Citations

Abstract Abstract 739 The combination of a calcineurin inhibitor and methotrexate has been the standard of care in graft-vs.-host disease (GVHD) prophylaxis for over 25 years, with resultant rates of grade II-IV acute GVHD between 30–50%. The mTOR inhibitor, sirolimus, has demonstrated promise in a number of Phase II trials as an immunosuppressant used for GVHD prophylaxis. The BMT CTN, sponsored by the NHLBI and NCI, conducted a multicenter, randomized controlled trial comparing the combination of tacrolimus and sirolimus (Tac/Sir) with tacrolimus and methotrexate (Tac/Mtx) as GVHD prophylaxis after matched, related donor (MRD) hematopoietic stem cell transplantation (HSCT). Methods: Eligible patients were between ages 2 – 60 years, and had acute leukemia in remission, myelodysplasia or chronic myeloid leukemia in chronic or accelerated phase. All had adequate organ function, and a 6/6 HLA-A, B, DRB1 matched sibling donor. 304 patients were randomly assigned to either Tac/Sir (n = 151) or Tac/Mtx (n = 153) as GVHD prophylaxis after TBI-based conditioning and MRD HSCT. An intent-to-treat analysis was performed on the primary endpoint of Grade II-IV GVHD-free survival 114 days from randomization. Ten subjects who received busulfan-based conditioning and were previously reported were excluded from analysis. Three subjects who did not undergo HSCT are included in the primary analysis, but not secondary analyses. Results: Treatment groups were well balanced. The median age of participants was 44 years (range 13 – 59) and 83% had acute leukemia. Neutrophil and platelet engraftment were both faster in the Tac/Sir group (14 vs. 16 days, p < 0.001; 16 vs. 19 days, p = 0.03, respectively), but this did not affect the time to first hospital discharge (20 vs. 21 days, p = 0.37). The incidence of grade II-IV and grade III-IV acute GVHD at 100 days were lower in the Tac/Sir group (26 vs. 34%, p = 0.17; 8 vs. 15%, p = 0.05). Day 100 treatment-related mortality was no different between groups (7 vs. 7%, p = 0.43). The primary endpoint of 114-day acute GVHD-free survival was not statistically different between groups (67 vs. 62%, p = 0.38, Figure). The cumulative incidence of relapse at 2 years from transplantation was not different between groups (27 vs. 30%, p = 0.81). The competing-risk cumulative incidence of chronic GVHD was higher in the Tac/Sir arm (54 vs. 43%, p =0.044). Overall toxicities were not different between groups, with two notable exceptions. The peak and average OMAS oral mucositis scores were lower in the Tac/Sir arm (peak 0.70 vs. 0.96, p < 0.001; average 0.31 vs. 0.47, p < 0.001), however, there was an increased rate of the endothelial injury syndromes, veno-occlusive disease (11 vs. 4%, p = 0.03), and thrombotic microangiopathy (5 vs. 1%, p = 0.05) in the Tac/Sir arm. Causes of death were not different between groups. At 2 years from transplantation, disease-free (DFS) and overall survival (OS) were not different between study arms (DFS 53 vs. 53%, p = 0.76; OS 60 vs. 61%, p = 0.44). Conclusions: No difference in 114-day acute GVHD-free survival was noted between treatment arms. Compared with Tac/Mtx in MRD HSCT, Tac/Sir is associated with more rapid engraftment, less severe acute GVHD and oral mucositis, excess chronic GVHD and endothelial injury syndromes, and similar long-term outcomes. Understanding the trade-offs between regimens, Tac/Sir can be used as an alternative to Tac/Mtx in MRD HSCT. Disclosures: Cutler: Pfizer, inc: Research Funding; Astellas, Inc: Consultancy, Research Funding. Off Label Use: Sirolimus - Prevention of GVHD Tacrolimus - Prevention of GVHD. Waller:Outsuka: Research Funding.

Abstract Aims Increases in extravascular lung water (EVLW) during exercise contribute to symptoms, morbidity, and mortality in patients with heart failure and preserved ejection fraction (HFpEF), but the mechanisms leading to pulmonary congestion during exercise are not well-understood. Methods and results Compensated, ambulatory patients with HFpEF (n = 61) underwent invasive haemodynamic exercise testing using high-fidelity micromanometers with simultaneous lung ultrasound, echocardiography, and expired gas analysis at rest and during submaximal exercise. The presence or absence of EVLW was determined by lung ultrasound to evaluate for sonographic B-line artefacts. An increase in EVLW during exercise was observed in 33 patients (HFpEFLW+, 54%), while 28 (46%) did not develop EVLW (HFpEFLW−). Resting left ventricular function was similar in the groups, but right ventricular (RV) dysfunction was two-fold more common in HFpEFLW+ (64 vs. 31%), with lower RV systolic velocity and RV fractional area change. As compared to HFpEFLW−, the HFpEFLW+ group displayed higher pulmonary capillary wedge pressure (PCWP), higher pulmonary artery (PA) pressures, worse RV-PA coupling, and higher right atrial (RA) pressures during exercise, with increased haemoconcentration indicating greater loss of water from the vascular space. The development of lung congestion during exercise was significantly associated with elevations in PCWP and RA pressure as well as impairments in RV-PA coupling (area under the curve values 0.76–0.84). Conclusion Over half of stable outpatients with HFpEF develop increases in interstitial lung water, even during submaximal exercise. The acute development of lung congestion is correlated with increases in pulmonary capillary hydrostatic pressure that favours fluid filtration, and systemic venous hypertension due to altered RV-PA coupling, which may interfere with fluid clearance. Clinical trial registration NCT02885636.

Abstract MYCN is a major driver for the childhood cancer, neuroblastoma, however, there are no inhibitors of this target. Enhanced MYCN protein stability is a key component of MYCN oncogenesis and is maintained by multiple feedforward expression loops involving MYCN transactivation target genes. Here, we reveal the oncogenic role of a novel MYCN target and binding protein, proliferation-associated 2AG4 (PA2G4). Chromatin immunoprecipitation studies demonstrated that MYCN occupies the PA2G4 gene promoter, stimulating transcription. Direct binding of PA2G4 to MYCN protein blocked proteolysis of MYCN and enhanced colony formation in a MYCN-dependent manner. Using molecular modeling, surface plasmon resonance, and mutagenesis studies, we mapped the MYCN–PA2G4 interaction site to a 14 amino acid MYCN sequence and a surface crevice of PA2G4. Competitive chemical inhibition of the MYCN–PA2G4 protein–protein interface had potent inhibitory effects on neuroblastoma tumorigenesis in vivo. Treated tumors showed reduced levels of both MYCN and PA2G4. Our findings demonstrate a critical role for PA2G4 as a cofactor in MYCN-driven neuroblastoma and highlight competitive inhibition of the PA2G4-MYCN protein binding as a novel therapeutic strategy in the disease. Significance: Competitive chemical inhibition of the PA2G4–MYCN protein interface provides a basis for drug design of small molecules targeting MYC and MYCN-binding partners in malignancies driven by MYC family oncoproteins.

Background: Sex and age have long been known to affect the ECG. Several biologic variables and anatomic factors may contribute to sex and age-related differences on the ECG. We hypothesized that a convolutional neural network (CNN) could be trained through a process called deep learning to predict a person’s age and self-reported sex using only 12-lead ECG signals. We further hypothesized that discrepancies between CNN-predicted age and chronological age may serve as a physiological measure of health. Methods: We trained CNNs using 10-second samples of 12-lead ECG signals from 499 727 patients to predict sex and age. The networks were tested on a separate cohort of 275 056 patients. Subsequently, 100 randomly selected patients with multiple ECGs over the course of decades were identified to assess within-individual accuracy of CNN age estimation. Results: Of 275 056 patients tested, 52% were males and mean age was 58.6±16.2 years. For sex classification, the model obtained 90.4% classification accuracy with an area under the curve of 0.97 in the independent test data. Age was estimated as a continuous variable with an average error of 6.9±5.6 years (R-squared =0.7). Among 100 patients with multiple ECGs over the course of at least 2 decades of life, most patients (51%) had an average error between real age and CNN-predicted age of <7 years. Major factors seen among patients with a CNN-predicted age that exceeded chronologic age by >7 years included: low ejection fraction, hypertension, and coronary disease ( P <0.01). In the 27% of patients where correlation was >0.8 between CNN-predicted and chronologic age, no incident events occurred over follow-up (33±12 years). Conclusions: Applying artificial intelligence to the ECG allows prediction of patient sex and estimation of age. The ability of an artificial intelligence algorithm to determine physiological age, with further validation, may serve as a measure of overall health.

AbstractBackground and ObjectivesConclusive evidence in favor of neoadjuvant therapy for those with non‐metastatic pancreatic ductal adenocarcinoma (PDAC) is still lacking. The objective of this study was to evaluate the survival benefit of neoadjuvant therapy vs upfront surgery for patients with non‐metastatic PDAC.MethodsThe study involved 565 patients undergoing neoadjuvant therapy or upfront surgery as the primary treatment for PDAC. Propensity score matching was performed between the neoadjuvant therapy group (NAT group) and the upfront surgery group (UFS group) using 20 clinical variables at diagnosis. Overall survival and surgical pathology were compared between the two treatment groups on an intent‐to‐treat basis.ResultsIn the matched cohort, the NAT group (n = 91) had a longer median overall survival than the UFS group (n = 91) (23.1 months vs 18.5 months, P = .043). The rate of patients undergoing surgical resection was lower in the NAT group (58% vs 80%, P = .001). Regarding surgical pathology, the NAT group had smaller tumor size (2.8 cm vs 4.0 cm, P = .001), lower incidence of positive surgical margins (8% vs 30%, P < .002), and less lymph node metastasis (45% vs 78%, P < .001).ConclusionsThe strategy of neoadjuvant therapy before surgical resection appears to offer pathologic effect and survival benefit for the patients presenting with non‐metastatic PDAC.

ABSTRACTArthrofibrosis is a common complication following total knee arthroplasty caused by pathologic fibroblast activation and excessive collagen deposition around a synovial joint leading to debilitating loss of motion. Treatment options are limited because the pathologic mechanisms remain to be characterized. Dysregulation of the inflammatory cascade may lead to communication between myofibroblasts and immune cells triggering tissue metaplasia, and excessive collagen deposition described clinically as arthrofibrosis. We explored the novel use of celecoxib (selective cyclooxygenase‐2 [COX‐2] inhibitor) to disrupt the downstream effects of the post‐traumatic inflammatory cascade and inhibit scar tissue formation in a validated rabbit model of arthrofibrosis combined with new parameters for quantifying the stiffness of the posterior capsule. Biomechanical and molecular analyses, of contracted rabbit knee posterior capsule tissue after COX‐2 inhibition revealed increased maximal passive extension and down‐regulation of collagen messenger RNA compared with controls. Histopathologic examination suggested a trend of decreased quantities of dense fibrous connective tissue with COX‐2 inhibition. These data may suggest that inhibiting the inflammatory cascade could potentially reduce pathologic myofibroblast activation, thereby reducing scar tissue formation and increasing the range of motion in arthrofibrotic joints. Implementing a multi‐modal pharmacologic approach may simultaneously target numerous cellular components contributing to the complex process of arthrofibrogenesis. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 37:2609–2620, 2019

Abstract Background Archived formalin fixed paraffin embedded (FFPE) samples are valuable clinical resources to examine clinically relevant morphology features and also to study genetic changes. However, DNA quality and quantity of FFPE samples are often sub-optimal, and resulting NGS-based genetics variant detections are prone to false positives. Evaluations of wet-lab and bioinformatics approaches are needed to optimize variant detection from FFPE samples. Results As a pilot study, we designed within-subject triplicate samples of DNA derived from paired FFPE and fresh frozen breast tissues to highlight FFPE-specific artifacts. For FFPE samples, we tested two FFPE DNA extraction methods to determine impact of wet-lab procedures on variant calling: QIAGEN QIAamp DNA Mini Kit ("QA"), and QIAGEN GeneRead DNA FFPE Kit ("QGR"). We also used negative-control (NA12891) and positive control samples (Horizon Discovery Reference Standard FFPE). All DNA sample libraries were prepared for NGS according to the QIAseq Human Breast Cancer Targeted DNA Panel protocol and sequenced on the HiSeq 4000. Variant calling and filtering were performed using QIAGEN Gene Globe Data Portal. Detailed variant concordance comparisons and mutational signature analysis were performed to investigate effects of FFPE samples compared to paired fresh frozen samples, along with different library preparations. In this study, we found that five times or more variants were called with FFPE samples, compared to their paired fresh-frozen tissue samples even after applying molecular barcoding error-correction and default bioinformatics filtering recommended by the vendor. We also found that QGR as an optimized FFPE-DNA extraction approach leads to much fewer discordant variants between paired fresh frozen and FFPE samples. Approximately 92% of the uniquely called FFPE variants were of low allelic frequency range (<5%), and collectively shared a “C>T|G>A” mutational signature known to be representative of FFPE artifacts resulting fromcytosine deamination. Based on control samples and FFPE-frozen replicates, we derived an effective filtering strategy with associated empirical false-discovery estimates. Conclusions Through this study, we demonstrated feasibility of calling and filtering genetic variants from FFPE tissue samples using a combined strategy with molecular barcodes, optimized DNA extraction, and bioinformatics methods incorporating genomics context such as mutational signature and variant allelic frequency.

Abstract Background: Prospective validation of breast cancer risk models integrating classical risk factors and genetic variants is required for risk-stratified prevention and screening strategies. The objective of this study was to validate a breast cancer risk model integrating classical risk factors and a 313-variant polygenic risk score (PRS) in multiple prospective cohort studies, and to project five-year risk of breast cancer in six different countries. Methods: The study population included 7,529 cases and 230,103 controls from 14 prospective cohort studies in Australia, Germany, the Netherlands, Sweden, UK, and USA. We used the Individualized Coherent Absolute Risk Estimator (iCARE) tool for risk model building, validation, and risk projection. Expected five-year risk of invasive or in situ breast cancer was compared to observed risk, overall and within deciles of expected risk using goodness of fit statistics. We evaluated calibration of the relative risk through meta-analysis across cohorts, and of the absolute risk within each cohort. Model discrimination was evaluated using the area under the curve (AUC), and percentages of women crossing risk thresholds. Projections of five-year risk distributions were estimated for women of European ancestry aged 50-70 years in the general populations of these six countries. Results: Analysis showed overall good calibration of the integrated iCARE-based model relative risk for both women younger than 50 years (χ2=14.9, P=0.09) and aged 50 years or older (χ2=14.6, P=0.10), with a small overestimation of risk for women in the highest decile of expected risk (RR = 3.5 expected vs 2.3 (95% CI 1.6 to 3.2) observed for women &lt;50 years; and 2.8 expected vs 2.3 (95% CI 2.0 to 2.7) observed for women 50+ years). The age-adjusted AUCs for the integrated model were 63.1 (95% CI 60.9 to 65.3) and 62.9 (95% CI 61.8 to 64.0), for the two age groups respectively. The calibration of absolute risk showed substantial variation across cohorts, particularly for the older group, but had no systematic bias. Model based projections in the general populations showed that compared to the population average, women in the 1st and 99th percentiles of the integrated risk score had relative risks 0.19 and 3.56 respectively. The proportion of women of European ancestry aged 50-70 years with a five-year risk greater than 3% (threshold for consideration of risk-lowering drugs by U.S. Preventive Services Task Force) ranged from 7.1% in Germany to 18.2% in the US, which corresponds to ~5.5 million women in the US. Conclusions: Five-year risk predictions from a model with classical risk factors and PRS are well calibrated and provide substantial risk stratification across multiple cohorts in six different countries. Further studies are needed to evaluate the clinical utility of the validated model for risk stratified screening and prevention of breast cancer. Citation Format: Parichoy Pal Choudhury, Amber Wilcox, Chi Gao, Brian Carter, Anika Husing, Mark Brook, Mikael Eriksson, Kara Martin, Chris Scott, Min Shi, Thomas Ahearn, Michael Jones, Nick Orr, Minouk Schoemaker, Kamila Czene, Jenny Chang-Claude, Jacques Simard, Doug Easton, Marjanka K. Schmidt, Dale Sandler, Clarice R. Weinberg, Celine Vachon, Roger Milne, Per Hall, Anthony Swerdlow, Rudolph Kaaks, Myrto Barrdahl, Mia Gaudet, Antonis Antoniou, Peter Kraft, Montserrat Garcia-Closas, Nilanjan Chatterjee. Validation of breast cancer risk model incorporating classical risk factors and polygenic risk scores in 14 prospective cohort studies in 6 countries [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 962.

Abstract Existing risk models underperform among women who have undergone a benign breast disease (BBD) biopsy (&gt; one million performed annually in the US) and with respect to estimating the risk of aggressive BCs. We've shown that molecular pathologic analysis of BBD biopsies can improve individual risk prediction, compared with standard risk models relying on self-reported factors, and provide insights into mechanisms mediating BC risk. BC risk factors such as obesity and ethanol use are proposed to increase production of cytokines and chemokines, resulting in chronic inflammation, and leading to production of DNA damaging free radicals and growth factors (e.g., VEGF, IGFs) that activate pro-carcinogenic pathways (e.g. NF-KB and JAK/STAT). The effects of most BC risk factors on immunity are poorly understood. Further, whereas experimental models implicate immunity throughout carcinogenesis, knowledge of immune markers and mechanisms related to BC development among women is limited. Therefore, we aim to define tissue immune cell content throughout BC development, and particularly at the earliest stages, to improve risk assessment and discover immune-based prevention strategies. Our study combines novel resources and technologies to define the immune landscape in: 1) normal breast tissues in relation to BC risk factors, 2) BBD with respect to BC risk, and 3) invasive BC in relation to neoantigen expression and molecular subtype. We will perform NanoString GeoMxTM Digital Spatial Profiling (DSP) to quantitatively map expression of key immune proteins in healthy breast tissues donated to the Komen Tissue Bank (KTB) in relation to BC risk factors and in BBD biopsies from two cohorts to predict BC risk. We will re-analyze top prognostic markers in BBD biopsies using Vectra multiplex IF staining with machine learning algorithms to refine how immune cell content and organization impacts prognosis, and assess critical immune pathways related to BBD progression with the NanoString PanCancer IO 360 panel, which provides RNA expression of 770 immune genes (13 signatures). Finally, we will evaluate immune responses in BCs categorized for predicted neoantigen load and underlying mutation type with our novel bioinformatics pipeline that enables accurate prediction of MHC class I and class II missense, fusion and frameshift mutation neoantigens generated through faulty DNA repair, aberrant DNA and RNA splicing, insertions, and deletions. Our project demonstrates a novel approach to defining the immune processes in early stages of BC development which may improve BC risk prediction and prevention. We present a plan to comprehensively analyze immunity throughout BC development and define changes that accompany transitions from normal to BBD and BBD to BC. Citation Format: Doug Hinerfeld, Keith Knutson, Derek Radisky, E. Aubrey Thompson, Yan Asmann, Kim McCoy, Amy Degnim, Jodi Carter, Stacey Winham, Michele Cote, Jeroen van der Laak, Mark Sherman. Analysis of the immune microenvironment to advance breast cancer risk prediction and prevention [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 651.