Citations

AbstractBackground/ObjectivesThe COVID‐19 pandemic prompted a rapid expansion in the use of telemedicine. This study aimed to assess the experiences of hemangioma specialists utilizing telemedicine during the COVID‐19 pandemic to evaluate and manage infantile hemangiomas (IH), including perceived effectiveness of different modalities and barriers to care delivery.MethodsMulticenter cross‐sectional study asking providers to describe their experiences using telemedicine for initial evaluation of IH from March to September 2020.ResultsThe study included 281 patients from 15 medical centers internationally. Median time from referral to evaluation was 17 days. Median physician confidence in performing evaluations via telemedicine was 95.0 (IQR 90.0–100.0). Most evaluations were performed via video communication with photographs or audio communication with photographs; when not initially available, photographs were requested in 51.4%. Providers preferred follow‐up modalities that included photographs.ConclusionsPhysicians with extensive expertise in managing IH are confident in their abilities to assess and manage IH via telemedicine including initiating treatment in patients without risk factors for beta‐blocker therapy. There was a preference for hybrid modalities that included photographs. The data suggest that telemedicine can be effective for managing IH and may decrease wait times and improve specialist reach to underserved areas.

Abstract Background Clinical differences between critical illness from influenza infection vs coronavirus disease 2019 (COVID-19) have not been well characterized in pediatric patients. Methods We compared demographics, clinical characteristics, and outcomes of US children (aged 8 months to 17 years) admitted to the intensive care or high-acuity unit with influenza or COVID-19. Using mixed-effects models, we assessed the odds of death or requiring life support for influenza vs COVID-19 after adjustment for age, sex, race and Hispanic origin, and underlying conditions including obesity. Results Children with influenza (n = 179) were younger than those with COVID-19 (n = 381; median, 5.2 years vs 13.8 years), less likely to be non-Hispanic Black (14.5% vs 27.6%) or Hispanic (24.0% vs 36.2%), and less likely to have ≥1 underlying condition (66.4% vs 78.5%) or be obese (21.4% vs 42.2%), and a shorter hospital stay (median, 5 days vs 7 days). They were similarly likely to require invasive mechanical ventilation (both 30.2%), vasopressor support (19.6% and 19.9%), or extracorporeal membrane oxygenation (2.2% and 2.9%). Four children with influenza (2.2%) and 11 children with COVID-19 (2.9%) died. The odds of death or requiring life support in children with influenza vs COVID-19 were similar (adjusted odds ratio, 1.30; 95% confidence interval, .78–2.15; P = .32). Conclusions Despite differences in demographics and clinical characteristics of children with influenza or COVID-19, the frequency of life-threatening complications was similar. Our findings highlight the importance of implementing prevention measures to reduce transmission and disease severity of influenza and COVID-19.

AbstractCongenital disorders of glycosylation are a continuously expanding group of monogenic disorders of glycoprotein and glycolipid glycan biosynthesis. These disorders mostly manifest with multisystem involvement. Individuals with ALG8‐CDG commonly present with hypotonia, protein‐losing enteropathy, and hepatic involvement. Here, we describe seven unreported individuals diagnosed with ALG8‐CDG based on biochemical and molecular testing and we identify nine novel variants in ALG8, bringing the total to 26 individuals with ALG8‐CDG in the medical literature. In addition to the typical multisystem involvement documented in ALG8‐CDG, our cohort includes the two oldest patients reported and further expands the phenotype of ALG8‐CDG to include stable intellectual disability, autism spectrum disorder and other neuropsychiatric symptoms. We further expand the clinical features in a variety of organ systems including ocular, musculoskeletal, dermatologic, endocrine, and cardiac abnormalities and suggest a comprehensive evaluation and monitoring strategy to improve clinical management.

PurposeTo evaluate multicenter repeatability and reproducibility of T1 and T2 maps generated using MR fingerprinting (MRF) in the International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology MRI system phantom and in prostatic tissues.MethodsMRF experiments were performed on 5 different 3 Tesla MRI scanners at 3 different institutions: University Hospitals Cleveland Medical Center (Cleveland, OH), Brigham and Women's Hospital (Boston, MA) in the United States, and Diagnosticos da America (Rio de Janeiro, RJ) in Brazil. Raw MRF data were reconstructed using a Gadgetron‐based MRF online reconstruction pipeline to yield quantitative T1 and T2 maps. The repeatability of T1 and T2 values over 6 measurements in the International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology MRI system phantom was assessed to demonstrate intrascanner variation. The reproducibility between the 4 clinical scanners was assessed to demonstrate interscanner variation. The same‐day test–retest normal prostate mean T1 and T2 values from peripheral zone and transitional zone were also compared using the intraclass correlation coefficient and Bland–Altman analysis.ResultsThe intrascanner variation of values measured using MRF was less than 2% for T1 and 4.7% for T2 for relaxation values, within the range of 307.7 to 2360 ms for T1 and 19.1 to 248.5 ms for T2. Interscanner measurements showed that the T1 variation was less than 4.9%, and T2 variation was less than 8.1% between multicenter scanners. Both T1 and T2 values in in vivo prostatic tissue demonstrated high test–retest reliability (intraclass correlation coefficient > 0.92) and strong linear correlation (R2 > 0.840).ConclusionProstate MRF measurements of T1 and T2 are repeatable and reproducible between MRI scanners at different centers on different continents for the above measurement ranges.

Abstract Background: Over 20 susceptibility single-nucleotide polymorphisms (SNP) have been identified for esophageal adenocarcinoma (EAC) and its precursor, Barrett esophagus (BE), explaining a small portion of heritability. Methods: Using genetic data from 4,323 BE and 4,116 EAC patients aggregated by international consortia including the Barrett's and Esophageal Adenocarcinoma Consortium (BEACON), we conducted a comprehensive transcriptome-wide association study (TWAS) for BE/EAC, leveraging Genotype Tissue Expression (GTEx) gene-expression data from six tissue types of plausible relevance to EAC etiology: mucosa and muscularis from the esophagus, gastroesophageal (GE) junction, stomach, whole blood, and visceral adipose. Two analytical approaches were taken: standard TWAS using the predicted gene expression from local expression quantitative trait loci (eQTL), and set-based SKAT association using selected eQTLs that predict the gene expression. Results: Although the standard approach did not identify significant signals, the eQTL set–based approach identified eight novel associations, three of which were validated in independent external data (eQTL SNP sets for EXOC3, ZNF641, and HSP90AA1). Conclusions: This study identified novel genetic susceptibility loci for EAC and BE using an eQTL set–based genetic association approach. Impact: This study expanded the pool of genetic susceptibility loci for EAC and BE, suggesting the potential of the eQTL set–based genetic association approach as an alternative method for TWAS analysis.

Abstract Background: As a key receptor tyrosine kinase (RTK) regulating tumorigenesis, AXL is overexpressed in multiple different types of cancers. AXL can be activated by homodimerization after binding with its ligand, growth arrest specific 6 protein (Gas6) or by heterodimerization with other RTKs. AXL overexpression and/or activation have been reported to promote cancer cell proliferation, migration/invasion and to induce resistance to targeted therapy. However, the AXL downstream signaling network has not yet been fully elucidated. Methods: We employed a CRISPR-CAS9-based knockout technology to create AXL inducible knockout cells in a human triple negative breast cancer (TNBC) cell line, HCC1395. In order to explore the downstream signaling events driven by AXL, we induced AXL knockout for 72 hours and used a 6-plex TMT-labeling-based quantitative proteomic approach to characterize the protein phosphorylation alterations induced by AXL knockout. IMAC and anti-phosphotyrosine antibody (pY1000)-based phosphopeptide enrichment methods were used to comprehensively quantify the phosphoproteome changes induced by AXL knockout. Results: In this study, we identified 38,525 phospho serine/threonine sites and 1,427 phosphotyrosine sites. In response to AXL knockout, 1,581 phospho serine/threonine sites and 40 phosphotyrosine sites showed significant differences between AXL-KO and AXL-WT groups with >1.5-fold change and p<0.05 as the cutoff. Among them, 982 phosphoserine/threonine sites and 30 phosphotyrosine sites were decreased, while 599 phospho serine/threonine sites and 10 phosphotyrosine sites were increased in AXL-KO cells. Signaling pathway enrichment analysis revealed that suppressing AXL expression could inhibit multiple important cancer related signaling pathways, including cell cycle, focal adhesion, MAPK signaling pathway, p53 signaling pathway and mTOR signaling pathway. Conclusion: Quantitative analysis of the phosphoproteome driven by AXL demonstrated that AXL plays a pivotal role in regulating canonical oncogenic signaling but also signaling pathways involved in RNA processing and DNA repair. Our data also suggest that targeting AXL could suppress these oncogenic signaling pathways and have therapeutic potential to improve clinical outcomes for patients with AXL overexpression. Citation Format: Li Wang, Nicole Pearson, Yuning Xiong, Santosh Renuse, Ran Cheng, Jodi M. Carter, Akhilesh Pandey, Xinyan Wu. Quantitative phosphoproteomic analysis of AXL signaling network in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3913.

Abstract Introduction: Patients at extremes of age may be particularly vulnerable to poor physical and mental health after treatment for breast cancer; we examined factors associated with poor health in older breast cancer survivors. Methods: The Mayo Clinic Breast Disease Registry (MCBDR) is a prospective longitudinal cohort study that enrolls Mayo Clinic Rochester patients diagnosed with stage 0-4 breast cancer (BC). In 2020, we mailed a survey to 1,273 MCBDR participants aged 77+. Three PROMIS-10 items were used to assess overall health, mental health, and physical function. Self-reported comorbidities were also collected. Multivariate logistic regression analysis was used to identify significant (p<0.05) predictors of overall health, mental health, and physical function. Independent variables included demographics, clinical characteristics, and comorbidities. Results: 848/1273 MCBDR participants (67%) responded, 842 reporting their overall health. Median age at survey return was 82 years (range 77-100), and the median time since cancer diagnosis was 12 years. Excellent or very good overall health, mental health, and physical function were reported by 48%, 63.5%, and 77.1%, respectively. Older age was associated with poorer self-reported overall health, as were heart disease, diabetes, and lung disease, reported by 18%, 14%, and 7%, respectively (p<0.05 for all; see Table). Only heart disease was statistically significantly associated with poorer mental health (p=0.03). Physical function was reportedly better in survivors without a history of diabetes, stroke, or lung disease (p<0.05 for all), and in those who were closer to their cancer diagnosis (p=0.03). Tumor characteristics were not associated with overall health, mental health, or physical function. Conclusions: Comorbidities are common and impact self-reported overall health, mental health, and physical function in older breast cancer survivors. Table: Multivariate logistic regression model for overall health Overall health self-reported as excellent or very good No Yes Total p-value (N=437) (N=405) (N=842) Age at survey (years) <.001 Mean (SD) 82.8 (4.22) 81.7 (3.85) 82.3 (4.08) Median (range) 82.0 (77.0, 96.0) 81.0 (77.0, 100.0) 82.0 (77.0, 100.0) Years from cancer diagnosis to survey 0.708 Mean (SD) 12.1 (5.16) 11.8 (5.47) 11.9 (5.31) Median (range) 12.2 (3.1, 36.9) 11.5 (3.1, 36.4) 12.0 (3.1, 36.9) T stage T2 or higher, n (%) 0.276 No 357 (81.7%) 337 (83.2%) 694 (82.4%) Yes 80 (18.3%) 68 (16.8%) 148 (17.6%) N stage N1 or higher, n (%) 0.185 No 376 (86.0%) 339 (83.7%) 715 (84.9%) Yes 61 (14.0%) 66 (16.3%) 127 (15.1%) M stage M1, n (%) 0.313 No 434 (99.3%) 404 (99.8%) 838 (99.5%) Yes 3 (0.7%) 1 (0.2%) 4 (0.5%) Race, n (%) 0.460 Non-White 13 (3.0%) 8 (2.0%) 21 (2.5%) White 424 (97.0%) 397 (98.0%) 821 (97.5%) Heart disease, n (%) <.001 No 334 (76.4%) 353 (87.2%) 687 (81.6%) Yes 103 (23.6%) 52 (12.8%) 155 (18.4%) High blood pressure, n (%) 0.376 No 168 (38.4%) 188 (46.4%) 356 (42.3%) Yes 269 (61.6%) 217 (53.6%) 486 (57.7%) Stroke, n (%) 0.195 No 407 (93.1%) 392 (96.8%) 799 (94.9%) Yes 30 (6.9%) 13 (3.2%) 43 (5.1%) Diabetes, n (%) <.001 No 355 (81.2%) 370 (91.4%) 725 (86.1%) Yes 82 (18.8%) 35 (8.6%) 117 (13.9%) Lung disease, n (%) 0.023 No 394 (90.2%) 386 (95.3%) 780 (92.6%) Yes 43 (9.8%) 19 (4.7%) 62 (7.4%) Liver disease, n (%) 0.345 No 425 (97.3%) 401 (99.0%) 826 (98.1%) Yes 12 (2.7%) 4 (1.0%) 16 (1.9%) Kidney disease, n (%) 0.240 No 408 (93.4%) 392 (96.8%) 800 (95.0%) Yes 29 (6.6%) 13 (3.2%) 42 (5.0%) Citation Format: Dhauna Karam, Robert A. Vierkant, Shawna Ehlers, Rachel A. Freedman, Nicole Larson, Charles L. Loprinzi, Fergus Couch, Janet Olson, Kathryn J. Ruddy. Physical function, mental health, and overall health of older breast cancer survivors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5262.