Faculty Bibliography

OBJECTIVES:The number of hospitalized coronavirus disease 2019 patients in March 2020 to April 2020 in our New York City hospital required increased physician staffing, including deployment of pediatricians to adult care. To improve the deployment process, we sought to understand the mindset, preparations for, and experience during deployment of pediatric faculty in our institution. METHODS:test were used to compare groups. Free-text responses were categorized by topic. Survey responses were shared with leadership in real time and adjustments to the deployment process made. RESULTS:= 16). Dissemination of details about schedules and role clarification before deployment were areas for improvement. CONCLUSIONS:Pediatric faculty facing deployment to adult care have concerns about the process of deployment as well as the work itself. Specific information distributed in advance, along with consistent and frequent communication, may help mitigate these fears.

PURPOSE/OBJECTIVE:Racial/ethnic minorities experience greater job loss than whites during periods of economic downturn and after a cancer diagnosis. Therefore, race/ethnicity-matched controls are needed to distinguish the impact of illness on job loss from secular trends METHODS: Surveys were administered during and 4-month post-completion of breast cancer treatment. Patients were pre-diagnosis employed women aged 18-64, undergoing treatment for stage I-III breast cancers, who spoke English, Chinese, Korean, or Spanish. Each patient was asked to: (1) nominate peers who were surveyed in a corresponding timeframe (active controls), (2) report a friend's work status at baseline and follow-up (passive controls). Both types of controls were healthy, employed at baseline, and shared the nominating patient's race/ethnicity, language, and age. The primary outcome was number of evaluable patient-control pairs by type of control. A patient-control pair was evaluable if work status at follow-up was reported for both individuals. RESULTS:Of the 180 patients, 25% had evaluable active controls (45 patient-control pairs); 84% had evaluable passive controls (151 patient-control pairs). Although patients with controls differed from those without controls under each strategy, there was no difference in the percentage of controls who were working at follow-up (96% of active controls; 91% of passive controls). However, only 65% of patients were working at follow-up. CONCLUSIONS:The majority of patients had evaluable passive controls. There was no significant difference in outcome between controls ascertained through either method IMPLICATIONS FOR CANCER SURVIVORS: Passive controls are a low-cost, higher-yield option to control for secular trends in racially/ethnically diverse samples.

Introduction: PD-L1 is an immune checkpoint protein that mediates immune evasion. In Non-Small Cell Lung Cancer (NSCLC), its expression is used to predict the outcome of treatment targeting PD-1/L1. However, clinical benefits do not occur uniformly, and new approaches are needed to assist in selecting patients for immunotherapy.
Method(s): 26 patients with known clinical response to pembrolizumab were selected from publicly available data (PMID:25765070) (Table 1). Mutation and copy number aberrations from individual cases served as input into the Cellworks Omics Biology Model (CBM) to generate a patient-specific protein network map from PubMed and other online resources. Disease-biomarkers unique to each patient were identified within protein network maps. Digital drug biosimulations were conducted by measuring the effect of pembrolizumab on a cell growth score comprised of a composite of cell proliferation, viability, apoptosis, metastasis, and other cancer hallmarks. Drug biosimulations were conducted to identify and evaluate therapeutic efficacy.
Result(s): Among 26 patients treated with pembrolizumab, 14 were clinical responders, defined as stable disease or partial response lasting longer than 6 months, and 12 non-responders. Notably, 9/12 non-responders were PD-L1 positive (Table 1). Cellworks biosimulation predicted response with 84.6% accuracy, 75% specificity, and 92.86% sensitivity. Positive predictive value was 81.25% and negative predictive value was 90%. CBM identified that response was influenced by pathways that impacted the tumor microenvironment (TME). Deletions of adenosine pathway genes were observed in responders, whereas CNVs for these genes were enriched in non-responders (Table 1). Loss of ENPP1, and INSIG1 and SENP2 CNV aberrations, all of which regulate the STING pathway, could play a significant role in governing immune checkpoint blockade (ICB) response. Although STK11 loss appears to be a biomarker for poor ICB response, it was equally enriched in responders (n=7) and non-responders (n=7) in this dataset. Notably, responders had STK11 mutations and chromosome 6 loss, whereas non-responders had STK11 loss with chromosome 6 wild type or gain. Finally, frameshift mutations (FSM) enhance neoepitope formation and were higher in responders (average FSM = 48) vs non-responders (average FSM = 19). [Formula presented]
Conclusion(s): Alterations of the adenosine and STING pathways play key roles in determining benefit from PD-1/L1 targeting and highlight therapeutic possibilities for improving outcome in specific patient subgroups based on PD-L1 expression. The Cellworks CBM captures a holistic picture of the TME using tumor omics and improves response prediction beyond PD-L1 testing. Keywords: Multi-omics Therapy Biosimulation, Personalized Cancer Therapy, Immunotherapy Biosimulation
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BACKGROUND:The World Trade Center (WTC) attacks on 11 September 2001 created a hazardous environment with known and suspected carcinogens. Previous studies have identified an increased risk of prostate cancer in responder cohorts compared with the general male population. OBJECTIVES:To estimate the length of time to prostate cancer among WTC rescue/recovery workers by determining specific time periods during which the risk was significantly elevated. METHODS:Person-time accruals began 6 months after enrolment into a WTC cohort and ended at death or 12/31/2015. Cancer data were obtained through linkages with 13 state cancer registries. New York State was the comparison population. We used Poisson regression to estimate hazard ratios and 95% CIs; change points in rate ratios were estimated using profile likelihood. RESULTS:The analytic cohort included 54 394 male rescue/recovery workers. We observed 1120 incident prostate cancer cases. During 2002-2006, no association with WTC exposure was detected. Beginning in 2007, a 24% increased risk (HR: 1.24, 95% CI 1.16 to 1.32) was observed among WTC rescue/recovery workers when compared with New York State. Comparing those who arrived earliest at the disaster site on the morning of 11 September 2001 or any time on 12 September 2001 to those who first arrived later, we observed a positive, monotonic, dose-response association in the early (2002-2006) and late (2007-2015) periods. CONCLUSIONS:Risk of prostate cancer was significantly elevated beginning in 2007 in the WTC combined rescue/recovery cohort. While unique exposures at the disaster site might have contributed to the observed effect, screening practices including routine prostate specific antigen screening cannot be discounted.

BACKGROUND:World Trade Center (WTC)-exposed responders may be eligible to receive no-cost medical monitoring and treatment for certified conditions, including cancer. The survival of responders with cancer has not previously been investigated. METHODS:This study compared the estimated relative survival of WTC-exposed responders who developed cancer while enrolled in two WTC medical monitoring and treatment programs in New York City (WTC-MMTP responders) and WTC-exposed responders not enrolled (WTC-non-MMTP responders) to non-responders from New York State (NYS-non-responders), all restricted to the 11-southernmost NYS counties, where most responders resided. Parametric survival models estimated cancer-specific and all-cause mortality. Follow-up ended at death or on December 31, 2016. RESULTS:From January 1, 2005 to December 31, 2016, there were 2,037 cancer cases and 303 deaths (248 cancer-related deaths) among WTC-MMTP responders, 564 cancer cases, and 143 deaths (106 cancer-related deaths) among WTC-non-MMTP responders, and 574,075 cancer cases and 224,040 deaths (158,645 cancer-related deaths) among the NYS-non-responder population. Comparing WTC-MMTP responders with NYS-non-responders, the cancer-specific mortality hazard ratio (HR) was 0.72 (95% confidence interval [CI] = 0.64-0.82), and all-cause mortality HR was 0.64 (95% CI = 0.58-0.72). The cancer-specific HR was 0.94 (95% CI = 0.78-1.14), and all-cause mortality HR was 0.93 (95% CI = 0.79-1.10) comparing WTC-non-MMTP responders to the NYS-non-responder population. CONCLUSIONS:WTC-MMTP responders had lower mortality compared with NYS-non-responders, after controlling for demographic factors and temporal trends. There may be survival benefits from no-out-of-pocket-cost medical care which could have important implications for healthcare policy, however, other occupational and socioeconomic factors could have contributed to some of the observed survival advantage.

BACKGROUND:A recent study of World Trade Center (WTC)-exposed firefighters and emergency medical service workers demonstrated that elevated thyroid cancer incidence may be attributable to frequent medical testing, resulting in the identification of asymptomatic tumors. We expand on that study by comparing the incidence of thyroid cancer among three groups: WTC-exposed rescue/recovery workers enrolled in a New York State (NYS) WTC-medical monitoring and treatment program (MMTP); WTC-exposed rescue/recovery workers not enrolled in an MMTP (non-MMTP); and the NYS population. METHODS:Person-time began on 9/12/2001 or at enrollment in a WTC cohort and ended at death or on 12/31/2015. Cancer data were obtained through linkages with 13 state cancer registries. We used Poisson regression to estimate rate ratios (RRs) and 95% confidence intervals (CIs) for MMTP and non-MMTP participants. NYS rates were used as the reference. To estimate potential changes over time in WTC-associated risk, change points in RRs were estimated using profile likelihood. RESULTS:The thyroid cancer incidence rate among MMTP participants was more than twice that of NYS population rates (RR = 2.31; 95% CI = 2.00-2.68). Non-MMTP participants had a risk similar to NYS (RR = 0.96; 95% CI = 0.72-1.28). We observed no change points in the follow-up period. CONCLUSION/CONCLUSIONS:Our findings support the hypothesis that no-cost screening (a benefit provided by WTC-MMTPs) is associated with elevated identification of thyroid cancer. Given the high survival rate for thyroid cancer, it is important to weigh the costs and benefits of treatment, as many of these cancers were asymptomatic and may have been detected incidentally.

BACKGROUND:Limited research has addressed the obesity-COVID-19 severity association in paediatric patients. OBJECTIVE:To determine whether obesity is an independent risk factor for COVID-19 severity in paediatric patients and whether age modifies this association. METHODS:SARS-CoV-2-positive patients at NYU Langone Health from 1 March 2020 to 3 January 2021 aged 0-21 years with available anthropometric measurements: weight, length/height and/or body mass index (BMI). Modified log-Poisson models were utilized for the analysis. Main outcomes were 1) hospitalization and 2) critical illness (intensive care unit [ICU] admission). RESULTS:One hundred and fifteen of four hundred and ninety-four (23.3%) patients had obesity. Obesity was an independent risk factor for critical illness (adjusted risk ratio [ARR] 2.02, 95% CI 1.17 to 3.48). This association was modified by age, with obesity related to a greater risk for critical illness in adolescents (13-21 years) [ARR 3.09, 95% CI 1.48 to 6.47], but not in children (0-12 years). Obesity was not an independent risk factor for hospitalization for any age. CONCLUSION/CONCLUSIONS:Obesity was an independent risk factor for critical illness in paediatric patients, and this association was modified by age, with obesity related to a greater risk for critical illness in adolescents, but not in children. These findings are crucial for patient risk stratification and care.

BACKGROUND:The authors sought to study the risk factors associated with severe outcomes in hospitalized coronavirus disease 2019 (COVID-19) patients with cancer. METHODS:The authors queried the New York University Langone Medical Center's records for hospitalized patients who were polymerase chain reaction-positive for severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) and performed chart reviews on patients with cancer diagnoses to identify patients with active cancer and patients with a history of cancer. Descriptive statistics were calculated and multivariable logistic regression was used to determine associations between clinical, demographic, and laboratory characteristics with outcomes, including death and admission to the intensive care unit. RESULTS:A total of 4184 hospitalized SARS CoV-2+ patients, including 233 with active cancer, were identified. Patients with active cancer were more likely to die than those with a history of cancer and those without any cancer history (34.3% vs 27.6% vs 20%, respectively; P < .01). In multivariable regression among all patients, active cancer (odds ratio [OR], 1.89; CI, 1.34-2.67; P < .01), older age (OR, 1.06; CI, 1.05-1.06; P < .01), male sex (OR for female vs male, 0.70; CI, 0.58-0.84; P < .01), diabetes (OR, 1.26; CI, 1.04-1.53; P = .02), morbidly obese body mass index (OR, 1.87; CI, 1.24-2.81; P < .01), and elevated D-dimer (OR, 6.41 for value >2300; CI, 4.75-8.66; P < .01) were associated with increased mortality. Recent cancer-directed medical therapy was not associated with death in multivariable analysis. Among patients with active cancer, those with a hematologic malignancy had the highest mortality rate in comparison with other cancer types (47.83% vs 28.66%; P < .01). CONCLUSIONS:The authors found that patients with an active cancer diagnosis were more likely to die from COVID-19. Those with hematologic malignancies were at the highest risk of death. Patients receiving cancer-directed therapy within 3 months before hospitalization had no overall increased risk of death. LAY SUMMARY/UNASSIGNED:Our investigators found that hospitalized patients with active cancer were more likely to die from coronavirus disease 2019 (COVID-19) than those with a history of cancer and those without any cancer history. Patients with hematologic cancers were the most likely among patients with cancer to die from COVID-19. Patients who received cancer therapy within 3 months before hospitalization did not have an increased risk of death.

The purpose of this study is to validate the seven-item wheezing module from the International Study of Asthma and Allergies in Children (ISAAC) in the nationally representative Population Assessment of Tobacco and Health Study. Adult participants with complete Wave 2-3 data were selected, including those with asthma but excluding those with COPD and other respiratory diseases (n = 16,295). We created a nine-point respiratory symptom index from the ISAAC questions, assessed the reliability of the index, and examined associations with self-reported asthma diagnosis. Threshold values were assessed for association with functional outcomes. The weighted prevalence for one or more respiratory symptom was 18.0% (SE = 0.5) for adults without asthma, 70.1% (SE = 1.3) for those with lifetime asthma, 75.7% (SE = 3.7) for adults with past-year asthma not on medications, and 92.6% (SE = 1.6) for those on medications. Cronbach's alpha for the respiratory symptom index was 0.86. Index scores of ≥2 or ≥3 yielded functionally important respiratory symptom prevalence of 7-10%, adequate sensitivity and specificity for identifying asthma, and consistent independent associations with all functional outcomes and tobacco use variables. Respiratory symptom index scores of ≥2 or ≥3 are indicative of functionally important respiratory symptoms and could be used to assess the relationship between tobacco use and respiratory health.