Citations

AbstractWound biofilms must be identified to target disruption and bacterial eradication but are challenging to detect with standard clinical assessment. This study tested whether bacterial fluorescence imaging could detect porphyrin‐producing bacteria within a biofilm using well‐established in vivo models. Mouse wounds were inoculated on Day 0 with planktonic bacteria (n = 39, porphyrin‐producing and non‐porphyrin‐producing species, 107 colony forming units (CFU)/wound) or with polymicrobial biofilms (n = 16, 3 biofilms per mouse, each with 1:1:1 parts Staphylococcus aureus/Escherichia coli/Enterobacter cloacae, 107 CFU/biofilm) that were grown in vitro. Mouse wounds inoculated with biofilm underwent fluorescence imaging up to Day 4 or 5. Wounds were then excised and sent for microbiological analysis. Bacteria‐matrix interaction was assessed with scanning electron microscopy (SEM) and histopathology. A total of 48 hours after inoculation with planktonic bacteria or biofilm, red fluorescence was readily detected in wounds; red fluorescence intensified up to Day 4. Red fluorescence from biofilms persisted in excised wound tissue post‐wash. SEM and histopathology confirmed bacteria‐matrix interaction. This pre‐clinical study is the first to demonstrate the fluorescence detection of bacterial biofilm in vivo using a point‐of‐care wound imaging device. These findings have implications for clinicians targeting biofilm and may facilitate improved visualisation and removal of biofilms.

BACKGROUND Type 1 diabetes (T1D) is a risk factor for dementia and structural brain changes. It remains to be determined whether transient insulin deprivation that frequently occurs in insulin-treated individuals with T1D alters brain function. METHODS We therefore performed functional and structural magnetic resonance imaging, magnetic resonance spectroscopy, and neuropsychological testing at baseline and following 5.4 ± 0.6 hours of insulin deprivation in 14 individuals with T1D and compared results with those from 14 age-, sex-, and BMI-matched nondiabetic (ND) participants with no interventions. RESULTS Insulin deprivation in T1D increased blood glucose, and β-hydroxybutyrate, while reducing bicarbonate levels. Participants with T1D showed lower baseline brain N-acetyl aspartate and myo-inositol levels but higher cortical fractional anisotropy, suggesting unhealthy neurons and brain microstructure. Although cognitive functions did not differ between participants with T1D and ND participants at baseline, significant changes in fine motor speed as well as attention and short-term memory occurred following insulin deprivation in participants with T1D. Insulin deprivation also reduced brain adenosine triphosphate levels and altered the phosphocreatine/adenosine triphosphate ratio. Baseline differences in functional connectivity in brain regions between participants with T1D and ND participants were noted, and on insulin deprivation further alterations in functional connectivity between regions, especially cortical and hippocampus-caudate regions, were observed. These alterations in functional connectivity correlated to brain metabolites and to changes in cognition. CONCLUSION Transient insulin deprivation therefore caused alterations in executive aspects of cognitive function concurrent with functional connectivity between memory regions and the sensory cortex. These findings have important clinical implications, as many patients with T1D inadvertently have periods of transient insulin deprivation. TRIAL REGISTRATION ClinicalTrials.gov NCT03392441. FUNDING Clinical and Translational Science Award (UL1 TR002377) from the National Center for Advancing Translational Science; NIH grants (R21 AG60139 and R01 AG62859); the Mayo Foundation.

Abstract Funding Acknowledgements Type of funding sources: None. Aims. Combined cardiopulmonary exercise test (CPET) and exercise stress echocardiography (ESE) provides a non-invasive tool to study cardiopulmonary pathophysiology. We analyzed how cardiac functional reserve during exercise relates to peak oxygen consumption (VO2). Methods and Results. We performed a symptom-limited graded ramp bicycle CPET-ESE in 30 healthy controls and 357 patients: 113 at risk of developing heart failure (American College of Cardiology/American Heart Association HF Stages A-B) and 244 in HF Stage C with preserved (HFpEF, n = 101) or reduced ejection fraction (HFrEF, n = 143). Peak VO2 significantly decreased from controls to Stage A-B and Stage C (Table 1). A multivariable regression model to predict peak VO2 revealed peak left ventricular systolic annulus tissue velocity (S"), peak TAPSE/PAPs (tricuspid annular plane systolic excursion/systolic pulmonary artery pressure) and low-load left atrial reservoir strain/E/e’ were independent predictors, in addition to peak heart rate, stroke volume and workload (adjusted R²=0.76, p < 0.0001). The model was successfully tested in subjects with atrial fibrillation (n = 49), and with (n = 224) and without (n = 163) beta-blockers (all p < 0.01). Peak S’ showed the highest accuracy in predicting peak VO2 < 10 mL/kg/min (cut-point ≤ 7.5 cm/s; AUC = 0.92, p < 0.0001) and peak VO2 > 20 mL/kg/min (cut-point > 12.5 cm/s; AUC = 0.84, p < 0.0001) in comparison to the other cardiac variables of the model (p < 0.05). Conclusions. A model incorporating different measures of cardiac mechanics is strongly related to peak aerobic capacity and may help in identifying different causes of effort intolerance from HF Stage A to C. Table 1 Variable Overall population (n = 387) Controls (n = 30) Stage A-B (n = 113) Stage C-HFpEF (n = 101) Stage c-HFrEF (n = 143) p-value Age, years 68.9 ± 11.1 67.1 ± 10.6 67.7 ± 10.4 70.5 ± 10.1 68.5 ± 11 0.1 Male, n (%) 247 (64) 18 (60) 70 (62) 57 (56) 102 (71) 0.1 VO2 @peak, mL/min/kg 15.7 (12.1-19.6) 23 (21.7- 29.7) 18 (15.4- 20.7)* 13.6 (11.8- 16.8)*† 14.2 (10.7- 17.5)*† <0.0001 Workload @peak, W 90 (65-120) 130 (115-195) 110 (84-130)* 70 (55-100)*† 80 (60-110)*† <0.0001 Heart rate @peak, bpm 123 ± 22 142 ± 12 130 ± 20* 115 ± 17*† 119 ± 23*† <0.0001 Stroke volume @peak, mL 83 (71-99) 98 (85-114) 86 (76-107) 83 (74-97)* 75 (63-95)*† <0.0001 Average S" @peak, cm/s 11.2 ± 3.8 17.1 ± 3.9 13.3 ± 2.9* 10.6 ± 2.5*† 8.7 ± 2.7*†‡ <0.0001 TAPSE/PAPs @peak, mm/mmHg 0.75 (0.46-0.97) 1.05 (0.93- 1.16) 0.81 (0.52- 0.91)* 0.52 (0.38- 0.83)*† 0.58 (0.41- 0.89)*† <0.0001 Left atrial reservoir strain/E/e" @low-load, % 2.25 (1.17-5.04) 6.23 (4.45-6.77) 4.34 (3.89- 5.58)* 2.23 (1.31- 2.86)*† 1.91 (1.07-2.44)*†‡ <0.0001 * p < 0.01 vs Controls; † p < 0.01 vs Stage A-B; ‡ p < 0.01 vs Stage C-HFpEF. PAPs systolic pulmonary artery pressure; TAPSE: tricuspid annular plane systolic excursion; VO2: oxygen consumption. Abstract Figure 1

Abstract Funding Acknowledgements Type of funding sources: None. Aims. We tested the prognostic role of a risk score including bio-humoral evaluation, cardiopulmonary-echocardiographic stress (CPET-ESE) and lung ultrasound, in patients with heart failure (HF) with reduced and preserved ejection fraction (HFrEF and HFpEF), and subjects at risk of developing HF (American College of Cardiology/American Heart Association Stages A and B). Methods and results. We evaluated 318 subjects: 94 in Stages A-B, 194 in Stage C (85 HFpEF and 109 HFrEF), and 30 age and sex-matched controls (Stage 0). During a median follow-up of 18.5 months, we reported 40 urgent HF visits, 31 HF hospitalisations and 10 cardiovascular deaths. Cox proportional-hazards regression for predicting adverse events identified five independent predictors and each was assigned a number of points proportional to its regression coefficient: Δstress-rest B-lines >10 (3 points), peak oxygen consumption <16 mL/kg/min (2 points), minute ventilation/carbon dioxide production slope ≥36 (2 points), peak systolic pulmonary artery pressure ≥50 mmHg (1 point) and resting N-terminal pro-brain natriuretic peptide (NT-proBNP) >900 pg/mL (1 point). We defined three risk categories: low-risk (<3 points), intermediate-risk (3-6 points), and high-risk (>6 points). The event-free survival probability for these three groups were 93%, 52% and 20%, respectively. Hazard Ratio was 4.55 for each risk category upgrade (95% confidence interval [CI], 3.44-5.93). The area-under-curve for the scoring system to predict events was 0.92 (95% CI 0.88-0.96). Conclusion. A multiparametric risk score including indices of exercise-induced pulmonary congestion, markers of cardiopulmonary dysfunction and NT-proBNP identifies patients at increased risk for HF events across the HF spectrum. Table 1 Variable EPYC score EPYC score <3 (low risk) n = 217 EPYC score 3-6 (intermediate risk) n = 70 EPYC score >6 (high risk) n = 31 p-value (between risk categories) Event-free (n = 244) 0 (0 - 2) 210 (97) 32 (46) 2 (6) <0.0001 With events (n = 74) 6 (4 - 9) 7 (3) 38 (54) 29 (94) <0.0001 p-value (event-free vs with events) <0.0001 <0.0001 <0.0001 <0.0001 Stage 0-Controls (n = 30) 0 (0 - 1) 30 0 0 <0.0001 Stages A-B (n = 94) 1 (0 - 2) 85 (45) 6 (9) 3 (10) <0.0001 Stage C-HFpEF (n = 85) 3 (1 - 6)*† 46 (25) 29 (41) 10 (32) <0.0001 Stage C-HFrEF (n = 109) 4 (2 - 7)*† 56 (30) 35 (50) 18 (58) <0.0001 p-value (between HF Stages) <0.0001 <0.0001 <0.0001 <0.0001 Values are mean ± standard deviation, n (%), or median [25th quartile, 75th quartile]. * p < 0.01 vs Stage 0-Controls; † p < 0.01 vs Stages A-B. Abstract Figure 1

Background: The association between obesity and prognosis in HER2-positive early breast cancer remains unclear, with limited data available. This study aimed to determine the impact of body mass index (BMI) at baseline and weight change after 2 years on outcomes of patients with HER2-positive early breast cancer. Methods: ALTTO was a randomized phase III trial in patients with HER2-positive early breast cancer. BMI was collected at randomization and 2 years after. WHO BMI categories were used: underweight, <18.5 kg/m2; normal weight, 18.5 to <25 kg/m2; overweight, ≥25 to <30 kg/m2; and obese ≥30 kg/m2. A weight change from baseline of ≥5.0% and ≤5.0% was categorized as weight gain and weight loss. The impact of BMI at randomization and of weight change on disease-free survival (DFS), distant disease-free survival (DDFS), and overall survival (OS) were investigated with multivariate analyses, adjusting for baseline patients and tumor characteristics. Results: A total of 8,381 patients were included: 187 (2.2%), 3,797 (45.3%), 2,690 (32.1%), and 1,707 (20.4%) were underweight, normal weight, overweight, and obese at baseline, respectively. Compared with normal weight, being obese at randomization was associated with a significantly worse DDFS (adjusted hazard ratio [aHR], 1.25; 95% CI, 1.04–1.50) and OS (aHR, 1.27; 95% CI, 1.01–1.60), but no significant difference in DFS (aHR, 1.14; 95% CI, 0.97–1.32). Weight loss ≥5.0% at 2 years after randomization was associated with significantly poorer DFS (aHR, 1.34; 95% CI, 1.05–1.71), DDFS (aHR, 1.46; 95% CI, 1.07–1.98), and OS (aHR, 1.83; 95% CI, 1.18–2.84). Hormone receptor and menopausal status but not anti-HER2 treatment type influenced outcomes. Toxicities were more frequent in obese patients. Conclusions: In patients with HER2-positive early breast cancer, obesity at baseline is a poor prognostic factor. Weight loss during treatment and follow-up negatively impacts clinical outcomes. Dietary counseling should be part of survivorship care programs.

Introduction Historically, nearly all relapses in classical Hodgkin lymphoma (cHL) occur within the first 5 years (Radford et al, BMJ 1997). In the phase 3 ECHELON-1 study (NCT01712490), treatment with brentuximab vedotin, doxorubicin, vinblastine, and dacarbazine (A+AVD) significantly improved modified progression-free survival (PFS) in patients (pts) with newly-diagnosed Stage III/IV cHL compared with doxorubicin, bleomycin, vinblastine, and dacarbazine (ABVD) (Connors et al, NEJM 2018). 3- and 4-year follow-up (Straus et al, Blood 2020; Bartlett et al, Blood 2019) reported durable PFS benefit with A+AVD vs ABVD in the intent-to-treat (ITT) population that was consistent across most key pt subgroups, irrespective of interim positron emission tomography (PET) scan status, disease stage, and baseline disease risk factor score. We report updated efficacy and safety results for pts in the ECHELON-1 study after a median follow-up of 55.6 months. Methods In this evaluation of longer follow-up, an exploratory analysis of PFS (time from randomization to relapse, progression, or death from any cause) per investigator (INV) was conducted, with a cutoff date of May 18, 2020. Pts with previously untreated Stage III or IV cHL were randomized 1:1 to receive up to six cycles of A+AVD (n=664) or ABVD (n=670) intravenously on days 1 and 15 of a 28-day cycle. An interim PET scan after cycle 2 (PET2) was required. Resolution and improvement (defined as improvement by ≥1 grade from worst grade as of the latest assessment) of peripheral neuropathy (PN) in pts with ongoing symptoms at the end of treatment (EoT) were monitored during the extended follow-up period. The rate of secondary malignancies, and the incidence and outcomes of pregnancies among pts and their partners were also assessed. Results With extended follow-up (median 55.6 months; 95% CI 55.2-56.7) estimated 5-year PFS rates were 82.0% (95% CI 78.7-84.8) for A+AVD and 75.2% (95% CI 71.5-78.4) for ABVD. Overall, PFS per INV favored A+AVD over ABVD (HR 0.691; 95% CI 0.543-0.880; p=0.003) (Table). Exploratory subgroup analyses by PET2 status and age demonstrated PFS benefits regardless of PET2 status (Table); estimated 5-year PFS per INV with A+AVD vs ABVD in the ITT population was 84.7% vs 78.8% in PET2-negative pts (HR 0.676; 95% CI 0.512-0.892; p=0.005), and 60.6% vs 45.9% in PET2-positive pts (HR 0.703; 95% CI 0.393-1.256; p=0.230). PFS benefit with A+AVD over ABVD was also independent of the number of International Prognostic Factors Project (IPFP) risk factors (Figure). After a median follow-up of almost 5-years, 84% (370/442) and 86% (245/286) of pts with treatment-emergent PN reported complete resolution or improvement of symptoms in the A+AVD and ABVD arms, respectively. Median time to complete resolution of PN events that were ongoing at EoT was 30 weeks (range 0-262) in the A+AVD arm and 16 weeks (range 0-267) in the ABVD arm; median time to improvement was 49 weeks (range 8-270) and 12 weeks (range 2-70) , respectively. Of the 132 (30%) pts with ongoing PN in the A+AVD arm, 77 (17%), 39 (9%), 15 (3%) and 1 (&lt;1%) experienced a maximum severity of grade 1, 2, 3 or 4, respectively. In the ABVD arm, PN was ongoing in 61 pts (21%); maximum severity was grade 1, 2, 3 or 4 in 40 (14%), 17 (6%), 4 (1%) and 0 pts, respectively. A total of 124 pregnancies were reported among pts and their partners (40 female and 30 male pts in the A+AVD arm; 26 female and 28 male pts in the ABVD arm). The proportion of live births was similar between arms for female pts (26/40 in the A+AVD arm and 17/26 in the ABVD arm, 65% in each arm) and for male pts' partners (19/30 [63%] in the A+AVD arm and 20/28 [71%] in the ABVD arm). No stillbirths were recorded. Additional follow-up at an estimated median of ~5 years and secondary malignancy data will be presented. Conclusions After a median follow-up of 55.6 months, A+AVD continues to demonstrate a robust and durable treatment benefit independent of disease stage, risk factor score, and PET2 status. In addition, compared with ABVD, treatment adaptation by interim PET2 status is not required and bleomycin exposure is avoided. The sustained PFS benefit with A+AVD is coupled with a manageable safety profile with symptoms of PN improving or resolving over time and similar pregnancy rates in both treatment arms. The benefits observed with A+AVD at this important milestone suggest that A+AVD is an attractive treatment option for all pts with previously untreated Stage III or IV cHL. Disclosures Straus: Karyopharm Therapeutics: Membership on an entity's Board of Directors or advisory committees; Imedex, Inc.: Speakers Bureau; Targeted Oncology: Consultancy, Speakers Bureau; NY Lymphoma Rounds: Consultancy; Takeda Pharmaceuticals: Research Funding, Speakers Bureau; OncLive: Speakers Bureau; Elsevier: Membership on an entity's Board of Directors or advisory committees, Other: CME writer; ASH: Other: Conference in December 2019 on HL to other physicians during ASH; Seattle Genetics: Consultancy, Membership on an entity's Board of Directors or advisory committees. Connors:Seattle Genetics: Other: Sponsorship to educational presentations; Takeda: Other: Sponsorship to educational presentations. Illés:Takeda, Seattle Genetics: Research Funding; Novartis, Janssen, Pfizer, Roche;: Other: Travel, Accommodations, Expenses; Janssen, Celgene, Takeda, Novartis Pharma SAS, Pfizer Pharmaceuticals Israel, Roche;: Consultancy, Honoraria; Celgene, Janssen, Novartis,Roche, Takeda: Consultancy. Lech-Marańda:Roche, Novartis, Takeda, Janssen-Cilag, Amgen, Gilead, AbbVie, Sanofi: Consultancy; Roche, Amgen, Gilead: Speakers Bureau. Feldman:KITE: Honoraria, Speakers Bureau; Abbvie: Honoraria, Speakers Bureau; Morphosis: Other: Ad board; AstraZeneca: Other: Ad board; BMS/Celgene: Consultancy, Honoraria, Speakers Bureau; Seattle Genetics: Consultancy, Speakers Bureau; Janssen: Honoraria, Speakers Bureau; Pharmacyclics: Honoraria, Speakers Bureau; Takeda: Honoraria, Speakers Bureau. Smolewski:Roche Poland: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Sandoz: Honoraria; Morphosis: Honoraria. Savage:Roche (institutional): Research Funding; Merck, BMS, Seattle Genetics, Gilead, AstraZeneca, AbbVie, Servier: Consultancy; BeiGene: Other: Steering Committee; Merck, BMS, Seattle Genetics, Gilead, AstraZeneca, AbbVie: Honoraria. Bartlett:Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics: Research Funding; Seattle Genetics: Consultancy, Research Funding; Roche/Genentech: Consultancy, Research Funding; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees, Research Funding; BTG: Consultancy; Acerta: Consultancy; Affimed Therapeutics: Research Funding; ADC Therapeutics: Consultancy; Autolus: Research Funding; BMS/Celgene: Research Funding; Forty Seven: Research Funding; Immune Design: Research Funding; Janssen: Research Funding; Kite, a Gilead Company: Research Funding; Merck: Research Funding; Millennium: Research Funding. Walewski:Gilead: Consultancy, Honoraria; Servier: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; Takeda: Consultancy, Honoraria, Research Funding; Roche: Consultancy, Honoraria, Other: Travel Support, Research Funding; Novartis: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; GSK: Research Funding. Ramchandren:Janssen: Research Funding; Pharmacyclics LLC, an AbbVie Company: Consultancy, Research Funding. Zinzani:Eusapharma: Consultancy, Speakers Bureau; Gilead: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Portola: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Immune Design: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Kyowa Kirin: Consultancy, Speakers Bureau; MSD: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Servier: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; AbbVie: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Immune Design: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; TG Therapeutics, Inc.: Honoraria, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau; EUSA Pharma: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Kirin Kyowa: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; ADC Therapeutics: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Verastem: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celltrion: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen-Cilag: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Sandoz: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Merck: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Sanofi: Consultancy, Membership on an entity's Board of Directors or advisory committees; Incyte: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Hutchings:Celgene, Genmab, Janssen, Novartis, F. Hoffmann-La Roche, Takeda: Research Funding; Genmab, F. Hoffmann-La Roche, Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees. Munoz:Juno/Celgene/BMS: Consultancy, Research Funding, Speakers Bureau; Alexion: Consultancy; Beigene: Consultancy, Speakers Bureau; Fosunkite: Consultancy; Innovent: Consultancy; Acrotech/Aurobindo: Speakers Bureau; Verastem: Speakers Bureau; AstraZeneca: Speakers Bureau; Kyowa: Consultancy, Honoraria, Speakers Bureau; Janssen: Consultancy, Research Funding, Speakers Bureau; Seattle Genetics: Consultancy, Honoraria, Research Funding, Speakers Bureau; Genentech/Roche: Research Funding, Speakers Bureau; AbbVie: Consultancy, Speakers Bureau; Merck: Research Funding; Portola: Research Funding; Incyte: Research Funding; Millenium: Research Funding; Pfizer: Consultancy; Kite, a Gilead Company: Consultancy, Research Funding, Speakers Bureau; Pharmacyclics: Consultancy, Research Funding, Speakers Bureau; Bayer: Consultancy, Research Funding, Speakers Bureau. Kim:JJ: Research Funding; Celltrion: Research Funding; Kyowa Kirn: Research Funding; Donga: Research Funding; Mundipharma: Research Funding; Pfizer: Research Funding; F. Hoffmann-La Roche: Research Funding. Advani:Astra Zeneca, Bayer Healthcare Pharmaceuticals, Cell Medica, Celgene, Genentech/Roche, Gilead, KitePharma, Kyowa, Portola Pharmaceuticals, Sanofi, Seattle Genetics, Takeda: Consultancy; Celgene, Forty Seven, Inc., Genentech/Roche, Janssen Pharmaceutical, Kura, Merck, Millenium, Pharmacyclics, Regeneron, Seattle Genetics: Research Funding. Ansell:Bristol Myers Squibb: Research Funding; Takeda: Research Funding; ADC Therapeutics: Research Funding; AI Therapeutics: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding. Younes:HCM: Consultancy; Daiichi Sankyo: Consultancy; Curis: Consultancy; BMS: Consultancy; Novartis: Consultancy; Epizyme: Consultancy; BioPath: Consultancy; AstraZeneca: Current Employment; Janssen: Consultancy; Takeda: Consultancy. Gallamini:Takeda: Other: Speaker. Liu:Takeda Pharmaceuticals: Current Employment. Little:Takeda: Current Employment, Current equity holder in publicly-traded company. Fenton:Seattle Genetics: Current Employment, Current equity holder in publicly-traded company. Fanale:Seattle Genetics: Current Employment, Current equity holder in publicly-traded company. Radford:GlaxoSmithKline: Current equity holder in publicly-traded company, Other: Spouse; AstraZeneca: Current equity holder in publicly-traded company, Other: Spouse; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Pfizer: Research Funding; ADCT: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Honoraria, Speakers Bureau; Novartis: Consultancy, Honoraria; Seattle Genetics: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau.

MYC gene amplification and somatic mutations are frequent in both adult and pediatric AML although how MYC drives and contributes to the development and maintenance of AML has not been resolved. Transcription factor EB (TFEB) is a master regulator of genes that control autophagy and lysosome biogenesis, a central catabolic recycling pathway that regulates cell survival. Given the oncogenic effects of MYC in AML and that the induction of autophagy compromises AML cell growth and survival, we tested if the oncogenic effect of MYC depends on its suppression of TFEB transcription programs in AML. In support of this hypothesis, inducible MYC expression in K562 and THP-1 leukemia cells was sufficient to suppress expression of TFEB and its target genes. Further and conversely, MYC knockdown in NB4 AML cells provoked increased expression of TFEB mRNA and protein, as well as increased expression of TFEB target genes. Notably, dose response studies demonstrated that expression of TFEBS211A, constitutively nuclear form of TFEB that is refractory to control by mTORC1 signaling, dramatically impairs proliferation of HL60, OCI-AML2 and OCI-AML3 AML cells. In addition, induction of TFEBS211A provoked rampant apoptosis. Of important, overexpression of TFEBS211A in HL-60 and OCI-AML3 cells was also sufficient to promote monocytic and granulocytic differentiation, as judged by morphological changes and the acquisition of mature monocytic and granulocytic markers including CD11b, Gr1, and CD15. To identify TFEB targets that might contribute to myeloid/granulocytic differentiation, we performed RNA-seq analysis of HL60 leukemia cells engineered to inducibly express the TFEBS211A transgene. Using a cut-off of fold change&gt;4 with q&lt;0.01, a total of 1152 genes were differentially regulated following the induction of TFEBS211A in HL60 cells. As expected, this included the robust induction of nearly all TFEB target genes associated with the autophagy-lysosome pathway, but also of STAT1, KLF4, KLF6, CEBPB, CSF1 and GATA2 genes that are necessary and/or sufficient to provoke terminal monocytic and granulocytic differentiation of AML cells. Surprisingly, among genes induced by TFEB in HL60 cells is IDH1, which catalyzes the production of α-ketoglutarate (α-KG), a required substrate of the TET family of dioxygenases that convert 5-methylcytosine (5mC) to 5-hydroxymethylcytosine (5hmC). In particular, TFEBS211A expression provoked increases in levels of α-KG and significant increases in global levels of 5hmC in genomic DNA of HL60 leukemia cells both ex vivo and in vivo. Furthermore, TFEB-mediated induction of IDH1/2 mRNA and protein, and of IDH1 promoter activity, was antagonized by inducible expression of MYC. To assess the global effects of TFEB on 5mC/5hmC landscapes, we performed paired reduced representation bisulfite (BS)- and oxidation bisulfite (oxBS)-sequencing. As predicted, TFEBS211A induced both loss and gains of 5mC, but there were more losses (n = 722) than gains (n = 459) across all 22 chromosomes. Quite remarkably, and consistent with TFEB-provoked increases of 5hmC signals, TFEBS211A exclusively induced 5hmC gains (in a total of 863 genes), and 37% and 36% of these 5hmC gains occurred in promoter regions and CpG islands, and across all 22 chromosomes. Comparison of BS- and oxBS-seq versus RNA-seq analyses of HL60 cells expressing TFEB revealed significant changes in mRNA levels and concomitant differential changes in 5mC and 5hmC marks in KLF4, KLF6, STAT3, TP73, andFOXO1 that have pivotal roles in controlling myeloid cell differentiation and death. Collectively, these findings demonstrate that MYC suppresses TFEB expression and function in AML cells, and that TFEB functions as a tumor suppressor that provokes AML cell differentiation and death. Strikingly, these responses rely on epigenetic control, where TFEB directly induces the transcription of IDH1 and IDH2 to provoke global hydroxylation of 5-methylcytosine and the expression of genes that drive terminal differentiation and apoptosis. Thus, a MYC-TFEB-IDH1/2-TET2 circuit controls AML cell fate. Disclosures Murphy: Merck: Research Funding; Puma Biotech: Research Funding. Ballabio:CASMA Therapeutics: Other: Co-Founder. Kaufmann:Takeda Pharmaceuticals: Research Funding.

ABSTRACT Background: Damage-associated molecular patterns (DAMPs) stimulate endothelial syndecan-1 shedding and neutrophil extracellular traps (NET) formation. The role of NETs in trauma and trauma-induced hypercoagulability is unknown. We hypothesized that trauma patients with accelerated thrombin generation would have increased NETosis and syndecan-1 levels. Methods: In this pilot study, we analyzed 50 citrated plasma samples from 30 trauma patients at 0 h (n = 22) and 6 h (n = 28) from time of injury (TOI) and 21 samples from healthy volunteers, for a total of 71 samples included in analysis. Thrombin generation was quantified using calibrated automated thrombogram (CAT) and reported as lag time (LT), peak height (PH), and time to peak (ttPeak). Nucleosome calibrated (H3NUC) and free histone standardized (H3Free) ELISAs were used to quantify NETs. Syndecan-1 levels were quantified by ELISA. Results are presented as median [interquartile range] and Spearman rank correlations. Results: Plasma levels of H3NUC were increased in trauma patients as compared with healthy volunteers both at 0 h (89.8 ng/mL [35.4, 180.3]; 18.1 ng/mL [7.8, 37.4], P = 0.002) and at 6 h (86.5 ng/mL [19.2, 612.6]; 18.1 ng/mL [7.8, 37.4], P = 0.003) from TOI. H3Free levels were increased in trauma patients at 0 h (5.74 ng/mL [3.19, 8.76]; 1.61 ng/mL [0.66, 3.50], P = 0.002) and 6 h (5.52 ng/mL [1.46, 11.37]; 1.61 ng/mL [0.66, 3.50], P = 0.006). Syndecan-1 levels were greater in trauma patients (4.53 ng/mL [3.28, 6.28]; 2.40 ng/mL [1.66, 3.20], P < 0.001) only at 6 h from TOI. H3Free and syndecan-1 levels positively correlated both at 0 h (0.376, P = 0.013) and 6 h (0.583, P < 0.001) from TOI. H3NUC levels and syndecan-1 levels were positively correlated at 6 h from TOI (0.293, P = 0.041). TtPeak correlated inversely to H3 NUC (−0.358, P = 0.012) and syndecan-1 levels (−0.298, P = 0.038) at 6 h from TOI. Conclusions: Our pilot study demonstrates that trauma patients have increased NETosis, measured by H3NUC and H3Free levels, increased syndecan-1 shedding, and accelerated thrombin generation kinetics early after injury.

Abstract Background Primary hyperoxaluria (PH) type 3 (PH3) is caused by mutations in the hydroxy-oxo-glutarate aldolase 1 gene. PH3 patients often present with recurrent urinary stone disease in the first decade of life, but prior reports suggested PH3 may have a milder phenotype in adults. This study characterized clinical manifestations of PH3 across the decades of life in comparison with PH1 and PH2. Methods Clinical information was obtained from the Rare Kidney Stone Consortium PH Registry (PH1, n = 384; PH2, n = 51; PH3, n = 62). Results PH3 patients presented with symptoms at a median of 2.7 years old compared with PH1 (4.9 years) and PH2 (5.7 years) (P = 0.14). Nephrocalcinosis was present at diagnosis in 4 (7%) PH3 patients, while 55 (89%) had stones. Median urine oxalate excretion was lowest in PH3 patients compared with PH1 and PH2 (1.1 versus 1.6 and 1.5 mmol/day/1.73 m2, respectively, P &lt; 0.001) while urine calcium was highest in PH3 (112 versus 51 and 98 mg/day/1.73 m2 in PH1 and PH2, respectively, P &lt; 0.001). Stone events per decade of life were similar across the age span and the three PH types. At 40 years of age, 97% of PH3 patients had not progressed to end-stage kidney disease compared with 36% PH1 and 66% PH2 patients. Conclusions Patients with all forms of PH experience lifelong stone events, often beginning in childhood. Kidney failure is common in PH1 but rare in PH3. Longer-term follow-up of larger cohorts will be important for a more complete understanding of the PH3 phenotype.