Citations

Background: The absence of residual monoclonal protein in the serum and urine by IFE using specific antibodies to individual heavy and light chains has been an important part of response assessment and is required for categorizing patients as in complete response. MASS FIX has been shown to be more sensitive in picking up small amounts of monoclonal protein and represents an advancement in the field. Ongoing studies are examining the clinical significance of this increased sensitivity. We designed the following study using clinically obtained test results to examine this question. Patients and methods: The clinical laboratory at Mayo Clinic changed the process from IFE to MASS FIX in July 2018. We identified patients who had sequential testing before and after this date and collected test results from the last IFE prior to the date of transition and the first MASS FIX after the date of transition. Patients were grouped based on the paired results and outcomes compared between groups. Time to next therapy (TTNT) was defined as the time from the first MASS FIX date to the start of next therapy. Survival was estimated by the Kaplan-Meier method and these curves were compared using log rank test. Results: Overall, 739 patients with MM, who had a MASS FIX within 12 months of the IFE were studied; 380 (51%) patients had a negative IFE prior to implementation of MASS FIX. These patients had a MASS FIX test performed at a median of 3.7 (0.03-12) months from the last available IFE. The gap between IFE and MASS FIX was <6 months and < 3 months in 71% and 54% of the patients. Among the patients with a negative IFE, 130 (34%) tested positive on the subsequent MASS FIX. Limiting the analysis to those with a MASS FIX within 6 and 3 months yielded 32% in each group that turned positive from previous negative IFE. The median TTNT from the MASS FIX date was significantly shorter for those who became positive on MASSFIX; median was not reached for either group (P=0.002) (Figure). We then limited the analysis to those patients with a gap of <6 months and <3 months between the tests. The results were consistent: P= 0.012 for those with a gap of < 6 months and P=0.01 for those with a gap < 3 months. Conclusion: The results suggest that the additional sensitivity offered by MASS FIX have clinical significance in patients with MM, given that patients with residual disease identified by MASS FIX have an earlier relapse. The data supports the use of the more sensitive MASS FIX in the laboratory compared to the conventional IFE approaches. The study has the disadvantage that the samples were not tested simultaneously and some of the samples turning positive may reflect an early evidence of relapse, but the relative consistency of the finding irrespective of the gap between the tests argues against that. Ongoing studies are examining MASS FIX on the samples that were called to be negative by IFE initially. Figure Disclosures Murray: The Binding Site: Patents & Royalties: Patent Use of Mass Spec to identify monoclonal proteins licensed to The Binding Site. Dispenzieri:Takeda: Research Funding; Celgene: Research Funding; Janssen: Research Funding; Intellia: Research Funding; Pfizer: Research Funding; Alnylam: Research Funding. Kapoor:Cellectar: Consultancy; Sanofi: Consultancy, Research Funding; Janssen: Research Funding; Celgene: Honoraria; Takeda: Honoraria, Research Funding; GlaxoSmithKline: Research Funding; Amgen: Research Funding. Gertz:Janssen: Other: personal fee; Spectrum: Other: personal fee, Research Funding; Springer Publishing: Patents & Royalties; Abbvie: Other; Appellis: Other: personal fee; Annexon: Other: personal fee; Celgene: Other; Amgen: Other: personal fee; Physicians Education Resource: Other: personal fee; Medscape: Other: personal fee, Speakers Bureau; Research to Practice: Other; Sanofi: Other; Teva: Speakers Bureau; Johnson and Johnson: Speakers Bureau; DAVA oncology: Speakers Bureau; Proclara: Other; Aurora Bio: Other; Prothena: Other: personal fee; Alnylam: Other: personal fee; Ionis/Akcea: Other: personal fee. Dingli:Karyopharm Therapeutics: Research Funding; Bristol Myers Squibb: Research Funding; Rigel: Consultancy; Sanofi-Genzyme: Consultancy; Millenium: Consultancy; Alexion: Consultancy; Janssen: Consultancy; Apellis: Consultancy. Lin:Janssen: Consultancy, Research Funding; Kite, a Gilead Company: Consultancy, Research Funding; Takeda: Research Funding; Gamida Cells: Consultancy; Sorrento: Consultancy, Membership on an entity's Board of Directors or advisory committees; Vineti: Consultancy; Legend BioTech: Consultancy; Merck: Research Funding; Novartis: Consultancy; Celgene: Consultancy, Research Funding; Bluebird Bio: Consultancy, Research Funding; Juno: Consultancy. Kumar:Dr. Reddy's Laboratories: Honoraria; AbbVie: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Carsgen: Other, Research Funding; BMS: Consultancy, Research Funding; Karyopharm: Consultancy; Tenebio: Other, Research Funding; Celgene/BMS: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Adaptive Biotechnologies: Consultancy; Kite Pharma: Consultancy, Research Funding; Sanofi: Research Funding; Takeda: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Janssen Oncology: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Genentech/Roche: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Merck: Consultancy, Research Funding; MedImmune: Research Funding; Cellectar: Other; Genecentrix: Consultancy; Oncopeptides: Consultancy, Other: Independent Review Committee; IRC member; Amgen: Consultancy, Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments, Research Funding; Novartis: Research Funding.

Background: The combination of a proteasome inhibitor (PI), an immunomodulatory drug (IMiD), and dexamethasone is the current standard induction therapy for myeloma. Daratumumab (Dara), a monoclonal antibody directed against CD38, is highly effective in treating myeloma and improves response rates and progression free survival (PFS) when added to PI or IMiD. Ixazomib, lenalidomide and dexamethasone (IRd) is an effective, all oral, induction regimen that has been studied in phase 2 and 3 trials. We designed this trial to examine the feasibility and efficacy of adding daratumumab to the IRd regimen as well as early discontinuation of dexamethasone in two sequential cohorts of patients. Patients and Methods: Patients with previously untreated MM who had measurable disease and adequate organ function were enrolled, irrespective of their autologous stem cell transplant eligibility. The primary objective was to determine the rate of complete response to IRD-Dara combination. A one-stage binomial design was utilized in each cohort independently to test the null hypothesis that the complete response rate is at most 25% against the alternative hypothesis that it is at least 45%, with 89% power and 9% type I error. Treatment involved 28-day cycles consisting of ixazomib 4 mg days 1, 8, 15; lenalidomide 25 mg days 1-21, dexamethasone 40 mg, weekly and Dara 16 mg/kg, weekly for two cycles, every other week during cycles 3-6 and then every 4 weeks thereafter during induction (12-cyles) followed by Dara-ixazomib maintenance (maximum 24 cycles). Cohorts A (n=38) and B (N=40) comprised the study population, with dexamethasone discontinued after two cycles in Cohort B. Results: Baseline demographics and patient disposition are outlined in the table. Among those who have gone off study, 9 and 5 patients, respectively in Cohort A and B went off for progression, while 11 and 8 patients in the respective arms discontinued treatment for an alternative therapy, most frequently a transplant. Responses were rapid and deepened over the duration of treatment as shown in the figure; 32% of patients in Cohort A and 28% of patients in Cohort B achieved a complete response; 29% and 23%, respectively, achieved a marrow minimal residual disease (MRD) negative status. After a median follow up was 31.4 months for Cohort A and 18.2 months for Cohort B, median PFS has not been reached in either cohort. A grade 3 or higher adverse event at least possibly attributed to the study drugs was seen in 58% of patients in Cohort A (hematologic in 47% and non-hematologic in 26%) and 50% of patients in Cohort B (hematologic 43%, non-hematologic 20%). The most common toxicities included fatigue, neutropenia, lymphopenia, peripheral neuropathy and nausea. Stem cells were collected in 30 and 31 patients in Cohorts A and B respectively; median (range) of CD34 cells/kg were 8.5 (2.8, 15.9) x 106and 7.0 (2.9, 12.1) x 106, with plerixafor used in 83% and 94% of patients, respectively, in Cohorts A and B as per our standard of care "on-demand" approach based on blood CD34 count. Conclusion: Dara-IRd is an active regimen in newly diagnosed MM, with high overall rates of response and deep responses that improved over time with therapy. Early discontinuation of dexamethasone does not appear to impact the depth, rate or kinetics of response. The toxicities were manageable, with dose modifications. All patients were able to collect stem cells when required, though majority needed plerixafor. Disclosures Kumar: Amgen: Consultancy, Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments, Research Funding; Kite Pharma: Consultancy, Research Funding; Carsgen: Other, Research Funding; Janssen Oncology: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Cellectar: Other; Dr. Reddy's Laboratories: Honoraria; Takeda: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; AbbVie: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Celgene/BMS: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Merck: Consultancy, Research Funding; Adaptive Biotechnologies: Consultancy; Tenebio: Other, Research Funding; Oncopeptides: Consultancy, Other: Independent Review Committee; IRC member; Genentech/Roche: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Genecentrix: Consultancy; Sanofi: Research Funding; Novartis: Research Funding; MedImmune: Research Funding; Karyopharm: Consultancy; BMS: Consultancy, Research Funding. Kapoor:GlaxoSmithKline: Research Funding; Celgene: Honoraria; Takeda: Honoraria, Research Funding; Amgen: Research Funding; Sanofi: Consultancy, Research Funding; Janssen: Research Funding; Cellectar: Consultancy. Dingli:Apellis: Consultancy; Karyopharm Therapeutics: Research Funding; Janssen: Consultancy; Bristol Myers Squibb: Research Funding; Alexion: Consultancy; Rigel: Consultancy; Millenium: Consultancy; Sanofi-Genzyme: Consultancy. Dispenzieri:Pfizer: Research Funding; Takeda: Research Funding; Alnylam: Research Funding; Janssen: Research Funding; Celgene: Research Funding; Intellia: Research Funding. Gertz:Abbvie: Other; Sanofi: Other; Celgene: Other; Research to Practice: Other; Physicians Education Resource: Other: personal fee; Amgen: Other: personal fee; Medscape: Other: personal fee, Speakers Bureau; Appellis: Other: personal fee; Annexon: Other: personal fee; Alnylam: Other: personal fee; Spectrum: Other: personal fee, Research Funding; Janssen: Other: personal fee; Teva: Speakers Bureau; Aurora Bio: Other; Springer Publishing: Patents & Royalties; Proclara: Other; DAVA oncology: Speakers Bureau; Johnson and Johnson: Speakers Bureau; Ionis/Akcea: Other: personal fee; Prothena: Other: personal fee.

Background: Newly diagnosed patients with AL Amyloidosis are a heterogeneous population, ranging from incidentally found nephrotic syndrome to delayed advanced cardiac disease. For the purposes of trials, patients with the highest-risk disease are often excluded from trial participation. An NT-proBNP of 8500 or higher is the criterion most often used for exclusion from clinical trials due to high rates of early death. It is well documented that overall survival is improving over time, in part due to earlier diagnosis and in part due to more effective therapies. It is our aim to describe outcomes of patients with very high (NT-proBNP >=8500) and advanced stage amyloidosis excluding very high (VH) NT-proBNP. Methods: To address this need, we examined the outcomes of AL patients diagnosed between 1/2012 and 7/2020 and seen at our institution within 90 days of diagnosis. Of the 1290 patients, 291 were seen beyond the 90 day threshold and were thus excluded. Another 170 patients were excluded due to missing biomarkers to calculate stage, leaving 829 patients for our analysis. Thresholds for troponins and BNPs were used according to Muchtar Blood 133(7):2019 to correct for assay used. The vast majority of patients had troponin T measured, and for them the 0.025 mcg/L and the 0.035 mcg/L cut-points were used for the 2012 and 2004 staging systems. A minority of patients did not have troponin T, but rather high sensitivity troponin T (n=129) or troponin I (n=23). For patients with high sensitivity troponin T, cut-points of 41 and 50 ng/L were used, respectively, for the 2012 and 2004 systems, and for patients with troponin I only, a cut-point of 0.1 mcg/L was used for both systems. In the 3 patients with no NT-proBNP but with BNP, 400 and 81 ng/L were used respectively for the 2012 and 2004 systems; otherwise, the 1800 ng/L and 332 ng/L cut-offs were used. A BNP of > 700 ng/L was considered equivalent to NT-proBNP greater than or equal to 8500 ng/L. For the 2012 system, the dFLC of 18 mg/dL was used as a cut-off. Survival estimates were done using the method of Kaplan-Meier, and differences in survival were by determined by Log-Rank. Results: The median age of patients was 65 (range 29, 89), and 65% were male. 148 (17%) of these newly diagnosed patients had a VH (>=8500) NT-proBNP. Patients with VH NT-proBNP were older (67 versus 64 years, p=0.004). Only 4% of the VH NT-proBNP patients received an ASCT in contrast to 34% without VH NT-proBNP. With a median follow-up of 30 months for surviving patients, median OS for VH NT-proBNP patients was 3.3 months in contrast to patients without VH NT-proBNP at 68.4 months (Figure). Breakdown of early death for patients with VH NT-proBNP by stage is shown in Table. Of Mayo 2012 patients staged I, II, III, and IV, the percent of patients with VH NT-proBNP was 0, 3, 20, and 45%. In contrast, for the European modification of the Mayo 2004 system, the percent with VH NT-proBNP by stage was 0, 6, 0, 100 for stages I, II, IIIa, and IIIb, respectively. Among the VH NT-proBNP patients, there was stage discrimination using the Mayo 2012 system (6-month death rates were 17, 45, and 66% for patients with stages II, III, and IV, respectively, p=0.02). In contrast, using the Mayo 2004 system in the patients with VH NT-proBNP, 6-month death rates were 45 and 60% for stage II and IIIb patients, respectively (p= 0.12). Among patients without VH NT-proBNP, rates of death in the first year were 21% including stage I patients whose death rates were well under 10%. Patients without VH NT-proBNP but Mayo 2012 Stage III and IV had 1-year mortalities of 28 and 43% respectively. Using the modified Mayo 2004 system, excluding the VH NT-proBNP 1-year mortality was 19 and 37% for patients with stage II and IIIa, respectively. Discussion: Establishing expected outcomes for the sickest AL amyloidosis patients is a means by which trials can be designed for these patients with an unmet need. Patients with VH NT-proBNP should be considered for specially designed trials, and patients without VH NT-proBNP and advanced stage should be included in trials for newly diagnosed patients regardless of stage as long as there is appropriate stratification. Disclosures Dispenzieri: Janssen: Research Funding; Pfizer: Research Funding; Intellia: Research Funding; Alnylam: Research Funding; Celgene: Research Funding; Takeda: Research Funding. Kumar:AbbVie: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Tenebio: Other, Research Funding; BMS: Consultancy, Research Funding; Sanofi: Research Funding; Carsgen: Other, Research Funding; Amgen: Consultancy, Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments, Research Funding; Oncopeptides: Consultancy, Other: Independent Review Committee; IRC member; Celgene/BMS: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Merck: Consultancy, Research Funding; Takeda: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Janssen Oncology: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Adaptive Biotechnologies: Consultancy; Dr. Reddy's Laboratories: Honoraria; Novartis: Research Funding; MedImmune: Research Funding; Genecentrix: Consultancy; Kite Pharma: Consultancy, Research Funding; Cellectar: Other; Karyopharm: Consultancy; Genentech/Roche: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments. Dingli:Karyopharm Therapeutics: Research Funding; Bristol Myers Squibb: Research Funding; Millenium: Consultancy; Janssen: Consultancy; Alexion: Consultancy; Rigel: Consultancy; Apellis: Consultancy; Sanofi-Genzyme: Consultancy. Kapoor:Celgene: Honoraria; Cellectar: Consultancy; Janssen: Research Funding; Sanofi: Consultancy, Research Funding; Amgen: Research Funding; Takeda: Honoraria, Research Funding; GlaxoSmithKline: Research Funding. Lin:Janssen: Consultancy, Research Funding; Kite, a Gilead Company: Consultancy, Research Funding; Novartis: Consultancy; Celgene: Consultancy, Research Funding; Bluebird Bio: Consultancy, Research Funding; Juno: Consultancy; Legend BioTech: Consultancy; Merck: Research Funding; Takeda: Research Funding; Gamida Cells: Consultancy; Sorrento: Consultancy, Membership on an entity's Board of Directors or advisory committees; Vineti: Consultancy. Gertz:Springer Publishing: Patents & Royalties; Amyloidosis Foundation: Research Funding; International Waldenstrom Foundation: Research Funding; NCI SPORE MM: Research Funding; Ionis/Akcea: Consultancy; Alnylam: Consultancy; Prothena: Consultancy; Sanofi: Consultancy; Janssen: Consultancy; Spectrum: Consultancy, Research Funding; Annexon: Consultancy; Appellis: Consultancy; Amgen: Consultancy; Medscape: Consultancy, Speakers Bureau; Physicians Education Resource: Consultancy; Data Safety Monitoring board from Abbvie: Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy; Johnson and Johnson: Speakers Bureau; DAVA oncology: Speakers Bureau; Advisory Board for Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Advisory Board for Proclara: Membership on an entity's Board of Directors or advisory committees; i3Health: Consultancy.

Introduction: MF is a clonal stem cell disorder characterized by progressive bone marrow failure, extramedullary hematopoiesis, and debilitating constitutional symptoms. Approximately 60% of pts develop anemia within a year of diagnosis; pts who are transfusion dependent have worse survival and quality-of-life outcomes (Tefferi A, et al. Mayo Clin Proc 2012;87:25-33). Treatment for MF-associated anemia remains critically unaddressed, with no approved therapies available. Luspatercept is a first-in-class erythroid maturation agent that has been shown to increase hemoglobin and reduce transfusion burden in pts with myelodysplastic syndromes (Fenaux P, et al. N Engl J Med 2020;382:140-51) and β-thalassemia (Cappellini MD, et al. N Engl J Med 2020;382:1219-31). Pts have also achieved multiple response episodes with luspatercept (Fenaux P, et al. Blood 2019;134:841). We previously reported findings from the ongoing open-label, phase 2 ACE-536-MF-001 trial evaluating luspatercept in pts with MF-associated anemia (Gerds AT, et al. Blood 2019;134:557). Here we report updated study results, focusing on response in pts requiring RBC transfusions. Methods: 79 pts with MF and anemia were enrolled (Figure). Of these, 43 were requiring RBC transfusions, defined as 6-12 U/84 d prior to treatment, or 4-12 U/84 d for the 3 expansion cohort pts. Pts in Cohort 3B received a stable ruxolitinib (RUX) dose for ≥ 16 wks (40 wks for expansion cohort pts), whereas Cohort 2 pts did not (Figure). Pts in Cohort 3B received a median RUX dose of 20 mg/d (range 5-40) for a median of 73 wks (range 29-359) prior to treatment. Pts received luspatercept every 21 d at a starting dose of 1.0 mg/kg (1.33 mg/kg for 3 expansion cohort pts in Cohort 3B), with doses escalating up to 1.75 mg/kg; 81% and 50% of pts in Cohorts 2 and 3B escalated to 1.75 mg/kg. Primary endpoint for pts receiving transfusions was RBC transfusion-independence (RBC-TI) for ≥ 12 consecutive wks within the first 24 wks on study. However, as responses were observed that did not fall within the 24-wk response window, additional assessments were conducted for the entire treatment duration for RBC-TI and ≥ 50% decrease in RBC transfusions (minimum 4 RBC U decrease) from baseline. Intent-to-treat data were analyzed as of March 29, 2020. Results: Pts in Cohorts 2 and 3B received a median of 8 cycles of luspatercept (range 1-39), for a median (mean) duration of 24 (31) and 23 (39) wks, respectively. Pts in Cohorts 2 and 3B received a median of 2.7 RBC U/28 d (range 1-5) and 2.7 RBC U/28 d (range 2-4), respectively. For the primary endpoint, 2/21 (10%) and 6/22 (27%) pts receiving transfusions in Cohorts 2 and 3B achieved RBC-TI over any consecutive 12 wks during the first 24 wks. Median time to start of first RBC-TI response period in Cohorts 2 and 3B was 1.5 days and 37 days; median duration of response was 49 wks (range 16-82) and 42 wks (range 12-111), respectively. When assessing the entire treatment period, 4/21 (19%) and 8/22 (36%) pts in Cohorts 2 and 3B achieved RBC-TI ≥ 12 wks, respectively. 25% of the RBC-TI responders in both cohorts experienced 2 separate episodes of RBC-TI ≥ 12 wks. Median cumulative duration of all individual ≥ 12-wk response episodes of RBC-TI was 59 wks (range 24-82) in Cohort 2 and 55 wks (range 12-116) in Cohort 3B. 8/21 (38%) and 10/22 (46%) pts in Cohorts 2 and 3B achieved a ≥ 50% reduction in RBC transfusion burden (minimum ≥ 4 U reduction) over 12 wks; of these, 38% and 20% of responders in Cohorts 2 and 3B experienced 2 separate ≥ 12-wk response episodes, and 1 pt (13%) in Cohort 2 experienced 3 separate responses. 4/15 (27%) and 8/14 (57%) pts in Cohorts 2 and 3B who reached 24 wks of treatment achieved clinical benefit and therefore continued luspatercept on an ongoing basis. Safety is reported for all 79 pts on study. 5 (6%) pts had grade 3-4 treatment-related adverse events (AEs); these AEs were diarrhea (n = 2), dehydration (n = 1), and hypertension (n = 3). There were no treatment-related deaths. Treatment-related AEs (any grade) occurring in ≥ 5% of pts were hypertension (13%), bone pain (9%), and diarrhea (5%). 8 (10%) pts had ≥ 1 AE leading to discontinuation. 16 (20%) pts remain on treatment. Conclusions: These longer-term findings suggest durable activity of luspatercept in pts with MF-associated anemia. In addition, a quarter of pts receiving transfusions achieved more than one ≥ 12-wk episode of RBC-TI response with luspatercept. The incidence of grade 3-4 AEs with luspatercept was low. Disclosures Gerds: Roche/Genentech: Research Funding; Celgene: Consultancy, Research Funding; Sierra Oncology: Research Funding; AstraZeneca/MedImmune: Consultancy; Incyte Corporation: Consultancy, Research Funding; Pfizer: Research Funding; Apexx Oncology: Consultancy; CTI Biopharma: Consultancy, Research Funding; Gilead Sciences: Research Funding; Imago Biosciences: Research Funding. Vannucchi:Novartis: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Blueprint: Membership on an entity's Board of Directors or advisory committees; AbbVie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene/BMS: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Incyte: Membership on an entity's Board of Directors or advisory committees. Passamonti:Novartis: Speakers Bureau; BMS: Speakers Bureau; Roche: Other: Support of parent study and funding of editorial support. Kremyanskaya:Constellation Pharmaceuticals: Research Funding; Incyte Corporation: Research Funding; Bristol Myers Squibb: Research Funding; Protagonist Therapeutics: Consultancy, Research Funding; Astex Pharmaceuticals: Research Funding. Gotlib:CTI Biopharma: Research Funding; Kartos: Consultancy, Honoraria, Research Funding; Incyte: Consultancy, Honoraria, Research Funding; Roche: Honoraria, Research Funding; BMS: Research Funding. McCaul:BMS: Speakers Bureau; Seattle Genetics: Speakers Bureau; Karyopharm: Speakers Bureau; Incyte: Speakers Bureau. Ribrag:argenX: Research Funding; pharmamar: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; servier: Consultancy; nanostring: Honoraria, Membership on an entity's Board of Directors or advisory committees; gilead: Honoraria, Membership on an entity's Board of Directors or advisory committees; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees; AZD: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other; Infinity: Honoraria, Membership on an entity's Board of Directors or advisory committees; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees; Incyte: Honoraria, Membership on an entity's Board of Directors or advisory committees; MSD: Honoraria, Membership on an entity's Board of Directors or advisory committees; gustave roussy comprehensive cancer center: Current Employment; EPZ: Honoraria, Membership on an entity's Board of Directors or advisory committees; epizyme (EPZ): Research Funding. Mead:Novartis: Consultancy, Honoraria, Other: travel, accommodations, expenses, Research Funding, Speakers Bureau; Celgene/BMS: Consultancy, Honoraria, Other: travel, accommodations, expenses, Research Funding; Abbvie: Consultancy; CTI: Consultancy; Gilead: Consultancy. Harrison:Roche: Honoraria; Gilead Sciences: Honoraria, Speakers Bureau; Incyte Corporation: Speakers Bureau; Janssen: Speakers Bureau; CTI Biopharma Corp: Honoraria, Speakers Bureau; Sierra Oncology: Honoraria; Celgene: Honoraria, Research Funding, Speakers Bureau; Shire: Honoraria, Speakers Bureau; Novartis: Honoraria, Research Funding, Speakers Bureau; AOP Orphan Pharmaceuticals: Honoraria; Promedior: Honoraria. Mesa:Sierra Oncology: Consultancy; LaJolla Pharmaceutical Company: Consultancy; Novartis: Consultancy; Incyte: Research Funding; CTI BioPharma: Research Funding; Genentech: Research Funding; AbbVie: Research Funding; Promedior: Research Funding; Bristol Myers Squibb: Research Funding; Samus Therapeutics: Research Funding. Kiladjian:AbbVie: Membership on an entity's Board of Directors or advisory committees; AOP Orphan: 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; Novartis: Membership on an entity's Board of Directors or advisory committees. Barosi:Bristol Myers Squibb: Consultancy. Gerike:Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Shetty:BMS: Current Employment, Current equity holder in publicly-traded company. Pariseau:Bristol Myers Squibb: Current Employment. Miranda:Bristol Myers Squibb: Current Employment. Schwickart:Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Giuseppi:BMS: Current Employment, Current equity holder in publicly-traded company. Zhang:Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Backstrom:Acceleron Pharma: Current Employment, Current equity holder in publicly-traded company; BMS: Current equity holder in publicly-traded company. Verstovsek:AstraZeneca: Research Funding; Roche: Research Funding; CTI Biopharma Corp: Research Funding; Genentech: Research Funding; Incyte Corporation: Consultancy, Research Funding; NS Pharma: Research Funding; Novartis: Consultancy, Research Funding; PharmaEssentia: Research Funding; ItalPharma: Research Funding; Sierra Oncology: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Blueprint Medicines Corp: Research Funding; Promedior: Research Funding; Gilead: Research Funding; Protagonist Therapeutics: Research Funding.

AbstractPurposeSevere adverse events (AEs), such as Guillain‐Barré syndrome (GBS) occur rarely after influenza vaccination. We identify highly associated AEs with GBS and develop prediction models for GBS using the US Vaccine Adverse Event Reporting System (VAERS) reports following trivalent influenza vaccination (FLU3).MethodsThis study analyzed 80 059 reports from the US VAERS between 1990 and 2017. Several AEs were identified as highly associated with GBS and were used to develop the prediction model. Some common and mild AEs that were suspected to be underreported when GBS occurred simultaneously were removed from the final model. The analyses were validated using European influenza vaccine AEs data from EudraVigilance.ResultsOf the 80 059 reports, 1185 (1.5%) were annotated as GBS related. Twenty‐four AEs were identified as having strong association with GBS. The full prediction model, using age, sex, and all 24 AEs achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 85.4% (90% CI: [83.8%, 86.9%]). After excluding the nine (e.g., pruritus, rash, injection site pain) likely underreported AEs, the final AUC became 77.5% (90% CI: [75.5%, 79.6%]). Two hundred and one (0.25%) reports were predicted as of high risk of GBS (predicted probability >25%) and 84 actually developed GBS.ConclusionThe prediction performance demonstrated the potential of developing risk‐prediction models utilizing the VAERS cohort. Excluding the likely underreported AEs sacrificed some prediction power but made the model more interpretable and feasible. The high absolute risk of even a small number of AE combinations suggests the promise of GBS prediction within the VAERS dataset.

Transcription factor E3-rearranged renal cell carcinoma (TFE3-RCC) has heterogenous morphologic and immunohistochemical (IHC) features. 131 pathologists with genitourinary expertise were invited in an online survey containing 23 questions assessing their experience on TFE3-RCC diagnostic work-up. Fifty (38%) participants completed the survey. 46 of 50 participants reported multiple patterns, most commonly papillary pattern (almost always 9/46, 19.5%; frequently 29/46, 63%). Large epithelioid cells with abundant cytoplasm were the most encountered cytologic feature, with either clear (almost always 10/50, 20%; frequently 34/50, 68%) or eosinophilic (almost always 4/49, 8%; frequently 28/49, 57%) cytology. Strong (3+) or diffuse (>75% of tumour cells) nuclear TFE3 IHC expression was considered diagnostic by 13/46 (28%) and 12/47 (26%) participants, respectively. Main TFE3 IHC issues were the low specificity (16/42, 38%), unreliable staining performance (15/42, 36%) and background staining (12/42, 29%). Most preferred IHC assays other than TFE3, cathepsin K and pancytokeratin were melan A (44/50, 88%), HMB45 (43/50, 86%), carbonic anhydrase IX (41/50, 82%) and CK7 (32/50, 64%). Cut-off for positive TFE3 fluorescent in situ hybridisation (FISH) was preferably 10% (9/50, 18%), although significant variation in cut-off values was present. 23/48 (48%) participants required TFE3 FISH testing to confirm TFE3-RCC regardless of the histomorphologic and IHC assessment. 28/50 (56%) participants would request additional molecular studies other than FISH assay in selected cases, whereas 3/50 participants use additional molecular cases in all cases when TFE3-RCC is in the differential. Optimal diagnostic approach on TFE3-RCC is impacted by IHC and/or FISH assay preferences as well as their conflicting interpretation methods.

Background and Aims Electronic health record (EHR)‐based research allows the capture of large amounts of data, which is necessary in NAFLD, where the risk of clinical liver outcomes is generally low. The lack of consensus on which International Classification of Diseases (ICD) codes should be used as exposures and outcomes limits comparability and generalizability of results across studies. We aimed to establish consensus among a panel of experts on ICD codes that could become the reference standard and provide guidance around common methodological issues. Approach and Results Researchers with an interest in EHR‐based NAFLD research were invited to collectively define which administrative codes are most appropriate for documenting exposures and outcomes. We used a modified Delphi approach to reach consensus on several commonly encountered methodological challenges in the field. After two rounds of revision, a high level of agreement (>67%) was reached on all items considered. Full consensus was achieved on a comprehensive list of administrative codes to be considered for inclusion and exclusion criteria in defining exposures and outcomes in EHR‐based NAFLD research. We also provide suggestions on how to approach commonly encountered methodological issues and identify areas for future research. Conclusions This expert panel consensus statement can help harmonize and improve generalizability of EHR‐based NAFLD research.

AbstractThree sets of criteria (International Society of Amyloidosis [ISA], Palladini and Kastritis) were independently developed for staging, progression and response criteria to predict renal survival in patients with AL amyloidosis. We evaluated these criteria using a cohort of 495 newly diagnosed AL amyloidosis patients with renal involvement using time to event competing risk analysis at baseline, 3, 6 and 12 months after treatment. Only Palladini and Kastritis had a staging system and both predicted a higher risk of end stage renal disease (ESRD) in the stage III vs stage I patients but only the Palladini model was predictive for stage II patients. At 3 months, risk of ESRD was significantly higher for Palladini and ISA renal progression (hazard ratio [HR] 2.8 [95% CI: 1.5–5.3, p = .001] and 2.5 [CI: 1.4–4.6, p = .004, respectively]), but renal response was not significantly protective; conversely, the risk of ESRD was not significantly higher for the Kastritis renal progression, but was significantly protective for the Kastritis renal responders (HR 0.38 [95% CI: 0.17–0.84], p = .017). Both progression and response with ISA, Palladini and Kastritis criteria were predictive of ESRD at 6 months and 12 months. While the Palladini staging criteria at baseline, and the ISA and Palladini criteria for progression at 3 months performed better than the Kastritis criteria at baseline and 3 months post‐treatment, the Kastritis criteria performed better for response 3 months after treatment. All three sets of criteria performed well at and after 6 months post‐treatment. These differences are important when choosing endpoints for clinical trials.

Abstract Objective To describe headache characteristics, medication use, disability, and quality of life in a large patient cohort from the United States who have chronic migraine (CM) and medication overuse headache (MOH). Methods In all, 610 adult patients were enrolled into the Medication Overuse Treatment Strategy trial from 34 healthcare clinics, including headache specialty, general neurology, and primary care clinics. Descriptive statistics characterize baseline demographics, headache characteristics, medication use, disability (Headache Impact Test 6 [HIT‐6] and Migraine Functional Impact Questionnaire [MFIQ]), pain interference (PROMIS Pain Interference), and quality of life (EQ‐5D‐5L). Relationships with headache frequency were assessed. Results Mean age was 45 years (SD 13) and 531/608 (87.3%) were females. Mean headache days per 30 was 24.3 (SD 5.5), including 13.6 (SD 7.1) with moderate to severe headache. Daily headaches were reported by 36.1% (219/607) of patients. Acute headache medications were used on 21.5 (SD 7.5) per 30 days. The most commonly overused medications were simple analgesics (378/607, 62% of patients), combination analgesics (246/607, 41%), and triptans (128/607, 21%). HIT‐6, MFIQ, PROMIS Pain Interference, and EQ‐5D‐5L scores demonstrated substantial negative impact from CM with MOH on patient functioning and quality of life. Higher headache frequency was associated with more moderate–severe headache days, more frequent acute headache medication use, greater headache‐related disability, and lower quality of life. Only 272/606 (44.9%) were taking migraine preventive medication. Conclusions CM with MOH is associated with a large burden on patients in the United States. Higher headache frequency is associated with greater impact on functioning, pain interference, and quality of life.

AbstractObjectiveMost seizure forecasting algorithms have relied on features specific to electroencephalographic recordings. Environmental and physiological factors, such as weather and sleep, have long been suspected to affect brain activity and seizure occurrence but have not been fully explored as prior information for seizure forecasts in a patient‐specific analysis. The study aimed to quantify whether sleep, weather, and temporal factors (time of day, day of week, and lunar phase) can provide predictive prior probabilities that may be used to improve seizure forecasts.MethodsThis study performed post hoc analysis on data from eight patients with a total of 12.2 years of continuous intracranial electroencephalographic recordings (average = 1.5 years, range = 1.0–2.1 years), originally collected in a prospective trial. Patients also had sleep scoring and location‐specific weather data. Histograms of future seizure likelihood were generated for each feature. The predictive utility of individual features was measured using a Bayesian approach to combine different features into an overall forecast of seizure likelihood. Performance of different feature combinations was compared using the area under the receiver operating curve. Performance evaluation was pseudoprospective.ResultsFor the eight patients studied, seizures could be predicted above chance accuracy using sleep (five patients), weather (two patients), and temporal features (six patients). Forecasts using combined features performed significantly better than chance in six patients. For four of these patients, combined forecasts outperformed any individual feature.SignificanceEnvironmental and physiological data, including sleep, weather, and temporal features, provide significant predictive information on upcoming seizures. Although forecasts did not perform as well as algorithms that use invasive intracranial electroencephalography, the results were significantly above chance. Complementary signal features derived from an individual's historic seizure records may provide useful prior information to augment traditional seizure detection or forecasting algorithms. Importantly, many predictive features used in this study can be measured noninvasively.