Citations

Abstract Cytotoxic CD8+ T cells (CTL) are a crucial component of the immune system notable for their ability to eliminate rapidly proliferating malignant cells. However, the T-cell intrinsic factors required for human CTLs to accomplish highly efficient antitumor cytotoxicity are not well defined. By evaluating human CD8+ T cells from responders versus nonresponders to treatment with immune checkpoint inhibitors, we sought to identify key factors associated with effective CTL function. Single-cell RNA-sequencing analysis of peripheral CD8+ T cells from patients treated with anti–PD-1 therapy showed that cells from nonresponders exhibited decreased expression of the cytolytic granule-associated molecule natural killer cell granule protein-7 (NKG7). Functional assays revealed that reduced NKG7 expression altered cytolytic granule number, trafficking, and calcium release, resulting in decreased CD8+ T-cell–mediated killing of tumor cells. Transfection of T cells with NKG7 mRNA was sufficient to improve the tumor-cell killing ability of human T cells isolated from nonresponders and increase their response to anti–PD-1 or anti–PD-L1 therapy in vitro. NKG7 mRNA therapy also improved the antitumor activity of murine tumor antigen–specific CD8+ T cells in an in vivo model of adoptive cell therapy. Finally, we showed that the transcription factor ETS1 played a role in regulating NKG7 expression. Together, our results identify NKG7 as a necessary component for the cytotoxic function of CD8+ T cells and establish NKG7 as a T-cell–intrinsic therapeutic target for enhancing cancer immunotherapy. See related article by Li et al., p. 154.

Background: We hypothesized that the Effective radiation Dose to the Immune Cells (EDIC) in circulating blood is a significant factor for the treatment outcome in patients with locally advanced non-small-cell lung cancer (NSCLC). Methods: This is a secondary study of a phase III trial, NRG/RTOG 0617, in patients with stage III NSCLC treated with radiation-based treatment. The EDIC was computed as equivalent uniform dose to the entire blood based on radiation doses to all blood-containing organs, with consideration of blood flow and fractionation effect. The primary endpoint was overall survival (OS), and the secondary endpoints were progression-free survival (PFS) and local progression-free survival (LPFS). The EDIC–survival relationship was analyzed with consideration of clinical significant factors. Results: A total of 456 patients were eligible. The median EDIC values were 5.6 Gy (range, 2.1–12.2 Gy) and 6.3 Gy (2.1–11.6 Gy) for the low- and high-dose groups, respectively. The EDIC was significantly associated with OS (hazard ratio [HR] = 1.12, p = 0.005) and LPFS (HR = 1.09, p = 0.02) but PFS (HR = 1.05, p = 0.17) after adjustment for tumor dose, gross tumor volume and other factors. OS decreased with an increasing EDIC in a non-linear pattern: the two-year OS decreased first with a slope of 8%/Gy when the EDIC < 6 Gy, remained relatively unchanged when the EDIC was 6–8 Gy, and followed by a further reduction with a slope of 12%/Gy when the EDIC > 8 Gy. Conclusions: The EDIC is a significant independent risk factor for poor OS and LPFS in RTOG 0617 patients with stage III NSCLC, suggesting that radiation dose to circulating immune cells is critical for tumor control. Organ at risk for the immune system should be considered during RT plan.

Background Acute changes in GFR can occur after initiation of interventions targeting progression of CKD. These acute changes complicate the interpretation of long-term treatment effects. Methods To assess the magnitude and consistency of acute effects in randomized clinical trials and explore factors that might affect them, we performed a meta-analysis of 53 randomized clinical trials for CKD progression, enrolling 56,413 participants with at least one estimated GFR measurement by 6 months after randomization. We defined acute treatment effects as the mean difference in GFR slope from baseline to 3 months between randomized groups. We performed univariable and multivariable metaregression to assess the effect of intervention type, disease state, baseline GFR, and albuminuria on the magnitude of acute effects. Results The mean acute effect across all studies was −0.21 ml/min per 1.73 m 2 (95% confidence interval, −0.63 to 0.22) over 3 months, with substantial heterogeneity across interventions (95% coverage interval across studies, −2.50 to +2.08 ml/min per 1.73 m 2 ). We observed negative average acute effects in renin angiotensin system blockade, BP lowering, and sodium-glucose cotransporter 2 inhibitor trials, and positive acute effects in trials of immunosuppressive agents. Larger negative acute effects were observed in trials with a higher mean baseline GFR. Conclusion The magnitude and consistency of acute GFR effects vary across different interventions, and are larger at higher baseline GFR. Understanding the nature and magnitude of acute effects can help inform the optimal design of randomized clinical trials evaluating disease progression in CKD.

AbstractBackgroundClostridioides difficile infection (CDI) is a significant cause of morbidity and mortality in recipients of solid organ transplant (SOT) or hematopoietic stem cell transplant (HSCT). In retrospective single center analyses, severe disease and relapse are common. We undertook an international, prospective cohort study to estimate the response to physician determined antibiotic treatment for CDI in patients with SOT and HSCT.MethodsAdults with a first episode of CDI within the first 2 years of SOT or HSCT were enrolled. Demographics, comorbidities, and medication history were collected, and over 90 days of follow‐up clinical cure, recurrences, and complications were assessed. Logistic regression was used to study associations of baseline predictors of clinical cure and recurrence. Odds ratios (ORs) and 95% confidence intervals (CIs) are cited.ResultsA total of 132 patients, 81 SOT and 51 HSCT (32 allogeneic), were enrolled with a median age of 56 years; 82 (62%) were males and 128 (97%) were hospitalized at enrollment. One hundred and six (80.3%) were diagnosed by DNA assay. CDI occurred at a median of 20 days post‐transplant (interquartile range, IQR: 6–133). One hundred and eight patients (81.8%) were on proton pump inhibitors; 126 patients (95.5%) received antibiotics within the 6 weeks before CDI. The most common initial CDI treatments prescribed, on or shortly before enrollment, were oral vancomycin alone (50%) and metronidazole alone (36%). Eighty‐three percent (95% CI: 76, 89) of patients had clinical cure; 18% (95% CI: 12, 27) of patients had recurrent CDI; global clinical cure occurred in 65.2%. Of the 11 patients who died, two (1.5% of total) were related to CDI. In multivariable logistic regression analyses, the type of initial treatment was associated with clinical cure (p = .009) and recurrence (p = .014). A history of cytomegalovirus (CMV) after transplant was associated with increased risk of recurrence (44% with versus 13% without CMV history; OR: 5.7, 95% CI: 1.5, 21.3; p = .01).ConclusionsAmong adults who develop CDI after SOT or HSCT, despite their immunosuppressed state, the percentage with clinical cure was high and the percentage with recurrence was low. Clinical cure and recurrence varied by type of initial treatment, and CMV viremia/disease was associated with an increased risk of recurrence.

Abstract Introduction: Multiple studies have demonstrated that diffuse large B-cell lymphoma (DLBCL) can be divided into subgroups based on their biology. However, these biological subgroups overlap clinically. While R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) remains the standard of care for treating patients with DLBCL, predicting which patients will not benefit from such therapy is important so that alternative therapy or clinical trials can be considered. Most of the studies stratifying patients select biomarkers first, then explore how these biomarkers can stratify patients based on outcome. We explored the potential of using machine learning to first group patients with DLBCL based on survival, then isolating the biomarkers necessary for predicting these survival subgroups. Methods: RNA was extracted from tissue paraffin blocks from 379 R-CHOP treated patients with de novo DLBCL, and from 247 patients with extranodal DLBCL. A targeted hybrid capture RNA panel of 1408 genes was used for next generation sequencing (NGS). Sequencing was performed using an Illumina NextSeq 550 System platform. Ten million reads per sample in a single run were required, and the read length was 2 × 150 bp. An expression profile was generated from the sequencing coverage profile of each individual sample using Cufflinks. A machine learning system was developed to classify patients into four groups based on their overall survival. This machine learning approach based on Naïve Bayesian algorithm was also used to discover the relevant subset of genes with which to classify patients into each of the four survival groups. To eliminate the underflow problem commonly associated with the standard Naïve Bayesian classifiers, we applied Geometric Mean Naïve Bayesian (GMNB) as the classifier to predict the survival group for each patient. Results: Using machine learning, patients were first divided into two groups: short survival (S) and long survival (L). To refine this model, we used the same approach and divided the patients in each group into two subgroups, generating four groups: long survival in the long group (LL), short survival in the long group (LS), long survival in the short group (SL), and short survival in the short group (SS). The hazard ratio for this model was 0.174 (confidence interval: 0.120-0.251), and P-value &lt;0.0001. After defining these four groups, a machine learning algorithm was used to discover the biomarkers from the expression data of the 1408 genes from NGS data. To reduce the effects of noise and avoid overfitting, we employed a 12-step cross validation to obtain a robust measure. For an individual gene, a generalized Naïve Bayesian classifier was constructed on the training of one of the 12 subsets and tested on the other 11 testing subsets. This allowed us to limit the prediction process to 60 genes for each separation step. Using the selected biomarkers, we classified the patients in the original set (379 patients) into LL, LS, SL, and SS groups and then evaluated the survival pattern of these groups. As shown in Fig. 1A, the selected biomarkers predicted survival as expected in the overall survival groups prior to biomarker selection. For additional validation of the system, we used the selected biomarkers to classify a completely new set of 247 samples of patients with extranodal DLBCL. As shown in Fig. 1B, these selected biomarkers successfully predicted the overall survival in this group of patients with an HR of 0.530 (confidence interval: 0.234-1.197, P=0.005). This classification correlated with cell of origin classification, TP53 mutation status, MYC expression, and IRF4 expression. However, in a multivariate analysis, only TP53 mutation was independent in predicting prognosis (P=0.005) and age (below or over 60) (P=0.01) along with the survival grouping (P&lt;0.000001). Conclusions: Using a novel machine learning approach with the expression levels of 180 genes, we developed a model that can reliably stratify patients with DLBCL treated with R-CHOP into four survival subgroups. This model can be used to identify patients who may not respond well to R-CHOP to be considered for alternative therapy and clinical trials. Figure 1 Figure 1. Disclosures Hsi: AbbVie Inc, Eli Lilly: Research Funding. Ferreri: Ospedale San Raffaele srl: Patents & Royalties; BMS: Research Funding; Pfizer: Research Funding; Beigene: Research Funding; Hutchison Medipharma: Research Funding; Amgen: Research Funding; Genmab: Research Funding; ADC Therapeutics: Research Funding; Gilead: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Membership on an entity's Board of Directors or advisory committees, Research Funding; PletixaPharm: Membership on an entity's Board of Directors or advisory committees; x Incyte: Membership on an entity's Board of Directors or advisory committees; Adienne: Membership on an entity's Board of Directors or advisory committees. Piris: Millenium/Takeda, EUSA, Jansen, NanoString, Kyowa Kirin, Gilead and Celgene.: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Winter: BMS: Other: Husband: Data and Safety Monitoring Board; Actinium Pharma: Consultancy; Janssen: Other: Husband: Consultancy; Agios: Other: Husband: Consultancy; Gilead: Other: Husband: Consultancy; Epizyme: Other: Husband: Data and Safety Monitoring Board; Ariad/Takeda: Other: Husband: Data and Safety Monitoring Board; Merck: Consultancy, Honoraria, Research Funding; Novartis: Other: Husband: Consultancy, Data and Safety Monitoring Board; Karyopharm (Curio Science): Honoraria.

Abstract BACKGROUND: E4402 was a randomized phase III study comparing two different rituximab dosing strategies for patients with previously untreated, low tumor burden follicular lymphoma (FL). The primary endpoint was time to treatment failure. The initial publication (Kahl, JCO 2014) demonstrated that a retreatment strategy utilized less drug and produced comparable time to treatment failure compared to a maintenance strategy. Here we provide long term follow up results, focusing on response duration, time to first cytotoxic therapy, overall survival, and risk of histologic transformation. METHODS: Eligible patients had untreated, low tumor burden (GELF criteria) FL. Patients received R 375 mg/m 2 weekly x 4 and responders were randomized to maintenance rituximab (MR) (single dose R q 3 months) or retreatment rituximab (RR) (R weekly x 4 doses at disease progression). Each strategy was continued until treatment failure. The primary endpoint, time to treatment failure, was defined as progression within 6 months of last R, no response to R retreatment, initiation of alternative therapy, or inability to complete protocol therapy. Secondary endpoints included time to first cytotoxic therapy, quality of life and safety. The ECOG Data Monitoring Committee recommended release of study results at a planned interim analysis in September 2011 and patients and providers were notified of results. Time to treatment failure data collection halted with release of the results but limited data collection on time to first cytotoxic therapy, response duration, and risk of histologic transformation continued. INITITIAL RESULTS: From 11/03 to 9/08, 384 patients with FL were enrolled. Complete or partial response was achieved in 289 patients (71%), who were then randomized to MR (n=146) or RR (n=143). Demographic features were similar in the two arms: median age 59 years; ECOG PS 0-1 in all patients, and FLIPI low-risk (14.9 vs. 16.4%), intermediate-risk (46.3 vs. 42.9%) and high-risk (38.8 vs. 40.7%) for MR vs. RR, respectively. At initial publication, with a median follow-up of 3.8 years, the time to treatment failure was 3.9 years for MR vs. 3.6 years for RR (p=NS). LONG TERM FOLLOW UP RESULTS: Immunoglobulin levels and risks for serious infections/late complications in MR patients will be updated at the annual meeting. For the endpoint of time to first cytotoxic therapy, the median follow up is 8.7 years. At 7 years, 83% of MR and 63% of RR remained free from first cytotoxic therapy (HR 2.37; 95% CI 1.50 - 3.76) [Figure 1]. For the endpoint of response duration, the median follow up is 12.1 years. At 10 years, 66% of the MR patients remained in their 1 st remission compared to 30% of the RR patients who remained in their 1 st remission [Figure 2]. The median response duration for RR patients receiving a single 4-week course of rituximab was 3.25 years. There was no difference in the overall survival at 10 years, 84% for MR vs. 83% for RR. There was a trend towards a lower risk of histologic transformation for patients receiving MR (n = 4) compared to RR (n = 11) (p = 0.11). CONCLUSIONS: With long term follow up, the RESORT data indicates that in previously untreated, low tumor burden, follicular lymphoma, MR was superior to RR for delaying time to first cytotoxic therapy and for response duration, with a trend towards reducing the risk of histologic transformation. MR did not improve the overall survival. The original publication concluded that the time to treatment failure is similar between the two dosing strategies. Due to study design, time to treatment failure could not be analyzed in this long term follow up analysis. Compared to the historical benchmark of 3 years median time to first cytotoxic therapy when watch and wait is utilized, single agent rituximab, administered by either dosing strategy, was highly effective at delaying the time to first cytotoxic therapy. Figure 1 Figure 1. Disclosures Kahl: AbbVie, Adaptive, ADCT, AstraZeneca, Bayer, BeiGene, Bristol-Myers Squibb, Celgene, Genentech, Incyte, Janssen, Karyopharm, Kite, MEI, Pharmacyclics, Roche, TG Therapeutics, and Teva: Consultancy; AbbVie, Acerta, ADCT, AstraZeneca, BeiGene, Genentech: Research Funding. Habermann: Incyte: Other: Scientific Advisory Board; Tess Therapeutics: Other: Data Monitoring Committee; Seagen: Other: Data Monitoring Committee; Morphosys: Other: Scientific Advisory Board; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Schuster: Abbvie: Consultancy, Research Funding; Acerta Pharma: Consultancy; AstraZeneca: Consultancy; Adaptive Biotechnologies: Research Funding; BeiGene: Consultancy; Celgene: Consultancy, Honoraria, Research Funding; DTRM: Research Funding; Genetech: Consultancy, Research Funding; Roche: Consultancy, Research Funding; Incyte: Research Funding; Juno Theraputics: Consultancy, Research Funding; Loxo Oncology: Consultancy; Merck: Research Funding; Nordic Nanovector: Consultancy; Novartis: Consultancy, Honoraria, Patents & Royalties, Research Funding; Pharmaclcyclics: Research Funding; Tessa Theraputics: Consultancy; TG Theraputics: Research Funding. Fenske: TG Therapeutics: Consultancy, Speakers Bureau; Servier Pharmaceuticals: Consultancy; Seattle Genetics: Speakers Bureau; Sanofi: Speakers Bureau; Pharmacyclics: Consultancy; MorphoSys: Consultancy; Kite (Gilead): Speakers Bureau; KaryoPharm: Consultancy; CSL Therapeutics: Consultancy; Bristol-Myers Squibb: Speakers Bureau; Biogen: Consultancy; Beigene: Consultancy; AstraZeneca: Speakers Bureau; ADC Therapeutics: Consultancy; Adaptive Biotechnologies: Consultancy; AbbVie: Consultancy.

Abstract Background: Real-world data (RWD) derived from electronic health records are becoming increasingly important for deriving insights from clinical practice to complement findings from clinical trials. Response to treatment in multiple myeloma (MM) is assessed using the International Myeloma Working Group (IMWG) response criteria based on MM-specific laboratory measures (i.e. monoclonal [M] protein in serum and urine, free light chain [FLC] levels), as well as radiological images and bone marrow (BM) investigations when appropriate. As healthcare providers do not routinely collect all the information required by the IMWG response criteria, RWD data are often incomplete. Here, we present a derived real-world response (dR) algorithm based on IMWG criteria that accounts for lab measures routinely collected in the clinical care of MM patients and evaluate its ability to accurately assess response to treatment using clinical trial patient-level data. Methods: Treatment response is assessed using a rule-based algorithm integrating longitudinal laboratory measures routinely captured in RWD (Xu et al. Pharmacoepidemiol Drug Saf 2021) as e.g. by the Flatiron MM database. The rules are based on 'relaxed' IMWG criteria, which entail exclusion of BM biopsies data and of imaging results, and reduction in either serum or urine M protein levels (rather than both) to assign partial response. This algorithm was applied to patient-level clinical trial data from the Bellini trial (Kumar et al. Lancet Oncol 2020) with response assignment made by an independent review committee (IRC) in the trial used as the 'ground truth'. Agreement between the IRC's and algorithm's assignments of response was estimated using Cohen's Kappa statistic. Differences in overall response rate (ORR) between treatment and placebo arms were calculated using stratified Cochran-Mantel-Haenszel tests based on strata at randomization with number of prior lines of therapy and previous proteasome inhibitor treatment status variables used for stratification. Results: The Bellini trial is a Phase 3 clinical trial with 2:1 design enrolling 194 and 97 patients in its treatment and placebo arms, respectively (291 patients in total). Regular assessments of M protein and FLC levels were performed (median 13 or 14 measurements), providing detailed trajectory of patient response (Table 1). Comparison between the IRC's and algorithm's assignments of responders classified as Partial Response or better (PR+) resulted in almost perfect agreement with Cohen's Kappa 0.82 (274/291 assignments in agreement). It is worth noting that the Cohen's Kappa between the IRC's and Investigator's assessments is 0.85, indicating that the algorithm's error is within some expected uncertainty. Due to the exclusion of BM information, agreement decreased to 0.56 when depths of response were considered separately as opposed to grouping PR+ patients together with most cases being overestimated as complete response (CR) or stringent CR. In assessing treatment effect, differences in ORR between the intervention and placebo arms in the trial based on IRC's assessment could be accurately recapitulated (68% [66/97] vs 71% [69/97] responders in the placebo arm and 82% [159/194] vs 87% [169/194] in the intervention arm for IRC's and algorithm's assessment, respectively). Based on these results, algorithm response assignment led to consistent conclusions about treatment efficacy in the Bellini trial. Implementation of criteria to characterize very good partial response or better (VGPR+) also led to a conclusion consistent with the Bellini trial IRC's assessment, while implementation of criteria of CR+ resulted in estimates of treatment efficacy that were higher, but directionally consonant (Table 2). Conclusions: We present a fully automated rule-based algorithm for response assessment in MM relying on longitudinal lab measurements during treatment. The algorithm uses 'relaxed' IMWG criteria to account for routine clinical practice settings and demonstrates very high agreement with the assessment of trained clinicians, while reliably reproducing the efficacy analysis in the Bellini trial when ORR and VGPR+ are considered. We envision that, if the concordance observed here is confirmed in other independent cohorts, this algorithm can be used in the assessment of response in RWD and can facilitate response assessment when suboptimal amounts of data are available. Figure 1 Figure 1. Disclosures Tyanova: F. Hoffmann-La Roche: Current Employment. Xu: F. Hoffmann-La Roche AG: Current Employment. Williamson: Amgen: Current equity holder in publicly-traded company; Genentech: Current Employment, Current equity holder in publicly-traded company. Rocci: Novartis: Other: Wife is and employee of Novartis and holds Novartis stocks; Sanofi Aventis: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees, Other: Received travel sponsorship ; Janssen-Cilag Ltd: Honoraria, Speakers Bureau; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Honoraria, Other: Received travel sponsorship ; Owner of 100% stocks of Harlock Healthcare Consulting Ltd (UK privately-held company currently not active).: Current holder of individual stocks in a privately-held company; Roche: Current equity holder in publicly-traded company; Takeda: Consultancy, Honoraria, Other: Received travel sponsorship , Speakers Bureau; Sanofi: Consultancy; NHS: Ended employment in the past 24 months; F. Hoffmann-La Roche Ltd.: Current Employment; AbbVie: Other: Received travel sponsorship . Hong: Imago BioSciences: Current Employment; Genentech, Inc.: Ended employment in the past 24 months; Stock options in Imago BioSciences: Current equity holder in publicly-traded company. Jin: Genentech Inc: Current Employment; Roche: Current equity holder in publicly-traded company. Karve: AbbVie: Current Employment, Current equity holder in publicly-traded company. Roose: Flatiron Health, Inc.: Current Employment; Roche: Current equity holder in publicly-traded company. Sawas: Seattle Genetics: Honoraria; Affimed: Research Funding; Roche: Current equity holder in publicly-traded company; Flat Iron Health: Current Employment; Acrotech: Honoraria; Daiichi-Sankyo: Speakers Bureau; Seattle Genetics: Speakers Bureau; Gilead: Speakers Bureau. Kumar: Antengene: Consultancy, Honoraria; Novartis: Research Funding; Adaptive: 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; Tenebio: Research Funding; Beigene: Consultancy; Oncopeptides: Consultancy; KITE: Consultancy, 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; Merck: Research Funding; Bluebird Bio: Consultancy; Roche-Genentech: Consultancy, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding.

Abstract Purpose: The aim of the study was to determine safety, antitumor activity, and pharmacodynamic profile of mogamulizumab, an anti-CCR4 monoclonal antibody targeting effector regulatory T cells (Treg) in combination with the checkpoint inhibitor nivolumab in patients with locally advanced or metastatic solid tumors. Patients and Methods: This was a multicenter, dose-finding (phase I), and dose expansion (phase II) study (NCT02705105) in patients with locally advanced or metastatic solid tumors. There were no dose-limiting toxicities in phase I with mogamulizumab 1 mg/kg every week for cycle 1 followed by 1 mg/kg every 2 weeks plus nivolumab 240 mg every 2 weeks intravenously, and cohort expansion occurred at this dose level. Results: All 114 patients treated with mogamulizumab 1 mg/kg plus nivolumab 240 mg in phases I (n = 4) and II (n = 110) were assessed for safety and efficacy. Mogamulizumab plus nivolumab showed acceptable safety and tolerability. Objective response rate was 10.5% [95% confidence interval (CI), 5.6–17.7; 3 complete and 9 partial responses]. Disease control rate was 36.8%. Median duration of response was 14.4 months. Median progression-free survival was 2.6 (95% CI, 2.3–3.1) months, and median overall survival was 9.5 (95% CI, 5.9–13.5) months. Conclusions: Combination of mogamulizumab with nivolumab for treatment of patients with locally advanced or metastatic solid tumors did not result in enhanced efficacy. Tolerability of mogamulizumab 1 mg/kg plus nivolumab 240 mg was acceptable.

Abstract Introduction/Objective The first human case of SARS-CoV-2 infection was reported in December of 2019. By late February 2020 evidence of community transmission in the United States was documented and soon it developed into a global pandemic. On March 11, 2020, the Mayo Clinic Arizona’s Microbiology Laboratory introduced a PCR test for diagnosis of SARS-CoV-2 infection. Even as testing demands exponentially increased, the hospital relied on timely results to assist in directing patient care. In order to provide timely results in the face of continuously emerging supply chain threats, we needed to increase our capacity for SARS-CoV-2 PCR testing by reducing technologist time and reagent waste, without impacting turnaround time or other quality metrics such as repeat rate. Methods/Case Report Technologist time was measured in hands-on time per specimen. Reagent use was defined by the volume of lysis buffer (the primary rate limiting reagent due to supply chain constraints) used per sample. Turnaround time was measured from receipt in lab until result verification and the rerun rate is the percent of specimens with an internal control (IC) failure, an indicator of specimen or process quality. Unnecessary steps were determined using a process map, spaghetti diagram and waste walk. Improvements included simplifying steps, changes to protocols and lab design. Impacts of the improvements were measured for 5 months post-implementation. Results (if a Case Study enter NA) Process improvements reduced technologist time per sample by 30.8 seconds and decreased critical reagent needs by 23.6%. Improvements led to $104,000 in just reagent savings over 5 months. Turnaround time and rerun rates were not negatively impacted. Conclusion By creating the leanest possible workflow, we were able to meet the demands for accurate and timely SARS-CoV-2 results. In future workflows, we will continue to implement lean processes and ensure the most efficient use of limited resources.

In the era of “fast-track’ liver transplantation (LT), neuromuscular blockade (NMB) antagonists such as neostigmine or sugammadex are used to achieve the restoration of neuromuscular function. While sugammadex reverses NMB faster than neostigmine, it has been shown to prolong prothrombin time (PT) and activated thromboplastin time (aPTT). However, this agent’s impact on coagulation during LT is not understood. We compare bleeding risk associated with sugammadex versus neostigmine during liver transplantation. This is a single-center, retrospective review of LT patients who received NMB antagonists intraoperatively between 01/01/2015 to 05/31/2018 at Mayo Clinic in Florida. The primary outcomes were postoperative day (POD) 0-1 bleeding events and POD 0 values of aPTT and INR. Total 241 patients were included, with 135 patients in the neostigmine group (NG) and 106 in the sugammadex group (SG). POD 0-1 postoperative bleeding requiring transfusion occurred in 20% of NG versus 10.4% in SG. POD 0-1 re-operation for bleeding occurred in 1.5% in NG vs. 0% in SG. POD 0 mean INR was 2.0±0.4 in both groups. POD 0 mean aPTT was 45.5±7.9 in NG vs. 49.3±9.0 in SG. Our retrospective study suggests that sugammadex is not associated with an increased risk of bleeding compared to neostigmine use.