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Background: Dex is a key component of induction regimens for NDMM despite common toxicities including insomnia and anxiety. The E4A03 randomized trial (Rajkumar, Lancet Oncol 2010) demonstrated that dex dosed at 40 mg weekly outperformed higher-intensity dex and established this regimen as the standard of care for NDMM. In standard practice as well as clinical trials, however, dex doses are often reduced to 20 mg weekly or lower (if not stopped entirely) among older patients or among patients experiencing toxicities. As a first step toward establishing the equipoise of planned dex dose reductions in NDMM, we investigated outcomes with patients from two previous SWOG trials to understand the impact of dex dose reductions on post-induction outcomes. Methods: This was a secondary analysis of data from two completed NDMM studies, S0777 (comparing VRd vs Rd) and S1211 (comparing Elo-VRd vs VRd). For this secondary analysis, we analyzed the subset of patients who completed all eight 3-week cycles of induction (or six 4-week cycles in the S0777 Rd arm). As shown in Figure 1A, we defined DEX fullDose as patients for whom the starting dex dose (either 40 mg once per week or 20 mg on Days 1, 2, 4, 5, 8, 9, 11, and 12 of 21-day cycles) did not change during induction. In contrast, DEX lowered was defined as patients for whom the end-of-induction dex dose and/or frequency were lower than their starting values. As an exploratory analysis, we also compared PFS and OS between the DEX fullDose (no changes to dex strength during induction) and DEX veryLow (dex dosing reduced ≥50% during induction) subgroups. All analyses were landmarked from the end of induction, defined as 24 weeks from registration, to account for the time-dependent nature of dex treatment received. Survival curves were based on the Kaplan-Meier method and comparisons were made using the log-rank test. Results: Of 541 evaluable patients ( n = 441 from S0777, n = 100 from S1211), dex was reduced during induction in 76% ( n = 410). Compared to DEX fullDose patients, DEX lowered patients were more likely to be anemic (37% vs 26%, p = 0.02) but had similar distributions of age ≥70 (24% of DEX lowered vs 26% of DEX fullDose), Performance Status > 1 (15% vs 12%), ISS stage 3 disease (33% vs 27%), ≥ 60% bone marrow plasma cell burden (37% vs 31%), M-spike > 3 g/dL (48% vs 48%) platelets < 150,000 cells/mm 3 (18% vs 14%), baseline LDH ≥ 190 U/L (36% vs 41%), and baseline CRP ≥ 8 mg/L (27% vs 30%). PFS (Figure 1B) was comparable between DEX lowered (median 36 months, 95% CI 29 to 39 months) and DEX fullDose (median 33 months, 95% CI 24 to 48 months) with p = 0.90. Similarly, OS (Figure 1C) was comparable between DEX lowered [median 77 months, 95% CI 68 months to not reached (NR)] and DEX fullDose (median 92 months, 95% CI 65 months to NR) with p = 0.93. In the DEX veryLow subgroup with ≥50% reduction in dex dosing during induction, median PFS was 39 months (95% CI 34 to 50 months) and median OS was NR (95% CI NR to NR). As shown in Figure 1D and Figure 1E, PFS and OS were comparable between these patients and DEX fullDose patients. Discussion: To our knowledge, this is the first analysis of dex dosing strength in NDMM since E4A03. In two separate randomized trials, over half of patients required dex reductions below a starting dose of ~40 mg weekly. Dex dose reductions during induction had no impact on post-induction PFS and OS, even in the subset of patients who required significant dose reductions. Limitations of our post hoc analysis include heterogeneity between dex dosing schedules and lack of cytogenetic data for all patients. We could not discern specifically why dex was lowered, and it is possible that dex might have been lowered disproportionately commonly in patients who had achieved early responses and thus independently did better. Regardless, these results strongly suggest that maintaining dex 40 mg weekly for the entirety of NDMM induction may be unnecessary in the modern era. Cooperative efforts to study a planned dex de-escalation protocol (i.e., once no longer needed for symptom management or as a pre-medication) alongside patient-reported outcome assessments of symptom burden are being planned. Funding: NIH/NCI/NCTN grants U10CA180888, U10CA180819, U10CA180821, U10CA180820; and in part by Millennium Pharmaceuticals, Inc. (now part of Takeda Pharmaceutical Company), and by Celgene.

Introduction Lymphoid malignancy (LM) is a heterogenous neoplasm with respect to biology, aggressiveness, and treatment with over 100 subtypes, including Hodgkin lymphoma (HL), non-Hodgkin lymphoma (NHL), and multiple myeloma (MM). Genome-wide association studies have identified common variants associated with risk of specific LM subtypes, but less is known about the contribution of rare inherited pathogenic variants (PV) in the genetic architecture of LM risk. Rare PV have been shown to contribute to the genetic architecture of solid tumor cancer risk, many of which have been found in genes that are important to the DNA-damage repair (DDR) pathway (e.g., BRCA1, BRCA1, ATM). Here, we investigated the prevalence of PV in cancer predisposition genes regularly included on multi-gene panel testing based on NCCN guidelines and correlated with risk of LM overall and by common subtypes. Methods In this association study, cases were newly diagnosed lymphomas (excluding chronic lymphocytic leukemia) from Mayo Clinic who were enrolled between 2002 and 2015 in the Lymphoma SPORE Molecular Epidemiology Resource (MER) and MM from Mayo Clinic enrolled primarily between 1998 and 2022 in a clinical registry. Controls were from the Mayo Clinic Biobank who were enrolled between 2009 and 2016, from which we excluded individuals with a history of hematologic malignancy. All participants provided a blood sample for whole exome sequencing, performed by Regeneron on an Illumina NovaSeq panel (mean coverage of 48X). Variants were called using GATK v4 and variant annotation was performed using BioR. All loss-of-function variants (i.e., nonsense, frameshift, and consensus splice sites) and pathogenic missense in 19 cancer predisposition genes with a minor allele frequency < 0.5% were included in the analyses. Logistic regression was used to estimate odds ratio (OR) and 95% confidence intervals (CI) for the association between mutation status by gene (burden test) and risk of LM overall, and by subtype that had more than 5 mutation carriers. All analyses were adjusted for age (at diagnosis for cases and at enrollment for controls) and sex. Results A total of 6990 cases and 42432 controls were included. Median age was 63 years for both cases and controls (case range 18-94 years, control range 18-99 years). Males accounted for 58.5% (n=4090) of the cases and 41.7% (n=17762) of the controls. MM accounted for 31% (n=2138) of the cases, followed by diffuse large B-cell lymphoma (DLBCL, n=1146) and follicular lymphoma (FL, n=1132) each accounting for 16% of cases. CHEK2 (1.0%), ATM (0.4%), BRCA2 (0.4%), and BRCA1 (0.3%) had the most PV carriers in controls; these frequencies are in the range of published control populations. A total of 1816 (3.7%) individuals had a PV across the 19 genes, with 4.7% of cases and 3.5% of controls carrying any PV (OR=1.34, 95% CI: 1.18-1.51). ATM (OR=1.86, 95% CI: 1.36-2.49), CHEK2 (OR=1.74, 95% CI: 1.42-2.13) and TP53 (OR=9.07, 95% CI: 4.51-18.87) were all significantly associated with increased risk of LM overall (Figure 1). When investigating subtypes (Table 1), CHEK2 (OR=1.93, 95% CI: 1.23-2.89) and TP53(OR=10.97, 95% CI: 3.06-31.43) were significantly associated with DLBCL. ATM (OR=2.22, 95% CI: 1.13-3.90) was significantly associated with FL. ATM (OR=1.97, 95% CI: 1.16-3.12), CHEK2 (OR=1.75, 95% CI: 1.24-2.41) and TP53 (OR=7.67, 95% CI: 2.43-20.72) were significantly associated with MM and ATM (OR=8.62, 95% CI: 4.47-15.12), NBN (OR=6.94, 95% CI: 2.41-15.76) and TP53 (OR=38.30, 95% CI: 10.27-116.69) were significantly associated with mantle cell lymphoma (MCL). When subset to LM cases overall, there was no evidence of enrichment for young onset (age <60 years), male sex, or family history of hematologic malignancy between PV carriers and non-PV carriers. However, FL cases with an ATM PV tended to be less than 60 years (OR=3.21, P=0.09) compared to non- ATM PV carriers. Similarly, MM cases with a TP53 PV tended to be less than 60 years of age (OR=7.39, P=0.07) compared non-carriers. Discussion In this large case-control study, PV in established cancer predisposition genes were associated with increased risk of LM overall, demonstrating that rare inherited variants may play an important etiologic role. This study also suggests that PV in cancer predisposition genes may be subtype specific. These results will help inform LM risk for individuals who undergo genetic testing of cancer predisposition genes.

CONCLUSION The most widely used multiple myeloma (MM) staging system, the Revised International Staging System (R-ISS), is based on lactate dehydrogenase (LDH), albumin, high-risk genetic markers by FISH, and beta-2 microglobulin (B2M). However, it does not account for additional risk impacted by multiple genetic abnormalities, or the risk imparted by presence of gain of chromosome 1q, and most patients are staged as R-ISS Stage 2. Two new staging systems, the Mayo Additive Staging System (MASS), and the Second Revision of the International Staging System (R2-ISS) have been recently proposed. A comparison of the performance of these two systems has not previously been done. We aimed to externally demonstrate the application of these staging systems and compare their performances in a large, contemporary cohort of patients in the United States. We conducted a retrospective cohort study using the nationwide Flatiron Health electronic health record (EHR)-derived de-identified database, comprising de-identified patient-level structured and unstructured data, curated via technology-enabled abstraction and originating from approximately 280 US cancer clinics (~800 sites of care). The study included newly diagnosed MM patients initiating treatment between January 1, 2016 and October 1, 2022. We used the Kaplan-Meier method to evaluate real-world overall survival (rwOS) and patients were followed from first-line treatment initiation (index date) until the first of death, end of the study period or last recorded activity. The log-rank test to compare survival curves across derived stages. Multivariable analysis was conducted using stratified Cox models to account for non-proportional hazards by age with covariate adjustment for race/ethnicity, sex, practice type (academic or community) and diagnosis year (2016-2019 vs 2020-2022). Discrimination and calibration were evaluated in crude models using Harrell's c-index and the Hosmer-Lemeshow goodness-of-fit test, respectively. There were 497 patients with MM included. Patient characteristics are listed in Panel A. The distribution of patients across R-ISS stages were as follows, 24%, 63% and 13% for Stage 1, 2 and 3 respectively. rwOS differed across R-ISS stage (log-rank p = 0.0006) and the median OS (OS) was not reached (NR), 63, and 37 months for R-ISS stage 1, 2, and 3, respectively. 34%, 35% and 31% for MASS 1, 2, and 3, respectively. The corresponding survival curves were significantly different (p < 0.0001), with associated mOS of 77, 61 and 45 months for MASS stages 1, 2, and 3, respectively. R2-ISS includes four risk categories (stages I-IV) and in our cohort, 20% (n = 100) were stage I (low), 126 (25%) were stage II (low-intermediate), 229 (46%) were stage III (intermediate), and 42 (9%) were stage IV (high). Survival across stages were significantly different (p<0.0001), though the survival curves for low and low-intermediate, and intermediate-high and high, were clustered, suggesting comparable outcomes in stages I-II and stages III-IV. mOS for R2-ISS stages I, II, III, and IV was NR, 69, 50 and 51 months respectively (Panel B). In fully adjusted analyses, as compared to MASS I, the hazards of death were 2.0 (95% CI 1.3-3.2) for MASS II and 2.7 (1.7-4.2) for MASS III. The hazards of death were similar for R2-ISS low and low-int (HR for low-int vs low: 1.2 [0.7, 2.3]). As compared to R2-ISS low, the hazards of death were similarly higher for int-high and high (HRs: 2.4 [1.4, 4.1] and 2.6 [1.3, 5.2], respectively). Discrimination and calibration were similar across all staging systems (c = 0.6, and Hosmer-Lemeshow p>0.05 for all three staging systems). Application of contemporary MM staging systems in a large cohort of US patients shows that they are prognostic for OS in MM. MASS demonstrated a greater separation for OS across stages, whereas R2-ISS intermediate-high and high were largely overlapping. Although our results demonstrate similar accuracy and reliability of MASS and R2-ISS, the discrimination of patients across different stages seen with the newer staging systems, as opposed to R-ISS, calls for greater use of these newer staging systems in routine clinical practice.

Background: Frailty is a state of decreased reserve affecting multiple organ systems, thereby increasing vulnerability to stressors. Several tools have been proposed to measure frailty in multiple myeloma (MM), such as the IMWG frailty index, and the Revised Myeloma Comorbidity Index. However, these are based on clinical trial datasets, and include chronological age as one of the indicators of frailty. A frailty index based on the principle of accumulation of health deficits (Rockwood doi:10.1093/gerona/62.7.738), provides a measure of physiologic age. This index can be readily calculated using various combinations of health deficits and has been shown to predict outcomes in several populations. Social support has been associated with improved outcomes in various populations, however its impact on survival in patients with MM has not been well studied. The objective of this study was to assess the impact of frailty measured by a deficit accumulation frailty index and the impact of relationship status on outcomes in patients with newly diagnosed MM. Methods: This is a retrospective study including patients diagnosed with MM between January 6 th, 2005, and September 19 th, 2018. We calculated a deficit accumulation frailty index using previously published methods (McNallan et. al doi.org/10.1016/j.ahj.2013.07.008, Searle et al. doi.org/10.1186/1471-2318-8-24). We used the electronic medical record system at Mayo Clinic in Rochester, MN, to extract data on activities of daily living (14 items) provided by patient-completed questionnaires, and data on comorbidities as recorded by treating providers (16 items), within 6 months prior to diagnosis. Each item was scored as 0, 0.5, or 1, with 1 indicating the presence of deficit (Table 1). The frailty index was defined as the sum of individual scores divided that by the total number of non-missing items. Patients who had N >2 missing items were excluded. Patients with a frailty index ≥0.15 were considered frail. We also extracted data on self-reported relationship status within 6 months prior to diagnosis. Variables were compared between groups using Fisher's exact test and Wilcoxon signed-rank test for categorical and continuous variables, respectively. Univariate and multivariate analysis were performed using Cox proportional hazard models. Overall survival (OS) was estimated using the Kaplan-Meier method and compared between groups using the Log-rank test. P values <0.05 were considered statistically significant. Results: We included 578 patients. Median age was 67 (interquartile range: 59-74) years, and 40% were female. Among all patients, 226 (39%) were frail. Compared to fit patients, patients who were frail were older (median age: 70 vs. 65 years, P<0.001) and more likely to have International Staging System (ISS) III (vs. I/II) disease (47% vs. 27%, P<0.001) and ECOG performance status ≥2 (vs. 0 or 1) (45% vs. 17%, P<0.001). There was no difference in R-ISS III disease or presence of high-risk cytogenetic abnormalities between fit and frail patients. Median OS was 7.6 and 4.1 years in fit and frail patient, respectively (P<0.001). Physician assessed ECOG performance status was available for 268 patients. Among patients with ECOG performance status 0 or 1 (193 patients), 136 (70%) were fit, and 57 (30%) were frail. The OS was 8.3 and 4.6 years in the 2 groups, respectively (P=0.006) (table 2). On multivariate analysis including age, frailty status, and R-ISS stage (III vs. I/II), all were associated with OS with frailty's hazard ratio (HR): 1.4 (95%CI [1.1-1.8]), P=0.004; the HR was 1.4 (95%CI [1.1-1.9]), P=0.02, when analysis was restricted to patients ≥60 years. Among all patients, those who were married or in a committed relationship (474 patients) had improved survival (median OS: 7.0 vs. 3.7 years, respectively, P<0.001) compared to patients who were divorced/separated, widowed, or single (96 patients). On multivariate analysis, which included age, R-ISS III, frailty status, and relationship status, all were associated with OS. The HR for relationship status was 1.4 (95%CI [1.0-1.8]), P=0.0465. Conclusion: Frailty, defined by the deficit accumulation frailty index, is independently associated with decreased survival in patients with newly diagnosed MM. Being married or in a committed relationship is associated with improved survival independent of age, disease stage, and frailty.

Abstract The approach to patients with high-risk smoldering multiple myeloma (SMM) varies among clinicians; while some advocate early intervention, others reserve treatment at progression to multiple myeloma (MM). We aimed to describe the myeloma-defining events (MDEs) and clinical presentations leading to MM diagnosis among SMM patients seen at our institution. We included 406 patients diagnosed with SMM between 2013-2022, seen at Mayo Clinic, Rochester, MN. The 2018 Mayo 20/2/20 criteria were used for risk stratification. Median follow-up was 3.9 years. Among high-risk patients who did not receive treatment in the SMM phase (n=71), 51 progressed by last follow-up; the MDEs included: bone lesions(37%), anemia(35%), hypercalcemia(8%), and renal failure(6%); 24% met MM criteria based on marrow plasmacytosis (≥60%) and/or free light chain ratio (>100); 45% had clinically significant MDEs (hypercalcemia, renal insufficiency, and/or bone lesions). MM diagnosis was made based on surveillance labs/imaging(45%), testing obtained due to provider suspicion for progression(14%), bone pain(20%), and hospitalization/ED presentations due to MM complications/symptoms(4%). The presentation was undocumented in 14%. A high proportion (45%) of patients with high-risk SMM on active surveillance develop end-organ damage at progression. About a quarter of patients who progress to MM are not diagnosed based on routine interval surveillance testing.

Abstract Objectives Fluorescence in situ hybridization (FISH) for plasma cell neoplasms (PCNs) requires plasma cell (PC) identification or purification strategies to optimize results. We compared the efficacy of cytoplasmic immunoglobulin FISH (cIg-FISH) and fluorescence-activated cell sorting FISH (FACS-FISH) in a clinical laboratory setting. Methods The FISH analysis results of 14,855 samples from individuals with a suspected PCN subjected to cytogenetic evaluation between 2019 and 2022 with cIg-FISH (n = 6917) or FACS-FISH (n = 7938) testing were analyzed. Results Fluorescence-activated cell sorting–FISH increased the detection rate of abnormalities in comparison with cIg-FISH, with abnormal results documented in 54% vs 50% of cases, respectively (P < .001). It improved the detection of IGH::CCND1 (P < .001), IGH::MAF (P < .001), IGH::MAFB (P < .001), other IGH rearrangements (P < .001), and gains/amplifications of 1q (P < .001), whereas the detection rates of IGH::FGFR3 fusions (P = .3), loss of 17p (P = .3), and other abnormalities, including hyperdiploidy (P = .5), were similar. Insufficient PC yield for FISH analysis was decreased between cIg-FISH and FACS-FISH (22% and 3% respectively, P < .001). Flow cytometry allowed establishment of ploidy status in 91% of cases. In addition, FACS-FISH decreased analysis times, workload efforts, and operating costs. Conclusions Fluorescence-activated cell sorting–FISH is an efficient PC purification strategy that affords significant improvement in diagnostic yield and decreases workflow requirements in comparison with cIg-FISH.