Biology subjects

Deek, R. A.

Publications and source records attributed to Deek, R. A..

3 recordsLinked to original sources

bioRxiv2026

Evaluation of harmonization methods to mitigate assay and cohort effects in plasma p-tau217

INTRODUCTIONThe growing number of assay platforms measuring blood-based biomarkers (BBMs) for Alzheimers disease (AD) has introduced challenges in interpretability and comparability across assays. Differences across studies also limit comparability of data. To address these challenges, a systematic evaluation of harmonization methods is needed to support BBM data integration within or across studies. METHODSTwo multisite studies, Alzheimers Disease Neuroimaging Initiative (ADNI, n = 219) and Human Connectome Project (HCP, n = 111), were used to evaluate harmonization methods for mitigating assay and cohort effects in plasma p-tau217 measurements. Methods includes various normalization, regression, and standardization approaches, including the recently developed CentiMarker. Assay effects were evaluated using repeated-measures data across assay platforms within each cohort, whereas cohort effects were assessed using pooled ADNI and HCP data. Harmonization performance was evaluated using distributional statistics and downstream modeling of p-tau217. RESULTSQuantile normalization and quantile mapping methods were most effective for mitigating assay effects, whereas conditional quantile mapping performed best for pooled multi-cohort data. These methods also preserved biological variability. In contrast, simple means adjustment and reference-based z-score standardization were least effective for mitigating assay effects, while simple means adjustment, z-score standardization, and quantile normalization were least effective for mitigating cohort effects. CentiMarker had minimal impact on assay or cohort effects. DISCUSSIONBased on our evaluation, we recommend (conditional) quantile mapping for p-tau217 studies integrating data across multiple assays or cohorts. In contrast, we caution against using CentiMarker and z-score-based methods, as they limit comparability and do not effectively mitigate technical variability.

neuroscience↗

bioRxiv2024

Integrated Analyses of Multi-omic Data Derived from Paired Primary Lung Cancer and Brain Metastasis Reveals the Metabolic Vulnerability as a Novel Therapeutic Target

Lung cancer brain metastases (LC-BrMs) are frequently associated with dismal mortality rates in patients with lung cancer; however, standard of care therapies for LC-BrMs are still limited in their efficacy. A deep understanding of molecular mechanisms and tumor microenvironment of LC-BrMs will provide us with new insights into developing novel therapeutics for treating patients with LC-BrMs. Here, we performed integrated analyses of genomic, transcriptomic, proteomic and metabolomic data which were derived from a total number of 174 patients with paired and unpaired primary lung cancer and LC-BrM, spanning four published and two newly generated patient cohorts on both bulk and single cell levels. We uncovered that LC-BrMs exhibited significantly higher intra-tumor heterogeneity. We also observed that mutations in a subset of genes were almost always shared by both primary lung cancers and LC-BrM lesions, including TTN, TP53, MUC16, LRP1B, RYR2, and EGFR. In addition, the genome-wide landscape of somatic copy number alterations was similar between primary lung cancers and LC-BrM lesions. Nevertheless, several regions of focal amplification were significantly enriched in LC-BrMs, including 5p15.33 and 20q13.33. Intriguingly, integrated analyses of transcriptomic, proteomic and metabolomic data revealed mitochondrial-specific metabolism was activated but tumor immune microenvironment was suppressed in LC-BrMs. Subsequently, we validated our results by conducting real-time quantitative reverse transcription PCR experiments, immunohistochemistry and multiplexed immunofluorescence staining of patients paired tumor specimens. Patients with a higher expression of mitochondrial metabolism genes but a lower expression of immune genes in their LC-BrM lesions tended to have a worse survival outcome. Therapeutically, targeting oxidative phosphorylation with gamitrinib in patient-derived organoids specific to LC-BrMs induced apoptosis and inhibited cell proliferation. The combination of gamitrinib plus anti-PD-1 immunotherapy significantly improved survival of mice bearing LC-BrMs. In conclusion, our findings not only provide comprehensive and integrated perspectives of molecular underpinnings of LC-BrMs but also contribute to the development of a potential, rationale-based combinatorial therapeutic strategy with the goal of translating it into clinical trials for patients with LC-BrMs.

cancer biology↗

bioRxiv2022

Mixture Margin Random-effects Copula Models for Inferring Temporally Conserved Microbial Co-variation Networks from Longitudinal Data

Longitudinal microbiome studies, in which data on a single subject is collected repeatedly over time, are becoming increasingly common in biomedical research. Such studies provide an opportunity to study the inherently dynamic nature of a microbiome in a way that cannot be done using cross-sectional studies. In this paper, we develop random-effects copula models with mixed zero-beta margins to identify biologically meaningful temporally conserved co-variation between two bacterial taxa, while accounting for the excessive zeros seen in 16S rRNA and metagenomic sequencing data. The model assumes a random-effects model for the dependence parameter in the copulas, which captures the conserved microbial co-variation while allowing for a time-specific dependence parameters. We develop a Monte Carlo EM algorithm for efficient estimation of model parameters and a corresponding Monte Carlo likelihood ratio test for the mean dependence parameter. Simulation studies show that our test controls the Type I error rate and provides an unbiased estimate of the mean dependence parameter. Additionally, we apply our method to a longitudinal pediatric cohort and identify changes in both local and global patterns of microbial co-variation networks in infants treated with antibiotics. Our analysis shows that the no antibiotics network is less dependent on individual taxon, thus making it more stable than the antibiotics network and more robust to both targeted and random attacks. Author summaryIdentification of co-variation between two microbes in microbial communities provides important insights into the community structure and stability. The commonly used measures of co-variation do not handle excessive zeros observed in the data and cannot be applied to longitudinal microbiome data directly. In this paper, we develop random-effects copula models with mixed zero-beta margins to identify biologically meaningful temporally conserved co-variation between two bacterial taxa, while accounting for the excessive zeros seen in 16S rRNA and metagenomic sequencing data. The model captures the conserved microbial co-variations while allowing for a time-specific dependence parameters. We develop an efficient Monte Carlo-based algorithm for parameter estimation and statistical inference. We analyze the data from a pediatric longitudinal cohort and identify changes in both local and global patterns of microbial co-variation networks in infants treated with antibiotics.

bioinformatics↗