Citations

Abstract Purpose: Multiple myeloma (MM) is a plasma cell malignancy characterized by a collection of cytogenetic abnormalities that drive clinical presentation, response to treatment, and prognosis. The most common immunoglobulin heavy chain (IGH) translocation found in MM, t(11;14), results in CCND1 overexpression. Although t(11;14) is considered a standard risk cytogenetic abnormality, heterogeneity in patient outcomes and response to therapy within the t(11;14) group has been reported. In contrast to t(11;14) mantle cell lymphoma, where most cases (73%) have a typical, balanced t(11;14) rearrangement, only 47% of t(11;14) MM have a typical, balanced t(11;14) FISH pattern; the remaining 53% of MM are characterized by an atypical t(11;14) FISH result (Dalland, Genes Chromosomes Cancer, 2021). Here, we compared the genetic characteristics of typical vs. atypical t(11;14) MM and the association with other genomic abnormalities, CCND1 expression, IGH breakpoint location and patient outcomes. Methods: We performed a retrospective study of newly diagnosed t(11;14) MM patients seen at Mayo Clinic (N=411) or enrolled in the MMRF CoMMpass trial (N=167). The t(11;14) FISH results of the Mayo Clinic samples were segmented into two groups: typical CCND1/IGH signal pattern (n=179, 3 red, 3 green, 2 fusion signals) and atypical CCND1/IGH signal pattern (n=232, any FISH pattern deviating from typical). The CoMMpass cases had both RNAseq and long-insert WGS data and were analyzed to detect gene expression, structural and copy number variants and IGH breakpoint locations. These data were used to classify the t(11;14) as typical (n=63) or atypical (n=104) and whether there was a single t(11;14) fusion (n=107) or a gain of the t(11;14) fusion (n=60), and whether these cases were associated with changes in gene expression or breakpoint location. Differences between the groups were measured using χ2 and Wilcoxon tests. Survival curves were estimated using Kaplan Meier and compared using the Log-Rank test. Statistical significance was determined at p<0.05. Results: Of the Mayo t(11;14) cohort, 232/411 (56%) had an atypical t(11;14) FISH pattern and 179/411 (44%) had a typical t(11;14) FISH pattern. Although an atypical t(11;14) FISH pattern was not significantly associated with monosomy 17/17p deletion, MYC rearrangements, 1q gain or ISS stage III, it was significantly associated with the presence of any trisomy in comparison to typical t(11;14) cases (40/222=18% vs.12/166=7%, p=0.002). Atypical t(11;14) cases had an increase in CCND1 gene expression compared to typical t(11;14) cases (median 375.0 RPKM vs. 266.4 RPKM, p<0.0001) and cases with a gain of the t(11;14) fusion were associated with even greater CCND1 expression compared to cases with a single t(11;14) fusion (median 459.4 RPKM vs. 263.2 RPKM, p<0.0001). We next evaluated whether the t(11:14) FISH pattern was associated with differences in IGH breakpoint locations. Nearly 80.0% of atypical t(11;14) cases had a breakpoint within the constant region of the IGH locus commonly reported for MM, while only 61.0% of the typical cases had constant region breakpoints (p=0.035); the remaining cases had breakpoints involving the VDJ region. We next evaluated the impact of atypical t(11;14) FISH on OS. In Mayo cases, OS was significantly shorter in patients with atypical FISH patterns compared to typical t(11;14) FISH patterns (6.9 years [95% CI: 5.1-7.8] vs. 9.0 years [95% CI: 7.0-11.6], p=0.019). Although atypical t(11;14) was associated with increased risk of death (RR 1.45, p=0.021), this finding was not retained in a multivariate model including high risk abnormalities such as age, ISS stage, 17p deletion and 1q gain demonstrating atypical t(11;14) is not an independent predictor of poor outcome when treated with standard therapies. Conclusions: Atypical t(11;14) FISH in MM is found in approximately half of t(11;14) cases, is associated with trisomies, higher CCND1 expression and is more likely to have a breakpoint involving the constant region of the IGH locus. The enrichment of VDJ breakpoints within typical t(11;14) MM is consistent with enrichment of VDJ IGH breakpoints in MCL, which are mostly characterized by typical t(11;14) FISH patterns. While not an independent predictor of outcome with standard therapies, it remains to be evaluated whether patients with typical or atypical t(11;14) have a differential response to novel agents, such as venetoclax. Disclosures Fonseca: Janssen: Consultancy; Novartis: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; Merck: Consultancy; Juno: Consultancy; Caris Life Sciences: Membership on an entity's Board of Directors or advisory committees; Kite: Consultancy; Aduro: Consultancy; OncoTracker: Consultancy, Membership on an entity's Board of Directors or advisory committees; GSK: Consultancy; AbbVie: Consultancy; Patent: Prognosticaton of myeloma via FISH: Patents & Royalties; Mayo Clinic in Arizona: Current Employment; Amgen: Consultancy; BMS: Consultancy; Celgene: Consultancy; Takeda: Consultancy; Bayer: Consultancy; Scientific Advisory Board: Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees. Bergsagel: Pfizer: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; GSK: Consultancy, Honoraria; Oncopeptides: Consultancy, Honoraria; Genetech: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Patents & Royalties: human CRBN mouse. Kumar: Merck: Research Funding; Carsgen: Research Funding; Roche-Genentech: Consultancy, Research Funding; Beigene: Consultancy; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bluebird Bio: Consultancy; Antengene: Consultancy, Honoraria; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Consultancy; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Tenebio: Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Sanofi: Research Funding.

Abstract Background: MASS-FIX is a novel method for detection, characterization and quantification of serum and urine monoclonal proteins in multiple myeloma (MM) which uses immunoglobulin enrichment coupled with matrix-assisted laser desorption ionization time-of-flight mass-spectrometry (MALDI-TOF). MASS-FIX has been endorsed by the International Myeloma Working Group (IMWG) as an alternative to immunofixation electrophoresis for diagnosis and disease monitoring in MM and has replaced immunofixation electrophoresis in our institution. This technique has demonstrated superior sensitivity and specificity compared to conventional gel-based methods, but its prognostic value is still unknown. We designed this study to evaluate whether serum M protein monitoring by MASS-FIX has prognostic significance in predicting disease progression in patients with MM. Methods: In the first part of the study, we included patients with MM who had a documented negative MASSFIX after first-line treatment and had serial MASS-FIX data available (n=187). We then compared the time to next treatment (TTNT) between patients who became positive by MASS-FIX and those who remained negative by last follow up. In the second part, we included patients who were positive by MASS-FIX within 90 days from diagnosis and had available serial MASS-FIX data (n=203). We compared TTNT between patients who became negative by MASS-FIX and those who remained positive by last follow up. TTNT was defined as the time of initiation of the first-line treatment to the time of initiation of second-line treatment or last follow up. Results: Part 1: We included 155 patients diagnosed with MM between January 2013 and December 2019 who had a documented negative MASSFIX with first-line treatment and had serial MASSFIX data available. After a median follow up of 3.1 (95%CI: 2.6-3.5) years, 44 (28%) patients became MASSFIX positive, while 111 (72%) still had negative MASS-FIX. The median time from treatment start to negative MASSFIX was 18.0 (IQR: 6.4-33.5) months. The median TTNT was 4.1 (95%CI: 3.2 - NR) years in patients who became MASSFIX positive and not reached (NR) (95%CI: NR-NR) in patients who were MASS-FIX negative by last follow up (P<0.001). The 2-year OS was 97% and 100% in the 2 groups, respectively (P=0.27). Part 2: We included 203 patients who had positive MASSFIX within 90 days from diagnosis and available serial MASSFIX data. The median follow-up was 1.5 (95%CI: 1.5-1.7) years. By last follow up, 39 (19%) patients became MASSFIX negative while 164 (81%) still had positive MASSFIX. The median TTNT was NR (95%CI: 2.0-NR) years in patients who developed negative MASSFIX and 2.3 (95%CI: 1.9 - NR) years in patients who were still MASS-FIX positive by last follow up (P=0.03). The 2-year OS was 97% and 94% in the 2 groups, respectively (P=0.84) Conclusion: The utilization of MASS-FIX in disease monitoring of MM patients provides prognostic information; among patients who become MASS-FIX negative after first line treatment, an earlier conversion to positive MASS-FIX predicts disease progression. Similarly, persistent MASS-FIX positivity after treatment initiation predicts earlier disease progression. Larger studies and longer follow up are needed to evaluate whether the MASS-FIX is associated with overall survival. Disclosures Murray: Mayo Clinic: Other: Has received patents for the Mass-Fix technology which has been licensed to the Binding Site with potential royalties.. Dispenzieri: Sorrento Therapeutics: Consultancy; Oncopeptides: Consultancy; Pfizer: Research Funding; Alnylam: Research Funding; Takeda: Research Funding; Janssen: Consultancy, Research Funding. Kapoor: Karyopharm: Consultancy; Cellectar: Consultancy; BeiGene: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; Amgen: Research Funding; Ichnos Sciences: Research Funding; Regeneron Pharmaceuticals: Research Funding; Glaxo SmithKline: Research Funding; Karyopharm: Research Funding; Sanofi: Research Funding; Takeda: Research Funding; AbbVie: Research Funding. Gertz: Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy; Akcea Therapeutics, Ambry Genetics, Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Karyopharm Therapeutics, Pfizer Inc (to Institution), Sanofi Genzyme: Honoraria; Ionis Pharmaceuticals: Other: Advisory Board; Aurora Biopharma: Other: Stock option; AbbVie Inc, Celgene Corporation: Other: Data Safetly & Monitoring. Dingli: Sanofi: Consultancy; GSK: Consultancy; Novartis: Research Funding; Apellis: Consultancy; Janssen: Consultancy; Alexion: Consultancy. Kumar: Amgen: Consultancy, Research Funding; Bluebird Bio: Consultancy; Antengene: Consultancy, Honoraria; BMS: Consultancy, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Beigene: Consultancy; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche-Genentech: Consultancy, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Carsgen: Research Funding; Merck: Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Consultancy; Novartis: Research Funding; Tenebio: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding.

Abstract Introduction : Despite recent advances, MM remains incurable and new therapeutic options are needed, particularly for pts with RRMM. IBER is a novel, potent oral cereblon E3 ligase modulator (CELMoD ®) compound with enhanced tumoricidal and immune-stimulatory effects compared with immunomodulatory (IMiD ®) agents. Preclinically, IBER demonstrated marked synergy with DEX and with other standard myeloma treatments. CC-220-MM-001 (NCT02773030) is an ongoing phase 1/2 study evaluating IBER with different treatment combinations in independent cohorts of pts with RRMM; in phase 1, the recommended phase 2 dose of IBER, when given in combination with DEX, was determined at 1.6 mg (Lonial S, et al. Blood 2019;134[suppl 1]:3119). Here we report results from the dose expansion of IBER + DEX in pts with heavily pretreated, triple-class exposed (including ≥ 1 IMiD agent, ≥ 1 proteasome inhibitor [PI], and ≥ 1 anti-CD38 monoclonal antibody [mAb]) RRMM. Methods : Eligible pts had RRMM; had received ≥ 3 prior lines of therapy, including lenalidomide (LEN), pomalidomide (POM), a PI, a glucocorticoid, and an anti-CD38 mAb; had experienced disease progression within 60 days of last myeloma therapy; and were refractory to an IMiD agent, a PI, a glucocorticoid, and an anti-CD38 mAb. Pts with central nervous system involvement were not eligible. Pts who had received prior anti-BCMA therapy were excluded, but included in a supportive cohort for safety and preliminary efficacy assessment. IBER (1.6 mg) was given orally on days (D) 1-21, in combination with DEX (40 mg; 20 mg if > 75 years of age) on D1, 8, 15, and 22 of each 28-day cycle. Thrombo-embolism prophylaxis was mandatory for all pts. Primary objective was to determine efficacy expressed as overall response rate (ORR). Secondary endpoints included additional efficacy and safety assessments. Exploratory endpoints included evaluation of health-related quality of life (HRQoL). Results : As of June 2, 2021, 107 pts had received IBER + DEX. Median age was 64 (44-83) years; median time since initial diagnosis was 6.9 (1.6-24.5) years. Extramedullary plasmacytomas were present in 25.2% of pts; 29.9% of pts had high-risk cytogenetics. Median number of prior regimens was 6 (3-23). All pts were triple-class exposed; prior therapies included autologous stem cell transplantation (78.5%), PIs (100%), IMiD agents (LEN [100%] and POM [100%]), and anti-CD38 mAbs (100%); 99.1% of pts were refractory to last myeloma regimen and 97.2% of pts were triple-class refractory. Median follow-up was 7.69 (0.5-17.5) months, with a median number of 4 (1-17) cycles received and 13 (12.1%) pts continuing treatment. Main reason for discontinuation was progressive disease (69.2%). ORR was 26.2%, with 1 (0.9%) stringent complete response, 8 (7.5%) very good partial responses, and 19 (17.8%) partial responses (Table); the clinical benefit rate (≥ minimal response) was 36.4% and disease control rate (≥ stable disease) was 79.4%. Median duration of response was 7.0 (4.5-11.3) months (Table), median progression-free survival was 3.0 (2.8-3.7) months, and median overall survival was 11.2 (9.0-not reached) months. Similar response rates were observed among a cohort of pts also exposed to BCMA therapies (N = 24, Table). Grade (Gr) 3-4 treatment-emergent adverse events (TEAEs) were reported in 88 (82.2%) pts. Most frequent (≥ 20% pts) hematologic Gr 3-4 TEAEs were neutropenia (44.9%; and 4.7% febrile neutropenia), anemia (28.0%), thrombocytopenia (21.5%), and leukopenia (20.6%). Gr 3-4 infections were reported in 27.1% of pts; Gr 3-4 pneumonia and COVID-19 occurred in 10.3% and 4.7% of pts, respectively. Occurrence of other Gr 3-4 non-hematologic TEAEs was generally low, including gastrointestinal disorders (5.6%), fatigue (2.8%), rash (1.9%). Fifty-six (52.3%) pts and 20 (18.7%) had IBER dose interruptions and reductions due to TEAEs, respectively. Five (4.7%) pts discontinued due to TEAEs. No pt discontinued IBER due to neutropenia. Overall, HRQoL was maintained in these pts. Conclusions : IBER + DEX demonstrated promising efficacy in pts with heavily pretreated, triple-class exposed and refractory RRMM, as well as in pts who had previously received anti-BCMA therapy; this combination was generally well tolerated and TEAEs were manageable with dose reductions and interruptions. These results support the further development of IBER in MM, including phase 3 trials in combination regimens. Figure 1 Figure 1. Disclosures Lonial: Abbvie: Consultancy, Honoraria; AMGEN: Consultancy, Honoraria; Takeda: Consultancy, Honoraria, Research Funding; GlaxoSmithKline: Consultancy, Honoraria, Research Funding; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees; Merck: Honoraria; BMS/Celgene: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding. Popat: GlaxoSmithKline: Consultancy, Honoraria, Research Funding; Abbvie, Takeda, Janssen, and Celgene: Consultancy; Takeda: Honoraria, Other: TRAVEL, ACCOMMODATIONS, EXPENSES; AbbVie, BMS, Janssen, Oncopeptides, and Amgen: Honoraria; Janssen and BMS: Other: travel expenses. Hulin: Sanofi: Honoraria; Celgene/BMS: Honoraria; Janssen: Honoraria; Takeda: Honoraria; abbvie: Honoraria. Jagannath: Legend Biotech: Consultancy; Bristol Myers Squibb: Consultancy; Karyopharm Therapeutics: Consultancy; Janssen Pharmaceuticals: Consultancy; Sanofi: Consultancy; Takeda: Consultancy. Oriol: Oncopeptides: Consultancy, Membership on an entity's Board of Directors or advisory committees; Sanofi: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; GSK: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Karyopharm: Consultancy, Membership on an entity's Board of Directors or advisory committees; BMS/Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees. Richardson: Karyopharm: Consultancy, Research Funding; Regeneron: Consultancy; AbbVie: Consultancy; Celgene/BMS: Consultancy, Research Funding; Oncopeptides: Consultancy, Research Funding; GlaxoSmithKline: Consultancy; Protocol Intelligence: Consultancy; Janssen: Consultancy; Secura Bio: Consultancy; Takeda: Consultancy, Research Funding; Sanofi: Consultancy; AstraZeneca: Consultancy; Jazz Pharmaceuticals: Consultancy, Research Funding. Weisel: Adaptive Biotechnologies: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Karyopharm: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Roche: Honoraria; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; GSK: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Sanofi: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol Myers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Consultancy; Novartis: Honoraria; Pfizer: Honoraria. Minnema: Cilag: Consultancy; Janssen: Consultancy; Alnylam: Consultancy; Celgene: Other: Travel expenses; Kite/Gilead: Consultancy; BMS: Consultancy. Badros: J&J: Research Funding; Janssen: Research Funding; BMS: Research Funding; GlaxoSmithKline: Research Funding. Knop: BMS/Celgene: Consultancy, Honoraria, Research Funding; Amgen: Research Funding; Janssen: Consultancy; Oncopeptides: Consultancy; Pfizer: Consultancy; Sanofi: Consultancy. Stadtmauer: Janssen: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Bristol Myers Squibb: Consultancy, Honoraria, Research Funding. Chen: Bristol Myers Squibb: Current Employment. Nguyen: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Amin: Bristol Myers Squibb: Current Employment. Kueenburg: Celgene a BMS company: Current Employment. Peluso: Celgene, a Bristol-Myers Squibb Company: Current Employment. van de Donk: BMS/Celgene: Consultancy, Honoraria; Janssen: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; Cellectis: Research Funding; Takeda: Consultancy; Roche: Consultancy; Novartis /bayer/servier: Consultancy.

Abstract Background: Systemic light chain (AL) amyloidosis is a plasma cell disorder characterized by multisystem deposition of misfolded immunoglobulin light chains produced by clonal plasma cells. Hematologic and organ responses with treatment have been shown to correlate with survival as early as 3 months from initiation of first-line treatment. Our group recently developed and validated a model integrating organ and hematologic responses for assessment of treatment outcomes at 6 months. Although current organ response criteria do not consider the depth of organ response, this has been shown to have prognostic utility in newly diagnosed patients. So, we designed this study to evaluate a composite hematologic and organ scoring system that also considers the depth of organ response at 1 to 6 months from initiation of first-line treatment. Methods: We included patients with AL amyloidosis who had at least one major organ involvement (cardiac, renal and/or liver involvement) and who had not started a second line treatment by 6 months from the time of initiating first-line treatment. For each patient, we calculated an organ response score and a hematologic response score at 1, 2, 3, 4, 5, and 6 months from initiation of first-line treatment. A score was assigned for each depth of hematologic response as follows: complete response=0, very good partial response=1, partial response=2, and no response/progressive disease=3). To calculate organ response, a score was assigned to each organ based on the depth of organ response (Muchtar et al. 2018): non-evaluable=0, complete response=1, very good partial response=2, partial response=3, and no response=4. The final organ response score was obtained by calculating the average of the individual involved organ scores. We then calculated the composite hematologic and organ response (HOR) score by adding the organ and hematologic responses at each interval and compared overall survival (OS) between patients with HOR score ≤ 5 (group 1) and those with score > 5 (group 2). Results: The cohort included 730 patients diagnosed with AL amyloidosis between February 10 th, 2006 and July 9 th, 2019. Median age was 63 (IQR: 56-69), and 65% were male. The involved light chain was Lambda in 75% of cases. Cardiac, renal, and liver involvement were found in 81%, 61%, and 17% of patients, respectively. Among all patients, 28% underwent autologous stem cell transplantation during their disease course. The median follow up in the entire cohort was 7.0 (95%CI: 6.4-8.0) years and OS was 3.6 (95%CI: 2.6-4.4) years. At 1 to 4 months, we observed a statistically significant difference in OS between patients with HOR score ≤5 vs. >5. However, there was no difference in OS between the 2 groups at 5 and 6 months. These results are presented in Table 1. Conclusion: A composite hematologic and organ response score that takes into consideration the depth of organ response can discriminate 2 groups of patients with distinct survival outcomes as early as 1 month from treatment initiation and maintains its predictive ability for up to 4 months. The lack of predictive ability beyond 4 months in this study may be due to limited sample size especially in group 2 as more patients achieve deeper responses with time. This approach can provide the basis for early changes in treatment but needs validation in future studies. Figure 1 Figure 1. Disclosures Dispenzieri: Janssen: Consultancy, Research Funding; Takeda: Research Funding; Sorrento Therapeutics: Consultancy; Oncopeptides: Consultancy; Pfizer: Research Funding; Alnylam: Research Funding. Gertz: Ionis Pharmaceuticals: Other: Advisory Board; Akcea Therapeutics, Ambry Genetics, Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Karyopharm Therapeutics, Pfizer Inc (to Institution), Sanofi Genzyme: Honoraria; Aurora Biopharma: Other: Stock option; Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy; AbbVie Inc, Celgene Corporation: Other: Data Safetly & Monitoring. Kapoor: Ichnos Sciences: Research Funding; Karyopharm: Consultancy; Glaxo SmithKline: Research Funding; Pharmacyclics: Consultancy; Amgen: Research Funding; Cellectar: Consultancy; Sanofi: Consultancy; BeiGene: Consultancy; Regeneron Pharmaceuticals: Research Funding; Karyopharm: Research Funding; Sanofi: Research Funding; Takeda: Research Funding; AbbVie: Research Funding. Dingli: GSK: Consultancy; Sanofi: Consultancy; Janssen: Consultancy; Novartis: Research Funding; Apellis: Consultancy; Alexion: Consultancy. Kumar: Oncopeptides: Consultancy; Merck: Research Funding; Antengene: Consultancy, Honoraria; Bluebird Bio: Consultancy; Roche-Genentech: Consultancy, Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Novartis: Research Funding; Carsgen: Research Funding; Tenebio: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Beigene: Consultancy; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding.

Abstract Venomous snakebite is an important cause of preventable death. The World Health Organization (WHO) set a goal to halve snakebite mortality by 2030. We used verbal autopsy and vital registration data to model the proportion of venomous animal deaths due to snakes by location, age, year, and sex, and applied these proportions to venomous animal contact mortality estimates from the Global Burden of Disease 2019 study. In 2019, 63,400 people (95% uncertainty interval 38,900–78,600) died globally from snakebites, which was equal to an age-standardized mortality rate (ASMR) of 0.8 deaths (0.5–1.0) per 100,000 and represents a 36% (2–49) decrease in ASMR since 1990. India had the greatest number of deaths in 2019, equal to an ASMR of 4.0 per 100,000 (2.3-5.0). We forecast mortality will continue to decline, but not sufficiently to meet the WHO’s goals. Improved data collection should be prioritized to help target interventions, improve burden estimation, and monitor progress.

Background: Several studies have evaluated preprocedural imaging predictors of left ventricular outflow tract obstruction (LVOTO) after transcatheter mitral valve replacement. The patient cohorts in these studies were heterogeneous and included patients with transcatheter mitral valve replacement in failed bioprostheses, annuloplasty rings, and severe mitral annular calcification (MAC). The goal of this study was to evaluate predictors of LVOTO specific to patients undergoing valve-in-MAC. Methods: This study included patients with severe MAC who underwent valve-in-MAC and had optimal quality preprocedural multidetector row computed tomography scans eligible for retrospective analysis. Baseline demographic, echocardiographic, and procedural data on these patients were collected. multidetector row computed tomography parameters were analyzed for association with LVOTO, defined as increase in mean LVOT gradient by ≥10 mm Hg with accompanying hemodynamic instability. Results: Seventy-one patients with optimal preprocedural computed tomography scans were included in this study (mean age, 72.5±13.5 years), 9 of which developed LVOTO (all female). Baseline mean LVOT area, neo-LVOT area (145.3 versus 270.9 mm 2 ; P =0.006), indexed neo-LVOT area (90.1 versus 157.4; P =0.05), and virtual transcatheter heart valve to septum distance (3.1 versus 6.9 mm; P =0.002) were lower in the LVOTO group. Expected % LVOT area reduction was higher in the latter group (58.3 versus 42.7%; P =0.008). In the univariable analysis, the baseline mean LVOT area, neo-LVOT area, indexed neo-LVOT area, and valve to septum distance were all significantly associated with LVOTO. Conclusions: The systolic mean LVOT area, neo-LVOT area, indexed neo-LVOT, expected percentage LVOT area reduction, and the valve to septum distance were associated with LVOTO after valve-in-MAC.

Background and Purpose: Brain cavernous angiomas with symptomatic hemorrhage (CASH) have a high risk of neurological disability from recurrent bleeding. Systematic assessment of baseline features and multisite validation of novel magnetic resonance imaging biomarkers are needed to optimize clinical trial design aimed at novel pharmacotherapies in CASH. Methods: This prospective, multicenter, observational cohort study included adults with unresected, adjudicated brain CASH within the prior year. Six US sites screened and enrolled patients starting August 2018. Baseline demographics, clinical and imaging features, functional status (modified Rankin Scale and National Institutes of Health Stroke Scale), and patient quality of life outcomes (Patient-Reported Outcomes Measurement Information System-29 and EuroQol-5D) were summarized using descriptive statistics. Patient-Reported Outcomes Measurement Information System-29 scores were standardized against a reference population (mean 50, SD 10), and one-sample t test was performed for each domain. A subgroup underwent harmonized magnetic resonance imaging assessment of lesional iron content with quantitative susceptibility mapping and vascular permeability with dynamic contrast-enhanced quantitative perfusion. Results: As of May 2020, 849 patients were screened and 110 CASH cases enrolled (13% prevalence of trial eligible cases). The average age at consent was 46±16 years, 53% were female, 41% were familial, and 43% were brainstem lesions. At enrollment, ≥90% of the cohort had independent functional outcome (modified Rankin Scale score ≤2 and National Institutes of Health Stroke Scale score <5). However, perceived health problems affecting quality of life were reported in >30% of patients (EuroQol-5D). Patients had significantly worse Patient-Reported Outcomes Measurement Information System-29 scores for anxiety ( P =0.007), but better depression ( P =0.002) and social satisfaction scores ( P =0.012) compared with the general reference population. Mean baseline quantitative susceptibility mapping and permeability of CASH lesion were 0.45±0.17 ppm and 0.39±0.31 mL/100 g per minute, respectively, which were similar to historical CASH cases and consistent across sites. Conclusions: These baseline features will aid investigators in patient stratification and determining the most appropriate outcome measures for clinical trials of emerging pharmacotherapies in CASH.

Abstract Purpose: Structural variants (SV) of the MYC gene region are common in multiple myeloma and influence disease progression. However, the prognostic significance of different MYC SVs in multiple myeloma has not been clearly established. Experimental Design: We conducted a retrospective study of multiple myeloma comparing MYC SV subtypes identified by next-generation sequencing (NGS) and FISH to MYC expression and disease survival using 140 cases from Mayo Clinic and 658 cases from the MMRF CoMMpass study. Results: MYC SVs were found in 41% of cases and were classified into nine subtypes. A correlation between the presence of a MYC SV and increased MYC expression was identified. Among the nine MYC subtypes, the non-immunoglobulin (non-Ig) insertion subtype was independently associated with improved outcomes, while the Ig insertion subtype, specifically involving the IgL gene partner, was independently associated with poorer outcomes compared with other MYC SV subtypes. Although the FISH methodology failed to detect approximately 70% of all MYC SVs, those detected by FISH were associated with elevated MYC gene expression and poor outcomes suggesting a different pathogenic role for FISH-detected MYC subtypes compared with other MYC subtypes. Conclusions: Understanding the impact of different MYC SVs on disease outcome is necessary for the reliable interpretation of MYC SVs in multiple myeloma. NGS approaches should be considered as a replacement technique for a more comprehensive evaluation of the multiple myeloma clone.