Biomedical subjects

John R de Almeida

Publications and source records attributed to John R de Almeida.

2 recordsLinked to original sources

PubMed2026

Genetically Proxied Inhibition of Cholesterol-Lowering Drug Targets and Survival in HPV-Positive and Non-HPV-Driven Head and Neck Cancer: A Multicentre MR Study.

BACKGROUND: Cholesterol pathways may influence head and neck squamous cell carcinoma (HNSCC) progression, but evidence on prognosis is inconsistent. We used Mendelian randomization (MR) to test whether genetically proxied inhibition of major low-density lipoprotein cholesterol (LDL-C)-lowering drug targets and circulating lipid traits affects overall survival (OS) in HPV-positive and non-HPV-driven HNSCCs. METHODS: We proxied lifelong LDL-C lowering using 55 cis-acting single-nucleotide polymorphisms in HMGCR, NPC1L1, PCSK9, and LDL-receptor (LDLR) from the updated 2021 Global Lipids Genetics Consortium and instrumented circulating lipid traits. Two-sample MR estimated effects on OS in 4,869 multicentre HNSCC cases (1,291 HPV-positive; 3,578 non-HPV-driven) using minimally adjusted Cox models. Sensitivity analyses additionally adjusted for tumor stage and treatment, assessed collider bias using an external HNSCC incidence genome-wide association study, and examined between-center heterogeneity and colocalization. RESULTS: Using the updated Global Lipids Genetic Consortium 2021 instruments, genetically proxied HMGCR inhibition showed a directionally protective but nonsignificant association with OS in HPV-positive oropharyngeal HNSCC in the primary analysis [inverse variance weighted (IVW) HR = 0.19; 95% confidence interval (CI), 0.03-1.18; P = 0.08], with directionally concordant weighted median results. No corresponding protective association was observed for HMGCR in non-HPV-driven disease (IVW HR = 1.68; 95% CI, 0.78-3.63; P = 0.19). No clear evidence of association was observed for NPC1L1, PCSK9, LDLR, or circulating lipid traits in either HPV stratum. Colocalization did not support a shared causal variant. CONCLUSIONS: These analyses provide suggestive evidence that genetically proxied HMGCR inhibition may influence survival in HPV-positive oropharyngeal HNSCC. IMPACT: HMGCR-related pathways may be relevant to prognosis in HPV-positive oropharyngeal HNSCC, whereas clear survival effects of other cholesterol-lowering targets were not supported.

Humans↗

PubMed2026

Toward a Better Paradigm for Head and Neck Cancer Treatment Applying AI (HNC-TACTIC): Protocol for an International Cohort Study of Electronic Health Records.

BACKGROUND: Head and neck squamous cell carcinomas (HNSCCs) cause considerable morbidity and mortality. Multimodal treatment strategies can cause significant toxicity, and therapy options are limited for recurrent disease. Immunotherapy has emerged as a promising approach. However, patient response variability underscores the need for better predictive markers. OBJECTIVE: This study aims to use artificial intelligence to develop two predictive models in patients with HNSCC to assess (1) progression or recurrence following primary curative treatment and (2) long-term survival after immunotherapy schemes in recurrent and metastatic disease. This study will also describe the characteristics of patients with early, locally advanced, and recurrent or metastatic cancers. METHODS: This is a retrospective, observational study of data captured in electronic health records (EHRs) from participating hospitals between January 1, 2014, and December 31, 2021. This study's population comprises adults diagnosed with HNSCC at any stage. Study variables, including demographics, comorbidities, clinical variables, treatments, and outcomes, will be extracted using EHRead, a technology that applies natural language processing and machine learning to extract and analyze structured and unstructured clinical information in deidentified EHRs. Predictive models based on dynamic risk stratification for treatment response and progression or recurrence will be developed using multivariable logistic regressions, decision tree classifiers, and random forest approaches. Descriptive and outcome analyses will be shown for different anatomic subsites and stratified by stage and treatment. RESULTS: This study began enrolling sites in July 2021 and is currently ongoing. By December 2025, data from 10 centers has been collected, comprising a total of 151,934,990 EHRs from 2,159,719 patients. CONCLUSIONS: Development of predictive models using artificial intelligence will advance clinical understanding of HNSCC to improve patient outcomes.

Humans↗