Addressing positive multi-cancer early detection tests in head and neck Surgery: Experience with head and neck work up for high-risk referrals.

OBJECTIVES: Blood-based multi-cancer early detection (MCED) tests are now commercially available. However, there are currently no consensus guidelines available for head and neck cancer (HNC) providers to direct work up or surveillance for patients with a positive MCED test. We seek to describe cases of patients with positive MCED tests suggesting HNC and provide insights for their evaluation. METHODS: Retrospective chart review of patients referred to Otolaryngology with an MCED result suggesting HNC. Patients enrolled in prospective MCED clinical trials were excluded. Cancer diagnoses were confirmed via frozen-section pathology. RESULTS: Five patients were included (mean age: 69.2 years, range 50-87; 4 male) with MCED-identified-high-risk for HNC or lymphoma. Only patient was symptomatic. After physical exam and follow-up head and neck imaging, circulating tumor HPV DNA testing, two patients were diagnosed with p16 + oropharyngeal squamous cell carcinomas and underwent appropriate therapy. A third patient had no evidence of head and neck cancer but was diagnosed with sarcoma of the thigh. The remaining two patients had no evidence of malignancy after in-depth workup. CONCLUSIONS: In this retrospective study, 2 of 5 patients referred to Otolaryngology with a positive MCED result were diagnosed with HPV + oropharyngeal squamous cell carcinoma. We recommend that positive HNC MCED work up include thorough head and neck examination with flexible laryngoscopy and focused CT or MRI imaging. Given the potential for inaccurate MCED tissue of origin classification, PET/CT may be useful in specific situations. For a patient with no cancer identified, development of clear guidelines is warranted.

Mathematical models of cystic fibrosis as a systemic disease.

Cystic fibrosis (CF) is widely known as a disease of the lung, even though it is in truth a systemic disease, whose symptoms typically manifest in gastrointestinal dysfunction first. CF ultimately impairs not only the pancreas and intestine but also the lungs, gonads, liver, kidneys, bones, and the cardiovascular system. It is caused by one of several mutations in the gene of the epithelial ion channel protein CFTR. Intense research and improved antimicrobial treatments during the past eight decades have steadily increased the predicted life expectancy of a person with CF (pwCF) from a few weeks to over 50 years. Moreover, several drugs ameliorating the sequelae of the disease have become available in recent years, and notable treatments of the root cause of the disease have recently generated substantial improvements in health for some but not all pwCF. Yet, numerous fundamental questions remain unanswered. Complicating CF, for instance in the lung, is the fact that the associated insufficient chloride secretion typically perturbs the electrochemical balance across epithelia and, in the airways, leads to the accumulation of thick, viscous mucus and mucus plaques that cannot be cleared effectively and provide a rich breeding ground for a spectrum of bacterial and fungal communities. The subsequent infections often become chronic and respond poorly to antibiotic treatments, with outcomes sometimes only weakly correlated with the drug susceptibility of the target pathogen. Furthermore, in contrast to rapidly resolved acute infections with a single target pathogen, chronic infections commonly involve multi-species bacterial communities, called "infection microbiomes," that develop their own ecological and evolutionary dynamics. It is presently impossible to devise mathematical models of CF in its entirety, but it is feasible to design models for many of the distinct drivers of the disease. Building upon these growing yet isolated modeling efforts, we discuss in the following the feasibility of a multi-scale modeling framework, known as template-and-anchor modeling, that allows the gradual integration of refined sub-models with different granularity. The article first reviews the most important biomedical aspects of CF and subsequently describes mathematical modeling approaches that already exist or have the potential to deepen our understanding of the multitude aspects of the disease and their interrelationships. The conceptual ideas behind the approaches proposed here do not only pertain to CF but are translatable to other systemic diseases. This article is categorized under: Congenital Diseases > Computational Models.

Antibiotics Drive Expansion of Rare Pathogens in a Chronic Infection Microbiome Model.

Chronic (long-lasting) infections are globally a major and rising cause of morbidity and mortality. Unlike typical acute infections, chronic infections are ecologically diverse, characterized by the presence of a polymicrobial mix of opportunistic pathogens and human-associated commensals. To address the challenge of chronic infection microbiomes, we focus on a particularly well-characterized disease, cystic fibrosis (CF), where polymicrobial lung infections persist for decades despite frequent exposure to antibiotics. Epidemiological analyses point to conflicting results on the benefits of antibiotic treatment yet are confounded by the dependency of antibiotic exposures on prior pathogen presence, limiting their ability to draw causal inferences on the relationships between antibiotic exposure and pathogen dynamics. To address this limitation, we develop a synthetic infection microbiome model representing CF metacommunity diversity and benchmark on clinical data. We show that in the absence of antibiotics, replicate microbiome structures in a synthetic sputum medium are highly repeatable and dominated by oral commensals. In contrast, challenge with physiologically relevant antibiotic doses leads to substantial community perturbation characterized by multiple alternate pathogen-dominant states and enrichment of drug-resistant species. These results provide evidence that antibiotics can drive the expansion (via competitive release) of previously rare opportunistic pathogens and offer a path toward microbiome-informed conditional treatment strategies. IMPORTANCE We develop and clinically benchmark an experimental model of the cystic fibrosis (CF) lung infection microbiome to investigate the impacts of antibiotic exposures on chronic, polymicrobial infections. We show that a single experimental model defined by metacommunity data can partially recapitulate the diversity of individual microbiome states observed across a population of people with CF. In the absence of antibiotics, we see highly repeatable community structures, dominated by oral microbes. Under clinically relevant antibiotic exposures, we see diverse and frequently pathogen-dominated communities, and a nonevolutionary enrichment of antimicrobial resistance on the community scale, mediated by competitive release. The results highlight the potential importance of nonevolutionary (community-ecological) processes in driving the growing global crisis of increasing antibiotic resistance.

Frequency of quorum-sensing mutations in Pseudomonas aeruginosa strains isolated from different environments.

Pseudomonas aeruginosa uses quorum sensing (QS) to coordinate the expression of multiple genes necessary for establishing and maintaining infection. It has previously been shown that lasR QS mutations frequently arise in cystic fibrosis (CF) lung infections, however, there has been far less emphasis on determining whether other QS system mutations arise during infection or in other environments. To test this, we utilized 852 publicly available sequenced P. aeruginosa genomes from the Pseudomonas International Consortium Database (IPCD) to study P. aeruginosa QS mutational signatures. To study isolates by source, we focused on a subset of 654 isolates collected from CF, wounds, and non-infection environmental isolates, where we could clearly identify their source. We also worked with a small collection of isolates in vitro to determine the impact of lasR and pqs mutations on isolate phenotypes. We found that lasR mutations are common across all environments and are not specific to infection nor a particular infection type. We also found that the pqs system proteins PqsA, PqsH, PqsL and MexT, a protein of increasing importance to the QS field, are highly variable. Conversely, RsaL, a negative transcriptional regulator of the las system, was found to be highly conserved, suggesting selective pressure to repress las system activity. Overall, our findings suggest that QS mutations in P. aeruginosa are common and not limited to the las system; however, LasR is unique in the frequency of putative loss-of-function mutations.

Modeling serological testing to inform relaxation of social distancing for COVID-19 control.

Serological testing remains a passive component of the public health response to the COVID-19 pandemic. Using a transmission model, we examine how serological testing could have enabled seropositive individuals to increase their relative levels of social interaction while offsetting transmission risks. We simulate widespread serological testing in New York City, South Florida, and Washington Puget Sound and assume seropositive individuals partially restore their social contacts. Compared to no intervention, our model suggests that widespread serological testing starting in late 2020 would have averted approximately 3300 deaths in New York City, 1400 deaths in South Florida and 11,000 deaths in Washington State by June 2021. In all sites, serological testing blunted subsequent waves of transmission. Findings demonstrate the potential benefit of widespread serological testing, had it been implemented in the pre-vaccine era, and remain relevant now amid the potential for emergence of new variants.

Causes and Consequences of COVID-19-Associated Bacterial Infections.

The COVID-19 literature highlights that bacterial infections are more common in fatal cases than recovered cases. If bacterial infections drive mortality in COVID-19, this has clear implications for patient management. However, it is possible that the enrichment of bacterial infections in COVID-19 fatalities is simply a by-product of late-stage pathology, leading to different advice for patient management. To address this question, we review current knowledge on bacterial infections in COVID-19, assess information from past viral respiratory pandemics, and simulate alternate causal models of interactions between virus, bacteria, and mortality in COVID-19. From these models, we conclude that currently available data are not sufficient to discriminate between these alternate causal pathways, and we highlight what data are required to determine the relative contribution of bacterial infection to COVID-19 morbidity and mortality. We further summarize the potential long-term consequences of SARS-CoV-2 infection.

Microbiome Data Enhances Predictive Models of Lung Function in People With Cystic Fibrosis.

BACKGROUND: Microbiome sequencing has brought increasing attention to the polymicrobial context of chronic infections. However, clinical microbiology continues to focus on canonical human pathogens, which may overlook informative, but nonpathogenic, biomarkers. We address this disconnect in lung infections in people with cystic fibrosis (CF). METHODS: We collected health information (lung function, age, and body mass index [BMI]) and sputum samples from a cohort of 77 children and adults with CF. Samples were collected during a period of clinical stability and 16S rDNA sequenced for airway microbiome compositions. We use ElasticNet regularization to train linear models predicting lung function and extract the most informative features. RESULTS: Models trained on whole-microbiome quantitation outperformed models trained on pathogen quantitation alone, with or without the inclusion of patient metadata. Our most accurate models retained key pathogens as negative predictors (Pseudomonas, Achromobacter) along with established correlates of CF disease state (age, BMI, CF-related diabetes). In addition, our models selected nonpathogen taxa (Fusobacterium, Rothia) as positive predictors of lung health. CONCLUSIONS: These results support a reconsideration of clinical microbiology pipelines to ensure the provision of informative data to guide clinical practice.

Modeling serological testing to inform relaxation of social distancing for COVID-19 control.

Serological testing remains a passive component of the current public health response to the COVID-19 pandemic. Using a transmission model, we examined how serology can be implemented to allow seropositive individuals to increase levels of social interaction while offsetting transmission risks. We simulated the use of widespread serological testing in three metropolitan areas with different initial outbreak timing and severity characteristics: New York City, South Florida, and Washington Puget Sound. In our model, we use realistic serological assay characteristics, in which tested seropositive individuals partially restore their social contacts and act as immunological 'shields'. Compared to a scenario with no intervention, beginning a mass serological testing program on November 1, 2020 was predicted to avert 15,000 deaths (28% reduction, 95% CrI: 0.4%-30.2%) in New York City, 3,000 (31.1% reduction, 95% CrI: 26.4%-33.3%) in South Florida and 10,000 (60.3% reduction, 95% CrI: 50.2%-60.7%) in Washington State by June 2021. In all three sites, widespread serological testing substantially blunted new waves of transmission. Serological testing has the potential to mitigate the impacts of the COVID-19 pandemic while also allowing a substantial number of individuals to safely return to social interactions and economic activity.

Modeling shield immunity to reduce COVID-19 epidemic spread.

The COVID-19 pandemic has precipitated a global crisis, with more than 1,430,000 confirmed cases and more than 85,000 confirmed deaths globally as of 9 April 20201-4. Mitigation and suppression of new infections have emerged as the two predominant public health control strategies5. Both strategies focus on reducing new infections by limiting human-to-human interactions, which could be both socially and economically unsustainable in the long term. We have developed and analyzed an epidemiological intervention model that leverages serological tests6,7 to identify and deploy recovered individuals8 as focal points for sustaining safer interactions via interaction substitution, developing what we term 'shield immunity' at the population scale. The objective of a shield immunity strategy is to help to sustain the interactions necessary for the functioning of essential goods and services9 while reducing the probability of transmission. Our shield immunity approach could substantively reduce the length and reduce the overall burden of the current outbreak, and can work synergistically with social distancing. The present model highlights the value of serological testing as part of intervention strategies, in addition to its well-recognized roles in estimating prevalence10,11 and in the potential development of plasma-based therapies12-15.