Citations

Objective: Faulty neuromuscular and biomechanical deficits of the knee are nearly ubiquitous in athletes after anterior cruciate ligament (ACL) reconstruction (ACLR). Knee biomechanical deficits are directly associated with an increased risk of second ACL injury, which typically occurs during a sports-related movement on a single limb. To date, the biomechanical effects of a neuromuscular training (NMT) program on knee biomechanics during a single-leg landing task have not been investigated. Design: Prospective Cohort Study. Setting: Controlled laboratory setting. Participants: Eighteen ACLR and 10 control athletes. Interventions: Neuromuscular training. Main Outcome Measures: Knee kinematics and kinetics. Results: There were no significant interactions of session and limb (P > 0.05) for the athletes with ACLR after training. However, there were several significant main effects of session (P < 0.05) for knee kinematics and kinetics during the single-leg landing task. After training, the athletes with ACLR landed with greater knee flexion angles, decreased knee abduction angles, increased knee flexion range of motion, and decreased knee excursion. Also, the ACLR athletes landed with lower knee flexion moments, greater knee adduction moments, and lower peak vertical ground reaction force. Post-training comparison of the ACLR and control cohorts found no significant interactions of group and limb (P > 0.05) and only a significant main effect of group (P < 0.05) for frontal plane knee angle at initial contact. The athletes with ACLR landed with greater knee adduction angles than the control group. Conclusions: Deficits in knee biomechanics that are associated with an increased risk of ACL injury are attenuated after completion of this NMT program.

Overexpression and long terminal repeat (LTR) polymorphism of the HRES‑1/Rab4 human endogenous retrovirus locus have been associated with T cell activation and disease manifestations in systemic lupus erythematosus (SLE). Although genomic DNA methylation is diminished overall in SLE, its role in HRES-1/Rab4 expression is unknown. Therefore, we determined how lupus-associated polymorphic rs451401 alleles of the LTR regulate transcription from the HRES-1/Rab4 promoter and thus affect T cell activation. The results showed that cytosine –119 is hypermethylated while cytosine –51 of the promoter and the LTR enhancer are hypomethylated in SLE. Pharmacologic or genetic inactivation of DNA methyltransferase 1 augmented the expression of HRES-1/Rab4. The minimal promoter was selectively recognized by metabolic stress sensor NRF1 when cytosine –119 but not cytosine –51 was methylated, and NRF1 stimulated HRES-1/Rab4 expression in human T cells. In turn, IRF2 and PSIP1 bound to the LTR enhancer and exerted control over HRES-1/Rab4 expression in rs451401 genotype– and methylation-dependent manners. The LTR enhancer conferred markedly greater expression of HRES-1/Rab4 in subjects with rs451401CC over rs451401GG alleles that in turn promoted mechanistic target of rapamycin (mTOR) activation upon T cell receptor stimulation. HRES-1/Rab4 alone robustly activated mTOR in human T cells. These findings identify HRES-1/Rab4 as a methylation- and rs451401 allele–dependent transducer of environmental stress and controller of T cell activation.

PurposeTo demonstrate the feasibility of pseudo‐continuous arterial‐spin–labeled (pCASL) imaging with 3D fast‐spin‐echo stack‐of‐spirals on a compact 3T scanner (C3T), to perform trajectory correction for eddy‐current–induced deviations in the spiral readout of pCASL imaging, and to assess the correction effect on perfusion‐related images with high‐performance gradients (80 mT/m, 700T/m/s) of the C3T.MethodsTo track eddy‐current–induced artifacts with Archimedean spiral readout, the spiral readout in pCASL imaging was performed with 5 different peak gradient slew rate (Smax) values ranging from 70 to 500 T/m/s. The trajectory for each Smax was measured using a dynamic field camera and applied in a density‐compensated gridding image reconstruction in addition to the nominal trajectory. The effect of the trajectory correction was assessed with perfusion‐weighted (ΔM) images and proton‐density–weighted images as well as cerebral blood flow (CBF) maps, obtained from 10 healthy volunteers.ResultsBlurring artifact on ΔM images was mitigated by the trajectory correction. CBF values on the left and right calcarine cortices showed no significant difference after correction. Also, the signal‐to‐noise ratio of ΔM images improved, on average, by 7.6% after correction (P < .001). The greatest improvement of 12.1% on ΔM images was achieved with a spiral readout using Smax of 300~400 T/m/s.ConclusionEddy currents can cause spiral trajectory deviation, which leads to deformation of the CBF map even in cases of low value Smax. The trajectory correction for spiral‐readout–based pCASL produces more reliable results for perfusion imaging. These results suggest that pCASL is feasible on C3T with high‐performance gradients.

Purpose: To describe and validate the dose calculation algorithm of an independent second-dose check software for spot scanning proton delivery systems with full width at half maximum between 5 and 14 mm and with a negligible spray component. Methods: The analytical dose engine of our independent second-dose check software employs an altered pencil beam algorithm with 3 lateral Gaussian components. It was commissioned using Geant4 and validated by comparison to point dose measurements at several depths within spread-out Bragg peaks of varying ranges, modulations, and field sizes. Water equivalent distance was used to compensate for inhomogeneous geometry. Twelve patients representing different disease sites were selected for validation. Dose calculation results in water were compared to a fast Monte Carlo code and ionization chamber array measurements using dose planes and dose profiles as well as 2-dimensional–3-dimensional and 3-dimensional–3-dimensional γ-index analysis. Results in patient geometry were compared to Monte Carlo simulation using dose–volume histogram indices, 3-dimensional–3-dimensional γ-index analysis, and inpatient dose profiles. Results: Dose engine model parameters were tuned to achieve 1.5% agreement with measured point doses. The in-water γ-index passing rates for the 12 patients using 3%/2 mm criteria were 99.5% ± 0.5% compared to Monte Carlo. The average inpatient γ-index analysis passing rate compared to Monte Carlo was 95.8% ± 2.9%. The average difference in mean dose to the clinical target volume between the dose engine and Monte Carlo was −0.4% ± 1.0%. For a typical plan, dose calculation time was 2 minutes on an inexpensive workstation. Conclusions: Following our commissioning process, the analytical dose engine was validated for all treatment sites except for the lung or for calculating dose–volume histogram indices involving point doses or critical structures immediately distal to target volumes. Monte Carlo simulations are recommended for these scenarios.

Abstract Background: Isatuximab (SAR650984) is a humanized IgG1 monoclonal antibody that binds selectively to a unique epitope on the human CD38 receptor. In the dose escalation part of the Phase I portion of the trial (NCT01749969), isatuximab ≥10 mg/kg IV given every other week (q2w) or 10 mg/kg weekly (qw) induced responses in 6/19 recipients (ORR 32%). Responses were noted at doses as low as 1 mg/kg q2w. The maximum tolerated dose (up to 20 mg/kg IV qw) was not reached. The most common treatment-emergent adverse events (TEAEs) were fatigue and nausea; few serious grade 3/4 TEAEs were reported (pneumonia 6%). The most common category of drug-related TEAE involved infusion-associated reactions (IARs) occurring in half of the patients, with grade 3/4 IARs in <5% of the patients. Based on promising Phase I activity, a Phase II dose finding study was added to investigate single-agent isatuximab in patients with relapsed or refractory myeloma (RRMM). Methods: In Phase II, eligible patients were originally randomly assigned (1:1:1) to one of 3 treatment groups: 3 mg/kg q2w, 10 mg/kg q2w, and 10 mg/kg q2w x 4 doses then q4w. A 4th treatment group was enrolled at a higher isatuximab dose with an optimized schedule of 20 mg/kg qw x 4 doses then q2w. Patient assignment was stratified by prior anti-myeloma therapy (Stratum 1: prior treatment with pomalidomide (POM) and/or carfilzomib (CAR); Stratum 2: no prior POM or CAR). Patients continued therapy until disease progression, unacceptable AEs or physician/patient decision. Eligible patients had measurable disease (serum M-protein >1 g/dL or urine M-protein ≥200 mg/24 h, or serum free light chain ≥10 mg/dL), prior exposure to ≥3 lines of anti-myeloma therapy or were double-refractory to immunomodulatory drugs (IMiDs) and proteasome inhibitors (PIs), prior treatment with an alkylating agent, and adequate baseline bone marrow reserve and organ function. Enrollment of 24 patients was planned for each treatment group. An interim efficacy analysis is planned when all patients have completed ≥2 disease assessments, with central review of these data currently ongoing. Enrollment has now been completed and all patients who completed at least 1 cycle of isatuximab at the cutoff date (5/29/15) are included in this preliminary analysis. Results: Patient characteristics (n=96): median age 62.5 yrs (range 38-85); median time from initial diagnosis to study treatment 5.85 yrs (1.2-24.1); 76% had IgA/G MM; 24% had light-chain only disease; and 22%, 22%, and 44% had Stage I, II, and III disease, respectively. The median number of prior lines of therapy was 5 (2-14) with 100%, 99%, and 98% having prior exposure to PIs, IMiDs, and alkylator-containing therapy, respectively. Overall, 78% of patients had previously received POM and/or CAR; 55% had been exposed to both of these agents. At data cut-off, 36.5% of patients remained on therapy and 53.1%, 6.3%, and 4.2% had discontinued therapy due to disease progression, AEs, or other reason, respectively. The median number of cycles received was 3 (1-9), with a median duration of treatment of 11.7 wks. Approximately 17% of patients required at least 1 dose delay and 7.3% of isatuximab infusions were interrupted. TEAEs occurred in 93% of patients, with 51% being grade ≥3. Drug-related TEAEs occurred in 61% of patients (grade ≥3, 10%). The most common drug-related TEAEs (>5% of patients) were nausea (14.6%), chills (14.6%), dyspnea (13.5%), chest discomfort (10.4%), flushing (7.3%), headache (7.3%), cough (6.3%), and vomiting (5.2%). The majority of the symptoms occurred within 24 h of isatuximab infusion and were attributed to an IAR. IARs occurred in 50% of patients, were predominantly grade 1/2 (grade ≥3, 3.1%), and were mostly limited to Cycle 1 (2 patients had IARs in Cycle 2). Pneumonia (6.3%) and sepsis (5.2%) were the most common serious TEAEs. Six deaths have occurred within 30 days of last dose, 4 due to disease progression, and 2 due to serious AEs unrelated to study therapy (cardiopulmonary arrest and cerebral hemorrhage). Conclusion: Isatuximab administered to heavily pre-treated patients with RRMM appears well tolerated. IARs are common in Cycle 1, but typically do not recur. Results by treatment arm, including interim analysis response data, will be presented. Disclosures Martin: Sanofi: Research Funding. Richter:Takeda: Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Speakers Bureau; Amgen: Speakers Bureau; Novartis: Speakers Bureau. Vij:Millennium: Honoraria, Speakers Bureau; Onyx: Consultancy, Honoraria, Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau; BMS: Consultancy; Takeda: Consultancy, Research Funding; Novartis: Consultancy; Sanofi: Consultancy; Janssen: Consultancy; Merck: Consultancy. Zonder:Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS, Amgen, Prothena, Seattle Genetics: Membership on an entity's Board of Directors or advisory committees. Kaufman:Spectrum: Consultancy; Merck: Research Funding; Onyx: Consultancy, Research Funding; Novartis: Consultancy; Celgene: Consultancy, Research Funding; Millenium: Consultancy. Bensinger:Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Acetylon: Research Funding; Sanofi: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Onyx: Research Funding, Speakers Bureau; Millenium: Research Funding; BMS: Membership on an entity's Board of Directors or advisory committees, Research Funding. Dimopoulos:Novartis: Honoraria; Amgen: Honoraria; Genesis: Honoraria; Janssen: Honoraria; Celgene: Honoraria; Onyx: Honoraria; Janssen-Cilag: Honoraria. San Miguel:Millennium, Celgene, Novartis, Janssen, Onyx, BMS, MSD: Membership on an entity's Board of Directors or advisory committees. Zimmerman:Onyx: Honoraria; Celgene: Honoraria, Speakers Bureau; Millennium: Honoraria, Speakers Bureau; Amgen: Honoraria, Speakers Bureau. Hari:Celgene: Consultancy; Takeda: Consultancy; BMS: Consultancy; Janssen: Consultancy; Novartis: Consultancy; Spectrum: Consultancy; Sanofi: Consultancy. Gasparetto:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Onyx: Honoraria; Millennium: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Kumar:Skyline, Noxxon: Honoraria; Celgene, Millennium, Onyx, Janssen, Noxxon, Sanofi, BMS, Skyline: Consultancy; Celgene, Millennium, Onyx, Novartis, Janssen, Sanofi: Research Funding. Hsu:Sanofi: Employment. Charpentier:Sanofi: Employment. Strickland:Alexion Pharmaceuticals: Other: Advisory Board Particpation; Amgen: Other: Advisory Board Particpation; Daiichi-Sankyo: Other: Advisory Board Particpation; Boehringer-Ingelheim: Other: Advisory Board Particpation; Sunesis Pharmaceuticals: Other: Steering Committee and Advisory Board Participation. Mikhael:Sanofi: Research Funding; Onyx: Research Funding; Celgene: Research Funding; AbbVie: Research Funding.

BACKGROUND: Angioimmunoblastic T cell lymphoma (AITL) has a unique histological phenotype with a relatively small number of malignant T-cells and an extensive infiltrate of normal cells including T-cells, B-cells, NK cells and FDCs. The malignant cell has a T follicular helper (Tfh) cell phenotype with increased expression of immune checkpoint molecules including PD-1 and ICOS. With the routine use of antibodies targeting immune checkpoint molecules, clinical results in limited numbers of AITL patients have thus far been mixed with some responding patients but also some who rapidly progress. Because both malignant T-cells and normal T-cells in AITL may be PD-1 positive, we conducted a study to compare the phenotype of normal T-cells and malignant T-cells within the tumor microenvironment. We also evaluated whether soluble immune checkpoint molecules in the peripheral blood differed between AITLs, controls, and other PTCL subgroups. METHODS: We designed a novel CyTOF antibody (Ab) panel comprised of 37 markers to identify and characterize cells of T, B, NK, macrophage and FDC lineages within a cohort of biopsy specimens from 6 AITL patients compared to 6 normal controls (2 LN, 4 tonsil). Soluble immune receptors and ligands were then measured by multiplex ELISA on peripheral blood from 8 AITLs, 5 controls, and 32 additional samples from PTCL subtypes including PTCL-NOS, ALCL and CTCL. RESULTS: Malignant T-cells in AITL specimens could clearly be identified by viSNE mapping based on CyTOF expression of CD4, CD5, BCL6, CD10 (variable), CXCR5 (variable), PD-1 and ICOS. Aberrant loss of CD3 and CD7 on malignant T-cells was commonly seen. Expression of other immune checkpoint molecules was assessed. Malignant cells expressed high levels of TIGIT and moderate levels of CD27, but malignant T-cell populations were negative for expression of TIM3, LAG3, and CTLA-4. A substantial proportion of normal T-cells also expressed immune checkpoint molecules including PD-1 and CD27, and a subset of these cells also expressed TIGIT and ICOS. A small minority of normal T-cells expressed TIM3, LAG3, and CTLA-4. As others have described, the normal Tfh cell population had an immunophenotype that was otherwise relatively similar to the malignant population, but distinguishable via viSNE mapping with our CyTOF Ab panel. Expression of PD-L1 could be detected on NK-cells, macrophages, and a few select populations of normal T cells, but not on B-cells or FDCs. Malignant T-cells did not express PD-L1. Due to the potential importance of immune checkpoint molecules in this disease, we then measured soluble checkpoint receptors and ligands in the serum. Soluble PD-1 was significantly increased in the peripheral blood of AITL when compared to normal controls (average 2637 pg/mL (SE = 512.1) in AITL vs 57.87 (SE = 16.88) in controls). Similarly, soluble PD-L1 was significantly increased in AITL compared to normal controls (13.8 (SE = 10.5) in AITL vs 0.94 (SE = 0.25) in controls). CONCLUSIONS: Malignant T-cells in AITL have a unique phenotype which was able to be reliably identified through use of viSNE mapping with our CyTOF Ab panel. Furthermore, malignant T-cells were found to consistently express additional immune regulatory molecules beyond PD-1 and ICOS, including TIGIT and CD27. However, a substantial subset of normal T-cells have a very similar phenotype suggesting that activation of T-cells in this disease will likely stimulate both normal and malignant T-cells. This may explain the rapid disease progression seen in a subset of AITL patients treated with PD-1 blockade. Furthermore, soluble PD-1 is significantly increased in the serum of AITL patients and may explain the overall lack of clinical efficacy of anti-PD-1 antibodies in treating AITL. Disclosures Cerhan: NanoString: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding. Ansell:LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; LAM Therapeutics: Research Funding.

Background: Progress in genome technology allows analysis of previously completed trials to identify subgroups potentially benefiting from therapy. Enzastaurin is a potent inhibitor of protein kinase C beta (PKC-β) and suppresses the phosphoinositide 3-kinase (PI3K)/AKT pathway. The safety and efficacy of Enzastaurin has been tested in more than 60 clinical trials including 2 major studies in DLBCL: (1) PRELUDE (A phase III maintenance trial of Enzastaurin vs Placebo, N=758) (Crump, 2016), and (2) S028 (A randomized phase II study of Enzastaurin/R-CHOP vs R-CHOP in frontline intermediate/high-risk DLBCL, N=101) (Hainsworth, 2016). DNA samples extracted from blood of patients from PRELUDE were retrospectively genotyped using whole genome SNP arrays. From the genome wide screening a novel genetic biomarker, DGM1, was identified showing high correlation with response to Enzastaurin treatment (Luo, ASH 2018). Importantly, these findings were replicated in the phase II S028 study. In the S028 study the hazard ratio (HR) for OS in high-risk (IPI ≥ 3) DGM1 positive (+) patients who received Enzastaurin/R-CHOP was 0.28 (0.1-0.81) when compared to subjects who received R-CHOP, a benefit favoring Enzastaurin (p=0.018). These data suggest that addition of Enzastaurin to R-CHOP may significantly improve outcome in frontline high-risk DGM1 (+) DLBCL. The ENGINE study was initiated to validate this finding in a prospective study. Study Design and Methods: Adult patients must have untreated CD20+ DLBCL, IPI ≥ 3. Patients are randomized 1:1 to Enzastaurin/R-CHOP or Placebo/R-CHOP for 6 cycles during combination phase. Each subject's treatment assignment will be unblinded after response assessment at the end of the combination phase. Subjects randomized to the investigational arm who have a complete or partial response will have the option to continue in the single agent phase to receive Enzastaurin for up to 2 additional years. The study intends to enroll approximately 235 patients with primary endpoint of OS in DGM1 (+) patients. The study is ongoing with 57 sites open in the US and China. As of 20 July 2019, 128 patients have been randomized. Clinical trial information: NCT03263026. Disclosures Nowakowski: Genentech, Inc.: Research Funding; Curis: Research Funding; Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding; MorphoSys: Consultancy, Research Funding. Brody:Acerta Pharma: Research Funding; Merck: Research Funding; Celldex Therapeutics: Research Funding; Genentech: Research Funding; Oncovir, Inc.: Research Funding; BMS: Research Funding; Kite Pharma: Research Funding. Lue:Kymera Therapeutics: Honoraria; Astex Pharmaceuticals: Honoraria. Luo:Denovo Biopharma LLC: Employment, Equity Ownership, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties. Zhang:Denovo Biopharma LLC: Employment. Han:Denovo Biopharma LLC: Employment. Jivani:TRACON Pharmaceuticals, Inc.: Other: Stock; Denovo Biopharma LLC: Employment. Liu:Denovo Biopharma LLC: Employment. Li:Denovo Biopharma LLC: Employment. Sun:Novartis: Other: Stock; Denovo Biopharma LLC: Employment.

Introduction: Selinexor is a novel, oral selective inhibitor of nuclear export (SINE) which forces nuclear retention and activation of tumor suppressor proteins. Selinexor plus low dose dexamethasone (Sel-dex) was recently approved in the United States based on data from the STORM study wherein, Sel-dex induced an overall response rate (ORR) of 26.2% in patients with penta-exposed, triple-class refractory multiple myeloma. The presence of high-risk cytogenetic abnormalities is known to have a poor prognosis in multiple myeloma, with transient responses. We performed post-hoc analyses to determine the outcomes in patients with relapsed/refractory myeloma treated with Sel-dex based on the baseline cytogenetic abnormalities. Methods: STORM was a phase 2b, open-label study which enrolled patients with relapsed and refractory myeloma. Selinexor 80 mg in combination with dexamethasone 20 mg was administered orally, twice weekly. The primary endpoint was ORR. For this analysis, we pooled 78 patients (48 quad-refractory and 30 penta-refractory) from STORM, part 1 and 122 patients (penta-exposed and triple class refractory) from STORM, part 2 to compare outcomes between high-risk and standard risk cytogenetics groups. High-risk cytogenetics was defined as having at least 1 of the following abnormalities by fluorescence in-situ hybridization (FISH) at baseline: del(17p), t(4;14), t(14;16), and gain(1q) in >5% of screened plasma cells. The FISH analyses were performed at a central laboratory and used to assess cytogenetic risk status. Results: Of the 200 patients, 122 (61%) had high-risk disease (del(17p): 36%, t(4;14): 18%, t(14;16): 5%, and gain(1q): 36%). The ORR in high-risk patients was 20.5% (very good partial response [VGPR)]: 5.7% and partial response [PR]: 14.8%) and the ORR was 29.5 % (complete response [CR]: 2.6%, VGPR: 3.8%, and PR: 23.1%) in standard-risk patients. The clinical benefit rate (CBR) was 35.2% in high-risk patients compared with 38.5% in standard-risk patients. Median duration of clinical benefit (DOCB) was 4.4 and 6.2 months in the high-risk and standard-risk patients respectively. Median progression-free survival (PFS) was 3.8 and 4.2 months and overall survival (OS) was 8.6 and 9.4 months in the high-risk and standard-risk patients, respectively. Efficacy by specific cytogenetic abnormality is presented in Table 1 below (Due to the small sample size (n=11), data for the t(14;16) subgroup are not presented separately). Conclusions: Sel-dex demonstrated a similar CBR in patients with high risk and standard risk disease and preserved clinical benefit in heavily pre-treated patients who had rapidly proliferative disease and high-risk cytogenetics at baseline. The benefit was maintained across cytogenetic risk subgroups with a higher ORR in the t(4;14) and gain(1q) subgroups. The DOR and OS was similar across all the subgroups. These analyses support the use of Sel-dex in patients with high-risk cytogenetics and warrant further evaluation of selinexor in combination with other anti-myeloma therapies in high-risk disease. Disclosures Nooka: GSK: Honoraria, Other: advisory board participation; Amgen: Honoraria, Other: advisory board participation; BMS: Honoraria, Other: advisory board participation; Janssen: Honoraria, Other: advisory board participation; Spectrum pharmaceuticals: Honoraria, Other: advisory board participation; Adaptive technologies: Honoraria, Other: advisory board participation; Celgene: Honoraria, Other: advisory board participation; Takeda: Honoraria, Other: advisory board participation. Yee:Adaptive: Consultancy; Karyopharm: Consultancy; Takeda: Consultancy; Bristol-Myers Squibb: Consultancy, Research Funding; Amgen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding. Huff:Karyopharm, Sanofi, MiDiagnostics: Consultancy; Member of Safety Monitoring Board for Johnson and Johnson: Membership on an entity's Board of Directors or advisory committees. Vogl:Karyopharm Therapeutics: Consultancy; Takeda: Consultancy; Celgene: Consultancy; Amgen: Consultancy; Janssen: Consultancy; Active Biotech: Consultancy. Chari:Novartis Pharmaceuticals: Research Funding; GlaxoSmithKline: Research Funding; Array Biopharma: Research Funding; Karyopharm: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Millennium/Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol-Myers Squibb: Consultancy; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics: Research Funding; Oncoceutics: Research Funding. Moreau:Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria. Dingli:Karyopharm: Research Funding; Rigel: Consultancy; Millenium: Consultancy; alexion: Consultancy; Janssen: Consultancy. Lonial:Genentech: Consultancy; Karyopharm: Consultancy; Takeda: Consultancy, Research Funding; Amgen: Consultancy; BMS: Consultancy; Janssen: Consultancy, Research Funding; GSK: Consultancy; Celgene Corporation: Consultancy, Research Funding. Dimopoulos:Sanofi Oncology: Research Funding. Vij:Takeda: Honoraria, Research Funding; Sanofi: Honoraria; Karyopharm: Honoraria; Janssen: Honoraria; Genentech: Honoraria; Celgene: Honoraria, Research Funding; Bristol-Myers Squibb: Honoraria, Research Funding. Tuchman:Karyopharm: Honoraria; Celgene: Honoraria, Research Funding, Speakers Bureau; Amgen: Research Funding; Merck: Research Funding; Prothena: Research Funding; Roche: Research Funding; Sanofi: Research Funding; Alnylam: Honoraria, Research Funding. Biran:Bristol Meyers Squibb: Research Funding; Amgen: Consultancy, Honoraria, Research Funding; Merck: Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria; Takeda: Consultancy, Honoraria. Siegel:Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Bristol-Myers Squibb Company: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Richardson:Karyopharm: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Research Funding; Oncopeptides: Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding. Liu:Karyopharm Therapeutics: Employment, Equity Ownership. Joshi:Karyopharm Therapeutics: Employment, Equity Ownership. Shah:Karyopharm Therapeutics: Employment, Equity Ownership. Shacham:Karyopharm Therapeutics Inc: Employment, Equity Ownership, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties. Kauffman:Karyopharm Therapeutics Inc: Employment, Equity Ownership, Membership on an entity's Board of Directors or advisory committees. Jagannath:Medicom: Speakers Bureau; Multiple Myeloma Research Foundation: Speakers Bureau; BMS: Consultancy; Celgene: Consultancy; Novartis: Consultancy; Merck: Consultancy.