Risk Of Myeloma Research Articles

Impact of Previous Cancer Therapy on Outcome of Patients with Multiple Myeloma or Smoldering Disease

Background: Multiple Myeloma (MM) is a rare hematologic malignancy estimated to account for 1.8% of all new cancer cases in 2018. Although there are many studies evaluating the risk of second malignancies following MM, very few data is available regarding the risk of secondary smoldering myeloma (SM) and symptomatic MM (i.e., after receiving chemotherapy or radiation therapy for other types of cancer). The previous cytotoxic therapy has potential to cause or modify the myelomagensis process. Hence, to delineate clinical characteristics and biological behavior of secondary disease we analyzed the data in National Cancer Database (NCDB) which covers more than 70% of cancer patients (pts) in United States. Methods: We reviewed pts in the NCDB who were diagnosed with MM between 2004 and 2014. Pts who had at least one malignancy prior to diagnosis were considered to have had secondary MM. Pts who were not receiving any treatment within first 6 months after the diagnosis and still alive were considered to have had SM. Overall survival (OS) was estimated by the Kaplan-Meier method and compared with the log-rank test. Cumulative incidence of progression from SM to symptomatic MM was calculated with death as competing risk and compared with the Gray's test. Multivariale Cox and logistic regression analyses was used to identify independent predictors of OS and progression from SM to symptomatic MM, respectively. Results: We identified 126,672 MM pts, 22,064 (17%) were SM, 18,864 (15%) were secondary MM, 57,529 (45%) were female, 26,256 (20%) were black, and 56,225 (44%) were diagnosed after 2010. Median age at diagnosis was 67 years (range: 19 - 90). The Charlson comorbidity index was 0, 1, and ≥2 for 96,554 (76%), 20,287 (16%), and 9845 (8%) pts, respectively. Durie-Salmon stage at diagnosis was unavailable, 1, 2, and 3 for 113,917 (90%), 2148 (2%), 3007 (2%), and 7613 (6%) pts, respectively. Patient and treatment characteristics are compared between primary and secondary MM in Table 1. Median age and percentages of pts with age >70 were higher in secondary MM (74 vs 66 and 61% vs 37%, p <0.0001 for both). More pts with primary MM received chemotherapy (67% vs 61%, p <0.0001) and bone marrow transplant (16% vs 10%, p <0.0001) compared to secondary MM. Median follow-up time of alive pts was 46 months. Five-year cumulative incidence of progression from secondary vs primary SM to symptomatic MM in pts with age of ≤70 and >70 years were 11.1% (95% CI: 9.3 - 12.9) vs 10.8% (95% CI: 10.2 - 11.4), p=0.89, and 4.7% (95% CI: 3.8 - 6.0) vs 4.9% (95% CI: 4.4 - 5.4), p=0.52, respectively (Figure 1). On multivariable analysis, only each 1-year decrease in age (OR 1.002, 95% CI: 1.002 - 1.003, p<0.001) was independently associated with progression of SM to symptomatic MM, after adjustment for secondary vs primary SM (OR 1.001, 95% CI: 0.992 - 1.011, p=0.77) and male vs female gender (OR 1.005, 95% CI: 0.998 - 1.012, p=0.17). Five-year OS of pts with primary and secondary MM who received systemic therapy were 46.2% (95% CI: 45.8 - 46.6) and 35.7% (95% CI: 34.7 - 36.7), respectively (p <0.0001). Figure 2 shows the comparison of OS between primary and secondary MM who received systemic therapy in several subgroups. Five-year OS of secondary vs primary MM pts in transplant and no-transplant cohorts were 73% (95% CI: 71 - 76) vs 73% (95% CI: 72 - 74), p=0.30, and 29% (95% CI: 28 - 30) vs 38% (95% CI: 37 - 38), p<0.0001, respectively (Figure 2-I). On multivariable analysis, each 1-year increase in age, any vs no insurance, academic vs non-academic hospital, each 1-quartile increase in household income, Charlson comorbidity index ≥3 vs <3, each 1-year increase in year of MM diagnosis, secondary vs primary MM, SM vs symptomatic MM, Durie-Salmon stage 3 vs 1/2, treatment with radio-, chemo- and immunotherapy were independent predictors of OS (Table 2). Conclusion: Here, we showed that the risk of progression of SM to symptomatic MM is not dependent on previous cancer therapy suggesting a potential lack of cause-and-effect association between chemotherapy/radiation and initial myelomagenesis. Also, secondary MM has worse prognosis than primary MM after adjusting for known unfavorable prognostic factors highlighting potential host-related factors predisposing to poor outcomes. Importantly, previous cancer was not an adverse prognostic factor for more fit and transplant-eligible pts who received bone marrow transplant. Disclosures Malek: Janssen: Speakers Bureau; Amgen: Speakers Bureau; Adaptive: Consultancy; Medpacto: Research Funding; Sanofi: Consultancy; Takeda: Consultancy; Celgene: Consultancy.

FDA Analysis: Impact of Body Mass Index (BMI) on Outcomes in Relapsed-Refractory Multiple Myeloma

Background: Obesity has been implicated as a risk factor for the development of certain types of cancers, including multiple myeloma (Wallin 2011). In a report published in NEJM in 2016, the relative risk of multiple myeloma for overweight to class 1 obese individuals was 1.2, versus a relative risk of 1.5 for class 2 to 3 obese individuals (Lauby-Secretan 2016). Recent reports indicate that BMI may impact prognosis in patients with newly diagnosed multiple myeloma (Beason 2013). Minimal information is available on impact of BMI and prognosis in patients with relapsed-refractory multiple myeloma. We analyzed the association between BMI and clinical outcomes in patients with relapsed/refractory multiple myeloma to determine if there is a difference in outcomes based on body weight. Methods: We conducted a retrospective analysis of four clinical trials evaluating novel therapeutics. These trials enrolled patients with relapsed/refractory multiple myeloma who had received one or more prior therapies. Patients were categorized into four groups, underweight (BMI <18.5 kg/m2), normal weight (BMI 18.5-24.9 kg/m2), overweight (BMI 25.0-29.9 kg/m2) and obese (BMI >30.0 kg/m2). The Kaplan-Meier method was used to estimate progression free survival and overall survival. Results: A total of 2392 subjects were included in this analysis. The median age was 65 years (range 30-91 years). A total of 28 (1.2%) subjects were underweight, 733 (30.6%) were normal weight, 1032 (43.1%) were overweight, and 599 (25.0%) were obese. More of the underweight subjects were female (82.1%), whereas more of the overweight and obese subjects were male (62.9% and 56.6%, respectively). The median PFS and OS K-M curves are displayed below. In this univariate analysis, there were no differences in PFS (p=0.61) or OS (p=0.7) among the four groups. There were some differences in the underweight population; however, the small sample size of this group precludes any meaningful conclusions. Univariate analyses by gender did not reveal any differences in outcomes based on body weight. Conclusion: In patients with relapsed/refractory multiple myeloma, body weight had no impact on outcomes, as measured by PFS and OS. These results are consistent with previous findings on the effect of BMI on survival in subjects with multiple myeloma after autologous stem cell transplant (Kocoglu 2018). Limitations of this analysis include the use of a univariate analysis, the small sample size for patients who were underweight, heterogeneity in the treatment regimens, and immaturity of the OS data. Future studies are needed to evaluate other variables such as the relationship between cytogenetics and body weight, as well as analyses of safety based on body weight in this relapsed/refractory patient population. Figure Disclosures Shah: Physicians' Education Resource: Honoraria. Mailankody:Juno: Research Funding; Celgene: Research Funding; Janssen: Research Funding; Takeda Oncology: Research Funding; CME activity by Physician Education Resource: Honoraria. Landgren:Sanofi: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Membership on an entity's Board of Directors or advisory committees; Merck: Other: IDMC; Theradex: Other: IDMC; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding.

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<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 > 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 (>4; 10%) with a median OS of 53 mos, 106 mos, and NR, respectively, p <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 & 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.

Association between a Polygenic Risk Score for Multiple Myeloma Risk and Overall Survival

Background Genome-wide association studies (GWAS) conducted in populations of European ancestry (EA) have identified and confirmed 23 germline susceptibility loci for multiple myeloma (MM). The effect sizes of single nucleotide polymorphisms (SNPs) at these loci are small, therefore combining them into a single summary measure, known as a polygenic risk score (PRS), may provide a more meaningful risk factor. We have previously shown a PRS comprised of the 23 SNPs for MM contributes to increased risk of MM, with a 2.7-fold increase for highest vs. lowest PRS quintiles. Whether the MM-PRS is also associated with overall survival (OS) in MM cases has not been evaluated. We examined the association between MM-PRS and OS in two EA studies. Methods The first study consisted of 2,179 EA MM cases from ten studies included in the Multiple Myeloma Working Group within the International Lymphoma Consortium (InterLymph). Cases were diagnosed between 1970 and 2015 and genotyped using multiple platforms (Oncoarray, Affymetrix, Human660W-quad Beadchip, and Illumina arrays); 885 cases also had stage [based on International Staging System (ISS)] available. Each of the GWAS was subjected to rigorous standard quality control independently (prior to imputation via the Michigan imputation server based on the Haplotype Reference Consortium (HRC). The second study consisted of 515 newly diagnosed EA MM cases from CoMMpass (Relating Clinical Outcomes in Multiple Myeloma to Personal Assessment of Genetic Profile), diagnosed from 2011-2013, who had whole genome sequencing (WGS) performed on germline DNA. The WGS data was used to call common germline genetic variants through the Mayo Clinic bioinformatics pipeline. Briefly, genetic variants were detected with GenomeGPS, aligned to the hg19 reference genome, called using the GATK (V3.6) Haplotype Caller, and merged for multiple-sample joint calling. To reduce the false positive variants, variant quality score recalibration (VQSR) was applied for both SNPs and INDELs. After quality control, 458 EA samples remained. Follow-up was available for both studies and consisted of time from MM diagnosis date until death or date of last known follow-up. The PRS was constructed from the 23 MM SNPs using the published per allele odds ratio associated with MM risk. The published log odds ratios for each SNP were multiplied by the number of risk alleles (0, 1, 2) for the corresponding SNP, and summed, resulting in a unique score per person. Kaplan-Meier curves and Cox proportional hazard models were used to assess the association between PRS with MM OS considering two models: 1) adjusted for age, sex, study and 2) additional adjustment by stage (ISS). Hazard ratios (OR) and 95% confidence intervals (CI) were estimated. The PRS was evaluated both as a continuous variable, per standard deviation (SD), and as a categorical variable (quintiles). Results MM cases (N=2,179) in the InterLymph study were 59% male and 41% female and the median age was 61.0 years (26-90 years). Median follow-up time was 57.2 months (1.0-509.0 months) with 868 reported deaths. MM cases with stage information available consisted of 20% stage I (n=178), 53% stage II (n=466), and 27% stage III (n=241). No association was observed between PRS and OS in MM patients regardless of adjustment for stage (continuous PRS (HR: 1.03, 95% CI: 0.83-1.28, P=0.80) or by quintile PRS (p>0.05)) (Table). The CoMMpass EA MM cases (n=458) had similar distributions for sex (61% male and 39% females) but were slightly older 65 years (27-93 years) and had shorter follow-up time (median=39.75 months (0.13-77.2)) with 117 deaths. Stage was available for 96% of CoMMpass cases including 36% stage I (n=159), 33% stage II (n=146), and 31% stage III (n=134). We also observed no association of PRS and OS in the CoMMpass study (HR=1.02, 95% CI: 0.72 -1.46, P= 0.89), adjusted for age, sex, and stage (Table). Discussion A PRS score for MM risk is not associated with OS for MM cases in two EA populations. Given that prior studies have shown association of genetic variation with MM survival, efforts to identify additional loci associated with OS or MM specific survival are warranted. Future studies should also consider germline variants impact on molecular subtypes, specific therapies, and outcomes. Disclosures Kumar: Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Takeda: Research Funding.

Phase 2 Trial of Daratumumab, Ixazomib, Lenalidomide and Modified Dose Dexamethasone in Patients with Newly Diagnosed Multiple Myeloma

Background: The current commonly used regimens in newly diagnosed multiple myeloma (NDMM) utilize steroids (dexamethasone or prednisone) in various combinations: bortezomib, lenalidomide and dexamethasone (VRd), bortezomib, thalidomide and dexamethasone (VTd), daratumumab, lenalidomide and dexamethasone (DRd) and daratumumab, bortezomib, melphalan and prednisone (DVMP). However, steroid therapy may be associated with various adverse effects, including, but not limited to mood changes, insomnia, hypertension, hyperglycemia, osteoporosis, adrenal suppression, muscle weakness, and increased risk of opportunistic infections. A recent trial demonstrated improved tolerability of a regimen (Rd-R) involving an abbreviated course of dexamethasone, without compromising the efficacy in patients with intermediate-fit NDMM (Lorocca A., et al., ASH 2018). We designed a phase 2 clinical trial to examine the safety and efficacy of daratumumab, an anti-CD38 monoclonal antibody in combination with an all-oral regimen of ixazomib, a proteasome inhibitor, lenalidomide, an immunomodulatory drug, and modified dose dexamethasone (IRd). Patients and Methods: NDMM patients with measurable disease and adequate organ function were enrolled, irrespective of their transplant eligibility. The primary objective was to determine the rate of complete response (CR) to daratumumab-IRd. Treatment consisted of daratumumab, 16 mg/kg, weekly for two cycles, every other week during cycles 3-6 and then every 4 weeks, ixazomib, 4 mg days 1, 8, 15, lenalidomide, 25 mg days 1-21, and dexamethasone upto 40 mg intravenously weekly for no more than two cycles, followed by use only as a prophylactic premedication for daratumumab-associated infusion reactions. Myeloma risk stratification was assessed by cytoplasmic immunoglobulin fluorescence in-situ hybridization (cIg FISH) analysis. Results: Overall, 40 patients were accrued, with data available on all patients for analysis at the cutoff date of July 19, 2019. The median age at enrollment was 64.5 (33-81) years; 37.5% were female. Eight (20%) patients were high risk by FISH. The median number of cycles was 6 (2-11) and the median follow up was 6.1 (2-11.7) months. Among 40 patients who had received at least 2 cycles of therapy, responses were attained rapidly; at the end of cycle 2, 88% patients achieved at least a partial response and 33% at least a very good partial response (VGPR) that improved to 52% at the end of 4 cycles among 29 patients who had completed at least 4 cycles. The overall best confirmed response rate among all 40 patients (Figure 1) was 95%, including 10% stringent CR, 5% CR and 23% near CR (13% VGPR excluding nCR). Stem cell collection was completed in 17 patients so far, all of whom required filgrastim and plerixafor. The median CD34+ cell count was 7.7 (range 2.9-11.6) million/kg . All patients were alive and 39 (97.5%) patients were progression-free at last follow up. Four (10%) patients proceeded to autologous stem cell transplantation off study, per patient and/or investigator discretion (1 in CR, 2 in PR, 1 with progressive disease). Overall, 224 cycles have been administered across the study, with dose reduction/ hold required in a subset of patients; ixazomib (10%), lenalidomide (20%), daratumumab (0%) and dexamethasone (13%); the most frequent reasons for dose adjustment were skin rash and hematologic toxicities. A grade 3 or higher adverse event, at least possibly attributed to the study drugs, was observed in 40% of patients; hematologic in 30% (lymphopenia 25%, neutropenia 15%, thrombocytopenia 5% and anemia 2.5%) and non-hematologic in 18% of patients (hyperglycemia 8%, diarrhea 5%, infections 5%, ileus 2.5%, maculopapular rash 2.5%, and fatigue 2.5%). Updated results with additional 6 months of follow up and minimal residual disease assessment related data will be presented at the meeting. Conclusion: Our early results suggest that the combination of daratumumab, ixazomib, lenalidomide and modified dose dexamethasone is well-tolerated, with excellent activity, and does not adversely impact stem-cell mobilization in patients with NDMM. Disclosures Kapoor: Takeda: Honoraria, Research Funding; Amgen: Research Funding; Sanofi: Consultancy, Research Funding; Celgene: Honoraria; Janssen: Research Funding; Glaxo Smith Kline: Research Funding; Cellectar: Consultancy. Gertz:Spectrum: Honoraria, Research Funding; Janssen: Honoraria; Celgene: Honoraria; Prothena: Honoraria; Alnylam: Honoraria; Ionis: Honoraria. Dingli:alexion: Consultancy; Janssen: Consultancy; Millenium: Consultancy; Rigel: Consultancy; Karyopharm: Research Funding. Leung:Takeda: Research Funding; Prothena: Membership on an entity's Board of Directors or advisory committees; Aduro: Membership on an entity's Board of Directors or advisory committees; Omeros: Research Funding. Dispenzieri:Pfizer: Research Funding; Janssen: Consultancy; Intellia: Consultancy; Akcea: Consultancy; Takeda: Research Funding; Celgene: Research Funding; Alnylam: Research Funding. Lacy:Celgene: Research Funding. Kumar:Takeda: Research Funding; Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding. OffLabel Disclosure: Daratumumab in combination with Ixazomib, lenalidomide and dexamethasone for the management of newly diagnosed multiple myeloma

Crowdsourced High-Risk Classifiers for Multiple Myeloma Patients Commonly Identify PHF19 As a Robust Progression Biomarker

Multiple myeloma (MM) is a hematological malignancy of terminally differentiated plasma cells residing within the bone marrow with 25,000-30,000 patients diagnosed in the United States each year. The disease's clinical course depends on a complex interplay chromosomal abnormalities and mutations within plasma cells and patient socio-demographic factors. Novel treatments extended the time to disease progression and overall survival for the majority of patients. However, a subset of 15%-20% of MM patients exhibit an aggressive disease course with rapid disease progression and poor overall survival regardless of treatment. Accurately predicting which patients are at high-risk is critical to designing studies with a better understanding of myeloma progression and enabling the discovery of novel therapeutics that extend the progression free period of these patients. To date, most MM risk models use patient demographic data, clinical laboratory results and cytogenetic assays to predict clinical outcome. High-risk associated cytogenetic alterations include deletion of 17p or gain of 1q as well as t(14;16), t(14;20), and most commonly t(4,14), which leads to juxtaposition of MMSET with the immunoglobulin heavy chain locus promoter, resulting in overexpression of the MMSET oncogene. While cytogenetic assays, in particular fluorescence in situ hybridization (FISH), are widely available, their risk prediction is sub-optimal and recently developed gene expression based classifiers predict more accurately rapid progression. To investigate possible improvements to models of myeloma risk, we organized the Multiple Myeloma DREAM Challenge, focusing on predicting high-risk, defined as disease progression or death prior to 18 months from diagnosis. This effort combined 4 discovery datasets providing participants with clinical, cytogenetic, demographic and gene expression data to facilitate model development while retaining 4 additional datasets, whose clinical outcome was not publicly available, in order to benchmark submitted models. This crowd-sourced effort resulted in the unbiased assessment of 171 predictive algorithms on the validation dataset (N = 823 unique patient samples). Analysis of top performing methods identified high expression of PHF19, a histone methyltransferase, as the gene most strongly associated with disease progression, showing greater predictive power than the expression level of the putative high-risk gene MMSET. We show that a simple 4 feature model composed of age, stage and the gene expression of PHF19 and MMSET is as accurate as much larger published models composed of over 50 genes combined with ISS and age. Results from this work suggest that combination of gene expression and clinical data increases accuracy of high risk models which would improve patient selection in the clinic. Disclosures Towfic: Celgene Corporation: Employment, Equity Ownership. Dalton:MILLENNIUM PHARMACEUTICALS, INC.: Honoraria. Goldschmidt:Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; John-Hopkins University: Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Mundipharma: Research Funding; Amgen: Consultancy, Research Funding; Chugai: Honoraria, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Molecular Partners: Research Funding; MSD: Research Funding; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive Biotechnology: Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Research Funding; Dietmar-Hopp-Stiftung: Research Funding; John-Hopkins University: Research Funding. Avet-Loiseau:takeda: Consultancy, Other: travel fees, lecture fees, Research Funding; celgene: Consultancy, Other: travel fees, lecture fees, Research Funding. Ortiz:Celgene Corporation: Employment, Equity Ownership. Trotter:Celgene Corporation: Employment, Equity Ownership. Dervan:Celgene: Employment. Flynt:Celgene Corporation: Employment, Equity Ownership. Dai:M2Gen: Employment. Bassett:Celgene: Employment, Equity Ownership. Sonneveld:SkylineDx: Research Funding; Takeda: Honoraria, Research Funding; Karyopharm: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; BMS: Honoraria; Amgen: Honoraria, Research Funding. Shain:Amgen: 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; Janssen: Membership on an entity's Board of Directors or advisory committees; AbbVie: Research Funding; 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; Adaptive Biotechnologies: Consultancy. Munshi:Abbvie: Consultancy; Takeda: Consultancy; Oncopep: Consultancy; Celgene: Consultancy; Adaptive: Consultancy; Amgen: Consultancy; Janssen: Consultancy. Morgan:Bristol-Myers Squibb, Celgene Corporation, Takeda: Consultancy, Honoraria; Celgene Corporation, Janssen: Research Funding; Amgen, Janssen, Takeda, Celgene Corporation: Other: Travel expenses. Walker:Celgene: Research Funding. Thakurta:Celgene: Employment, Equity Ownership.

Validation of a New Clinical Prediction Model for Outcome in Newly Diagnosed Multiple Myeloma Patients Not Eligible for Autologous Stem-Cell Transplantation; A Population-Based Study from the Danish National Multiple Myeloma Registry

Background : The UK Myeloma Research Alliance recently introduced a new clinical prediction model for outcome in newly diagnosed multiple myeloma (MM) patients not eligible for autologous hematopoietic stem-cell transplantation (ASCT) (Lancet Haematology 2019; 6: e154-66). The score or Myeloma Risk Profile, MRP, includes WHO performance status (PS), the International Staging System (ISS), age, and C-reactive protein (CRP) as prognostic variables. First a score is calculated by the formula: Score = (PS - 2) * 0.199 + (age - 74.4) * 0.0165 + (ISS - 2) * 0.212 + (log(CRP + 1) - 2.08) * 0.0315, where PS and ISS are defined as numbers between 0-4 and 1-3, respectively, and CRP is in mg/L. Next, three risk groups are defined as 1) low risk: score < -0.256, 2) medium risk: -0.256 ≤ score ≤ -0.0283, or 3) high risk: score > -0.0283. The MRP score was generated based on two prospective clinical trial cohorts, the NRCI-Myeloma XI study (ISRCTN49407852) as training set or internal validation, and the NRCI-Myeloma IX study (Blood 2011; 118, 1231-38) as test set or external validation. Both trials investigated conventional oral alkylating agents, cyclophosphamide or melphalan, in combination with thalidomide, lenalidomide, and/or bortezomib; thus including drugs typically used in treatment of elderly MM patients. Establishment of the model included 1852 patients in the training set, and 520 patients in the test set. All patients were recruited as part of clinical trials and therefore fulfilled defined inclusion and exclusion criteria. To validate the MRP score in a population-based setting we performed a study of the entire cohort of transplant ineligible MM patients in the Danish National MM Registry. Methods : The Danish MM registry started 01 January 2005. It includes registration of all diagnosed MM patients in Denmark and given first- and second-line treatment. A data validation study has been performed (J Clin Epidemiology, 2016; 8: 583-587). At 31 December 2014, 2,926 newly diagnosed treatment demanding MM patients were registered, hereof 1,803 patients were above 65 years and found ineligible for ASCT, and constituted the patient population for this study. Results: Of 1,803 transplant in-eligible but treatment demanding newly diagnosed MM patients above 65 years 426 patients had one or more missing values for calculation of the MRP score, most often this was caused by missing ISS. Thus, 1,377 patients were evaluable with a median follow-up of 40.9 months. Patients were treated according to standard of care in Denmark during the 10-years registration period which included upfront conventional alkylating agent, mostly melphalan in 37.7%, thalidomide-based in 25.6%, bortezomib-based in 26.1%, lenalidomide based in 2.7%, and only palliative, mostly steroid-based in 7.9%. The distribution of the risk groups according to MRP was as follows: low risk 28.5%, medium-risk 25.1%, and high-risk 46.4%. Ccompared to the UK datasets we had a higher proportion of high-risk patients which undoubtedly reflects that our cohort is population based. Median survivals for the 3 risk groups are presented in Table 1 and overall survival curves illustrated in Figure 1. The model performed well in separating the patients into subgroups with different survival risks. In conclusion, our real life population-based data confirm that the MRP score is a robust and valuable risk assessment tool for elderly newly diagnosed MM patients older than 65 and not eligible for ASCT. An important advantage of the MRP score is that it is calculated from simple parameters that should be part of everyday diagnostic work-up. Disclosures Vangsted: Oncopeptides: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria; Jansen: Honoraria. Plesner:Takeda: Consultancy; Oncopeptides: Consultancy; Genmab: Consultancy; AbbVie: Consultancy; Celgene: Consultancy; Janssen: Consultancy, Research Funding. Frederiksen:Novartis: Research Funding; Janssen: Research Funding; Gilead: Research Funding; Alexion: Research Funding; Abbvie: Research Funding. Abildgaard:Amgen: Research Funding; Takeda: Research Funding; Celgene: Research Funding; Janssen: Research Funding.

The Association of GSTT1, GSTM1, and TNF-α Polymorphisms With the Risk and Outcome in Multiple Myeloma.

Oxidative stress and systemic inflammation are closely linked with increased risk of cancer development. Tumor necrosis factor alpha (TNF-α) is one of the pro-inflammatory cytokines. Glutathione S-transferases (GSTs) are enzymes involved in oxidative stress handling. Polymorphisms of genes encoding mentioned molecules may potentially influence the risk and the outcome in neoplastic diseases. Multiple myeloma (MM) is a hematological malignancy characterized by clonal, atypical plasma cell proliferation. In the present study we investigated the association of deletion polymorphisms in GSTT1/GSTM1 genes and single nucleotide polymorphisms (SNPs) in the TNF-α gene at positions −308/−238 with the risk and outcome in MM and sensitivity to bortezomib under in vitro conditions. One hundred newly diagnosed MM patients and 100 healthy blood donors were genotyped by means of multiplex PCR (for GSTs) and PCR-RFLP (for TNF-α). In a subgroup of 50 MM patients, bone marrow cells were treated with bortezomib in vitro. Patients with −238GA+AA or GSTT1-null genotypes had 2.0 (p = 0.002) or 2.29 (p = 0.013) fold increased risk of MM. The interaction effects and risk of MM were observed in GSTT1/GSTM1-null (OR = 2.82, p = 0.018), −308/−238GA+AA (OR = 5.63, p < 0.001), as well as in all combinations of −308 with GSTs. The −308/−238GA+AA genotypes in comparison to GG were associated with earlier MM onset−61.14 vs. 66.86 years (p = 0.009) and 61.72 vs. 66.52 years (p = 0.035), respectively. Patients with GSTM1-present had shorter progression-free-survival (15.17 vs. 26.81 months, p = 0.003) and overall-survival (22.79 vs. 34.81 months, p = 0.039) compared with GSTM1-null. We did not observe relationship between response rate and studied polymorphisms. The in vitro study revealed significantly higher number of apoptotic cells at 12 nM of bortezomib in GSTT1-present, GSTM1-null/present, −308GG and −238GG/GA+AA genotypes. Our findings comprise large analysis of studied polymorphisms in MM.

A prospective study of circulating chemokines and angiogenesis markers and risk of multiple myeloma and its precursor

Experimental and clinical studies have implicated certain chemokines and angiogenic cytokines in multiple myeloma (MM) pathogenesis. To investigate whether systemic concentrations of these markers are associated with future MM risk and progression from its precursor, monoclonal gammopathy of undetermined significance (MGUS), we conducted a prospective study within the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial.

Occupational exposure to pesticides and multiple myeloma in the AGRICAN cohort.

Epidemiological studies have found an increased risk of multiple myeloma (MM) in farmers. Few studies have investigated the detailed circumstances of occupational pesticide exposure which could explain these increased risks (pesticide use on crops, seeds or on animals, contact with treated crops) and the role of other exposures. In the Agriculture and Cancer cohort (AGRICAN), we assessed the associations between MM and crop- or animal-related activities, with specific attention to pesticide exposure via use on animals and crops or contact with treated crops and to disinfectant exposure. Analyses concerned 155,192 participants, including 269 incident MM identified by cancer registries from enrolment (2005-2007) to 2013. Cox models using attained age as time scale were run to calculate hazard ratios (HR) and 95% confidence intervals (CI). MM risk was increased in farmers (i) who started using pesticides on crops in the 1960s, especially among those applying pesticides on corn (≥ 20years: HR 1.73, 95% CI 1.08, 2.78, p for trend < 0.01) and (ii) using insecticides on animals (HR 1.48, 95% CI 1.11, 1.98), especially among horse farmers (≥ 10years: HR 2.77, 95% CI 1.22-6.27, p for trend = 0.01). We also observed significant elevated risks with disinfectant use in animal barns. Findings support the role of pesticide use on crops and animals in the occurrence of MM risk in farmers.

Familial Cancer: How to Successfully Recruit Families for Germline Mutations Studies? Multiple Myeloma as an Example

IntroductionIdentification of germline mutations related to an increased cancer risk enables diagnostic, preventive, and therapeutic measures for individuals carrying the disease variant. However, recruitment of families for studies on these mutations can be challenging. Herein we present some of the obstacles that can arise during such studies. We suggest solutions for overcoming or avoiding these difficulties, enabling an efficient and ethically correct family recruitment. Patients and MethodsWe describe a study on germline mutations associated with familial risk of multiple myeloma using next-generation sequencing of the whole genome. To date, the study has recruited 54 participants/16 families from different centers in Germany. It was performed at the University Hospital of Heidelberg and German Cancer Research Center. ResultsWe were confronted with ethical/psychological concerns of patients and family members, a large number of ineligible families, a profound time investment by the participants and the study team, incidental findings, and participants’ death. We present solutions to these difficulties such as: knowledge of and adherence to the laws protecting participants’ rights, an exact clarification of the inclusion and exclusion criteria, a clear division of tasks within members of the study team, a collaboration with general practitioners/oncologists and patients’ support groups, a detailed and understandable informed consent including information about incidental findings, and a choice of a representative in case of participant’s death. ConclusionA successful recruitment for studies on familial cancer is challenging, yet possible. It can be facilitated by applying the previously mentioned strategies.

Abstract 5037: Erythrocyte levels of cadmium and lead and risk of B-cell non-Hodgkin lymphoma and multiple myeloma

Abstract Heavy metals such as cadmium and lead are ubiquitous in the environment and have been classified as carcinogens by the International Agency for Research on Cancer. While research is limited, associations of heavy metals with risk of lymphoid malignancies is plausible given that other environmental factors have been associated with these cancers. To further explore associations of cadmium and lead exposure with risk of lymphoid malignancies, we conducted a nested case-control study within the Cancer Prevention Study-II (CPS-II) Nutrition cohort, a U.S. prospective cohort of men and women. Incident B-cell non-Hodgkin lymphomas (B-NHL) and multiple myelomas (MM) were identified from 32,704 CPS-II participants who were cancer-free at time of blood collection between 1998-2001. Cases included the following histologic subtypes: chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL), diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), multiple myeloma (MM), and other B-cell lymphoma. Each case was matched to two eligible controls that were selected among those who were cancer-free at the time of the case’s diagnosis. The matching factors included race, age, gender, and blood draw date. Cadmium and lead levels were measured in stored erythrocytes from the blood draw. Conditional logistic regression models were used to estimate relative risks (RR) and 95% confidence intervals (CI) for lymphoid malignancy overall and stratified by NHL subtype. The final analytic cohort consisted of 375 B-NHL or MM cases (95 DBLCL, 90 CLL/SLL, 62 FL, 76 MM, and 52 other B-cell lymphoma), and 750 matched controls. The cohort was 56% male, and an average of 69.9 years of age at the time of blood draw. There was a significant, positive association between erythrocyte levels of lead and risk of lymphoid malignancy overall (RR: 1.17, 95% CI: 1.03 - 1.33 per 1 standard deviation (SD; 17.6 µg/L) increase in lead level). This association appeared to be driven by FL (per SD of lead RR: 1.76, 95% CI: 1.21 - 2.57). No associations between lead and DLBCL, CLL/SLL, MM, or other B-cell lymphoma were observed. Results appeared similar when additionally controlling for alcohol use and smoking status. There was no association between cadmium and lymphoid malignancies overall; however, cadmium was inversely associated with risk of multiple myeloma (Q4 vs Q1 RR: 0.30, 95% CI: 0.12 - 0.75). Preliminary results from this nested case-control analysis suggests that a positive association between erythrocyte lead levels and risk of lymphoid malignancies (particularly follicular lymphoma) may exist. Reasons for an inverse association between cadmium and risk of multiple myeloma are unclear but might be due to chance. Further research is needed to fully explore and confirm these findings. Citation Format: Emily L. Deubler, Susan M. Gapstur, W. Ryan Diver, Mia M. Gaudet, James M. Hodge, Victoria L. Stevens, Marjorie L. McCullough, Laura G. Haines, Keith E. Levine, Lauren R. Teras. Erythrocyte levels of cadmium and lead and risk of B-cell non-Hodgkin lymphoma and multiple myeloma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 5037.

Abstract 2686: Associations between a polygenic risk score and risk of multiple myeloma and its precursor

Abstract Genome-wide association studies (GWAS) conducted among populations of European ancestry (EA) have identified 23 common single nucleotide polymorphisms (SNPs) associated with multiple myeloma (MM) risk. We hypothesize that the combination of these SNPs in a polygenic risk score (PRS) is likely to be a strong risk factor for MM. However, it is unclear whether the genetic variation associated with MM susceptibility also predisposes to monoclonal gammopathy of undetermined significance (MGUS). Thus, we calculated a PRS and evaluated the association with risk of MM and its precursor, MGUS. We pooled genotype data for 2434 MM and 3446 controls from ten MM GWAS of individuals of EA within the Interlymph Consortium, for 23 MM risk SNPs identified by prior GWAS. An additional 754 MGUS cases were ascertained from Mayo Clinic and MD Anderson clinical practices. To calculate the PRS, we used the risk estimates corresponding to the 23 SNP associations from the largest published MM GWAS. The log of the odds ratio (OR) for each SNP was multiplied by the respective number of risk alleles and summed to generate a PRS for each individual. The PRS was examined continuously, per one standard deviation (SD), and as quintiles, based on the PRS distribution in the controls. Associations of PRS with MM and MGUS risk were examined separately, using multivariable logistic regression assuming an additive model to assess ORs and 95% confidence intervals adjusted for age, sex, and site. We also evaluated age and sex stratified models. The distribution of sex within MM cases, MGUS cases and controls were each ~60% male and ~40% female. The median age was 61, 66, and 66 years for MM cases, MGUS cases and controls, respectively. PRS ranged from 1.52-4.91, with a median PRS of 3.21 for MM cases, 3.19 for MGUS cases, and 3.05 for controls. PRS was significantly associated with MM risk when assessed continuously (OR=1.19 per SD, p=2.2x10-16) and categorically; compared with the middle quintile (Q3), individuals in the highest quintile (Q5) had a 66% increased MM risk (OR=1.66, p=2.3x10-9) and those in the lowest quintile (Q1) had a 38% decreased MM risk (OR=0.62, p=1.3x10-6). PRS was also significantly associated with MGUS risk (OR=1.19 per SD, p=1.7x10-11); individuals with the highest PRS (Q5) had a 77% increased risk (OR=1.77, p=4.0x10-4) and those with lowest PRS (Q1) had 30% decreased risk (OR=0.70, p=0.04), compared with Q3. When stratified by age and sex, similar associations and trends were found. Using an independent sample of MM / MGUS cases and controls, we showed that a PRS constructed from 23 common genetic variants for MM risk is associated with risk of both MM and MGUS, regardless of age or sex. A future direction of this work is testing associations with PRS and clinical characteristics of the MM cases, as well as differences between MGUS cases that progress and those that do not. Our results suggest that common genetic variation may predispose to MGUS as the precursor to MM. Citation Format: Alyssa I. Clay-Gilmour, Michelle A. Hildebrandt, Nicola J. Camp, Elad Ziv, Elizabeth E. Brown, Jonathan N. Hofmann, John J. Spinelli, Graham G. Giles, Parveen Bhatti, Wendy Cozen, Xifeng Wu, Dennis P. Robinson, Aaron D. Norman, Jason P. Sinnwell, Shaji K. Kumar, S Vincent Rajkumar, Susan L. Slager, Celine M. Vachon. Associations between a polygenic risk score and risk of multiple myeloma and its precursor [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2686.

Abstract 610: A longitudinal investigation of hematologic perturbations among permethrin-exposed pesticide applicators in the Biomarkers of Exposure and Effect in Agriculture Study

Abstract Background: In the Agricultural Health Study, a large prospective cohort of pesticide applicators, high lifetime use of the insecticide permethrin was associated with an increased risk of multiple myeloma. However, the biological mechanisms through which this association occurs remain to be fully understood. Methods: Hematologic perturbations were assessed among 33 permethrin-exposed male pesticide applicators enrolled in the Biomarkers of Exposure and Effect in Agriculture study, a molecular substudy within the Agricultural Health Study. Serial blood samples were collected from each participant coinciding with permethrin use including visits during the offseason, an exposure visit the day after use, and for 27 participants a follow-up visit approximately three weeks after use. Complete blood count with white blood cell differential and lymphocyte subsets were collected at each visit. Linear mixed models were used to assess the relationship between permethrin exposure and hematologic parameters, controlling for potential confounders including season of blood draw, age, body mass index, recent cold/infection, and use of other pesticides. Results: A statistically significant increase in the adjusted mean immature granulocyte count (37%, 95% CI=6%, 76%) was observed the day after permethrin use compared to an offseason visit. Statistically significant (P&amp;lt;0.05) alterations in several red blood cell (RBC) parameters were also observed on the day after permethrin use, including decreased RBC count and hemoglobin and increased mean corpuscular volume and RBC distribution width-SD. Decreases in RBC count and hemoglobin, and increased levels of RBC distribution width-SD persisted approximately three weeks after permethrin use. Conclusions: Altered hematologic parameters could be indicative of disrupted hematopoiesis, providing insights into the potential biologic plausibility of the observed association between permethrin use and multiple myeloma risk among pesticide applicators. Impact/Significance: Given the widespread use of permethrin in both residential and commercial settings, our findings may have public health implications beyond occupationally exposed pesticide applicators. Citation Format: Joe Shearer, Laura Beane Freeman, Danping Liu, Gabriella Andreotti, Jennifer Hamilton, Julie Happel, Charles Lynch, Michael Alavanja, Jonathan Hofmann. A longitudinal investigation of hematologic perturbations among permethrin-exposed pesticide applicators in the Biomarkers of Exposure and Effect in Agriculture Study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 610.

Hypertensive nephropathy is associated with an increased risk of myeloma, skin, and renal cancer.

Previous studies suggest an increased cancer risk in hypertension. Patients with hypertensive nephropathy have not been studied. A national registry study was performed to assess the presence and size of this association. Clinical data and cancer diagnoses for all patients with biopsy-proven hypertensive nephropathy between 1985 and 2015 in Denmark were extracted from four national registries and compared with age- and sex-adjusted national cancer rates. The risk of cancer was twice the background population. It was raised for renal cancer (odds ratio 10.4), myeloma (13.2), skin cancer (7.9), and other/unspecified (1.8). No increase in incidence was seen until 1year before renal biopsy and then rose rapidly. It was again normal 5years after biopsy. Hypertensive nephropathy is associated with an increased risk of myeloma, skin, renal, and other cancers. Screening of patients with hypertensive nephropathy, in the presence of reduced renal function or significant proteinuria, may be indicated.

Germline Risk Contribution to Genomic Instability in Multiple Myeloma.

Genomic instability, a well-established hallmark of human cancer, is also a driving force in the natural history of multiple myeloma (MM) – a difficult to treat and in most cases fatal neoplasm of immunoglobulin producing plasma cells that reside in the hematopoietic bone marrow. Long recognized manifestations of genomic instability in myeloma at the cytogenetic level include abnormal chromosome numbers (aneuploidy) caused by trisomy of odd-numbered chromosomes; recurrent oncogene-activating chromosomal translocations that involve immunoglobulin loci; and large-scale amplifications, inversions, and insertions/deletions (indels) of genetic material. Catastrophic genetic rearrangements that either shatter and illegitimately reassemble a single chromosome (chromotripsis) or lead to disordered segmental rearrangements of multiple chromosomes (chromoplexy) also occur. Genomic instability at the nucleotide level results in base substitution mutations and small indels that affect both the coding and non-coding genome. Sometimes this generates a distinctive signature of somatic mutations that can be attributed to defects in DNA repair pathways, the DNA damage response (DDR) or aberrant activity of mutator genes including members of the APOBEC family. In addition to myeloma development and progression, genomic instability promotes acquisition of drug resistance in patients with myeloma. Here we review recent findings on the genetic predisposition to myeloma, including newly identified candidate genes suggesting linkage of germline risk and compromised genomic stability control. The role of ethnic and familial risk factors for myeloma is highlighted. We address current research gaps that concern the lack of studies on the mechanism by which germline risk alleles promote genomic instability in myeloma, including the open question whether genetic modifiers of myeloma development act in tumor cells, the tumor microenvironment (TME), or in both. We conclude with a brief proposition for future research directions, which concentrate on the biological function of myeloma risk and genetic instability alleles, the potential links between the germline genome and somatic changes in myeloma, and the need to elucidate genetic modifiers in the TME.

Tumor Necrosis Factor Alpha-308G/A Polymorphism and the Risk of Multiple Myeloma: A Meta-Analysis of Pooled Data from Twelve Case-Control Studies

Objective:Tumor necrosis factor alpha (TNF-α) is an important cytokine involved in inflammation, immune response, and other biological processes. The association between polymorphism -308G/A in its promoter and the risk of multiple myeloma (MM) is not clear. Thus, we conducted a meta-analysis to clarify this question.Materials and Methods:Twelve eligible studies, which included 2204 MM cases and 3478 controls, were enrolled in our meta-analysis by searching the PubMed, China National Knowledge Infrastructure, Scopus, Web of Science, and Google Scholar databases up to December 2018. The effect of polymorphism -308G/A on MM risk was evaluated by calculating the pooled odds ratio (OR) and the 95% confidence interval (CI). Furthermore, the Q-test and I2 statistical analyses were used to estimate the degree of heterogeneity. Sensitivity analysis was conducted to test the robustness of the meta-analysis results. Publication bias was assessed by Egger’s test and visual inspection of a funnel plot.Results:In the dominant model, -308G/A polymorphism was associated with reduced MM risk (OR=0.80, 95% CI: 0.65-0.97), and it also demonstrated a significant protective effect with a pooled OR of 0.82 (95% CI: 0.68-0.99) in the Caucasian subgroup. Because of the limited number of individual studies with AA genotype carriers, only eight studies were included in the recessive model, and no significant difference was observed. Moreover, the meta-analysis of the allele frequency demonstrated that the A allele has a protective effect against MM risk with a pooled OR of 0.83 (95% CI: 0.69-0.99). Sensitivity analysis suggested that the synthesized effect size was not influenced by any individual study. Moreover, the Egger’s test statistical analysis suggested that publication bias was not obvious in the present analysis.Conclusion:Overall, the -308G/A polymorphism was associated with reduced MM risk in the dominant model and allele frequency. Further investigation is needed to gain better insight.

Tumor Necrosis Factor Alpha (TNF-α) –308G/A Polymorphism and the Risk of Multiple Myeloma: A Meta-analysis of Pooled Data from 12 Case-control Studies

Tumor Necrosis Factor Alpha (TNF-α) –308G/A Polymorphism and the Risk of Multiple Myeloma: A Meta-analysis of Pooled Data from 12 Case-control Studies

Dietary Pattern and Risk of Multiple Myeloma in Two Large Prospective US Cohort Studies.

BackgroundThe limited data on specific dietary components and risk of multiple myeloma (MM) show no consistent association. Studies have not examined the association of dietary pattern with MM risk.MethodsIn prospective cohorts of 69 751 women (Nurses’ Health Study, 1984–2014) and 47 232 men (Health Professionals Follow-up Study, 1986–2014), we examined the association between dietary pattern and risk of MM using Cox proportional hazard models. Diet was assessed repeatedly every 4 years with food frequency questionnaires and was used to calculate dietary patterns including the Alternate Healthy Eating Index-2010, Alternate Mediterranean Diet, Dietary Approaches to Stop Hypertension, Prudent and Western patterns, the empirical dietary inflammatory pattern (EDIP), and empirical dietary indices for insulin resistance (EDIR) and hyperinsulinemia (EDIH).ResultsDuring 2 792 257 person-years of follow-up, we identified 478 incident MM cases (215 women, 263 men). In men, high EDIP was statistically significantly associated with a 16% increase in MM risk (hazard ratio [HR] = 1.16, 95% confidence interval [CI] = 1.02 to 1.32 per 1-SD increase). Moreover, EDIR and EDIH had a suggestive positive association (EDIR: HR = 1.09, 95% CI = 0.96 to 1.24; and EDIH: HR = 1.11, 95% CI = 0.97 to 1.28 per 1-SD increase). We observed no other associations with MM risk in men and no associations for any dietary pattern with MM risk in women.ConclusionsWe present the first evidence for a role of diets with higher inflammatory or insulinemic potential in MM development. Further studies are warranted to explore these associations in other populations, including the apparent restriction to men.

Immune marker changes and risk of multiple myeloma: a nested case-control study using repeated pre-diagnostic blood samples.

Biomarkers reliably predicting progression to multiple myeloma (MM) are lacking. Myeloma risk has been associated with low blood levels of monocyte chemotactic protein-3 (MCP-3), macrophage inflammatory protein-1 alpha (MIP-1α), vascular endothelial growth factor (VEGF), fibroblast growth factor-2 (FGF-2), fractalkine, and transforming growth factor-alpha (TGF-α). In this study, we aimed to replicate these findings and study the individual dynamics of each marker in a prospective longitudinal cohort, thereby examining their potential as markers of myeloma progression. For this purpose, we identified 65 myeloma cases and 65 matched cancer-free controls each with two donated blood samples within the Northern Sweden Health and Disease Study. The first and repeated samples from myeloma cases were donated at a median 13 and 4 years, respectively, before the myeloma was diagnosed. Known risk factors for progression were determined by protein-, and immunofixation electrophoresis, and free light chain assays. We observed lower levels of MCP-3, VEGF, FGF-2, and TGF-α in myeloma patients than in controls, consistent with previous data. We also observed that these markers decreased among future myeloma patients while remaining stable in controls. Decreasing trajectories were noted for TGF-α (P=2.5 × 10−4) indicating progression to MM. Investigating this, we found that low levels of TGF-α assessed at the time of the repeated sample were independently associated with risk of progression in a multivariable model (hazard ratio = 3.5; P=0.003). TGF-α can potentially improve early detection of MM.