Screening for HPV-related oropharyngeal, anal, and penile cancers in middle-aged men: Initial report from the HOUSTON clinical trial

BACKGROUND

The Human papillomavirus (HPV)-related Oropharyngeal and Uncommon Cancers Screening Trial of Men (HOUSTON) was designed to determine the prevalence of IgG antibodies to HPV type 16 E proteins (HPV16EAbs), to screen for persistence of HPV and/or detect HPV-related premalignancies and cancers, and to assess acceptance of screening among middle-aged men.

METHODS

HOUSTON consists of a cross-sectional study and a longitudinal cohort study of men aged 50-64 years. Serologic HPV16EAb status and oral rinse HPV16 status were determined. All HPV16EAb-positive (HPV16EAb+) men and a matched cohort of HPV16EAb-negative (HPV16EAb-) men as well as all oral rinse HPV16-positive (HPV16+) men were included in the longitudinal study (blinded to their results) and underwent oropharyngeal screening every 6 months as well as one-time anal and penile screening.

RESULTS

Of 553 men enrolled in the cross-sectional study, six (1.1%) were HPV16EAb+ (two were also oral rinse HPV16+), and 41 (7.4%) were HPV16EAb- but oral rinse HPV16+. These 47 men, along with five matched controls, were invited to participate in the longitudinal study, and 42 (81%) agreed and completed baseline in-person screening, with 93% and 90% completeing 6-month and 12-month follow-up visits. One HPV16EAb+ (also oral rinse HPV16+) man, who declined participation in the longitudinal study, presented 4 months after enrollment with an early-stage HPV16-related pharyngeal cancer. Additionally, one HPV16EAb+ (oral rinse HPV16-) man and two oral rinse HPV16+ (HPV16EAb-) men were diagnosed with oncogenic HPV-associated anal dysplasia.

CONCLUSIONS

This biomarker panel deserves further prospective study to explore potential utility for HPV-related cancer screening among men.

Identifying dehydration risk in head and neck cancer patients undergoing radiation therapy using remote, sensor-based monitoring: A randomized controlled trial

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Background: Early identification and mitigation of cancer therapy-induced complications can improve quality of life and reduce complications and health care costs. We are evaluating the efficacy of a system, called CYCORE (CYberinfrastructure for Comparative effectiveness Research), to systematically and accurately collect daily weight, blood pressure, pulse and symptom data from head and neck cancer (HNC) survivors during their 6 to 7-week radiation treatment (RT) regimen using home-based biometric and other sensors integrated with a cyber-physical system. Aims include: 1) evaluate CYCORE’s efficacy in reducing hospitalization and emergency room visits related to dehydration; and, 2) evaluate related impact on costs related to treating dehydration. Methods: Remote, sensor-based monitoring via CYCORE identifies HNC survivors at increased risk for dehydration (i.e., reduced blood pressure and weight, increased pulse, and patient-reported outcomes (PROs) including decreased food and fluid intake). Upon initation of RT, HNC survivors are randomized to either: 1) standard care plus use of the CYCORE system, wherein biometric sensor data and PROs (e.g., pain, nausea, medications, food/fluid intake) are collected ; or, 2) standard care. Outcomes include hospital and emergency room admissions, and related costs. Results: To date, 113 survivors are enrolled (88.9% recruitment and 90.3% retention rates). Those randomized to CYCORE take daily home readings of weight, blood pressure and pulse via Bluetooth-enabled devices, and complete daily PRO assessments on a mobile application. Survivors’ data are available for viewing and clinical decision-making via a Web-based interface by their physicians and other providers. Previously, 59% of HNC survivors on RT showed physiologic signs of dehydration while using the CYCORE system. Worse ratings of nausea and vomiting predicted dehydration risk. Conclusions: CYCORE may serve as one model for integrating mobile and sensor technologies during critical periods of cancer care that occur outside of the clinic setting, and may improve management of treatment side effects, quality of life, and greater satisfaction with care. Clinical trial information: NCT02253238.

High-throughput DNA methylation datasets for evaluating false discovery rate methodologies

When analyzing high-throughput genomic data, the multiple comparison problem is most often addressed through estimation of the false discovery rate (FDR), using methods such as the Benjamini & Hochberg, Benjamini & Yekutieli, the q-value method, or in controlling the family-wise error rate (FWER) using Holm's step down method. To date, research studies that have compared various FDR/FWER methodologies have made use of limited simulation studies and/or have applied the methods to one or more microarray gene expression dataset(s). However, for microarray datasets the veracity of each null hypothesis tested is unknown so that an objective evaluation of performance cannot be rendered for application data. Due to the role of methylation in X-chromosome inactivation, we postulate that high-throughput methylation datasets may provide an appropriate forum for assessing the performance of commonly used FDR methodologies. These datasets preserve the complex correlation structure between probes, offering an advantage over simulated datasets. Using several methylation datasets, commonly used FDR methods including the q-value, Benjamini & Hochberg, and Benjamini & Yekutieli procedures as well as Holm's step down method were applied to identify CpG sites that are differentially methylated when comparing healthy males to healthy females. The methods were compared with respect to their ability to identify CpG sites located on sex chromosomes as significant, by reporting the sensitivity, specificity, and observed FDR. These datasets are useful for characterizing the performance of multiple comparison procedures, and may find further utility in other tasks such as comparing variable selection capabilities of classification methods and evaluating the performance of meta-analytic methods for microarray data.