Citations

3021 Background: 17DMAG is a water-soluble analog of 17-allylamino-17-demethoxygeldanamycin (17AAG), the first geldanamycin compound to enter clinical trials. 17DMAG causes oncoprotein degradation by binding to heat shock protein 90 (HSP90) & has greater in-vitro activity than 17AAG. In animals treated with 17DMAG, liver toxicity is dose-limiting. The objectives of this first-in-human study are to: establish the dose-limiting toxicity (DLT); recommend a phase 2 dose; & characterize the PK & PD of 17DMAG. Methods: Using a modified accelerated schema, escalating doses of 17DMAG are given IV over 1 h daily x 5 (schedule A) or daily x 3 (schedule B) every 3 weeks. Plasma 17DMAG concentrations during cycle 1 are quantitated by LC/MS. HSP70 & ILK levels in peripheral mononuclear cells are measured by western blot at baseline & 24 h on d 1. Results: 37 pts have been enrolled. On schedule A, dose levels are 1.5, 3, 6, 9, 12 & 16 mg/m2/d (n = 20). On schedule B, the starting dose was 2.5 mg/m2/d. Based on safety information from schedule A, subsequent schedule B dose levels were 14, 19, 25 & 34 mg/m2/d (n = 17). One DLT was noted in schedule A (16 mg/m2) & consisted of reversible liver enzyme elevation. End-of-infusion (EOI) 17DMAG concentrations (33–2074 ng/ml) & AUC0–24 (200–5200 ng/ml x h) increase linearly with dose. Interpatient PK variability was substantial, with CV% of 16–59%. The daily administration schedule precluded precise definition of 17DMAG t1/2 & clearance, but estimates of t1/2 & clearance ranged from 10.8 to 77 h & 19–160 ml/min/m2, respectively. Between 0 & 24 h, 20.5 ± 8.6% (range 4–43.8%) of the administered dose was excreted in urine. HSP70 increased between 0.2 & 3.7 fold above baseline in PBMCs at 24 h at 12, 16, & 25 mg/m2. There is an indication of ILK degradation in 3 of 4 patients evaluated at 25 mg/m2. Conclusions: 17DMAG is well tolerated at current dose levels & maximal tolerated dose has not been reached. 17DMAG plasma PK are linear over the doses delivered to date, & there is evidence of a target effect as manifested by↑HSP70 & ↓ILK. Support: U01CA099168–01, U01CA62502, U01CA69912, R01CA90390 & NIH/NCCR/GCRC grants #5M01 RR 00056 & M01 RR00080 No significant financial relationships to disclose.

Abstract Eating disorders and substance use disorders frequently co‐occur. Twin studies reveal shared genetic variance between liabilities to eating disorders and substance use, with the strongest associations between symptoms of bulimia nervosa and problem alcohol use (genetic correlation [ r g ], twin‐based = 0.23‐0.53). We estimated the genetic correlation between eating disorder and substance use and disorder phenotypes using data from genome‐wide association studies (GWAS). Four eating disorder phenotypes (anorexia nervosa [AN], AN with binge eating, AN without binge eating, and a bulimia nervosa factor score), and eight substance‐use‐related phenotypes (drinks per week, alcohol use disorder [AUD], smoking initiation, current smoking, cigarettes per day, nicotine dependence, cannabis initiation, and cannabis use disorder) from eight studies were included. Significant genetic correlations were adjusted for variants associated with major depressive disorder and schizophrenia. Total study sample sizes per phenotype ranged from ~2400 to ~537 000 individuals. We used linkage disequilibrium score regression to calculate single nucleotide polymorphism‐based genetic correlations between eating disorder‐ and substance‐use‐related phenotypes. Significant positive genetic associations emerged between AUD and AN ( r g = 0.18; false discovery rate q = 0.0006), cannabis initiation and AN ( r g = 0.23; q < 0.0001), and cannabis initiation and AN with binge eating ( r g = 0.27; q = 0.0016). Conversely, significant negative genetic correlations were observed between three nondiagnostic smoking phenotypes (smoking initiation, current smoking, and cigarettes per day) and AN without binge eating ( r gs = −0.19 to −0.23; qs < 0.04). The genetic correlation between AUD and AN was no longer significant after co‐varying for major depressive disorder loci. The patterns of association between eating disorder‐ and substance‐use‐related phenotypes highlights the potentially complex and substance‐specific relationships among these behaviors.

ObjectivePublished predictive models of disease outcomes in idiopathic inflammatory myopathies (IIMs) are sparse and of limited accuracy due to disease heterogeneity. Computational methods may address this heterogeneity by partitioning patients based on clinical and biological phenotype.MethodsTo identify new patient groups, we applied similarity network fusion (SNF) to clinical and biological data from 168 patients with myositis (64 adult polymyositis [PM], 65 adult dermatomyositis [DM], and 39 juvenile DM [JDM]) in the Rituximab in Myositis trial. We generated a sparse proof‐of‐concept bedside classifier using multinomial regression and identified characteristics that distinguished these groups. We conducted χ2 tests to link new patient groups with the myositis subtypes.ResultsSNF identified five patient groups in the discovery cohort that subdivided the myositis subtypes. The sparse multinomial regressor to predict patient group assignments (areas under the receiver operating characteristic curve = [0.78, 0.97]; areas under the precision‐recall curve = [0.55, 0.96]) found that autoantibody enrichment defined four of these groups: anti–Mi‐2, anti–signal recognition peptide (SRP), anti–nuclear matrix protein 2 (NXP2), and anti‐synthetase (Syn). Depletion of immunoglobulin M (IgM) defined the fifth group. Each group was associated with one subtype, with adult DM being associated with anti–Mi‐2 and anti‐Syn autoantibodies, JDM being associated with anti‐NXP2 autoantibodies, and adult PM being associated with IgM depletion and anti‐SRP autoantibodies. These associations enabled us to further resolve the current myositis subtypes.ConclusionUsing unsupervised machine learning, we identified clinically and biologically homogeneous groups of patients with IIMs, forming the basis of an integrated disease classification based on both clinical and biological phenotype, thus validating other approaches and what has been previously described.

APOE4 exacerbates α-synuclein pathology in a synucleinopathy mouse model, and APOE4 genotype associates with increased α-synuclein pathology in humans.

746 Background: Pelareorep is a proprietary formulation of live, replication-competent, naturally occurring Reovirus Type 3 Dearing strain. A randomized phase II trial of pelareorep in combination with carboplatin and paclitaxel in first-line treatment of metastatic PDAC (NCT01280058) was performed. Although pelareorep did not improve the primary endpoint of progression-free survival compared to carboplatin and paclitaxel alone, impressive durable responses were seen in the pelareorep arm in some patients (pts). Further, prior studies have noted the immunomodulatory carcinoembryonic antigen-related cell adhesion molecule (CEACAM6/CD66c) as a receptor for specific viral subtypes. We thus speculated that altered CEACAM6 levels may be predictive for pelareorep sensitivity. Methods: Pre-treatment tissue biopsies were collected prior enrolment for all 73 pts on study. Evaluable pts with transcriptomic data was available for only 31 pts. RNA was purified from FFPE tissue and gene expression analysis was performed using SensationPlus FFPE Amplification and WT labelling kit and the Human Transcriptome Array 2.0. CEACAM6 protein expression was determined by immunohistochemistry. Differential gene expression and survival analysis using were performed in R/Bioconductor. Appropriate corrections for multiplicity were performed. Results: When comparing extraordinary responders in the pelareorep treated arm to those with poor outcomes, low levels of CEACAM6 mRNA expression were associated with prolonged PFS in pelareorep-treated pts (adjusted p = 0.05). This effect was not seen in non-pelareorep treated pts. The luminal, but not the cytoplasmic immunohistochemistry score, was highly correlated with mRNA expression levels of CEACAM6, p = 0.001. Modulation of CEACAM6 in vitro and in vivo are underway. Conclusions: CEACAM6 may be a candidate biomarker of sensitivity to pelareorep and, in theory, could improve viral trafficking of this compound in tumor cells. Clinical trial information: NCT01280058 . [Table: see text]

Background Low tube potential-high tube current computed tomography (CT) imaging allows reduction in iodine-based contrast dose and may extend the benefit of routine contrast-enhanced CT exams to patients at risk of nephrotoxicity. Purpose To determine the ability of an iodine contrast reduction algorithm to maintain diagnostic image quality for contrast-enhanced abdominal CT. Material and Methods CT exams with iodine contrast reduction were prescribed for patients at risk for renal dysfunction. The iodine contrast reduction algorithm combines weight-based contrast volume reduction with patient width-based low tube potential selection and bolus-tracking. Control exams with routine iodine dose were selected based on weight, width, and scan protocol. Three radiologists evaluated image quality and diagnostic confidence using a 4-point scale (<2 acceptable). Another radiologist assessed contrast reduction indications and measured portal vein and liver contrast-to-noise ratios. Results Forty-six contrast reduction algorithm and control exams were compared (mean creatinine 1.6 vs. 1.2 mg/dL, P ≤ 0.0001). Thirty-nine contrast reduction patients had an eGFR <60 mL/min/1.73m 2 and 15 had single or transplanted kidney. Mean iodine contrast dose was lower in the contrast reduction group (20.9 vs. 39.4 g/mL, P < 0.0001). Diagnostic confidence was rated as acceptable in 95% (131/138) of contrast reduction and 100% of control exams (1.18–1.28 vs. 1.02–1.13, respectively; P > 0.06). Liver attenuation and contrast-to-noise ratio (CNR) were similar ( P = 0.08), but portal vein attenuation and CNR were lower with contrast-reduction (mean 176 vs. 198 HU, P = 0.02; 13 vs. 16, P = 0.0002). Conclusion This size-based contrast reduction algorithm using low kV and bolus tracking reduced iodine contrast dose by 50%, while achieving acceptable image quality in 95% of exams.

Objective The aim of this study was to grade cartilage damage in a swine model of osteoarthritis using a whole-body photon-counting detector (PCD) CT. Materials and Methods A multienergy phantom containing gadolinium (Gd) (2, 4, 8, and 16 mg/mL) and hydroxyapatite (200 and 400 mg/cc) was scanned using a PCD-CT system (48 × 0.25 mm collimation, 80 kV, 800 mAs, D50 reconstruction kernel) to serve as calibration for material decomposition and to assess quantification accuracy. Osteoarthritis was induced in Yucatan miniswine (n = 8) using 1.2 mg monoiodoacetate (MIA) injected into a randomized knee, whereas the contralateral control knee received saline. Twenty-one days later, a contrast bolus (gadoterate meglumine, 4 mL/knee) was intra-articularly administered into both knees. The knees were simultaneously scanned on the PCD-CT system (48 × 0.25 mm collimation, 80 kV, 800 mAs). Multienergy images were reconstructed with a sharp “V71” kernel and a quantitative “D50” kernel. Image denoising was applied to the V71 images before grading cartilage damage, and an iterative material decomposition technique was applied to D50 images to generate the Gd maps. Two radiologists blinded to the knee injection status graded the cartilage integrity based on a modified International Cartilage Repair Society scoring system. Histology was performed on excised cartilage using methylene blue/basic fuchsin. Statistical analysis of grade distribution was performed using an exact test of omnibus symmetry with P < 0.05 considered significant. Results Material decomposed images from the multienergy phantom scan showed delineation and quantification of Gd and hydroxyapatite with a root-mean-squared error of 0.3 mg/mL and 18.4 mg/cc, respectively. In the animal cohort, the radiologists reported chondromalacia in the MIA knees with International Cartilage Repair Society scores ranging from grade 1 (cartilage heterogeneity, n = 4 knees) to grade 3 (up to 100% cartilage loss, n = 4 knees). Grade 1 was characterized by cartilage heterogeneity and increased joint space in the patellofemoral compartment, whereas grade 3 was characterized by cartilage erosion and bone-on-bone articulation in the patellofemoral compartment. All control knees were scored as grade 0 (normal cartilage). Significant difference (P = 0.004) was observed in the grade distribution between the MIA and control knees. Gross examination of the excised knees showed cartilage lesions in the grade 3 MIA knees. The Gd maps from material decomposition showed lower contrast levels in the joint space of the MIA knee compared with the contralateral control knee due to joint effusion. Histology revealed chondrocyte loss in the MIA knee cartilage confirming the chondrotoxic effects of MIA on cartilage matrix. Conclusions We demonstrated a high-resolution and quantitative PCD-CT arthrography technique for grading cartilage damage in a large animal model of osteoarthritis. Photon-counting detector CT offers simultaneous high-resolution and multienergy imaging capabilities that allowed morphological assessment of cartilage loss and quantification of contrast levels in the joint as a marker of joint disease. Cartilage damage in the MIA knees was graded using PCD-CT images, and the image-based findings were further confirmed using histology and gross examination of the excised knees.

Abstract Objective The study sought to determine the dependence of the Electronic Medical Records and Genomics (eMERGE) rheumatoid arthritis (RA) algorithm on both RA and electronic health record (EHR) duration. Materials and Methods Using a population-based cohort from the Mayo Clinic Biobank, we identified 497 patients with at least 1 RA diagnosis code. RA case status was manually determined using validated criteria for RA. RA duration was defined as time from first RA code to the index date of biobank enrollment. To simulate EHR duration, various years of EHR lookback were applied, starting at the index date and going backward. Model performance was determined by sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve (AUC). Results The eMERGE algorithm performed well in this cohort, with overall sensitivity 53%, specificity 99%, positive predictive value 97%, negative predictive value 74%, and AUC 76%. Among patients with RA duration &lt;2 years, sensitivity and AUC were only 9% and 54%, respectively, but increased to 71% and 85% among patients with RA duration &gt;10 years. Longer EHR lookback also improved model performance up to a threshold of 10 years, in which sensitivity reached 52% and AUC 75%. However, optimal EHR lookback varied by RA duration; an EHR lookback of 3 years was best able to identify recently diagnosed RA cases. Conclusions eMERGE algorithm performance improves with longer RA duration as well as EHR duration up to 10 years, though shorter EHR lookback can improve identification of recently diagnosed RA cases.