Biology subjects

Osazuwa-Peters, N.

Publications and source records attributed to Osazuwa-Peters, N..

3 recordsLinked to original sources

bioRxiv2026

GARD: Genomic Data based Drug Repurposing in Head and Neck Cancer with Large Language Model Validation

Background/ObjectivesHead and neck cancer (HNC) represents the seventh most common cancer diagnosis globally, yet current treatments, including surgery, radiation, and immunotherapy, have shown limited improvement in outcomes. Drug repurposing offers a cost-effective strategy to identify new therapeutic options by leveraging existing medications with known safety profiles. Within this study we developed the GARD pipeline (Genomic Alteration-based Repurposing for Drugs), designed to uncover repurposing candidates for HNC using genomic and network-based approaches. MethodsGARD integrates multi-omics data from The Cancer Genome Atlas (TCGA), including copy number variation (CNV) and somatic mutations (SOM). The cohort was stratified by human papillomavirus (HPV) status. Risk-associated genes were identified and then expanded via high-confidence protein-protein interaction (PPI) networks. Top candidate genes were filtered through comprehensive analysis of publicly available literature data in PubMed using LLM to validate the relationship between the identified genes and HNC. The validated top risk genes and their network-expanded neighbors were mapped against DrugBank, and through statistical significance testing, established significant drug-gene associations. ResultsSignificant genes associated with HNC, inferred by genomics alteration, were identified across HPV-positive and HPV-negative subgroups, such as PIK3CA, SOX2, TP53, EIF4G1, TLR7, CLDN1, PRKCI, and EPHA2. Further expansion through the PPI network identified other targetable genes such as EGFR, ERBB2, and the FGFRs. Literature based validation efforts ensured provided confidence in the gene-disease association. Drug-gene mapping revealed candidates spanning already in clinical trials for HNC (e.g. Afatinib, Cabozantinib, Dasatinib, Brigatinib, Lenvatinib, Capivasertib, Erdafitinib) and emerging or repurposing candidates (Amuvatinib, XL765 (Voxtalisib), Golotimod, Artenimol, Quercetin, and Acetylsalicylic Acid), offering opportunities for precision repurposing. ConclusionsThe GARD pipeline demonstrates a genomics-driven, network-informed framework for systematic drug repurposing in HNC. HPV stratification enhances precision, literature-based validation strengthens confidence, and integrated drug mapping enables refinement of existing therapies and discovery of novel candidates for personalized treatment strategies. Code AvailabilityThe full implementation of the GARD pipeline, including preprocessing scripts, statistical analysis modules, and visualization tools, is publicly available on GitHub at: https://github.com/pvtanike/Genomic-Landscape-Based-Drug-Repurposing.git Simple SummaryHead and neck cancer (HNC) is among the most prevalent and challenging cancers world-wide. Developing new drugs is expensive and time-consuming, so this study explored a faster, cost-effective approach utilizing existing medications with established safety profiles: drug repurposing. We developed the GARD pipeline (Genomic Alteration-based Repurposing for Drugs), which utilizes large-scale genomic data from The Cancer Genome Atlas (TCGA) to identify key genomics changes in HNC. These genes are expanded through protein-protein interaction networks to capture related pathways and then validated using evidence from thousands of PubMed articles extracted by large language model (LLM) tools. Finally, validated genes are matched with drugs using the DrugBank database. This approach uncovered both known cancer drugs and promising new candidates. These included targeted therapies such as Fostamatinib, Nintedanib, Brigatinib, Regorafenib, and Lenvatinib, as well as emerging compounds like Artenimol, Quercetin, and Acetylsalicylic Acid (Aspirin). Through a combination of genomic analysis, network expansion, and literature validation, the GARD pipeline offers a powerful way to accelerate personalized cancer treatments while reducing cost and development time.

bioinformatics↗

bioRxiv2025

Whole Metagenome Sequencing: not Deep Enough for Complete Microbial Function Recovery

BackgroundWhole metagenome shotgun sequencing (WMS) is widely used to profile microbial function. However, technical variability in sequencing and analysis often obscures true biological patterns. Large-scale studies are particularly susceptible to batch effects, such as differences in sequencing depth and platform and annotation strategies, as well as sample-to-flow-cell assignments. However, the relative effects of these factors on functional inference in such studies have yet to be systematically evaluated. We analyzed oral-rinse WMS data from a study cohort including 671 Nigerian youths aged 9-18, sequenced on two Illumina platforms. Microbial molecular functionality encoded in these data were annotated using the mi-faser/Fusion pipeline, to capture the broad functional repertoire, and HUMAnN 3/EC numbers pipeline to characterize curated enzymatic activities. We then quantified how technical factors and batch effects shaped the recovery of microbial functionality. ResultsThree findings of our work were most salient. First, we observed that the choice of annotation strategy traded off between breadth and specificity of functional coverage. Second, we found that low-prevalence functions were disproportionately lost at shallow sequencing depths, indicating that in e.g. case-control studies with few representatives of the minor class, sequencing depth could critically impact study resolution. Finally, using our newly developed model relating sequencing depth to functional recovery, we demonstrated that increasing sequencing depth does not directly or proportionally improve functional recall. That is, at as little as 10% of this studys sequencing depth, 30% of the estimated complete microbiome functional repertoire was detectable. However, even at the full depth used in this study, we were only able to recover an estimated 60% of that complete functional repertoire. ConclusionsTogether, these findings and our depth-to-function mapping framework provide practical guidelines for the design and interpretation of WMS studies. Coordinating sequencing depth planning with annotation strategy, experimental design, and rigorous batch control is thus essential for robust detection of microbial functions and for ensuring reproducible microbiome insights.

bioinformatics↗

bioRxiv2022

Deconstructing olfactory epithelium developmental pathways in esthesioneuroblastoma

ImportanceEsthesioneuroblastoma (ENB) is a rare tumor arising from the olfactory cleft region of the nasal cavity. Due to the low incidence of this tumor, as well as an absence of established reagents such as cell lines or murine models, understanding the mechanisms driving ENB pathobiology has been challenging. ObjectiveHere, we sought to apply advances from research on the human olfactory epithelial neurogenic niche, along with new biocomputational approaches, to better understand the cellular and molecular factors in low and high grade ENB and how specific transcriptomic markers may predict prognosis. Design, Setting, and ParticipantsThis was a retrospective biocomputational analysis utilizing human ENB and normal olfactory mucosal tissue and clinical outcomes data. The setting was a large academic medical center. Participants were selected based on available datasets, and included ENB patients across all four tumor grades, and patients with normal olfactory mucosa. Main Outcomes and MeasuresOutcomes include deconvolution analysis, differential gene expression analysis, overall survival, and immunohistochemistry. A machine learning model was used to predict cell-type proportion signatures in ENB, and Kaplan-Meier curves with associated log-rank tests were used to estimate differences in overall survival. ResultsWe analyzed a total of 19 ENB samples (9 low grade I/II, 10 high grade III/IV) with available bulk RNA-Sequencing and survival data, along with 10 samples from normal olfactory mucosa (3 bulk RNA-Sequencing, 7 single cell RNA-Sequencing). The bulk RNA-Sequencing deconvolution model identified a significant increase in globose basal cell (GBC) and CD8 T cell identities in high grade tumors (GBC from approximately 0% to 8%, CD8 T cell from 0.7% to 2.2%), and significant decreases in mature neuronal, Bowmans gland, and olfactory ensheathing programs, in high grade tumors (mature neuronal from 3.7% to approximately 0%, Bowmans gland from 18.6% to 10.5%, olfactory ensheathing from 3.4% to 1.1%). Trajectory analysis identified potential regulatory pathways in proliferative ENB cells, including PRC2. Survival analysis guided by gene expression in bulk RNA-Sequencing data identified favorable prognostic markers such as SOX9, S100B, and PLP1 expression. Conclusions and RelevanceOur analyses provide a basis for additional translational research on ENB management, as well as identification of potential new prognostic markers.

cancer biology↗