Citations

AbstractPrevious transcriptome‐wide association studies (TWAS) have identified breast cancer risk genes by integrating data from expression quantitative loci and genome‐wide association studies (GWAS), but analyses of breast cancer subtype‐specific associations have been limited. In this study, we conducted a TWAS using gene expression data from GTEx and summary statistics from the hitherto largest GWAS meta‐analysis conducted for breast cancer overall, and by estrogen receptor subtypes (ER+ and ER−). We further compared associations with ER+ and ER− subtypes, using a case‐only TWAS approach. We also conducted multigene conditional analyses in regions with multiple TWAS associations. Two genes, STXBP4 and HIST2H2BA, were specifically associated with ER+ but not with ER– breast cancer. We further identified 30 TWAS‐significant genes associated with overall breast cancer risk, including four that were not identified in previous studies. Conditional analyses identified single independent breast‐cancer gene in three of six regions harboring multiple TWAS‐significant genes. Our study provides new information on breast cancer genetics and biology, particularly about genomic differences between ER+ and ER− breast cancer.

BACKGROUND Determination of blood donor hemoglobin (Hb) levels is a pre‐requisite to ensure donor safety and blood product quality. We aimed to identify Hb measurement practices across blood donation services and to what extent differences associate with low‐Hb deferral rates. METHODS An online survey was performed among Biomedical Excellence for Safer Transfusion (BEST) Collaborative members, extended with published data. Multivariable negative‐binomial regression models were built to estimate adjusted associations of minimum donation intervals, Hb cut‐offs (high, ≥13.5 g/dL in men or ≥ 12.5 g/dL in women, vs. lower values), iron monitoring (yes/no), providing or prescribing iron supplementation (yes/no), post‐versus pre‐donation Hb measurement and geographical location (Asian vs. rest), with low‐Hb deferral rates. RESULTS Data were included from 38 blood services. Low‐Hb deferral rates varied from 0.11% to 8.81% among men and 0.84% to 31.85% among women. Services with longer minimum donation intervals had significantly lower deferral rates among both women (rate ratio, RR 0.53, 95%CI 0.33‐0.84) and men (RR 0.53, 95%CI 0.31‐0.90). In women, iron supplementation was associated with lower Hb deferral rates (RR 0.47, 95%CI 0.23‐0.94). Finally, being located in Asia was associated with higher low‐Hb deferral rates; RR 9.10 (95%CI 3.89‐21.27) for women and 6.76 (95%CI 2.45‐18.68) for men. CONCLUSION Differences in Hb measurement and eligibility criteria, particularly longer donation intervals and iron supplementation in women, are associated with variations in low‐Hb deferral rates. These insights could help improve both blood donation service efficiency and donor care.

Background Artificial Intelligent Virtual Assistants (AIVA) is a segment of artificial intelligence that is rapidly developing. However, its utilization to address patients' frequently asked questions remains unexplored. Methods We developed an AIVA to answer questions related to 10 frequent topics asked by plastic surgery patients in our institution. Between July 27, 2018, and August 10 of 2018, we recruited subjects with administrative positions at our health care institution to chat with the virtual assistant. They asked, with their own words, 1 question for each topic and filled out a satisfaction questionnaire. Postsurvey analysis of questions and answers allowed assessment of the virtual assistant's accuracy. Results Thirty participants completed the survey. The majority was female (70%), and the mean age was 27.76 years (SD, 8.68 [19–51] years). The overall accuracy of the plastic surgery AIVA was 92.3% (277/294 questions), and participants considered the answer correct in 83.3% of the time (250/294 answers). Most of the participants considered the AIVA easy to use, answered adequately, and could be helpful for patients. However, when asked if this technology could replace a human assistant, they stayed neutral.

New Findings What is the central question of this study? Are sex difference in the central airways present in healthy paediatric patients? What is the main finding and its importance? In patients ≤12 years we found no sex differences in central airway luminal area. After 14 years, the males had significantly larger central airway luminal areas than the females. The sex differences were minimized, but preserved when correcting for height. Luminal area is the main determinant of airway resistance and our finding could help explain sex differences in pulmonary system limitations to exercise in paediatric patients. Abstract Cross‐sectional airway area is the main determinant of resistance to airflow in the respiratory system. In paediatric patients (<18 years), previous evidence for sex differences in cross‐sectional airway area was limited to patients with history of pulmonary disease or cadaveric studies with small numbers of subjects. These studies either only report tracheal data and do not include a range of ages or correct for height. Therefore, we sought to assess sex differences in airway luminal area utilizing paediatric patients of varying ages and no history of respiratory disease. Using three‐dimensional reconstructions from high‐resolution computed tomography scans, we retrospectively assessed the cross‐sectional airway area in healthy paediatric females ( n = 97) and males ( n = 128) over a range of ages (1–17 years). The areas of the trachea, left main bronchus, left upper lobe, left lower lobe, right main bronchus, intermediate bronchus and right upper lobe were measured at three discrete points by a blinded investigator. No differences between the sexes were noted in the cross‐sectional areas of the youngest (ages 1–12 years) patients ( P > 0.05). However, in patients ≥14 years the cross‐sectional areas were larger in the males compared to females in most airway sites. For instance, the cross‐sectional size of the trachea was 25% (218 ± 44 vs . 163 ± 24 mm 2 , P < 0.01) larger in males vs . females among ages 13–17 years. When accounting for height, these sex differences in airway areas were attenuated, but persisted. Our results indicate that sex differences in paediatric airway cross‐sectional area manifest after age ≥14 years and are independent of height.

Abstract Background The number of patients requiring home enteral nutrition (HEN) continues to increase. Many of these patients are interested in using blended food instead of, or in addition to, commercial enteral formula (CEF). Increased risk of food‐borne illness is a concern of blenderized tube‐feeding (BTF). This project assessed a standard procedure for minimizing bacterial growth of BTF prepared in the home setting. Methods Fifty participants prepared BTF in their kitchens using a standard preparation procedure to minimize bacterial contamination. BTF was assessed for growth of aerobic microorganisms, Escherichia coli, Staphylococcus aureus , and coliforms at baseline, 24‐hour, and 48‐hour intervals after preparation for a total of 150 colony forming units (CFU) counts performed. Results No sample had zero aerobic microbial counts; yet no substantial increase in microbial counts was observed during the 48 hours. At baseline and 24 hours, 5/50 (10%) had a CFU count of >10 4 , and at 48 hours, 6/50 (12%) exceeded 10 4 CFUs. Out of 150 CFU counts, 2 (1.3%) were just over 10 5 CFU/mL. Samples exceeding 10 4 CFU/mL were likely contaminated by common endospore‐forming bacteria found in soil or by bacteria in milk that was close to its expiration date. Conclusion In this study, 88% of the samples met the US Food Code criteria for safe food consumption; 10.7% met guidelines for marginal safety by other standards; and 1.3% slightly exceeded 10 5 CFUs. Established safe food‐handling procedures can minimize bacterial contamination of BTF and consequently reduce risk of food‐borne infection in HEN patients.

Background. Currently, no surveillance guidelines for hepatocellular carcinoma (HCC) recurrence after liver transplantation (LT) exist. In this retrospective, multicenter study, we have investigated the role of surveillance imaging on postrecurrence outcomes. Methods. Patients with recurrent HCC after LT from 2002 to 2016 were reviewed from 3 transplant centers (University of California San Francisco, Mayo Clinic Florida, and University of Toronto). For this study, we proposed the term cumulative exposure to surveillance (CETS) as a way to define the cumulative sum of all the protected intervals that each surveillance test provides. In our analysis, CETS has been treated as a continuous variable in months. Results. Two hundred twenty-three patients from 3 centers had recurrent HCC post-LT. The median follow-up was 31.3 months, and median time to recurrence was 13.3 months. Increasing CETS was associated with improved postrecurrence survival (hazard ratio, 0.94; P < 0.01) as was treatment of recurrence with resection or ablation (hazard ratio, 0.31; P < 0.001). An receiver operating characteristic curve (area under the curve, 0.64) for CETS covariate showed that 252 days of coverage (or 3 surveillance scans) within the first 24 months provided the highest probability for aggressive postrecurrence treatment. Conclusions. In this review of 223 patients with post-LT HCC recurrence, we found that increasing CETS does lead to improved postrecurrence survival as well as a higher probability for aggressive recurrence treatment. We found that 252 days of monitoring (ie, 3 surveillance scans) in the first 24 months was associated with the ability to offer potentially curative treatment.

AbstractBackgroundThe accurate separation of undifferentiated wide complex tachycardias (WCTs) into ventricular tachycardia (VT) or supraventricular wide complex tachycardia (SWCT) using conventional, manually‐applied 12‐lead electrocardiogram (ECG) interpretation methods is difficult.PurposeWe sought to devise a new WCT differentiation method that operates solely on automated measurements routinely provided by computerized ECG interpretation software.MethodsIn a two‐part analysis, we developed and validated a logistic regression model (ie, VT Prediction Model) that utilizes routinely available computerized measurements derived from patients' paired WCT and baseline ECGs.ResultsThe derivation cohort consisted of 601 paired WCT (273 VT, 328 SWCT) and baseline ECGs from 421 patients. The VT Prediction Model, composed of WCT QRS duration (ms) (P < .0001), QRS duration change (ms) (P < .0001), QRS axis change (°) (P < .0001) and T axis change (°) (P < .0001), yielded effective VT and SWCT differentiation (area under the curve [AUC]: 0.924; confidence interval [CI]: 0.903‐0.944) for the derivation cohort. The validation cohort comprised 241 paired WCT (97 VT, 144 SWCT) and baseline ECGs from 177 patients. The VT Prediction Model's implementation on the validation cohort yielded effective WCT differentiation (AUC: 0.900; CI: 0.862‐0.939) with overall accuracy, sensitivity, and specificity of 85.0%, 80.4%, and 88.2%, respectively.ConclusionThe VT Prediction Model is an example of how readily available ECG measurements may be used to distinguish VT and SWCT effectively. Further study is needed to develop and refine newer WCT differentiation approaches that utilize computerized measurements provided by ECG interpretation software.

29 Background: Uterine clear cell carcinoma (UCCC) is a high-grade endometrial carcinoma. The current treatment is hysterectomy with post-operative chemotherapy and/or radiation. The 5-year disease free survival remains dismal for UCCC with 65% for early-stage and 54% for advanced stage disease. In addition, UCCC may be more resistant to chemotherapy or radiation therapy than the endometrioid subtype. The aim of the current study is to investigate the HER2 gene amplification status in UCCC and its role for targeted therapy in UCCC. Methods: Twenty-nine cases of UCCC were retrieved from surgical pathology archives of Mayo Clinic at Rochester between 2011 and 2015. All cases except one case were hysterectomy specimens. The blocks contain the most characteristic morphology of UCCC were selected and corresponding paraffin sections were subjected to fluorescent in situ hybridization for amplification of HER2 gene (Hercept, Abbott Molecular) and parallel immunohistochemical (IHC) study. Results: A total of 9 (of 29; 31%) UCCCs showed HER2 amplification and 4 (of 29; 14%) were considered equivocal for HER2 amplification by FISH. A total of 3 (10%) tumors showed 3+ HER2 overexpression while 11 (38%) UCCCs showed 2+ HER2 overexpression, 9 (31%) showed 1+ expression with the remaining cases showing no expression of HER2. Importantly, we observed significant intratumoral heterogeneity with regard to HER2 expression. Comparing the results of IHC with HER2 gene status as determined by FISH, 2 (66%) of the 3 cases that showed 3+ HER2 expression also showed amplification for HER2 by FISH, while 1 (33%) was equivocal for HER2 amplification. Of the 11 tumors that showed 2+ HER2 expression, 6 (55%) were amplified by FISH and 1 (9%) was equivocal. Conclusions: This is the largest number of UCCC cases that has been studied on the HER2 amplification and corresponding protein overexpression. Our results indicate that the HER2 overexpression is common in UCCCs and is frequently associated with HER2 amplification. These results also suggest that targeted adjuvant therapy with trastuzumab-based immunotherapy should be evaluated in patients with UCCC showing HER2 protein overexpression or HER2 gene amplification.

Introduction Approximately 20% of patients (pts) with classical Hodgkin lymphoma (cHL) are age ≥ 60 years (yrs) at diagnosis (dx). Standard anthracycline-based therapies (ABTx) have historically shown lower efficacy and greater toxicity in this older population compared with younger pts, especially for unfit or frail pts. However, prior analyses have pre-dated the use of brentuximab vedotin (BV) and other novel targeted agents in the front-line setting and there remains no clearly defined or unified standard of care for this pt population. Studies in older lymphoma pts have also defined specific categories of pt fitness that may potentially guide therapy, although continued validation of these definitions in cHL are needed. We performed a large, multicenter retrospective study to identify current practice patterns in the US in older untreated cHL pts with specific attention to pt fitness using established definitions and to outcomes with ABTx versus non-ABTx. Methods Following IRB approval at each site, detailed pathologic and clinical data, including geriatric assessments (GAs) for the latter, were collected across 10 US academic medical centers. Eligible pts included: cHL dx on or after 1/1/2010, age ≥ 60 yrs at dx, and no preceding hematologic malignancy. Subjects with inadequate clinicopathologic and outcomes data or with dx other than cHL were excluded. A multivariable Cox proportional hazards model was developed to study the association of PFS and OS with various factors of interest, and survival rates were estimated by Kaplan-Meier using SPSS. Associations were considered significant for two-sided P values ≤ 0.05. Results Among 254 eligible older cHL pts with newly diagnosed disease, clinical features included: median age 67 yrs (range 60-90+), 54% male, 64% stage III/IV, 46% B symptoms, and 86.1% ECOG PS 0-1. 68.9% had nodular sclerosis, 18.9% mixed cellularity, and 12.2% other subtypes; EBV was detectable by IHC or peripheral blood in 39.3% of evaluable cases (n=98). In terms of geriatric measures, 31 pts (12.2%) had loss of at least one ADL; 40 (15.8%) had a geriatric syndrome; and 16 (6.3%) had both. Of note, only one pt underwent a comprehensive GA prior to treatment. Using established fitness definitions in lymphoma (Tucci et al, 2015), pts were classified as "Fit" (n=129; 50.8%), "Unfit" (n=8; 3.2%), or "Frail" (n=112 (44.1%); 5 (2%) were unclassifiable due to incomplete data (Table 1). Front-line regimens included ABVD (n=145; 57.1%), AVD (n=33; 13.0%), and CHOP (n=18; 7.1%). 6 pts (2.4%) received BV monotherapy; 21 (8.3%) received BV with AVD, the latter either concurrently (N=11) or sequentially (n=10). 11 pts did not receive systemic tx, of whom 9 received radiation alone; 58 pts received radiation with systemic tx. ABVD recipients received an average of 7.4 doses of bleomycin (range 1-14) and 18.4% (n=26) had pulmonary toxicity. In addition, 56.7% (n=130) of pts experienced at least one tx-related toxicity, which was similar across all fitness groups (Table 1). 15.1% (n=38) of pts stopped tx due to toxicity, which was noted more often in "frail" pts though it was relatively uncommon. 11 pts (4.3%) experienced treatment-related mortality (TRM). Compared with pts receiving non-ABTx, ABTx was associated with significantly improved PFS (HR 0.391, P=0.008) and OS (HR 0.195, P=0.0004). ECOG PS >1 was associated with inferior PFS (HR=1.96, P=0.017) and OS (HR=3.09, P=0.002). Pts defined as "frail" had no apparent difference in PFS compared with fit pts (Figure 1A;P=0.169), although decreased OS was seen (Figure 1B; P=0.01). Discussion To the best of our knowledge, these data represent one of the largest analyses of older cHL pts in the modern era. Our real-world analysis shows that in those who are able to receive them, anthracyclines provide meaningful survival benefit in this older cHL patient population and can be well-tolerated for many pts. The impact of dose intensity and backbone regimen are not yet known. In addition, current definitions of "fitness" incompletely identified pts at risk for poor outcomes or greater toxicity. Further studies are planned to better delineate fitness in this population. Disclosures Bartlett: Celgene: Research Funding; ADC Therapeutics: Consultancy, Research Funding; Affimed: Research Funding; Merck: Research Funding; Janssen: Research Funding; Immune Design: Research Funding; Gilead: Research Funding; Millenium: Research Funding; Pfizer: Research Funding; Pharmacyclics: Research Funding; Seattle Genetics: Research Funding; Forty Seven: Research Funding; Genenetech: Research Funding; Bristol-Myers Squibb: Research Funding. Grover:Seattle Genetics: Consultancy. Bennani:Adicet Bio: Other: Advisory board; Bristol-Myers Squibb: Research Funding; Adicet Bio: Other: Advisory board; Seattle Genetics: Other: Advisory board; Seattle Genetics: Other: Advisory board; Purdue Pharma: Other: Advisory board; Seattle Genetics: Other: Advisory board; Purdue Pharma: Other: Advisory board; Kite Pharma: Other: Advisory board; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Kite Pharma: Other: Advisory board; Kite Pharma: Other: Advisory board; Purdue Pharma: Other: Advisory board; Adicet Bio: Other: Advisory board. Hill:Kite: Consultancy, Honoraria; Gilead: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; AstraZeneca: Consultancy, Honoraria; Celegene: Consultancy, Honoraria, Research Funding; Seattle Genetics: Consultancy, Honoraria; Takeda: Research Funding; Amgen: Research Funding; TG therapeutics: Research Funding; Abbvie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Consultancy, Research Funding. Advani:Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Gilead Sciences, Inc./Kite Pharma, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Millennium: Research Funding; Kura: Research Funding; Janssen: Research Funding; Autolus: Consultancy, Membership on an entity's Board of Directors or advisory committees; Bayer: Consultancy, Membership on an entity's Board of Directors or advisory committees; Merck: Research Funding; Regeneron: Research Funding; Infinity Pharma: Research Funding; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Research Funding; Celmed: Consultancy, Membership on an entity's Board of Directors or advisory committees; Forty-Seven: Research Funding; Roche/Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Kyowa Kirin Pharmaceutical Developments, Inc.: Consultancy; Agensys: Research Funding; Stanford University: Employment, Equity Ownership; Seattle Genetics: Consultancy, Research Funding; Cell Medica, Ltd: Consultancy; AstraZeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Svoboda:AstraZeneca: Consultancy; Celgene: Research Funding; Incyte: Research Funding; Pharmacyclics: Consultancy, Research Funding; Kyowa: Consultancy; Merck: Research Funding; BMS: Consultancy, Research Funding; Seattle Genetics: Consultancy, Research Funding. Magarelli:Tevan Oncology: Speakers Bureau. Feldman:Takeda: Honoraria, Speakers Bureau; Celgene: Honoraria, Research Funding, Speakers Bureau; Seattle Genetics: Consultancy, Honoraria, Other: Travel expenses, Speakers Bureau; AbbVie: Honoraria, Other: Travel expenses, Speakers Bureau; Pharmacyclics: Honoraria, Other: Travel expenses, Speakers Bureau; Janssen: Honoraria, Speakers Bureau; Kite Pharma: Honoraria, Other: Travel expenses, Speakers Bureau; Bayer: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Research Funding; Cell Medica: Research Funding; Roche: Research Funding; Corvus: Research Funding; Eisai: Research Funding; Kyowa Hakko Kirin: Research Funding; Pfizer: Research Funding; Portola Pharma: Research Funding; Roche: Research Funding; Trillium: Research Funding; Viracta: Research Funding. Cohen:Astra Zeneca: Research Funding; ASH: Research Funding; UNUM: Research Funding; Hutchison: Research Funding; Takeda Pharmaceuticals North America, Inc.: Research Funding; Gilead/Kite: Consultancy; Lymphoma Research Foundation: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics, Inc.: Consultancy, Research Funding; Bristol-Meyers Squibb Company: Research Funding; Janssen Pharmaceuticals: Consultancy; Genentech, Inc.: Consultancy, Research Funding. Evens:Seattle Genetics: Consultancy, Honoraria, Research Funding; Tesaro: Research Funding; Epizyme: Consultancy, Honoraria; Verastem: Consultancy, Honoraria; Pharmacyclics: Consultancy, Honoraria. Portell:AbbVie: Research Funding; Pharmacyclics: Consultancy; Janssen: Consultancy; Genentech: Consultancy, Research Funding; Amgen: Consultancy; Bayer: Consultancy; BeiGene: Consultancy, Research Funding; Kite: Consultancy, Research Funding; Acerta/AstraZeneca: Research Funding; TG Therapeutics: Research Funding; Xencor: Research Funding; Roche/Genentech: Research Funding; Infinity: Research Funding.