Diagnostic Accuracy of HPV16 Early Antigen Serology For HPV-Driven Oropharyngeal Cancer is Independent of Age and Sex

Funding information: This project was funded in part by NIH/NIDCR R01 DE025712 (Paul Brennan, Brenda Diergaarde and Neil Hayes). The Alcohol-Related Cancers and Genetic Susceptibility Study in Europe (ARCAGE) was funded by the European Commission’s fifth framework program (QLK1-2001-00182), the Italian Association for Cancer Research, Compagnia di San Paolo/FIRMS, Region Piemonte and Padova University (CPDA057222). We thank Dr. Wolfgang Ahrens, PhD (Universität Bremen, Germany) for his support in ARCAGE study. The Carolina Head and Neck Cancer Epidemiology (CHANCE) study was supported in part by the National Cancer Institute (R01-CA90731). The Head and Neck 5000 study was a component of independent research funded by the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research scheme (RP-PG-0707-10034). The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. Core funding was also provided through awards from Above and Beyond, University Hospitals Bristol and Weston Research Capability Funding and the NIHR Senior Investigator award to Professor Andy Ness. Human papillomavirus (HPV) serology was supported by a Cancer Research UK Programme Grant, the Integrative Cancer Epidemiology Programme (grant number: C18281/A19169). The University of Pittsburgh head and neck cancer case-control study is supported by US National Institutes of Health grants P50CA097190 and P30CA047904. The MSH-PMH study was supported by Canadian Cancer Society Research Institute and Lusi Wong Programs at the Princess Margaret Hospital Foundation.Peer reviewedPublisher PD

A risk prediction model for head and neck cancers incorporating lifestyle factors, HPV serology and genetic markers

Head and neck cancer is often diagnosed late and prognosis for most head and neck cancer patients remains poor. To aid early detection, we developed a risk prediction model based on demographic and lifestyle risk factors, human papillomavirus (HPV) serological markers and genetic markers. A total of 10 126 head and neck cancer cases and 5254 controls from five North American and European studies were included. HPV serostatus was determined by antibodies for HPV16 early oncoproteins (E6, E7) and regulatory early proteins (E1, E2, E4). The data were split into a training set (70%) for model development and a hold-out testing set (30%) for model performance evaluation, including discriminative ability and calibration. The risk models including demographic, lifestyle risk factors and polygenic risk score showed a reasonable predictive accuracy for head and neck cancer overall. A risk model that also included HPV serology showed substantially improved predictive accuracy for oropharyngeal cancer (AUC = 0.94, 95% CI = 0.92-0.95 in men and AUC = 0.92, 95% CI = 0.88-0.95 in women). The 5-year absolute risk estimates showed distinct trajectories by risk factor profiles. Based on the UK Biobank cohort, the risks of developing oropharyngeal cancer among 60 years old and HPV16 seropositive in the next 5 years ranged from 5.8% to 14.9% with an average of 8.1% for men, 1.3% to 4.4% with an average of 2.2% for women. Absolute risk was generally higher among individuals with heavy smoking, heavy drinking, HPV seropositivity and those with higher polygenic risk score. These risk models may be helpful for identifying people at high risk of developing head and neck cancer

Machine Learning–Based Predictive Modeling of Anxiety and Depressive Symptoms During 8 Months of the COVID-19 Global Pandemic: Repeated Cross-sectional Survey Study

BackgroundThe COVID-19 global pandemic has increased the burden of mental illness on Canadian adults. However, the complex combination of demographic, economic, and lifestyle factors and perceived health risks contributing to patterns of anxiety and depression has not been explored. ObjectiveThe aim of this study is to harness flexible machine learning methods to identify constellations of factors related to symptoms of mental illness and to understand their changes over time during the COVID-19 pandemic. MethodsCross-sectional samples of Canadian adults (aged ≥18 years) completed web-based surveys in 6 waves from May to December 2020 (N=6021), and quota sampling strategies were used to match the English-speaking Canadian population in age, gender, and region. The surveys measured anxiety and depression symptoms, sociodemographic characteristics, substance use, and perceived COVID-19 risks and worries. First, principal component analysis was used to condense highly comorbid anxiety and depression symptoms into a single data-driven measure of emotional distress. Second, eXtreme Gradient Boosting (XGBoost), a machine learning algorithm that can model nonlinear and interactive relationships, was used to regress this measure on all included explanatory variables. Variable importance and effects across time were explored using SHapley Additive exPlanations (SHAP). ResultsPrincipal component analysis of responses to 9 anxiety and depression questions on an ordinal scale revealed a primary latent factor, termed “emotional distress,” that explained 76% of the variation in all 9 measures. Our XGBoost model explained a substantial proportion of variance in emotional distress (r2=0.39). The 3 most important items predicting elevated emotional distress were increased worries about finances (SHAP=0.17), worries about getting COVID-19 (SHAP=0.17), and younger age (SHAP=0.13). Hopefulness was associated with emotional distress and moderated the impacts of several other factors. Predicted emotional distress exhibited a nonlinear pattern over time, with the highest predicted symptoms in May and November and the lowest in June. ConclusionsOur results highlight factors that may exacerbate emotional distress during the current pandemic and possible future pandemics, including a role of hopefulness in moderating distressing effects of other factors. The pandemic disproportionately affected emotional distress among younger adults and those economically impacted

Out‐of‐pocket costs associated with head and neck cancer treatment

Abstract Background Out‐of‐pocket costs (OOPC) associated with treatment have significant implications on quality of life and survival in cancer patients. Head and neck cancer patients face unique treatment‐related challenges, but to date OOPC have been understudied in this population. Aims This study aims to identify and measure OOPC for patients with head and neck cancer (HNC) in Ontario. Methods HNC patients between 2015 and 2018 at Princess Margaret Cancer Centre in Toronto were recruited. Participants completed OOPC questionnaires and lost income questions during radiation, post‐surgery, and 3, 6, 12, and 24 months after completion of treatment. Associations between OOPC and treatment modality and disease site were tested with multivariable hurdle regression. Results A total of 1545 questionnaires were completed by 657 patients. Median estimated OOPC for the total duration of treatment for participants undergoing chemoradiation was $1452 [$0–14 616], for surgery with adjuvant radiation or chemoradiation (C/RT) was $1626, for radiation therapy alone was$635, and for surgery alone was $360. The major expenses for participants at the mid‐treatment time‐point was travel (mean$424, standard error of the mean [SEM] $34) and meals, parking, and accommodations (mean$617, SEM $67). In multivariable analysis, chemoradiation, surgery with C/RT, and radiation were associated with significantly higher OOPC than surgery alone during treatment (791% higher, p < .001; 539% higher, p < .001; 370% higher, p < .001 respectively) among patients with non‐zero OOPC. Participants with non‐zero OOPC in the laryngeal cancer group paid 49% lower OOPC than those with oropharyngeal cancers in adjusted analysis (p = .025). Conclusions Patients undergoing treatment for HNC pay significant OOPC. These costs are highest during treatment and gradually decrease over time. OOPC vary by patient demographics, clinical factors, and, in particular, treatment modality

Real-World Treatment Sequencing, Toxicities, Health Utilities, and Survival Outcomes in Patients with Advanced ALK-Rearranged Non-Small-Cell Lung Cancer.

OBJECTIVES This real-world analysis describes treatment patterns, sequencing and clinical effectiveness, toxicities, and health utility outcomes in advanced-stage, incurable ALK-positive NSCLC patients across five different ALK-TKIs. MATERIALS AND METHODS Clinicodemographic, treatment, and toxicity data were collected retrospectively in patients with advanced-stage ALK-positive NSCLC at Princess Margaret Cancer Centre. Patient-reported symptoms, toxicities, and health utilities were collected prospectively. RESULTS Of 148 ALK-positive NSCLC patients seen July 2009-May 2021, median age was 58.9 years; 84 (57%) were female; 112 (76%) never-smokers; 54 (47%) Asian and 40 (35%) white; 139 (94%) received at least one ALK-TKI: crizotinib (n = 74; 54%) and alectinib (n = 61; 44%) were administered mainly as first-line ALK-TKI, ceritinib, brigatinib and lorlatinib were administered primarily after previous ALK-TKI failure. Median overall survival (OS) was 54.0 months; 31 (21%) patients died within two years of advanced-stage diagnosis. Treatment modifications were observed in 35 (47%) patients with crizotinib, 19 (61%) with ceritinib, 41 (39%) with alectinib, 9 (41%) with brigatinib and 8 (30%) with lorlatinib. Prevalence of dose modifications and self-reported toxicities were higher with early versus later generation ALK-TKIs (P<.05). The presence of early treatment modification was not negatively associated with progression-free survival (PFS) and OS analyses. CONCLUSION Serial ALK-TKI sequencing approaches are viable therapeutic options that can extend quality of life and quantity-of-life, though a fifth of patients died within two years. No best single sequencing approach could be determined. Clinically relevant toxicities occurred across all ALK-TKIs. Treatment modifications due to toxicity may not necessarily compromise outcomes, allowing multiple approaches to deal with ALK-TKI toxicities

A Comparison of Hypofractionated and Twice-Daily Thoracic Irradiation in Limited-Stage Small-Cell Lung Cancer: An Overlap-Weighted Analysis

Despite evidence for the superiority of twice-daily (BID) radiotherapy schedules, their utilization in practice remains logistically challenging. Hypofractionation (HFRT) is a commonly implemented alternative. We aim to compare the outcomes and toxicities in limited-stage small-cell lung cancer (LS-SCLC) patients treated with hypofractionated versus BID schedules. A bi-institutional retrospective cohort review was conducted of LS-SCLC patients treated with BID (45 Gy/30 fractions) or HFRT (40 Gy/15 fractions) schedules from 2007 to 2019. Overlap weighting using propensity scores was performed to balance observed covariates between the two radiotherapy schedule groups. Effect estimates of radiotherapy schedule on overall survival (OS), locoregional recurrence (LRR) risk, thoracic response, any ≥grade 3 (including lung, and esophageal) toxicity were determined using multivariable regression modelling. A total of 173 patients were included in the overlap-weighted analysis, with 110 patients having received BID treatment, and 63 treated by HFRT. The median follow-up was 20.4 months. Multivariable regression modelling did not reveal any significant differences in OS (hazard ratio [HR] 1.67, p = 0.38), LRR risk (HR 1.48, p = 0.38), thoracic response (odds ratio [OR] 0.23, p = 0.21), any ≥grade 3+ toxicity (OR 1.67, p = 0.33), ≥grade 3 pneumonitis (OR 1.14, p = 0.84), or ≥grade 3 esophagitis (OR 1.41, p = 0.62). HFRT, in comparison to BID radiotherapy schedules, does not appear to result in significantly different survival, locoregional control, or toxicity outcomes