Speakers Bureau; Astellas: Speakers Bureau; Abbvie: Speakers Bureau; Celgene: Research Funding, Speakers Bureau; Forty Seven, Inc.: Research Funding. Thakurta:Celgene: Employment, Equity Ownership.

Interactome Analysis of APOBEC3B in Multiple Myeloma

Apolipoprotein B mRNA-editing enzyme catalytic polypeptide-like (APOBEC) family proteins restrict retroviruses and retrotransposons by inducing hypermutation or degradation of the replication intermediates through their DNA cytidine deaminase activity. APOBECs can also act as endogenous sources of DNA damage that mutate many human cancers. Accumulation of APOBEC signature mutations is associated with disease progression and poor overall survival in multiple myeloma (Walker et al. Nat Commun, 2015). Among APOBEC3 enzymes, APOBEC3B (A3B) is the only family member that is predominantly located in the nucleus throughout the cell cycle. We previously reported that A3B knockdown decreased cytidine deaminase activity in myeloma cells, suggesting that among APOBECs, A3B plays a major role in cytidine deamination-related mutagenesis in myeloma cells (Yamazaki et al., Sci Rep, 2019). Recent studies showed that cofactors of A3B could affect the functions of A3B: heterogeneous nuclear ribonucleoproteins (hnRNPs) interact with surface hydrophobic residues of the N-terminal domain in order to bind to A3B (Xiao et al., Nuc. Acids Res, 2017; Zhang al., Cell Microbiol, 2008); BORF2, an Epstein-Barr viral protein, interacts with the A3B catalytic domain and inhibits A3B DNA cytidine deaminase activity (Cheng et al., Nat Microbiol, 2018). However, the biological mechanisms of how endogenous A3B induces mutations in genomic DNA are still unclear. In this study, we aim to ascertain the cofactors for nucleic acid binding and elucidate the regulatory mechanisms that prevent APOBEC-mediated genomic mutagenesis. Because of the high homology between APOBEC3 proteins, a specific antibody against A3B is not available, and it is difficult to analyze A3B-interaction at the endogenous expression level. To overcome this technical impediment, we used a lentiCRISPR system to insert a FLAG-tag sequence at the C-terminus of the A3B gene in A3B highly expressing myeloma cell lines (AMO1 and RPMI8226). We then conducted A3B-immunoprecipitation with the anti-FLAG M2 antibody, followed by mass spectrometry (MS) analysis to identify potential A3B interacting proteins. MS analysis identified 40 putative interacting proteins and these proteins were clustered largely into two interaction networks: ribonucleoprotein complex and ribosomal-associated proteins. We also performed Gene Ontology (GO) enrichment analysis and revealed that spliceosome, ribosome, and RNA transport were significantly enriched terms. We confirmed the binding between A3B and selected A3B interacting proteins: hnRNPs, interleukin enhancer-binding factor 2 and 3 (ILF2, ILF3) in myeloma cell lines by co-immunoprecipitation assays. Next, we tested the intracellular colocalization of overexpressed A3B and interacting proteins in Hela cells by immunofluorescence microscopy. We found that ILF2 presents strong colocalization with A3B in the nuclei of cells. We also employed density-gradient sedimentation analysis to test if these proteins form high molecular mass (HMM) complexes with A3B in the nucleus using HEK293T cells expressing FLAG-tagged A3B. We detected that ILF2 is one of the components of HMM A3B complexes. To check whether these putative interacting proteins affect A3B cytidine deaminase activity, we next performed an in vitro luminescence-based screening assay (AlphaScreen)using a FLAG-GST protein library which was produced by the wheat cell-free protein production system. We found that hnRNP A1 and ILF2 decreased A3B cytidine deaminase activity. This study provides for the first time a proteomic characterization of A3B interactome in a myeloma cell context. Our findings reveal putative A3B cofactors in myeloma cell lines which may regulate the catalytic activity of A3B. We discuss how these proteins bind A3B and affect its activity in myeloma cells. Disclosures Takaori-Kondo: Pfizer: Honoraria; Janssen: Honoraria; Novartis: Honoraria; Celgene: Honoraria, Research Funding; Bristol-Myers Squibb: Honoraria, Research Funding; Ono: Research Funding; Takeda: Research Funding; Chugai: Research Funding; Kyowa Kirin: Research Funding.

APOBEC3B Induction Following DNA Damage Response Modulates the Survival and Treatment Response in Human Multiple Myeloma

Apolipoprotein B mRNA editing catalytic polypeptide-like 3B (APOBEC3B, A3B) is one of 7-membered DNA cytosine deaminase family, causing cytosine-to-uracil (C-to-U) deamination in single-stranded DNA and promoting mutations in multiple human cancers including multiple myeloma (MM). High APOBEC3B expression is found in a significant portion of MM patients with MAF overexpression among t(14;16) and t(14;20). A3B upregulation is further associated with poor prognosis in MM, suggesting its role in the MM pathophysiology. However, approximately 23% MM patients with high APOBEC3 activity are associated with MAF/MAFA/MAFB translocations, the remainder of patients with high APOBEC3 carry neither translocations nor overexpression of these genes. Besides, studies are lacking on how A3B is regulated and the role of A3B in drug responses in MM. We here defined new mechanisms controlling A3B expression and further characterized its impact on treatment responses to current anti-MM therapies. Using qRT-PCR, A3B transcript is significantly higher than other members of the APOBEC3 gene family in MM cell lines (n=19) and MM patients, indicating that A3B may play a major role in MM. Using immunoblotting analysis, A3B protein expression was further confirmed in MM cell lines with various levels (n=10). Importantly, A3B mRNA upregulation by 1.34-42.64 folds was observed in CD138-purified cells from majorities of MM patients (83.3%) when compared to PBMC from the same individual (n=12). In MM cell lines without MAF/MAFA/MAFB translocation as a study model, higher A3B protein expression is associated with higher DNA damage levels as evidenced by higher γ-H2AX. These results suggest that A3B expression might be influenced by DNA damage levels in MM cells. Following a short time treatment of gamma-irradiation to cause DNA damages, A3B expression in viable MM cells was enhanced in a dose-dependent manner. We next treated MM cells (n=5) with common anti-MM drugs such as Melphalan (Mel) and Bortizomib (btz), both of which induce DNA damages, followed by examination of changes in A3B and γ-H2AX. Under sublethal treatment conditions of Mel or btz, A3B was consistently induced at both mRNA and protein levels in multiple MM cell lines regardless of the baseline A3B expression. Significantly, A3B was upregulated and associated with increased γ-H2AX in patient MM cells treated with Mel or btz under sub-lethal doses. Since DNA damages activate the ATR/ATM pathway, we next investigated whether these kinases mediate A3B induction following treatments with these compounds in MM cells. The presence of ATM or ATR inhibitors blocked A3B upregulated by these DNA damage-inducing treatments in MM cell lines (n=3), indicating an ATM/ATR-dependent pathway for A3B changes. Next, gene-specific CRISPR knock out (KO) and inducible-shRNA knockdown (KD) were used to determine the functional impact of perturbation of A3B in proliferation and survival of MM cells. Both KO and KD of A3B decreased growth and viability of MM cell lines regardless of sensitive or resistant to dexamethasone or lenalidomide. Using LIVE/DEAD fixable Aqua Stain and annexin V-based flow cytometric analysis, A3B inhibition enhanced growth arrest followed by apoptosis in MM cells. Significantly, A3B KD by its shRNA in RPMI8226 MM cells enhanced sensitivity to pomalidomide. Taken together, these data indicate that increased A3B level plays a critical role in MM cell survival and drug responses. DNA damages triggered by IR, Mel, or btz further enhance A3B expression via ATM/ATR pathway, which in turn increases subclonal diversity leading to drug resistance. The role of A3B in disease pathophysiology and progression, coupled with its function in mediating treatment response, suggest potential utility of targeting A3B in MM. Disclosures Munshi: Celgene: Consultancy; Abbvie: Consultancy; Oncopep: Consultancy; Adaptive: Consultancy; Amgen: Consultancy; Janssen: Consultancy; Takeda: Consultancy. Anderson:Sanofi-Aventis: Other: Advisory Board; Bristol-Myers Squibb: Other: Scientific Founder; Oncopep: Other: Scientific Founder; Amgen: Consultancy, Speakers Bureau; Janssen: Consultancy, Speakers Bureau; Takeda: Consultancy, Speakers Bureau; Celgene: Consultancy, Speakers Bureau.

Estimating Relationships between Response, Progression, and Survival in Relapsed-Refractory Multiple Myeloma in Pooled Clinical Trials Database

BACKGROUND: Despite an increasing number of treatment options available and in development, Relapsed-Refractory Multiple Myeloma (RRMM) remains an incurable disease with survival less than 12 months (Kumar SK et al., 2012). In a recent study by Moreau, et al. (2016), a relationship between response and survival was demonstrated in RRMM patients treated with pomalidomide. Understanding the relationships between initial response and long-term prognosis can potentially inform patient treatment changes or guide development of new therapeutic compounds. In a prior presentation by Berry, et al. (2017) clinical trial Study Data Tabulation Data Model (STDM) standards were used to effectively pool clinical trial data in Acute Myeloid Leukemia (AML) to show correlations between response and survival. OBJECTIVES: In this study, we expand upon the analysis of Moreau, et al. (2016) in a pooled clinical trial dataset of RRMM patients. Within this expanded, standardized patient pool, we assess the relationship between response, progression and survival both overall and within patient sub-populations based on patient profiles and prior treatment regimens. METHODS: A retrospective pooled analysis was conducted in a dataset from the Medidata Enterprise Data Store. Subjects were selected based on the inclusion/exclusion criteria from the NIMBUS trial (Moreau et al., 2016). Descriptive statistics were calculated to characterize differences between the overall pooled population and the study group. Response, Progression-free survival (PFS), and Overall Survival (OS) were extracted. Patients were stratified by several covariates including age, gender, number of prior regimens, and prior treatments received. Log-rank tests were conducted to compare PFS and OS in patient sub-populations. Both survival measures were assessed at 90, 180, and 240 days after first day of patient's most recent regimen. Cox proportional hazard models were developed to assess predictors of PFS and OS. Safety was characterized for common potentially treatment-limiting adverse events, such as leukopenia, neutropenia, and thrombocytopenia. Factors associated with development of neutropenia were assessed using logistic regression. Covariates included patient demographics, comorbidities, and treatment regimens (current and prior). RESULTS: Within the pooled analysis, PFS and OS rates were consistent with published literature rates, at ~4 months and ~12 months, respectively. Pooled analysis demonstrated a significant association between response, PFS, and OS. Results were consistent with findings of Moreau, et al. (2016), showing little difference between patients with Stable Disease and Partial Response, and lower overall survival in patients with Progressive Disease versus Stable Disease. Neutropenia was seen in approximately one-fourth of overall patients, and was associated with male patients, older age, and treatment regimen. CONCLUSIONS: The use of SDTM for pooled clinical trial analyses represents an effective way to overcome individual trial sample size limitations, expanding the range of populations, relative treatment outcomes, and safety event rates that can be studied. By working directly with individual patient-level data, there is also a potential for greater matching between trials than with meta-analysis approaches using aggregated data. Disclosures Galaznik: Medidata Solutions: Employment. Rusli:Medidata Solutions: Employment. Davi:Medidata Solutions: Employment.

A Novel Approach to Risk Stratification in Multiple Myeloma Using ISS Stage and FISH

Background: A variety of risk factors have been described in multiple myeloma and current risk assessment incorporates ISS stage with specific FISH results and serum LDH (R-ISS). However, this model does not include all the current abnormalities described as prognostic for survival in multiple myeloma. Importantly, the impact of many of these high-risk abnormalities are not uniform. We examined if we can better integrate FISH results into a risk assessment tool to better predict the outcomes of newly diagnosed MM. Patients and methods: We studied a cohort of 1316 patients with FISH done within 6 months of diagnosis of MM, in whom results for commonly observed abnormalities were available. We specifically examined the individual impact of common translocations involving chromosome 14, MYC rearrangements, chromosome 1q gain (single or multiple duplication) and del13q/monosomy 13. A risk assessment system was developed, weighting each abnormality according to their Risk Ratio and integrating ISS stage and serum LDH into the final model construction. Overall survival was calculated from diagnosis, with those alive at last follow up being censored. Results: We first examined the impact of each of the above FISH abnormalities: 1) high risk translocations [t(4;14), t(14;16), or t(14,20)], 2) trisomies, 3) t(11;14), 4) MYCrearrangements, 5) del13q/monosomy 13, and 6) 1q gain . Each of the abnormalities, except for t(11;14), was prognostic for survival (Table 1 with the risk ratios). For 1q gain, the median OS was NR, 105 mos and 79 mos respectively for no abnormality, duplication of 1 copy and duplication of multiple copies, (p&lt;0.001). On multivariate analysis, t(11;14) and trisomies were no longer prognostic for overall survival (Table 1). The cumulative impact of abnormalities demonstrated worsening survival in the presence of increasing numbers of abnormalities (Figure 1). Including ISS stage 3 and LDH &gt; ULN as additional variables for prognostication indicated both were individually prognostic for OS. In a multivariate analysis, including these two and FISH abnormalities, 1q gain and LDH were not independently prognostic. The final model consisted of HR translocations, MYCrearrangements, del17p/monosomy 17, del13q/monosomy 13, and ISS stage 3. Each of these variables was weighted using their risk ratio and a composite score was developed using 998 patients for whom all variables were available (range: 0-7.9; median 1.8). Three patient groups were characterized: group 1 (0; 32%), group 2 (1-4; 58%) and group 3 (&gt;4; 10%) with a median OS of 53 mos, 106 mos, and NR, respectively, p &lt;0.001 (Figure 2). Conclusion: Using the most relevant FISH and laboratory factors, in a large cohort of patients, we refined the current system to develop a risk stratification system that predicts survival in patients with newly diagnosed MM treated with contemporary treatment regimens. This needs validation in future studies. Disclosures Kumar: Janssen: Consultancy, Research Funding; Takeda: Research Funding; Celgene: Consultancy, Research Funding. Gertz:International Waldenstrom Foundation: Research Funding; Annexon: Consultancy; Medscape: Consultancy, Speakers Bureau; Amyloidosis Foundation: Research Funding; Abbvie: Other: personal fees for Data Safety Monitoring board; i3Health: Other: Development of educational programs and materials; Springer Publishing: Patents &amp; Royalties; Physicians Education Resource: Consultancy; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Proclara: Membership on an entity's Board of Directors or advisory committees; Ionis/Akcea: Consultancy; Alnylam: Consultancy; Prothena Biosciences Inc: Consultancy; Celgene: Consultancy; Janssen: Consultancy; Spectrum: Consultancy, Research Funding; Appellis: Consultancy; Research to Practice: Consultancy; Teva: Speakers Bureau; Johnson and Johnson: Speakers Bureau; DAVA oncology: Speakers Bureau; Amgen: Consultancy. Dispenzieri:Akcea: Consultancy; Janssen: Consultancy; Intellia: Consultancy; Pfizer: Research Funding; Takeda: Research Funding; Celgene: Research Funding; Alnylam: Research Funding. Lacy:Celgene: Research Funding. Dingli:alexion: Consultancy; Janssen: Consultancy; Millenium: Consultancy; Rigel: Consultancy; Karyopharm: Research Funding. Kapoor:Celgene: Honoraria; Janssen: Research Funding; Sanofi: Consultancy, Research Funding; Glaxo Smith Kline: Research Funding; Takeda: Honoraria, Research Funding; Amgen: Research Funding; Cellectar: Consultancy. Leung:Prothena: Membership on an entity's Board of Directors or advisory committees; Takeda: Research Funding; Omeros: Research Funding; Aduro: Membership on an entity's Board of Directors or advisory committees. Bergsagel:Celgene: Consultancy; Ionis Pharmaceuticals: Consultancy; Janssen Pharmaceuticals: Consultancy.

The Prognostic Factors for Early Fatal Outcome in Patients with Multiple Myeloma

Introduction: Survival of Multiple Myeloma (MM) patients has improved in recent decades due to the advent of novel agents and broader utilization of stem cell transplant. Fatal outcomes occur mostly after multiple lines of therapy due to increasingly resistant disease. Indeed, early mortality after diagnosis is unusual in front line treatment MM trials, where patients are often selected on the basis of good performance status and absence of organ dysfunction. The real world incidence of early deaths is largely unknown. Hereby, we analyze the factors impacting early mortality in the National Cancer Database (NCDB) which covers more than 70% of MM patients in United States. Methods: Patients with MM diagnosed from 2005-2014 were identified in the NCDB. Early mortality (EM) was defined as death within six months of MM diagnosis. The effect of demographic and clinical factors, insurance type, distance to treatment facility, medical facility and facility volume on the probability of EM was estimated by univariate logistic model. The effect of these factors on the probability of EM was further estimated by multivariable logistic regression. The overall performance of multivariable logistic regression was evaluated using the receiver operating characteristic curve (ROC) analysis. The difference of continuous measurements between groups was examined using t-test. The association between categorical variables was examined using Chi-square test. All tests are two-sided and p-value ≤ 0.05 were considered statistically significant. Results: 90,258 patients with ICD-O 9732 were included. 49949 (55%) were male. EM was reported in 15729 (17%) within the first 6 months after MM diagnosis. Median age at diagnosis was 67 years (19-90). 15729 patients (17%) died in the first 6 months after diagnosis. There was no significant change in the percentage of EM over the years from 2005-2014. 11963 (76%) of patients with EM were older than 65 years while 3766 (23%) patients were less than 65 years. 12300 (78.1%) of the patients with EM were white and 2822 (17.9%) were African American. In a multivariable analysis, variables that confer the worst prognosis were increasing age, higher charlson co-morbidity score, lower annual median income of less than 30,000, and being treated in a community hospital. Controlling the effects of other factors (charlson co-morbidity score, hospital type and transplant), the odds ratio of EM was 1.04 per year increase of age (p&amp;lt;0.0001), 0.52 with treatment in academic center compared to community hospital (p&amp;lt;0.0001). Conclusion: Taken together our data suggest almost 1 out of 6 MM patients died at the first 6 months after diagnosis. Therefore, EM remains high in MM and a major barrier for continued improvement in survival outcomes. In addition to well-known MM-specific determinants of survival, socioeconomic factors were significantly associated with early mortality. Disclosures Malek: Adaptive: Consultancy; Medpacto: Research Funding; Janssen: Speakers Bureau; Sanofi: Consultancy; Celgene: Consultancy; Takeda: Consultancy; Amgen: Speakers Bureau.

RNA-Sequencing Based Assessment of Targets, Risk and Long Term Survival for Personalized Treatment of Multiple Myeloma

Aim of our study is to implement RNA-sequencing as basis for risk adapted and targeted treatment-strategies in multiple myeloma. We establish assessment of malignant plasma cell proliferation and risk-stratification de novo (HDHRS) by RNA-sequencing, transfer microarray-based risk scores to RNA-sequencing, and report expression of actionable including immune-oncological targets. Methods. Transcriptome profiling was performed on CD138 purified malignant plasma cell samples from previously untreated symptomatic multiple myeloma patients undergoing high-dose melphalan treatment and autologous stem cell transplantation. RNA-sequencing was performed on an Illumina NextSeq500-platform using 5ng of total RNA (n=535), and, in the same cohort, by gene expression profiling using Affymetrix U133 2.0 DNA-microarrays (n=534). iFISH was performed using a ten-probe panel (n=535). Clinical data and survival of patients treated with high-dose therapy and autologous stem cell transplantation were assessed (n=534/533/534 for RNA-seq/GEP/iFISH) with median follow up of 64 months. Data were validated using the independent COMPASS cohort (n=767 pts., median follow up of 31 months). Results. RNA-sequencing is feasible in more than 90% of myeloma patients. It allows assessment of proliferation (RPI) and risk, both de novo (HDHRS) and regarding microarray-based scores (UAMS GEP70-, EMC92-, Rs-score). All scores are significantly predictive for overall and event-free survival in GEP are likewise in RNA-seq with hazard ratio for high risk to low risk group of 3.06, 4.3, 2.84, 3.93 and 3.30 in overall survival of test group. (See as example gene expression based proliferation index / RNA-seq based proliferation index, Fig. 1). The de novo RNA-seq risk stratification (HDHRS) superiorly delineates three significantly different groups of 40%, 38%, and 22% of patients with 5-year survival rates of 84%, 67%, and 32%, respectively (Fig.2). It likewise discerns a group of long term surviving patients with a survival rate of 57% and 49%, at 10 and 12 years, respectively. Targets expressed in normal plasma cells with varying expression height in malignant include BCMA and CD38, those aberrantly expressed CSF1, CD20 and HGF. Mutated actionable transcripts include BRAF V600E/K (target), the proteasome and cereblon (resistance). In conclusion, RNA-seq allows assessment of risk, long term survival and targets in multiple myeloma both in phase III multicenter trials as well as in clinical Routine. Disclosures Moreaux: Diag2Tec: Other: Co-founder of Diag2Tec company.

P53 Pathway Activation Mediated High c-MAF Expression Is Associated with Overall and Post-Progression Survival in Multiple Myeloma

Background: Chromosomal abnormalities are strongly associated with prognosis of multiple myeloma (MM). Among them, t (14;16) resulting in high expression of c-MAF, and t (14;20) resulting in high expression of MAFB, lead to poor prognosis. However, clinical significance and mechanisms underlying high c-MAF and MAFB expression without these translocations have not been fully elucidated. Methods: A total of 96 MM and 38 MGUS patients, 10 controls, and 9 MM cell lines were included in this study. RNA was extracted from purified CD138+ plasma cells from bone marrow (BM) mononuclear cells. c-MAF, MAFB, p53, and p21 RNA expressions were determined by RQ-PCR. Their expression levels were normalized against ACTB levels and calculated with 2-ΔΔCt value. Inhibition studies using BRD4 inhibitor JQ1, and MYC inhibitor10058-F4, and MM cell lines expressing doxycycline-inducible p53 (Tet-on p53) or c-MAF knockdown (KD) with siRNA were used for in vitro studies. Results: c-MAF expression in MM patients was higher than in MGUS patients and control (median level 0.043, 0.025, 0.002, p&lt;0.001), but MAFB expression did not differ between MM, MGUS, and control (p=0.371). Although c-MAF expression level was significantly higher in MM with t (14;16) (p=0.0018), high c-MAF expression was observed in patients without the translocations, suggesting a role of c-MAF in MM progression. Other cytogenetic abnormalities such as del 17p, or t (4;14), did not affect c-MAF expression. Overall survival (OS) of MM patients with high c-MAF expression was significantly inferior compared to the patients with low c-MAF expression (HR 2.46, p=0.002, 2 years survival rate 42.9% vs. 72.7%, median 20.3 months vs. not reached), but progression-free survival (PFS) did not differ (p=0.551). Instead, post-progression survival (PPS) was significantly shorter in the patients with high c-MAF expression (HR 4.67, p&lt;0.001, two years survival 0% vs. 60.2%, median 3.6 months vs. 49.3 months) suggesting that c-MAF confers drug resistance to residual disease. MAFB expression did not affect OS, PFS, and PPS. Reduction of cyclin D2 expression by c-MAF KD in vitro and a positive correlation between c-MAF and cyclin D2 expression (r=0.29, p&lt;0.001) in vivo in patients, support previous reports describing that MM proliferation is induced by c-MAF via cyclin D2. c-MAF was not correlated with APOBEC3B expression. c-MAF expression was positively correlated with MDM2 and MYC (r=0.387, p=0.03 and r=0.221, p=0.019, respectively) but not with p53 and PUMA. A tendency of positive correlation was also observed with p21/CDK1NA (r=0.304, p=0.085). p53 overexpression from the Tet-on p53 and p53 stabilization by nutlin-3 increased c-MAF expression in three cell lines with t (14;16). c-MAF KD together with p53 overexpression significantly suppressed the proliferation, implying synthetic lethality. These findings suggest that c-MAF expression is upregulated in response to proliferation arrest induced by the p53 pathway; suppressing this responsive c-MAF expression abrogates cell growth more efficiently. JQ1 and CPI203 increased c-MAF expression in cell lines with t (14;16), suggesting that c-MAF expression is not controlled by super-enhancers. Conclusion: Although the mechanism of high expression of c-MAF and MAFB has previously been reported in IgH translocation, the relationship between c-MAF and drug resistance remains to be determined. The induction of c-MAF expression by tumor suppressor gene p53 is suggested to be a mechanism underlying poor prognosis of MM with t (14;16), and suppressing c-MAF expression with chemotherapeutic drugs might prevent emergence of drug resistance in residual tumor cells. Disclosures Handa: Ono: Research Funding.

Ixazomib or Lenalidomide Maintenance Following Autologous Stem Cell Transplantation and Ixazomib, Lenalidomide, and Dexamethasone (IRD) Consolidation in Patients with Newly Diagnosed Multiple Myeloma: Results from a Large Multi-Center Randomized Phase II Trial

Ixazomib or Lenalidomide Maintenance Following Autologous Stem Cell Transplantation and Ixazomib, Lenalidomide, and Dexamethasone (IRD) Consolidation in Patients with Newly Diagnosed Multiple Myeloma: Results from a Large Multi-Center Randomized Phase II Trial

Incidence and Survival of Multiple Myeloma; A Population-Based Study on 10 961 Patients Diagnosed 1982-2017

Background: Several population-based studies on multiple myeloma (MM) have shown an improvement in relative survival (RS) for patients aged 65 years or younger at the time of diagnosis. In patients aged 75 years or older, the improvement in long-term RS is absent in most reports or much less pronounced. To our knowledge, only 2 studies based on the Surveillance, Epidemiology and End Results (SEER) database have demonstrated a significant increase in RS for this age-group. (Pulte et al. 2011, Costa et al. 2017) We performed a population-based study on Norwegian MM-patients to provide up-to-date estimates on changes in relative survival during the past 3 decades. Methods: The Cancer Registry of Norway (CRN) was established in 1951 and provides high-quality nationwide cancer statistics. For MM, the reported completeness of case-ascertainment is above 95%. Incidence data and all reported cases of MM diagnosed between January 1, 1982 and December 31, 2017 were retrieved from the CRN (n=10 961). Nationwide drug consumption statistics for drugs used in myeloma treatment were retrieved from the Norwegian Drug Wholesales Statistics and the Norwegian Prescriptions Database, Norwegian Institute of Public Health. Follow-up ended December 31, 2017. The time of MM-diagnosis was divided into 7 categories: 1982-1987, 1988-1992 (melphalan-prednisone), 1993-1997(early high-dose melphalan with autologous stem-cell transplant (HDM-ASCT)), 1998-2002 (introduction of thalidomide), 2003-2007 (early thalidomide upfront, introduction of bortezomib), 2008-2012 (thalidomide and bortezomib upfront, introduction of lenalidomide), 2013-2017 (lenalidomide upfront, early pomalidomide, daratumumab, panobinostat and carfilzomib). Age at diagnosis was divided into 3 categories; &lt;65 years, 65-79 years and 80 years or older. We estimated relative survival ratios (RSR) with 95% confidence intervals 5 and 10 years after the time of diagnosis by the Ederer-II method. The analysis was stratified by age-group and calendar period of diagnosis. Cohort analysis was applied to calendar periods with complete follow-up, and period analysis was applied where complete-follow up was not available (2013-2017 and 10-year estimates for 2008-2012). Non-overlapping confidence intervals were considered statistically significant. Results: The age-standardized incidence rate was stable until approximately year 2000, followed by an increasing tendency reaching 8.4 cases per 100 000 persons in 2017. Figure 1. Patients diagnosed before the age of 65 had a steady increase in both 5 and 10-year RSR across all calendar periods. For patients aged 65-79 years, the 5- and 10-year RSR's were stable at approximately 0.3 and 0.1, respectively, until the calendar period 1998-2002. In the following years, an improvement in both 5- and 10-year RSR was observed. The 5-year RSR improved significantly from 0.31 (95% CI; 0.27-0.35) in the first calendar period to 0.43 (95% CI;0.39- 0.47) during 2008-2012. The predicted 5-year RSR for 2013-2017 was 0.48 (95% CI; 0.44-0.52). There were also signs of improved 10-year RSR, predicted to 0.23 (95% CI; 0.18-0.27) during 2013-2017 compared to 0.11 (95% CI; 0.08-0.14) during 1982-1987. The 5-year RSR for patients aged 80+ years was 0.11 (95% CI; 0.01-0.17) during 1982-1987 and the 10-year RSR was 0.03 (95% CI; 0.01-0.11). In the following 4 calendar periods the RSR-estimates fluctuated before a rising tendency during the last 2 periods. The 5-year RSR improved significantly to 0.26 (95% CI; 0.20-0.32) during 2008-2012 and further rising to a predicted value of 0.32 (95% CI; 0.25-0.38) during 2013-2017. Figure 2. Additionally, complete annual prescription statistics for the oral agents thalidomide, lenalidomide and pomalidomide were obtained. Conclusions: We provide real-world observations on changes in RS in a population-based cohort of 10 961 MM-patients. We demonstrate an improvement in 5-year relative survival across all age-groups, including patients aged 80 years or older. For patients &lt;65 years, there was a steady increase in RS since 1982 and we see no obvious impact of the implementation of HDM-ASCT. For the other age-groups, the improvement in RS coincides with the introduction of new drugs. The increase in incidence since 2000 may be due to increased testing for M-protein. We can point to the introduction of new drugs and increased incidence of indolent cases as possible reasons for increasing RS. Disclosures No relevant conflicts of interest to declare.

Prognostic Implication of Inflammatory Factor-Based Staging System in Multiple Myeloma in the New Agent Era

B ackground The advent of new agents has contributed to the prolonged survival of multiple myeloma (MM) patients. From the Durie-Salmon staging system, international staging system (ISS) and recently, revised ISS (R-ISS) have been developed to better prognosticate the survival of MM. R-ISS includes chromosomal abnormalities (CA) detected by interphase fluorescent in situ hybridization (iFISH) and lactate dehydrogenase (LDH) in addition to the ISS. However, the R-ISS has still some limitations in the real clinical settings, as it has enrolled patients exclusively on clinical trials which includes more young patients (age &lt; 65 years old), contains non-standardized iFISH results and has short median follow-up duration. The first-line drugs used in the patients could also affect the prognosis. Therefore, a more detailed and standardized but also convenient prognostic system is needed with clinical findings commonly observed in MM patients treated with new agents in the real clinical world. Patient and Method Adult patients who were confirmed multiple myeloma between January, 2011 to December, 2017 and patients who had received thalidomide and/or bortezomib or lenalidomide-based chemotherapy as a first-line treatment were included for the analysis. A total of 861 patients from 13 centers participating Korean multiple myeloma working party were analyzed. Baseline serum albumin, serum ß2-microglobulin, cytogenetics by iFISH, LDH (lactate dehydrogenase), ALC (absolute lymphocyte count), CRP (C-reactive protein) and ferritin were measured within 4 weeks before beginning the first line of chemotherapy. Each factors of age ≥ 65 years, serum ß2-microglobulin ≥ 5.5 mg/L, LDH &gt; normal, cytogenetic high risk by FISH (del17p, t(4;14), t(14;16)), ALC &lt; 1500/mm3, CRP ≥ 1.5 mg/dL, and ferritin ≥ 500 ng/mL were defined as abnormal findings, which were given 1 point (Table 1) and the sum of points were used to discriminate inflammatory factor-based staging system (IFBSS). IFBSS were defined as follows: Stage I (point 0), stage II (point 2-3), stage III (point 4-5), and stage IV (point 5-7). Overall survival (OS) was defined by the date of MM diagnosis to the date of death by any cause or follow-up loss. Results Baseline characteristics of the patients were as listed in Table 2. With a median follow-up duration of 22.70 months (range, 0.20-86.80 months), age ≥ 65 years, serum ß2-microglobulin ≥ 5.5 mg/L, LDH &gt; normal, cytogenetic high risk by FISH (del17p, t(4;14), t(14;16)), ALC &lt; 1500/mm3, CRP ≥ 1.5 mg/dL, and ferritin ≥ 500 ng/mL showed significant higher OS by univariate and multivariate analysis. Albumin &lt; 3.5 mg/dL were excluded into the staging system due the unpredictability to OS in univariate analysis (Table 3). Study groups of inflammatory factor-based scoring system comprised of 61 patients in stage I, 395 patients in stage II, 189 patients in stage III and 36 patients in stage IV. The median OS were not reached in stage I and II, stage III and IV showed a median OS of 36.57 months (95%CI, 33.96-.39.18) and 17.60 months (95%CI, 9.16-.26.04). The OS according to the IFBSS showed significant differences (P &lt; 0.001), which predicted OS better compared with conventional ISS and R-ISS (Table 4). Conclusion In the new agent era, inflammatory factors incorporated into the staging system can better discriminate prognosis of multiple myeloma patients compared to the ISS or R-ISS. Validation of this finding in a larger cohort is needed to expand usage of this new staging system. Disclosures Yoon: Novartis: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Kyowa Hako Kirin: Research Funding; Janssen: Consultancy; Yuhan Pharma: Research Funding; Genentech, Inc.: Research Funding; MSD: Consultancy.

Moffitt Cancer Center 2-Year Single-Institution Experience with Next-Generation Sequencing Minimal Residual Disease Detection: Clinical Utility, Application, and Correlation with Outcomes in Plasma Cell and Lymphoid Malignancies

Moffitt Cancer Center 2-Year Single-Institution Experience with Next-Generation Sequencing Minimal Residual Disease Detection: Clinical Utility, Application, and Correlation with Outcomes in Plasma Cell and Lymphoid Malignancies

Minimal Residual Disease Evaluation By Multiparameter Flow Cytometry and Next Generation Sequencing in the Forte Trial for Newly Diagnosed Multiple Myeloma Patients

Minimal Residual Disease Evaluation By Multiparameter Flow Cytometry and Next Generation Sequencing in the Forte Trial for Newly Diagnosed Multiple Myeloma Patients

Composition of Immune Subpopulations Influence Multiple Myeloma Disease Progression

Immune dysfunction is an important feature of multiple myeloma (MM). Characterization of lymphocytic infiltrates, comparing precursor, early &amp; later disease stages, indicate that the anti-myeloma immune response evolves in conjunction with the progression of disease, while distinctive patterns identified in the infiltrating immune population have prognostic relevance (Pessoa de Magalhaes, Vidriales et al. 2013, Paiva, Mateos et al. 2016).The primary objective of this project is to characterize the marrow-infiltrating lymphocytic and myeloid populations to identify specific patterns of immune dysregulation &amp; a biomarker signature that will guide predictions of efficacy of different immunotherapeutic modalities.(Willenbacher et al. 2016, Kini Bailur, Mehta et al. 2017). Patient samples are identified for inclusion based on clinical assessment parameters: detection of a monoclonal gammopathy, the presence or absence of end-organ damage indicative of active/symptomatic MM, and &lt;2 prior lines of therapy. 15 Healthy donor samples were acquired from volunteers with no evidence of hematologic malignancy. Disease status was categorized as precursor MGUS stage, newly diagnosed untreated MM (NDMM), pre-transplant MM (PreT-MM) and 90 days post-transplant MM (D90-MM).Infiltrating leukocyte populations are isolated from 20ml of fresh bone marrow aspirates and analyzed by multi-parameter flow cytometry. Lymphocytes are analyzed for subset composition, memory/effector maturation &amp; exhaustion.The myeloid component analyzes the monocytic and granulocytic myeloid-derived suppressor cells, M1/M2 macrophages, dendritic cells and neutrophils. To date, 38 samples for lymphoid and 56 for the myeloid panel have been analyzed. All calculations and data analysis are performed in the industry standard bioinformatics platform, FlowJo v10.6.Dimensionality reduction is run for visualization of high parameter data using the t-distributed stochastic neighbor embedding (tSNE) algorithm. X-shift unsupervised clustering is employed to identify populations in an unbiased manner. Unsupervised clusters are compared and contrasted relative to known biomarkers of interest within the ClusterExplorer platform and batched across tSNE space in contrast to: subjects, conditions &amp; manually defined gating. The immunologic parameters are compared to histologic, molecular and genetic features of disease as detailed by the biopsy report &amp; to the extended longitudinal clinical course of the patient (disease progression &amp; response to therapy). Classical flow analysis of the myeloid panel clearly reveals significant changes occurring in the G-MDSC population with disease progression. tSNE-based unsupervised analysis show increases in CD4 T effector cells in NDMM patients, but the differences in the CD4 memory compartment in MGUS suggest that these variations in T cells may originate during premalignancy. The expression of CD95 on CD4 cells is downregulated with disease progression. In addition to changes in T cells, we observe changes in innate cells- NDMM samples show an increase in a CD194+NK cell population which may represent a transitional population expanded during pathological tumor growth. With recent studies suggesting that innate cells could be targets for immune-modulatory drugs, this opens a new avenue. Taken together, this data illustrates complex alterations in the immune landscape from a premalignant to malignant stage, implicating that engaging the immune system early in the evolution of clinical MM may improve the potential of durable remissions. Kini Bailur, J., et al. (2017). "Changes in bone marrow innate lymphoid cell subsets in monoclonal gammopathy: target for IMiD therapy." Blood Adv1(25): 2343-2347. Paiva, B., et al. (2016). "Immune status of high-risk smoldering multiple myeloma patients and its therapeutic modulation under LenDex: a longitudinal analysis." Blood127(9): 1151-1162. Pessoa de Magalhaes, R. J., et al. (2013). "Analysis of the immune system of multiple myeloma patients achieving long-term disease control by multidimensional flow cytometry." Haematologica98(1): 79-86. Willenbacher, W., et al. (2016). "Bone marrow microenvironmental CD4 + and CD8 + lymphocyte infiltration patterns define overall- and progression free survival in standard risk multiple myeloma--an analysis from the Austrian Myeloma Registry." Leuk Lymphoma57(6): 1478-1481. Disclosures Shain: AbbVie: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Consultancy; Amgen: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Sanofi Genzyme: Membership on an entity's Board of Directors or advisory committees. Brayer:Janssen: Consultancy, Speakers Bureau; BMS: Consultancy, Speakers Bureau.

Real-World Determinants of Survival in Multiple Myeloma: Experience of a Referral Center in Latin America

Introduction In the last decades Multiple Myeloma (MM) has had a surge in clinical research and novel therapies with a subsequent impact in clinical outcomes. Nonetheless, these changes have stagnated and remain slow-occurring in populations without wide accessibility to therapies. Worse outcomes have been related to multiple factors, both patient and myeloma-related. There is scarce data regarding resource-poor settings like in Latin America; where economic disadvantage, multiple comorbidities, and higher disease aggressiveness could all play competing roles towards unfavorable results. The aim of this study was to analyze the contemporary relevance and impact of social, economic, and biological factors on the real-world outcomes in Latin American patients treated in a referral center with limited resources. Methods Retrospective single center study that included &gt;18 year-old patients with MM diagnosis, defined by IMWG criteria, treated and followed at our institution from January 2006 to December 2018. We determined the impact of patient- and myeloma-related factors on the decision of induction therapy and overall survival. Patient-related factors were: age at diagnosis, socioeconomic status (SES) according patient´s income, performance status measured by ECOG, and comorbidities at diagnosis scored by Charlson Comorbidity Index (CCI). Myeloma-related factors included: ISS, Durie-Salmon (DSS), cytogenetics, response to treatment, type of induction therapy, and access to hematopoietic stem cell transplantation (HSCT). Results A total of 245 patients were included. In our cohort 84.7% were considered of low SES (household income &lt;180 USD/month), 29.8% had a low performance status (ECOG &gt;2), and 89.4% had ISS ≥2. Additional baseline characteristics are presented in Figure 1. Thirty patients (12.24%) were managed only with best supportive care. Factors associated with a decreased likelihood of undergoing induction treatment included ECOG&gt;2 (p=0.031), any comorbidity at diagnosis (p=0.028), and a CCI≥2 (p=0.002). Age ≥65 years (p=0.213), SES (p=0.287), as well as myeloma-related factors including ISS (p=0.720), DSS (p=0.699), and high-risk cytogenetics (p=0.089) had no discernible impact on this decision (Figure 2). The 215 patients that received therapy were included in the survival analysis (Figure 3). Median OS was 44.8 months. Patient-related factors for decreased survival were age≥65 years (p=0.04), and ECOG&gt;2 (p=0.001). Myeloma-specific factors including high ISS (p=0.002), DSS-B (p=0.029), induction without novel drugs (p=0.019), no consolidation with HSCT (p=0.005), and less profound responses (p&lt;0.001) had a negative impact on OS. On multivariate analysis: ECOG&gt;2 (HR 1.96; CI95% 1.27 -3.04; p=0.004), DSS-B (HR 1.51; CI95% 1.03 - 2.21; p=0.04), and induction without novel drugs (HR 1.49; CI95% 1.08 - 2.06; p=0.016) were all associated with worse OS. More profound responses to therapy (HR 0.27; CI95% 0.20 - 0.37; p&lt;0.001) showed a protective effect. Conclusion This is a study conveying outcomes in a resource-poor setting in a very low-income population with scant access to novel therapies. Interestingly, patient-related factors including performance status and CCI were useful in determining initiation of therapy, regardless of age and myeloma-related factors. Once patients underwent therapy, survival outcomes were determined by myeloma-related factors and patient's performance status. Despite not being statistically significant, possibly due to limited number of patients, high SES showed a trend towards increased access to multiple therapies and OS. These are real-world-setting results that emphasize that it is imperative to develop strategies that allow the homogenization of access to novel drugs and HSCT world-wide. Disclosures Martinez-Banos: Celgene, Amgen: Other: Advisory Board.