Citations

Abstract So far, 23 germline susceptibility loci have been associated with multiple myeloma (MM) risk. It is unclear whether the genetic variation associated with MM susceptibility also predisposes to its precursor, monoclonal gammopathy of undetermined significance (MGUS). Leveraging 2434 MM cases, 754 MGUS cases, and 2 independent sets of controls (2567/879), we investigated potential shared genetic susceptibility of MM and MGUS by (1) performing MM and MGUS genome-wide association studies (GWAS); (2) validating the association of a polygenic risk score (PRS) based on 23 established MM loci (MM-PRS) with risk of MM, and for the first time with MGUS; and (3) examining genetic correlation of MM and MGUS. Heritability and genetic estimates yielded 17% (standard error [SE] ±0.04) and 15% (SE ±0.11) for MM and MGUS risk, respectively, and a 55% (SE ±0.30) genetic correlation. The MM-PRS was associated with risk of MM when assessed continuously (odds ratio [OR], 1.17 per SD; 95% confidence interval [CI], 1.13-1.21) or categorically (OR, 1.70; 95% CI, 1.38-2.09 for highest; OR, 0.71; 95% CI, 0.55-0.90 for lowest compared with middle quintile). The MM-PRS was similarly associated with MGUS (OR, 1.19 per SD; 95% CI, 1.14-1.26 as a continuous measure, OR, 1.77, 95%CI: 1.29-2.43 for highest and OR, 0.70, 95%CI: 0.50-0.98 for lowest compared with middle quintile). MM and MGUS associations did not differ by age, sex, or MM immunoglobulin isotype. We validated a 23-SNP MM-PRS in an independent series of MM cases and provide evidence for its association with MGUS. Our results suggest shared common genetic susceptibility to MM and MGUS.

LBA3 Background: Bortezomib (btz) combined with lenalidomide (len) and dexamethasone (dex) (VRd) is a standard initial therapy for NDMM. Carfilzomib (cfz), a next-generation proteasome inhibitor, in combination with len-dex (KRd) has shown higher efficacy in phase II trials. This randomized phase III trial was designed to examine if KRd improves progression free survival (PFS) compared to VRd in NDMM (current results), and whether indefinite maintenance with len improves OS compared with two-year maintenance (to be analyzed once data matures). Methods: Patients (Pts) with NDMM, were randomized to receive VRd or KRd in a 1:1 fashion for 36 weeks followed by a second randomization (1:1) to indefinite versus two years of len maintenance. Pts without del17p, t (14;16), t(14;20), plasma cell leukemia or high-risk GEP70 profile, were enrolled. VRd arm included btz 1.3 mg/m2 on days(d) 1, 4, 8, and 11 (d 1, 8 for cycles 9-12), len 25 mg d 1-14, and dex 40 mg d 1, 2, 4, 5, 8, 9, 11, 12 of a 3-week (wk) cycle for 12 cycles, while pts in the KRd arm received cfz 36 mg/m2 d 1, 2, 8, 9, 15, 16 with len 25 mg daily on d 1-21 and dex 40 mg wkly, in 4 wk cycles for 9 cycles. Maintenance included len 15mg d 1-21 every 4 wks. The study was designed to detect a hazard ratio (HR)=0.75 with 80% power at 1-sided 2.5% alpha and 399 PFS events (progression or death regardless of intervening therapy). Results: The study accrued 1087 pts (VRd=542, KRd=545). The median age was 65y. Treatment, efficacy, and toxicity data are in the table. At the second of 3 planned interim analyses, with PFS HR=1.04 (95% CI, 0.8 to 1.3, p=0.74), futility was met. Median PFS was VRd=34.4m and KRd=34.6m; no differences were seen based on age (<65 or ≥65), presence or absence of t(4;14) or ISS stage. The three-year OS (95% CI) was similar: VRd 84% (80 to 88) and KRd 86% (82 to 89). Conclusions: In this randomized phase 3 trial, KRd did not improve PFS compared with VRd in NDMM. A significantly higher rate of cardio-pulmonary and renal toxicity was observed with KRd, while neuropathy rates were higher with VRd. VRd remains the standard triplet induction regimen in standard and intermediate risk NDMM, and a suitable backbone for 4 drug combinations. Clinical trial information: NCT01863550 . [Table: see text]

e20520 Background: Cytogenetic abnormalities detected by FISH are found in the majority of multiple myeloma (MM) patients. Although their prognostic value has been studied extensively, less is known about their association with disease characteristics and treatment response. Methods: To address these questions, we designed a retrospective study including 2031 Mayo Clinic patients diagnosed with MM from 2004 to 2018. We compared baseline characteristics and treatment outcomes between primary cytogenetic groups: t(11;14), t(4;14), t(14;16), (14;20), t(6;14), unknown IgH translocation/del and trisomy (without IgH translocation). These included 373, 177, 78, 20, 18, 228 and 791 patients respectively. Kruskal-Wallis and Fisher’s exact tests were used for categorical and continuous variables respectively. Time to next treatment (TTNT) was estimated using Kaplan-Meier method and compared using Log-Rank test. Results: t(4;14), t(14;16), t(6;14) and t(14;20) groups were associated with hemoglobin < 10 g/dL, beta2microglobulin > 5.5 µg/ml, ISS stage 3 and ≥50% bone marrow plasma cells. The latter 3 groups were also associated with renal dysfunction (Cr ≥2 mg/dL) and higher urinary monoclonal protein. t(4;14) was associated with IgA isotype, serum monoclonal protein ≥1g/dL and plasma cell labeling index ≥1%. Light chain myeloma was more prevalent in patients with t(11;14). Overall response rate (ORR) to proteasome inhibitor (PI) induction was higher for those with IgH translocations (any) compared to trisomies (85% vs 77% P = 0.02), while ORR was higher for those with trisomies with immunomodulatory drug (IMiD) induction (90% vs 78% P < 0.01). The rate of ≥ very good partial response was higher for patients with high risk IgH translocations [t(4;14), t(14;16) or t(14;20)] compared to standard risk with PI-IMiD combination treatment (88% vs 65% P < 0.01). Otherwise, response rates did not differ between these 2 groups. TTNT was longer in patients with trisomies compared to those with IgH translocation with IMiD or PI-IMiD treatments (32.2 vs 19 and 44 vs 27.4 months, respectively P < 0.01). For all cytogenetic groups, better treatment responses and longer TTNT were seen with PI-IMiD combinations compared to other treatments. Conclusions: t(4;14), t(14;16), t(6;14), and t(14;20) are associated with high risk disease characteristics. Patients with IgH translocations may have better response to PI induction compared to those with trisomies, while those with trisomies may have better response to IMiD treatment, with best outcomes for both seen with PI-IMiD combinations.

e20515 Background: IgA monoclonal proteins (MCPs), unlike IgG MCPs, often migrate in the beta region on serum protein electrophoresis (SPEP) which can lead to underestimation of their size due to the co-migration with physiologic proteins. In IgA multiple myeloma (MM), the utility of quantitative IgA levels in assessing disease response in comparison to SPEP is not well studied. Methods: We retrospectively analyzed 304 IgA MM patients, diagnosed between 2004 and 2018, with available serial MCP and quantitative IgA levels. Kaplan Meier analysis was used to estimate the median progression free survival (mPFS) using the IMWG criteria and our study definition of IgA progression (2 consecutive IgA values that are > 25% above the nadir IgA value and above upper limit of normal (ULN) of 356 mg/dL, and a detectable IgA MCP on serum immunofixation). The mPFS was defined as the time from treatment initiation until disease progression or death. Results: IgA MCP migrated in the beta region in 134 (44%) patients, and in the gamma region in 150 (56%) patients. At diagnosis the median MCP was 3 (IQR 1.9-4) g/dL and the median IgA was 3240 (IQR 2008-4420) mg/dL. The median time from treatment initiation to MCP nadir was 80 (IQR 42-144) days and median time to IgA nadir was 154 (IQR 90-238) days. At MCP nadir 40% of patients had an IgA above the ULN. All complete responders (n = 104) had normal IgA levels, with a median IgA of 54 (IQR 27-88) g/dL. A ≥90% decrease in IgA between treatment initiation and IgA nadir, compared to a < 90% decrease, was associated with a longer mPFS (34 vs. 20 months, p = 0.006) and overall survival (97 vs. 33 months, p = 0.003). Patients with serial MCP and IgA levels available prior to progression (n = 195) were used to compare the mPFS using the IMWG and IgA progression criteria. The mPFS using the IgA criteria was 32 (95% CI 29-39) months, versus 39 (95% CI 33-45) months using IMWG criteria. Overall, 92 (47%) patients progressed by both IMWG and IgA criteria. At the time of progression using the IgA criteria compared to at IMWG progression, the median hemoglobin was higher (13.3 vs. 11.6 g/dL, respectively, p < 0.001) and fewer patients had new symptomatic bone lesions (2% vs. 33%, respectively, p < 0.001). Conclusions: In IgA MM patients, monitoring quantitative IgA levels predicts disease response and allows for earlier detection of disease progression, prior to the development of end organ damage.

e20510 Background: pPCL is a rare and aggressive form of multiple myeloma (MM) with dismal survival outcomes compared to the remainder of MM patients. Several studies have validated the optimal cutoff for defining pPCL to be >5% circulating plasma cells (cPCs) on a peripheral blood smear due to equally poor outcomes in this less restrictive cohort. We evaluated the clinical outcomes and cytogenetic features of patients diagnosed with pPCL at our institution that were treated with novel agent induction therapies. Methods: We evaluated patients with pPCL diagnosed between 2000 – 2018 (using the 5% cut off) at Mayo Clinic, Minnesota. Data was extracted from a prospectively maintained database and from the review of electronic medical records. Patients were categorized as having high risk (HR) cytogenetics if any of the following abnormalities were present: del 17p, t(4;14), t(14;16) or t(14;20). Chi-square tests and Fisher exact tests were used to compare differences between sub-groups. Survival analysis was performed by the Kaplan-Meier method and differences assessed using the log rank test. Results: This cohort consisted of 67 patients with pPCL with a median age of 62 years (range: 34-91) of which 33 (46%) were male. The median follow up was 46 months (95% CI: 41 – 90). The median bone marrow plasma cell involvement was 84% (Range: 10 – 100) and the median cPCs percentage on the peripheral blood was 23% (range: 5 - 93). Data on primary cytogenetic abnormalities were available in 60 (85%) patients and the distribution was as follows: t(11;14) – 27 (45%), t(4;14) – 5 (8%), t(14;16) – 8 (13%), t(14;20)- 3(5%) and del 17p- 15(25%). All patients received novel agent induction therapy with 36 (54%) having received autologous stem cell transplant and 4 undergoing an allogeneic stem cell transplant. The median time to next therapy (TTNT) and overall survival (OS) for all patients was 13 months (95% CI: 9 – 17) and 24 months (95% CI: 19 – 40) respectively; 16 months and 51 months for standard risk (SR) vs. 10 months and 19 months for HR (P = 0.005 for OS), when stratified by cytogenetic risk. There were only 17 (31%) patients who were alive for ≥48 months since diagnosis (i.e. twice the median OS of this cohort); The absence of high risk cytogenetics (P = 0.049) and non-elevated LDH level (P = 0.047) at diagnosis predicted for achieving this survival goal. Conclusions: The outcomes of patients diagnosed with pPCL remain poor despite the use of novel agent induction therapy. However, some patients appear to do better than expected and this phenomenon may be influenced by the presence of HR cytogenetics.

8507 Background: The introduction of immunomodulatory agents, proteasome inhibitors, and autologous stem cell transplantation (ASCT) has improved outcomes for patients with multiple myeloma (MM), but those with high risk MM (HRMM) have a poor long-term prognosis. To date, no trials have addressed optimal treatment for these patients. Methods: S1211 is a randomized phase II trial (NCT01668719) comparing 8 cycles of lenalidomide, bortezomib and dexamethasone (RVd) induction followed by dose-attenuated RVd maintenance until disease progression with or without elotuzumab. Stem cell collection was allowed, but ASCT was deferred until progression. HRMM was defined by one of the following: gene expression profiling high-risk (GEPhi), t(14; 16), t(14; 20), del(17p) or amplification 1q21, primary plasma cell leukemia (pPCL) and elevated serum LDH (> 2X ULN). Median progression-free survival (PFS) was the primary end-point, using a one-sided stratified log-rank test at a one-sided significance level of 0.1. Secondary endpoints included overall response rate (ORR), adverse events (AE), serious adverse events (SAE) and OS. Response was assessed using the IMWG 2009 criteria. Results: S1211 enrolled 103 evaluable patients, RVd n = 54, RVd-Elo n = 49. 75% had ISS II/III, 47% amp1q21, 38% del17p, 12% t(14; 16), 9% GEPhi, 7% pPCL, 5% t(14; 20) and 4% elevated serum LDH (18.5% > 1 feature). With median follow-up of 53 months (mos.), no difference in median PFS was observed [RVd-Elo = 31 mos., RVd = 34 mos.,HR = 0.968 (80% CI = 0.697-1.344), p = 0.449]. No difference in OS was observed [RVd-Elo = 68 mos, RVd = not reached, HR = 1.279 (80% CI: 0.819, 2.000), p-value = 0.478]. 72% pts had > Grade 3 AEs, no differences in the safety profile were observed except >Grade 3 infections (RVd 8%, RVd-Elo 16%), >Grade 3 sensory neuropathy (RVd 8%, RVd-Elo 13%). Conclusions: In the first randomized HRMM study reported to date, the addition of elotuzumab to RVd induction and maintenance did not improve patient outcomes. However, the PFS and OS seen in both arms of the study exceeded the original statistical assumptions and support the role for PI/IMiD combination maintenance therapy for this patient population. The S1211 data will serve as an important benchmark for future HRMM clinical trials. Clinical trial information: NCT01668719 .

e19112 Background: We sought to study the longitudinal QOL, financial burden and perception of care (PoC) in patients with multiple myeloma (MM) or lymphoma (LYM) enrolled on CTs vs. those receiving standard care (Non-CT). Methods: QOL was evaluated using the FACT-G questionnaire (baseline, 1, 2, 3, 6 & 12 months). Financial burden was evaluated using 10 questions adapted from the MEPS survey and PoC was evaluated using 6 questions adapted from the CAHPS survey (baseline, 3, 6 & 12 months). Results: 35 (28%) of 123 enrolled patients were treated on CTs. Demographics, QOL and financial burden are shown in the Table. Baseline QOL scores were similar in CT vs non-CT groups. Age, gender and self-reported performance status were significantly associated with baseline QOL, while lines of therapy & cancer type were not. There were no significant differences in change in QOL vs. baseline in patients treated on CTs vs. non-CTs at 1, 2, 3, 6 and 12 months from baseline, including the total FACT-G score and the subdomains of functional, physical, emotional and social well-being. Within each group, there was a decline in QOL over the first 3 months, specifically physical WB, which gradually returned to baseline. Baseline answers to financial burden questions were similar, except patients on CTs reported less need for taking extended time-off from work (22% vs. 46%, p = 0.02). The only statistically significant difference in individual Qs over 1 year was ‘worry about having to pay large medical bills’ at the 6-month timepoint (CT: 0%, non-CT: 52%, p = 0.01). Using a composite score from 4 MEPS questions, financial burden over time was lower in the CT group, though the differences were not statistically significant. There were no significant differences in PoC at baseline, 3, 6 and 12 months in the two groups based on answers to each of the six questions adapted from the CAHPS survey. Conclusions: Over 1 year, patients on CTs experienced a similar QOL and perceived their care to be similar as non-CT patients. Financial burden was lower in the CT group, though differences were not statistically significant. [Table: see text]

2054 Background: Systematic reviews that summarize the toxicity of Immune checkpoint inhibitors (ICIs) become outdated very soon after publication. Therefore, we reported results of a toxicity meta-analysis at 2019 ASCO meeting and informed the intent to create a living systematic review (LSR). LSRs combine human and machine effort and support rapid evidence synthesis and living clinical practice guidelines. Now, we report our experience maintaining a LSR on toxicity of ICIs. Methods: Steps include quarterly literature searches to identify new clinical trials reporting ICI-associated adverse events (AEs), AI-enabled screening of new citations which meet the inclusion criteria, automated cumulative meta-analysis and an online reporting platform. Standard data formats and protocols were designed for inputting text, tables and graphics. Software was written to interpret these data and output the information in the appropriate format, such as a forest plot and summary tables. Finally, a dynamic interface that enables user inputs and displays the associated output was designed. Results: The LSR is continuously updated incorporating toxicity data from new clinical trials as it becomes available. We have screened 8000 relevant citations and summarized the odds of Grade 3 or higher AEs and AEs of special interest in patient receiving ICIs. The results are updated on quarterly basis and are available online. The results are updated on quarterly basis and will be available on a website at the time of publication. Prototype with dummy data is available at this link . This interface can also be manipulated via user input to organize and sort data tables and forest plots by type of cancer, name or mechanism (PD-1 or PD-L1) of ICI agent, single agent or combination, type of control arm, line of treatment and several other clinically relevant filters. For example, a user can instantaneously generate a meta-analysis summarizing the risk of colitis or pneumonitis in metastatic lung cancer trials with pembrolizmuab. Conclusions: This LSR engine can prospectively synthesize toxicity data from ICI trials in an efficient manner providing accurate and timely information for advanced clinical decision support at point-of-care. Efforts are ongoing to improve efficiency of screening, improve AI-enabled processes for automated screening and data abstraction, and test across multiple clinical questions.