Citations

AbstractBackgroundWolff–Parkinson–White (WPW) syndrome is a relatively common arrhythmia affecting ~1–3/1,000 individuals. Mutations in PRKAG2 have been described in rare patients in association with cardiomyopathy. However, the genetic basis of WPW in individuals with a structurally normal heart remains poorly understood. Sudden death due to atrial fibrillation (AF) can also occur in these individuals. Several studies have indicated that despite ablation of an accessory pathway, the risk of AF remains high in patients compared to general population.MethodsWe applied exome sequencing in 305 subjects, including 65 trios, 80 singletons, and 6 multiple affected families. We used de novo analysis, candidate gene approach, and burden testing to explore the genetic contributions to WPW.ResultsA heterozygous deleterious variant in PRKAG2 was identified in one subject, accounting for 0.6% (1/151) of the genetic basis of WPW in this study. Another individual with WPW and left ventricular hypertrophy carried a known pathogenic variant in MYH7. We found rare de novo variants in genes associated with arrhythmia and cardiomyopathy (ANK2, NEBL, PITX2, and PRDM16) in this cohort. There was an increased burden of rare deleterious variants (MAF ≤ 0.005) with CADD score ≥ 25 in genes linked to AF in cases compared to controls (P = .0023).ConclusionsOur findings show an increased burden of rare deleterious variants in genes linked to AF in WPW syndrome, suggesting that genetic factors that determine the development of accessory pathways may be linked to an increased susceptibility of atrial muscle to AF in a subset of patients.

Abstract Context Glucagon increases energy expenditure; consequently, glucagon receptor agonists are in development for the treatment of obesity. Obesity negatively affects the reproductive axis, and hypogonadism itself can exacerbate weight gain. Therefore, knowledge of the effects of glucagon receptor agonism on reproductive hormones is important for developing therapeutics for obesity; but reports in the literature about the effects of glucagon receptor agonism on the reproductive axis are conflicting. Objective The objective of this work is to investigate the effect of glucagon administration on reproductive hormone secretion in healthy young men. Design A single-blinded, randomized, placebo-controlled crossover study was conducted. Setting The setting of this study was the Clinical Research Facility, Imperial College Healthcare NHS Trust. Participants Eighteen healthy eugonadal men (mean ± SEM: age 25.1 ± 1.0 years; body mass index 22.5 ± 0.4 kg/m2; testosterone 21.2 ± 1.2 nmol/L) participated in this study. Intervention An 8-hour intravenous infusion of 2 pmol/kg/min glucagon or rate-matched vehicle infusion was administered. Main Outcome Measures Luteinizing hormone (LH) pulsatility; LH, follicle-stimulating hormone (FSH), and testosterone levels were measured. Results Although glucagon administration induced metabolic effects (insulin area under the curve: vehicle 1065 ± 292 min.µU/mL vs glucagon 2098 ± 358 min.µU/mL, P < .001), it did not affect LH pulsatility (number of LH pulses/500 min: vehicle 4.7 ± 0.4, glucagon 4.2 ± 0.4, P = .22). Additionally, there were no significant differences in circulating LH, FSH, or testosterone levels during glucagon administration compared with vehicle administration. Conclusions Acute administration of a metabolically active dose of glucagon does not alter reproductive hormone secretion in healthy men. These data are important for the continued development of glucagon-based treatments for obesity.

Cellular senescence, a permanent arrest of cell proliferation, is characterized by a senescence-associated secretory phenotype (SASP), which reinforces senescence and exerts noxious effects on adjacent cells. Recent studies have suggested that transplanting small numbers of senescent cells suffices to provoke tissue inflammation. We hypothesized that senescent cells can directly augment renal injury. Primary scattered tubular-like cells (STCs) acquired from pig kidneys were irradiated by 10 Gy of cesium radiation, and 3 wk later cells were characterized for levels of senescence and SASP markers. Control or senescent STCs were then prelabeled and injected (5 × 105 cells) into the aorta of C57BL/6J mice. Four weeks later, renal oxygenation was studied in vivo using 16.4-T magnetic resonance imaging and function by plasma creatinine level. Renal markers of SASP, fibrosis, and microvascular density were evaluated ex vivo. Per flow cytometry, irradiation induced senescence in 80–99% of STCs, which showed increased gene expression of senescence and SASP markers, senescence-associated β-galactosidase staining, and cytokine levels (especially IL-6) secreted in conditioned medium. Four weeks after injection, cells were detected engrafted in the mouse kidneys with no evidence for rejection. Plasma creatinine and renal tissue hypoxia increased in senescent compared with control cells. Senescent kidneys were more fibrotic, with fewer CD31+ endothelial cells, and showed upregulation of IL-6 gene expression. Therefore, exogenously delivered senescent renal STCs directly injure healthy mouse kidneys. Additional studies are needed to determine the role of endogenous cellular senescence in the pathogenesis of kidney injury and evaluate the utility of senolytic therapy.

BackgroundPretreatment determination of renal cell carcinoma aggressiveness may help to guide clinical decision‐making.PurposeTo evaluate the efficacy of residual convolutional neural network using routine MRI in differentiating low‐grade (grade I–II) from high‐grade (grade III–IV) in stage I and II renal cell carcinoma.Study TypeRetrospective.PopulationIn all, 376 patients with 430 renal cell carcinoma lesions from 2008–2019 in a multicenter cohort were acquired. The 353 Fuhrman‐graded renal cell carcinomas were divided into a training, validation, and test set with a 7:2:1 split. The 77 WHO/ISUP graded renal cell carcinomas were used as a separate WHO/ISUP test set.Field Strength/Sequence1.5T and 3.0T/T2‐weighted and T1 contrast‐enhanced sequences.AssessmentThe accuracy, sensitivity, and specificity of the final model were assessed. The receiver operating characteristic (ROC) curve and precision‐recall curve were plotted to measure the performance of the binary classifier. A confusion matrix was drawn to show the true positive, true negative, false positive, and false negative of the model.Statistical TestsMann–Whitney U‐test for continuous data and the chi‐square test or Fisher's exact test for categorical data were used to compare the difference of clinicopathologic characteristics between the low‐ and high‐grade groups. The adjusted Wald method was used to calculate the 95% confidence interval (CI) of accuracy, sensitivity, and specificity.ResultsThe final deep‐learning model achieved a test accuracy of 0.88 (95% CI: 0.73–0.96), sensitivity of 0.89 (95% CI: 0.74–0.96), and specificity of 0.88 (95% CI: 0.73–0.96) in the Fuhrman test set and a test accuracy of 0.83 (95% CI: 0.73–0.90), sensitivity of 0.92 (95% CI: 0.84–0.97), and specificity of 0.78 (95% CI: 0.68–0.86) in the WHO/ISUP test set.Data ConclusionDeep learning can noninvasively predict the histological grade of stage I and II renal cell carcinoma using conventional MRI in a multiinstitutional dataset with high accuracy.Level of Evidence3Technical Efficacy Stage2

Abstract Background: In an adaptive randomized trial, when new treatment combinations are being tested, it is important to be able to identify patients who are progressing on treatment so that they can be changed to a different therapeutic regimen. We know that even within the molecularly high risk patients in I-SPY 2, there is considerable variation in biology. In this study, we will present results of using MRI-calculated functional tumor volume (FTV) to identify tumor progression for each breast cancer subtype. Methods: Patients (n=990) enrolled in the I-SPY 2 TRIAL who were randomized to the graduated experimental drug arms or controls from 2010 to 2016 were analyzed. Four MRI exams were performed for each patient: pre-NAC (T0), after 3 weeks of NAC (T1), between regimens (T2), and post-NAC (T3). Functional tumor volume (FTV) was calculated at each exam by summing voxels meeting enhancement thresholds. Tumor progression at T1, T2 or T3 was identified by a positive FTV change relative to T0. Visual inspection was used to exclude false progression due to strong background parenchymal enhancement post-contrast, prominent vessels, motion, or insufficient image quality. pCR was defined as no invasive disease in the breast and lymph nodes. Negative predictive value for pCR was defined as:NPV=number of true non-pCRs / number of patients with MRI assessed tumor progressions, where “true non-pCRs” referred to patients who were non-pCRs at surgery and were assessed as progressors by MRI. The analysis was performed in the full cohort and in sub-cohorts defined by HR and HER2 statuses. Results: Out of 990 patients, 878 had pCR outcome data (pCR or non-pCR, pCR rate = 35%). Total and non-pCR numbers for each subtype, number of patients with tumor progression assessed by MRI at T1, T2, and T3, and NPVs, are shown in Table 1. In the full cohort, the NPV increased consistently over treatment, from T1 (NPV=83%) to T2 (93%), and to T3 (100%). The HER2+ cancer subtypes showed fewer MRI-assessed tumor progressions than HER2- subtypes: e.g. 10/209 (5%) vs. 108/669 (16%) at T1. NPV was 100% for HER2+ subtypes at T1 and T2 except for a single misclassification of a HR- tumor at T1. Only 6 tumor progressors, all HER2- were identified at T3, and all were confirmed at surgery as non-pCRs (NPV=100%). For HR+/HER2-, the NPV increased slightly from 89% at T1 to 91% at T2, while triple negative subtype had a more substantial increase, from 78% to 92%. Conclusions: Our study showed strong association between tumor progressors assessed by MRI with true non-pCRs after NAC. For HER2+ tumors, although MRI progressors are rare, they strongly indicate non-pCR at all treatment time points, while HER2- subtypes show more accurate results later in treatment. We are evaluating MRI change at 6 weeks to determine if that time point is sufficient to predict progressors. Table 1 MRI assessed tumor progression at different treatment time pointN/non-pCRs/%non-pCRMRI assessed tumor progressionT1 (after 3 weeks)T2 (inter-regimen)T3 (post-NAC)NNPV (%)NNPV (%)NNPV (%)Full cohort878/572/65%11883.14192.76100%HR+/HER2-344/280/81%4588.91190.93100%HR+/HER2+134/85/63%610021000N/AHR-/HER2+75/23/31%47521000N/Atriple negative325/184/57%6377.82692.33100% Citation Format: Wen Li, Natsuko Onishi, David C Newitt, Jessica Gibbs, Lisa J Wilmes, Ella F Jones, Bonnie N Joe, Laura S Sit, Christina Yau, A. Jo Chien, Elissa Price, Kathy S Albain, Theresa Kuritza, Kevin Morley, Judy C Boughey, Kathy Brandt, Sadia Choudhery, Amy S Clark, Mark Rosen, Elizabeth S McDonald, Anthony D Elias, Dulcy Wolverton, Kelly Fountain, David M Euhus, Heather S Han, Bethany Niell, Jennifer Drukteinis, Julie E Lang, Janice Lu, Jane L Meisel, Zaha Mitri, Rita Nanda, Donald W Northfelt, Tara Sanft, Erica Stringer-Reasor, Rebecca K Viscusi, Anne M Wallace, Douglas Yee, Rachel Yung, Smita M Asare, Michelle E Melisko, Jane Perlmutter, Hope S Rugo, Richard Schwab, W. Fraser Symmans, Laura J van't Veer, Donald A Berry, Angela DeMichele, Hiroyuki Abe, Deepa Sheth, Kirsten K Edmiston, Erin D Ellis, Richard Ha, Ralph Wynn, Erin P Crane, Charlotte Dillis, Michael Nelson, An Church, Claudine Isaacs, Qamar J Khan, Karen Y Oh, Neda Jafarian, Dae Hee Bang, Christiane Mullins, Stefanie Woodard, Kathryn W Zamora, Haydee Ojeda-Fornier, Pulin Sheth, Linda Hovanessian-Larsen, Mohammad Eghtedari, Michael Spektor, Marina Giurescu, Mary S Newell, Michael A Cohen, Elise Berman, Constance Lehman, William Smith, Kim Fitzpatrick, Marisa H Borders, Wei Yang, Basak Dogan, Sally Goudreau, Thelma Brown, Laura J Esserman, Nola M Hylton. Breast cancer subtype specific association of pCR with MRI assessed tumor volume progression during NAC in the I-SPY 2 trial [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr PD9-04.

Abstract Background: Strong background parenchymal enhancement (BPE) may cause overestimation in tumor volume measured from dynamic contrast-enhanced (DCE) MRI, which may adversely affect the ability of MR tumor volume to predict treatment outcome for patients undergoing neoadjuvant chemotherapy (NAC). Specifically, an overestimation of tumor volume can result in misclassification of patients with complete pathologic response (pCR) as non-responders, leading to less confidence in MRI prediction. As well, overestimation of extent of disease might lead to more aggressive surgical therapy than necessary. This study investigated whether high BPE in the contralateral breast influences the predictive performance of MRI-measured functional tumor volume (FTV) for patients with locally advanced breast cancer undergoing NAC. Methods: patients (n=990) enrolled in the I-SPY 2 TRIAL who were randomized to the graduated experimental drug arms or controls from 2010 to 2016 were analyzed. Each patient had 4 MRI exams: pre-NAC (T0), after 3 weeks of NAC (T1), between NAC regimens (T2), and post-NAC (T3). FTV was calculated at each MRI exam by summing voxels meeting enhancement thresholds. Background parenchymal enhancement (BPE) in the contralateral breast was calculated automatically as mean percentage enhancement on the early (nominal 150sec post-contrast) image in the fibroglandular tissue segmented from 5 continuous axial slices centered in the inferior-to-superior stack. For each treatment time point, patients having both FTV and BPE measurements were included in the analysis. The area under the ROC curve (AUC) was estimated as the association between FTV and pCR at T1, T2, and T3. The analysis was conducted in the full patient cohort and in sub-cohorts defined by hormone receptor (HR) and HER2 status. In each patient cohort, a cut-off BPE value was selected to classify patients with high vs. low BPE by testing AUCs estimated with low-BPE patients reached maximum when the cut-off value varied from median to maximum in steps of 10%. Results: Out of 990 patients, 878 had pCR outcome data (pCR or non-pCR, pCR rate = 35%). Table 1 shows the number of patients, pCR rate, and AUC of FTV for predicting pCR using all patients available vs. a subset patients with low BPE (< BPE cut-off). In the full cohort, AUC increased slightly across all time points after patients with high BPE were removed. In the HR+/HER2- subtype, AUC increased at T1 after removal of cases with high BPE (0.65 vs. 0.71). For HR-/HER2+, AUC increased substantially after removal of high BPE cases (0.65 to 0.86 at T1, 0.71 to 0.87 at T2, and 0.71 to 0.89 at T3), with greater improvement at the early time point (T1) compared to later time points (T2 and T3). Only a slight improvement in the AUC was observed in the HR+/HER2+ and HR-/HER2- subtypes across all time points. Conclusions: High background parenchymal enhancement adversely affected the predictive performance of functional tumor volume measured by DCE-MRI, at early treatment time point for HR+/HER2- and across all time points for HR-/HER2+ cancer subtype. The adverse effect might be offset using subtype-optimized enhancement threshold in calculating functional tumor volume. Table 1 Effect of BPE on the prediction of pCR using FTV at various treatment time pointsT1T2T3npCR rateAUCBPE cut-offnpCR rateAUCBPE cut-offnpCR rateAUCBPE cut-offFullAll64734%0.662762334%0.701761134%0.6925Subset45334%0.6831133%0.7230534%0.72HR+/HER2-All26218%0.651924918%0.718225518%0.7519Subset13118%0.7124818%0.7120419%0.76HR+/HER2+All10636%0.642110538%0.62269634%0.7120Subset5332%0.668438%0.665740%0.73HR-/HER2+All5175%0.65204774%0.71204973%0.7116Subset3073%0.862871%0.872475%0.89HR-/HER2-All22842%0.682822243%0.751821143%0.6916Subset15940%0.7111137%0.7810540%0.75 Citation Format: Wen Li, Natsuko Onishi, David C Newitt, Roy Harnish, Ella F Jones, Lisa J Wilmes, Jessica Gibbs, Elissa Price, Bonnie N Joe, A. Jo Chien, Donald A Berry, Judy C Boughey, Kathy S Albain, Amy S Clark, Kirsten K Edmiston, Anthony D Elias, Erin D Ellis, David M Euhus, Heather S Han, Claudine Isaacs, Qamar J Khan, Julie E Lang, Janice Lu, Jane L Meisel, Zaha Mitri, Rita Nanda, Donald W Northfelt, Tara Sanft, Erica Stringer-Reasor, Rebecca K Viscusi, Anne M Wallace, Douglas Yee, Rachel Yung, Michelle E Melisko, Jane Perlmutter, Hope S Rugo, Richard Schwab, W. Fraser Symmans, Laura J van't Veer, Christina Yau, Smita M Asare, Angela DeMichele, Sally Goudreau, Hiroyuki Abe, Deepa Sheth, Dulcy Wolverton, Kelly Fountain, Richard Ha, Ralph Wynn, Erin P Crane, Charlotte Dillis, Theresa Kuritza, Kevin Morley, Michael Nelson, An Church, Bethany Niell, Jennifer Drukteinis, Karen Y Oh, Neda Jafarian, Kathy Brandt, Sadia Choudhery, Dae Hee Bang, Christiane Mullins, Stefanie Woodard, Kathryn W Zamora, Haydee Ojeda-Fornier, Mohammad Eghedari, Pulin Sheth, Linda Hovanessian-Larsen, Mark Rosen, Elizabeth S McDonald, Michael Spektor, Marina Giurescu, Mary S Newell, Michael A Cohen, Elise Berman, Constance Lehman, William Smith, Kim Fitzpatrick, Marisa H Borders, Wei Yang, Basak Dogan, Laura J Esserman, Nola M Hylton. The effect of background parenchymal enhancement on the predictive performance of functional tumor volume measured in MRI [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P6-02-01.

AbstractBackgroundGranuloma annulare (GA) and the related annular elastolytic giant cell granuloma (AEGCG) and interstitial granulomatous dermatitis (IGD) are idiopathic histiocytic inflammatory disorders, which are frequently recalcitrant to treatment.ObjectivesEvaluate the efficacy of sulphasalazine in treating GA, AEGCG and IGD.MethodsSixteen patients were identified with granulomatous disease who were treated with sulphasalazine between September 2015 and September 2019. Outcomes were based on patients’ and providers’ subjective evaluations.ResultsSixteen patients were included in the study (ages 56–89, four male and twelve female). Previous treatments were attempted in fifteen patients. Clinical improvement was seen in fourteen patients (87.5%). Initial improvement was noted within a mean (SD) of 66.4 (35.1) days after starting therapy, with increasing benefits over time. Ten patients (62.5%) reported complete or near‐complete clearance, three patients (18.8%) reported significant improvement, and one (6.3%) reported partial improvement. Twelve patients elected to stop or reduce therapy, resulting in relapse or worsening in five patients.ConclusionsSulphasalazine may be considered as treatment for GA and GA‐related conditions.

Abstract Background The risks of breast cancer in African American (AA) women associated with inherited mutations in breast cancer predisposition genes are not well defined. Thus, whether multigene germline hereditary cancer testing panels are applicable to this population is unknown. We assessed associations between mutations in panel-based genes and breast cancer risk in 5054 AA women with breast cancer and 4993 unaffected AA women drawn from 10 epidemiologic studies. Methods Germline DNA samples were sequenced for mutations in 23 cancer predisposition genes using a QIAseq multiplex amplicon panel. Prevalence of mutations and odds ratios (ORs) for associations with breast cancer risk were estimated with adjustment for study design, age, and family history of breast cancer. Results Pathogenic mutations were identified in 10.3% of women with estrogen receptor (ER)-negative breast cancer, 5.2% of women with ER-positive breast cancer, and 2.3% of unaffected women. Mutations in BRCA1, BRCA2, and PALB2 were associated with high risks of breast cancer (OR = 47.55, 95% confidence interval [CI] = 10.43 to >100; OR = 7.25, 95% CI = 4.07 to 14.12; OR = 8.54, 95% CI = 3.67 to 24.95, respectively). RAD51D mutations were associated with high risk of ER-negative disease (OR = 7.82, 95% CI = 1.61 to 57.42). Moderate risks were observed for CHEK2, ATM, ERCC3, and FANCC mutations with ER-positive cancer, and RECQL mutations with all breast cancer. Conclusions The study identifies genes that predispose to breast cancer in the AA population, demonstrates the validity of current breast cancer testing panels for use in AA women, and provides a basis for increased referral of AA patients for cancer genetic testing.