Decker, Paul A.

Pulse pressure (PP), defined as the difference between systolic and diastolic blood pressure, is considered a marker of large artery stiffness and has been shown to independently predict cardiovascular disease. Differences among ethnicities with regard to PP have been previously described, yet the underlying biological mechanisms are poorly understood. In addition, biomarkers of inflammation and matrix remodeling have been shown to be involved in the development of arterial stiffness. The aim of this study was to investigate the role of adhesion pathway components in the development of arterial stiffness measured by PP among four ethnicities. E-cadherin, HGF, L-selectin, P-selectin, 6-Ckine, MMP1, MMP2, RANTES, SDF1, SLPI, TGFβ1, TIMP2 and VCAM were measured in a random sample of 2402 participants (579 African Americans, 619 Caucasians, 600 Chinese and 604 Hispanics) in the Multi-Ethnic Study of Atherosclerosis (MESA) study at Exam 2. PP was measured concurrently and its association with each adhesion protein level was assessed via linear regression in each ethnicity. All analyses were adjusted for age, sex, and traditional cardiovascular risk factors. Mean PP was significantly different among ethnicities: 55.9±18.0 mmHg in African Americans, 52.1± 16.1 mmHg in Caucasians, 51.4±16.4 mmHg in Chinese, and 55.0±17.5 in Hispanics, p<0.001. MMP2 was positively associated with PP among African Americans (β=2.3 (0.6), p<0.001) and Caucasians (β=1.7 (0.5), p=0.001); TIMP2 among Caucasians (β=1.5 (0.5), p=0.002) and to a lesser extent among African Americans (β=1.2 (0.6) p=0.050) and Chinese (β=1.3 (0.6), p=0.034). SDF1 was associated to PP in African Americans (β=1.4 (0.6), p=0.017) and in Chinese (β=1.1 (0.5), p=0.042); finally an association existed between TGFβ1 and PP in C (β=1.1 (0.5), p=0.025). None of the studied proteins was associated with PP in Hispanics. In conclusion, components of the adhesion pathway were correlated to PP in this large multi-ethnic cohort, possibly giving new insight on the pathophysiology of arterial stiffness. In addition, heterogeneity by ethnicity suggests different mechanisms in the development of arterial stiffness.

Abstract Background Appropriately designed preclinical patient-derived xenograft (PDX) experiments are important to accurately inform human clinical trials. There is little experimental design guidance regarding choosing the number of PDX lines to study, and the number of mice within each PDX line. Methods Retrospective data from IDH-wildtype glioblastoma preclinical experiments evaluating a uniform regimen of fractionated radiation (RT), temozolomide (TMZ) chemotherapy, and concurrent RT/TMZ across 27 PDX lines were used to evaluate experimental designs and empirically estimate statistical power for ANOVA and Cox regression. Results Increasing the number of PDX lines resulted in more precise and reproducible estimates of effect size. To achieve 80% statistical power using ANOVA, experiments using a single PDX line required subsampling of 6 mice per PDX for each treatment group to detect a difference in survival of 135 days, and 9 mice per PDX to detect a difference of 100 days. Alternatively, a design that used 10 PDX lines had greater than 80% power to detect a difference of 135 days with a single mouse per PDX per treatment group, a difference of 100 days with 2 mice per PDX per treatment, and 35 days with more than 10 mice per PDX per treatment. Power for Cox regression was slightly smaller than ANOVA for very small experiments regardless of effect size and slightly higher than ANOVA for detecting a smaller effect size of 35 days difference in survival for moderate-to-large experiments. Conclusions Experimental designs using few mice across many PDX lines can provide robust results and account for inter-tumor variability.

Abstract Background Antibody drug conjugates (ADCs) targeting the epidermal growth factor receptor (EGFR), such as depatuxizumab mafodotin (Depatux-M), is a promising therapeutic strategy for glioblastoma (GBM) but recent clinical trials did not demonstrate a survival benefit. Understanding the mechanisms of failure for this promising strategy is critically important. Methods PDX models were employed to study efficacy of systemic vs intracranial delivery of Depatux-M. Immunofluorescence and MALDI-MSI were performed to detect drug levels in the brain. EGFR levels and compensatory pathways were studied using quantitative flow cytometry, Western blots, RNAseq, FISH, and phosphoproteomics. Results Systemic delivery of Depatux-M was highly effective in nine of 10 EGFR-amplified heterotopic PDXs with survival extending beyond one year in eight PDXs. Acquired resistance in two PDXs (GBM12 and GBM46) was driven by suppression of EGFR expression or emergence of a novel short-variant of EGFR lacking the epitope for the Depatux-M antibody. In contrast to the profound benefit observed in heterotopic tumors, only two of seven intrinsically sensitive PDXs were responsive to Depatux-M as intracranial tumors. Poor efficacy in orthotopic PDXs was associated with limited and heterogeneous distribution of Depatux-M into tumor tissues, and artificial disruption of the BBB or bypass of the BBB by direct intracranial injection of Depatux-M into orthotopic tumors markedly enhanced the efficacy of drug treatment. Conclusions Despite profound intrinsic sensitivity to Depatux-M, limited drug delivery into brain tumor may have been a key contributor to lack of efficacy in recently failed clinical trials.

Background: Prevention strategies for Alzheimer’s disease and Alzheimer’s disease related dementias (AD/ADRDs) are urgently needed for reducing incidence. Intra-patient variability in lipid levels is a potentially modifiable risk factor for incident AD/ADRD. Although regular lipid measurements are a part of common clinical practice and longitudinal data routinely available in electronic health records (EHR), research examining this association between AD/ADRD and lipid variability across multiple lipid types remains scarce. Methods: All residents living in Olmsted County, MN on 1/1/2006 age ≥60 years without an AD/ADRD diagnosis were identified using Centers for Medicare & Medicaid Services diagnostic codes. Persons with ≥3 lipid measurements (total cholesterol or triglycerides) in the 5 years prior to index date were retained. Lipid variability was quantified using variability independent of the mean (VIM). Models were adjusted for traditional risk factors. Associations between lipid variability quintiles and incident AD/ADRD were assessed using Cox proportional hazards regression. Multiple imputation was used for missing covariates. Participants were followed through 2018 for incident AD/ADRD. Results: The final analysis included data on 11,551 participants with total cholesterol and 11,502 participants with triglycerides. Participants had a mean age of 71 (range 60-99) years, and were primarily white (96%). Females (54%) were also slightly more frequent than males. Median follow up was 12.9 years (range: 0-13). Participants in the highest quintile of variability for total cholesterol and triglycerides had a 20% increased risk of incident AD/ADRD. Similar results were found in the subset with complete covariate data. Conclusion: In a large EHR derived cohort, persons in the highest quintile of lipid variation had an increased risk of incident AD/ADRD. Further studies to identify the mechanisms behind this risk factor and replication of these results across a more diverse population are needed.

Background: Larger within-patient variability of lipid levels has been associated with an increased risk of cardiovascular disease (CVD). However, measures of lipid variability are not currently used clinically. We investigated the feasibility of calculating lipid variability within a large electronic health record (EHR)-based population cohort and assessed associations with incident CVD. Methods: We identified all individuals ≥40 years of age who resided in Olmsted County, MN on 1/1/2006 (index date) without prior CVD. CVD was defined as myocardial infarction, coronary artery bypass graft surgery, percutaneous coronary intervention or stroke. Patients with ≥3 measurements of total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and/or triglycerides during the 5 years before the index date were retained in the analyses. Lipid variability was calculated using variability independent of the mean (VIM). Patients were followed through 9/30/2017 for incident CVD (including CVD death). Cox regression was used to investigate the association between quintiles of lipid VIMs and incident CVD. Results: We identified 18,642 individuals (mean age 60; 55% female) who were free of CVD at baseline and VIM calculated for at least one lipid measurement. After adjustment, those in the highest VIM quintiles of total cholesterol had a 25% increased risk of CVD (Q5 vs. Q1 HR: 1.25, 95% CI: 1.08-1.45; Table). We observed similar results for LDL-C (Q5 vs. Q1 HR: 1.20, 95% CI: 1.04-1.39) and HDL-C (Q5 vs. Q1 HR: 1.25, 95% CI: 1.09-1.43). There was no association between triglyceride variability quintiles and CVD risk. Conclusion: In a large EHR-based population cohort, high variability in total cholesterol, HDL-C and LDL-C was associated with an increased risk of CVD, independently of traditional risk factors, suggesting it may be a target for intervention. Lipid variability can be calculated in the EHR environment but more research is needed to determine its clinical utility.

Abstract Tesevatinib is a potent oral brain penetrant EGFR inhibitor currently being evaluated for glioblastoma therapy. Tesevatinib distribution was assessed in wild-type (WT) and Mdr1a/b(-/-)Bcrp(-/-) triple knockout (TKO) FVB mice after dosing orally or via osmotic minipump; drug–tissue binding was assessed by rapid equilibrium dialysis. Two hours after tesevatinib dosing, brain concentrations in WT and TKO mice were 0.72 and 10.03 μg/g, respectively. Brain-to-plasma ratios (Kp) were 0.53 and 5.73, respectively. With intraperitoneal infusion, brain concentrations were 1.46 and 30.6 μg/g (Kp 1.16 and 25.10), respectively. The brain-to-plasma unbound drug concentration ratios were substantially lower (WT mice, 0.03–0.08; TKO mice, 0.40–1.75). Unbound drug concentrations in brains of WT mice were 0.78 to 1.59 ng/g. In vitro cytotoxicity and EGFR pathway signaling were evaluated using EGFR-amplified patient-derived glioblastoma xenograft models (GBM12, GBM6). In vivo pharmacodynamics and efficacy were assessed using athymic nude mice bearing either intracranial or flank tumors treated by oral gavage. Tesevatinib potently reduced cell viability [IC50 GBM12 = 11 nmol/L (5.5 ng/mL), GBM6 = 102 nmol/L] and suppressed EGFR signaling in vitro. However, tesevatinib efficacy compared with vehicle in intracranial (GBM12, median survival: 23 vs. 18 days, P = 0.003) and flank models (GBM12, median time to outcome: 41 vs. 33 days, P = 0.007; GBM6, 44 vs. 33 days, P = 0.007) was modest and associated with partial inhibition of EGFR signaling. Overall, tesevatinib efficacy in EGFR-amplified PDX GBM models is robust in vitro but relatively modest in vivo, despite a high brain-to-plasma ratio. This discrepancy may be explained by drug-tissue binding and compensatory signaling.

Abstract Introduction Tumefactive multiple sclerosis (MS) and glioblastoma (GBM) can be difficult to differentiate. Clinical presentations are nonspecific and may occur in patients with both diseases. Magnetic resonance imaging (MRI) scans are routinely used to generate an initial differential diagnosis. Although imaging features can help distinguish these entities, there are no pathognomonic signs, neither on traditional nor advanced MRI. Moreover, the ability to differentiate these entities depends on the radiologists’ experience. Ensuring the correct diagnosis has important therapeutic and prognostic implications, as the management of these diseases is very different. To date, a brain biopsy often represents the only option to obtain a diagnosis, but it may be inconclusive and could add unnecessary morbidity. We hypothesize that a machine-learning tool based on routinely performed MRI scans could guarantee a more reliable, fast, and reproducible diagnosis. Methods Only patients with a histological diagnosis were included in the study. Patients that underwent any form of radiation therapy before the surgery were excluded to avoid confounding factors due to treatment-related effects. A total of 99 MS and 257 GBMs were included, and preoperative T2-weighted images were collected. For each patient, the lesions' segmentation was performed, including both the contrast-enhancing and non-enhancing areas of the lesions. The segmentation masks were used to extract radiomic features from the T2-weighted sequences. All MRI images were standardized to the same mean and standard deviation before feature extraction and resampled to a voxel size of 1.0 × 1.0 × 1.0 mm. Features extraction was performed using Pyradiomics (v. 3.0.1) and included first order, shape, and texture features, totaling 100 features. The classifier consisted of a support vector machine (SVM) model. Patients were randomly assigned to the training and test sets with an 80-20% proportion for each class. To select the best hyperparameters for the SVM classifier, a grid-search analysis was performed on the training set using a nested cross-validation scheme (k=5). All experiments were performed using Python (v. 3.5) and the Scikit-learn library (v. 0.23.1). Results The mean accuracy and area under the ROC curve for the best set of hyperparameters on the validation set were 0.83 and 0.86, respectively. The weighted-average precision, recall, and F1 scores on the test set were 0.78, 0.76, and 0.77, respectively. Conclusions Radiomic features extracted from a routinely acquired MRI sequence showed promising results in differentiating tumefactive multiple sclerosis from glioblastoma, opening the possibility to obtain a reliable diagnosis without the need for a brain biopsy. Future experiments Future experiments will focus on using post-contrast T1-weighted images for the radiomic features extractions, testing multiple machine learning models and features selection algorithms, and including more diagnostic classes (i.e., metastasis, lymphomas). Citation Format: Gian Marco Conte, Jeanette E. Eckel-Passow, W. Oliver Tobin, Paul Decker, Daniel H. Lachance, Robert B. Jenkins, Bradley J. Erickson. Differentiation of tumefactive multiple sclerosis and glioblastoma using radiomics features extracted from magnetic resonance imaging and machine learning [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-023.

Inflammatory breast cancer is a highly aggressive form of breast cancer that forms clusters of tumor emboli in dermal lymphatics and readily metastasizes. These cancers express high levels of E-cadherin, the major mediator of adherens junctions, which enhances formation of tumor emboli. Previous studies suggest that E-cadherin promotes cancer when the balance between apical and basolateral cadherin complexes is disrupted. Here, we used immunohistochemistry of inflammatory breast cancer patient samples and analysis of cell lines to determine the expression of PLEKHA7, an apical adherens junction protein. We used viral transduction to re-express PLEKHA7 in inflammatory breast cancer cells and examined their aggressiveness in 2D and 3D cultures and in vivo. We determined that PLEKHA7 was deregulated in inflammatory breast cancer, demonstrating improper localization or lost expression in most patient samples and very low expression in cell lines. Re-expressing PLEKHA7 suppressed proliferation, anchorage independent growth, spheroid viability, and tumor growth in vivo. The data indicate that PLEKHA7 is frequently deregulated and acts to suppress inflammatory breast cancer. The data also promote the need for future inquiry into the imbalance between apical and basolateral cadherin complexes as driving forces in inflammatory breast cancer.

Substantial gaps remain in the evidence base for prenatal tobacco use interventions among Indigenous women. Using a cluster randomized controlled trial (RCT), the Healthy Pregnancies Project evaluated a community-level intervention for Alaska Native (AN) women in rural western Alaska. Sixteen villages were randomly assigned to usual care (control, n = 8 villages) or usual care plus a community-level intervention delivered by local AN “Native Sisters” (n = 8 villages). Outcomes were tobacco use rate at delivery and at 2 and 6 months postpartum, with biochemical confirmation obtained at 6 months. The program had high reach, enrolling 73% of all eligible women screened. Of the 352 participants, 67% used tobacco at baseline. No significant differences emerged between study groups on follow-up in tobacco use rates. More intervention than control participants made a quit attempt at 2 months postpartum (70% vs. 51%, respectively, p = 0.012). Participants in both study groups reported the program helped to raise awareness of healthy pregnancies in the study villages. This trial supports the reach of a community-level intervention, but not its efficacy for reducing tobacco use during pregnancy or postpartum. Efforts to sustain early quit attempts appear warranted. The community involvement, and reported impact on raising awareness of the importance of healthy pregnancies, supports the value of the research program in this community.

Abstract Background Face-to-face tobacco cessation has had limited reach and efficacy in Alaska Native (AN) communities. We describe our two-phased approach to develop content for Connecting Alaska Native People to Quit Smoking, a Facebook group intervention to reduce barriers to evidence-based smoking cessation treatment for AN people in Alaska. Methods Phase 1 included semi-structured telephone interviews with 30 AN people who smoke and ten stakeholders. They provided feedback on existing content from the Centers for Disease Control and Prevention Tips campaign and AN digital stories. Phase 2 included an online survey with a new group of 40 AN smokers who provided feedback on existing content via a measure of perceived effectiveness and cultural relevance. Results Phase I results revealed participants evaluated content based upon story strength, relevance to AN culture, emotional appeal, relatability to AN people, and favorite video. No single posting was rated highly across all themes. All perceived effectiveness (PE) and cultural relevance median scores fell between 3.5 and 4.4 (range 1–5). PE scores varied across participant demographic groups. Conclusions Content embodying characteristics perceived to be most appealing, effective, and culturally relevant were selected for the private Facebook group content library with refinements made to incorporate images of AN people engaged in AN activities. PE scores indicate a need for a wide variety of content that moderators could pull from when conducting the intervention. Implications Social media content targeting specific population sectors, such as American Indian/AN people for tobacco cessation needs to be culturally tailored. Our approach provides a model others can follow to determine what is appealing, relevant, and effective messaging. Clinical Trial Registration NCT 03645941.