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Melanoma is an aggressive malignancy with a high rate of metastasis. It is the most common cause of death from skin cancer. (1,2) Early diagnosis and resection is often associated [...], Context.--Detecting copy number variations (CNVs) at certain loci can aid in the diagnosis of histologically ambiguous melanocytic neoplasms. Droplet digital polymerase chain reaction (ddPCR) is a rapid, automated, and inexpensive method for CNV detection in other cancers, but not yet melanoma. Objective.--To evaluate the performance of a 4-gene ddPCR panel that simultaneously tests for ras responsive binding element protein 1 (RREB1) gain; cyclin-dependent kinase inhibitor 2A (CDKN2A) loss; MYC proto-oncogene, bHLH transcription factor (MYC) gain; and MYB protooncogene, transcription factor (MYB) loss in melanocytic neoplasms. Design.--One hundred sixty-four formalin-fixed, paraffin-embedded skin samples were used to develop the assay, of which 65 were used to evaluate its performance. Chromosomal microarray analysis (CMA) data were used as the gold standard. Results.--ddPCR demonstrated high concordance with CMA in detecting RREB1 gain (sensitivity, 86.7%; specificity, 88.9%), CDKN2A loss (sensitivity, 80%; specificity, 100%), MYC gain (sensitivity, 70%; specificity, 100%), and MYB loss (sensitivity, 71.4%; specificity, 100%). When one CNV was required to designate the test as positive, the 4-gene ddPCR panel distinguished nevi from melanomas with a sensitivity of 78.4% and a specificity of 71.4%. For reference, CMA had a sensitivity of 86.2% and a specificity of 78.6%. Our data also revealed interesting relationships with histology, namely (1) a positive correlation between RREB1 ddPCR copy number and degree of tumor progression; (2) a statistically significant correlation between MYC gain and nodular growth; and (3) a statistically significant correlation between MYB loss and a sheetlike pattern of growth. Conclusions.--With further validation, ddPCR may aid both in our understanding of melanomagenesis and in the diagnosis of challenging melanocytic neoplasms. doi: 10.5858/arpa.2024-0027OA

Introduction: Head and neck cancer (HNC) is a significant global health burden, with late presentation leading to complex treatment. While human papillomavirus (HPV) infection has been implicated in HNC, data from low‐ and middle‐income countries (LMICs) are limited. In this study, we investigated the prevalence and role of HPV in head and neck cancers diagnosed in Rwanda. Methods: A retrospective cross‐sectional study was conducted using Rwanda Cancer Registry from January 2011 through December 2020. p16 immunohistochemistry as a surrogate for HPV was performed on a randomly selected case. p16‐positive cases were genotyped. Results: A total of 1001 patients with HNC were identified; 82% (n = 819) had squamous cell carcinoma. The mean age at diagnosis was 51.1 years, with a majority being males (58%). Oral cavity and lip (27%) were the most common primary cancer sites. Stage was unknown in most cases (75%, n = 747). HIV status was known in 33% (n = 334) of patients with 10% (n = 33) HIV‐positive; 22% of 202 randomly selected cases were p16‐positive; 34% of the p16‐positive cases were oropharynx. PCR analysis of p16‐positive cases showed 19% HPV positivity, and HPV16 was the most common high‐risk HPV strain, and 55.5% were recorded HPV‐positive by PCR. Conclusions: HNC cases in Rwanda have been increasing from 2011 to 2020, with a significant portion being HPV‐positive. Strategies to implement routine testing for p16, especially in oropharynx cancer patients, improved preservation of tissue samples, collection of comprehensive information including cancer risk factors, staging, and treatment are needed in Rwanda. [ABSTRACT FROM AUTHOR]

Nearly all cervical cancers are caused by persistent high-risk human papillomavirus (hrHPV) infection. There are 14 recognized hrHPV genotypes (HPV 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 66, and 68), and hrHPV genotypes 16 and 18 comprise approximately 66% of all cases worldwide. An additional 15% of cervical cancers are caused by hrHPV genotypes 31, 33, 45, 52, and 58. Screening patients for hrHPV as a mechanism for implementation of early treatment is a proven strategy for decreasing the incidence of HPV-related neoplasia, cervical cancer in particular. Here, we present population data from an HPV screening initiative in Kosovo designed to better understand the prevalence of the country's HPV burden and local incidence of cervical cancer by hrHPV genotype. Nearly 2000 women were screened for hrHPV using a real-time polymerase chain reaction (real-time PCR) assay followed by melt curve analysis to establish the prevalence of hrHPV in Kosovo. Additionally, DNA was extracted from 200 formalin-fixed, paraffin embedded cervical tumors and tested for hrHPV using the same method. Cervical screening samples revealed a high prevalence of hrHPV genotypes 16 and 51, while cervical cancer specimens predominantly harbored genotypes 16, 18, and 45. This is the first comprehensive screening study for evaluating the prevalence of hrHPV genotypes in Kosovo on screening cervical brush samples and cervical neoplasms. Given the geographic distribution of hrHPV genotypes and the WHO's global initiative to eliminate cervical cancer, this study can support and direct vaccination efforts to cover highly prevalent hrHPV genotypes in Kosovo's at-risk population. [ABSTRACT FROM AUTHOR]

Cyanobacteria produce a variety of secondary metabolites, including toxins that may contribute to the development of disease. Previous work was able to detect the presence of a cyanobacterial marker in human nasal and broncoalveolar lavage samples; however, it was not able to determine the quantification of the marker. To further research the relationship between cyanobacteria and human health, we validated a droplet digital polymerase chain reaction (ddPCR) assay to simultaneously detect the cyanobacterial 16S marker and a human housekeeping gene in human lung tissue samples. The ability to detect cyanobacteria in human samples will allow further research into the role cyanobacteria plays in human health and disease. [ABSTRACT FROM AUTHOR]

Wastewater - based epidemiology (WBE) can be used to monitor the community presence of infectious disease pathogens of public health concern such as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Viral nucleic acid has been detected in the stool of SARS-CoV-2-infected individuals. Asymptomatic SARS-CoV-2 infections make community monitoring difficult without extensive and continuous population screening. In this study, we validated a procedure that includes manual pre-processing, automated SARS-CoV-2 RNA extraction and detection workflows using both reverse-transcriptase quantitative polymerase chain reaction (RT-qPCR) and reverse transcriptase droplet digital PCR (RT-ddPCR). Genomic RNA and calibration materials were used to create known concentrations of viral material to determine the linearity, accuracy, and precision of the wastewater extraction and SARS-CoV-2 RNA detection. Both RT-qPCR and RT-ddPCR perform similarly in all the validation experiments, with a limit of detection of 50 copies/mL. A wastewater sample from a care facility with a known outbreak was assessed for viral content in replicate, and we showed consistent results across both assays. Finally, in a 2-week survey of two New Hampshire cities, we assessed the suitability of our methods for daily surveillance. This paper describes the technical validation of a molecular assay that can be used for long-term monitoring of SARS-CoV-2 in wastewater as a potential tool for community surveillance to assist with public health efforts.IMPORTANCEThis paper describes the technical validation of a molecular assay that can be used for the long-term monitoring of SARS-CoV-2 in wastewater as a potential tool for community surveillance to assist with public health efforts., Competing Interests: J.A.L. is an advisor for Bio-Rad and owns publicly traded stock in Bio-Rad and Thermo Fisher.

Spatially resolved characterization of the transcriptome and proteome promises to provide further clarity on cancer pathogenesis and etiology, which may inform future clinical practice through classifier development for clinical outcomes. However, batch effects may potentially obscure the ability of machine learning methods to derive complex associations within spatial omics data. Profiling thirty-five stage three colon cancer patients using the GeoMX Digital Spatial Profiler, we found that mixed-effects machine learning (MEML) methods† may provide utility for overcoming significant batch effects to communicate key and complex disease associations from spatial information. These results point to further exploration and application of MEML methods within the spatial omics algorithm development life cycle for clinical deployment.