Biomedical subjects

Xiaoyan Zhu

Publications and source records attributed to Xiaoyan Zhu.

4 recordsLinked to original sources

PubMed2026

PScnv: personalized self-normalizing CNV detection with a hierarchical multi-phase framework.

MOTIVATION: Accurate detection of copy number variations (CNVs) from targeted panel sequencing remains challenging due to limited genomic coverage and pronounced sample-specific biases. Existing normalization strategies, including baseline-cohort, matched-control, and single-sample approaches, often struggle to balance noise suppression with adaptability, leading to inconsistent performance across heterogeneous samples. RESULTS: We present PScnv, a personalized self-normalizing framework for robust CNV detection from panel sequencing data. PScnv integrates a pre-built panel-of-normals (PoN) with sample-intrinsic stable chromosomes through ridge-regression normalization to generate individualized log2 ratio profiles with reduced systematic variation. CNVs are then identified using a hierarchical multi-phase segmentation pipeline incorporating z-score pre-partitioning, kernel-based correction, and circular binary segmentation. In 139 clinical tumor samples with orthogonal FISH validation at MET, ERBB2, and MTAP, PScnv showed improved accuracy and robustness over existing methods that do not require patient-matched normal samples, provided that a pre-built PoN cohort is available. AVAILABILITY: Source code is available for academic use at https://github.com/lvws/PScnv.

DNA Copy Number Variations↗

PubMed2025

MRDtarget: A heuristic Gaussian approach for optimizing targeted capture regions to enhance Minimal Residual Disease detection.

Molecular residual disease (MRD) detection, initially developed for hematologic malignancies, has become a critical biomarker for monitoring solid tumors. MRD detection primarily relies on circulating tumor DNA (ctDNA) analysis using next-generation sequencing, offering high sensitivity and broad genomic coverage. However, challenges remain in designing cost-effective panels that maximize mutation detection while maintaining biological relevance. Fixed panels often lack sufficient patient-specific mutation coverage, while WES-based personalized MRD assays, despite their high sensitivity, are costly and less accessible. We developed a tumor comprehensive genomic profiling (CGP)-informed personalized MRD assay to detect tumor-derived mutations, which allowed us to design patient-specific personalized panels and meanwhile, provide a cost-effective alternative to whole exome sequencing (WES). To address these limitations, we developed MRDtarget, a heuristic multivariate Gaussian model-based targeted capture region selection method. By expanding beyond traditional hotspot regions, MRDtarget optimizes variant tracking for MRD detection, significantly improving sensitivity. Using a Bayesian inference-based heuristic approach, MRDtarget integrates multi-feature informativeness rates to identify optimal genomic regions for capture. Experimental results demonstrate that MRDtarget enables the detection of more variants per patient. This study underscores the importance of rational panel design to improve MRD sensitivity and provides a novel approach to enhance precision diagnostics and treatment for solid tumor patients.

Humans↗

PubMed2025

MRDagent: iterative and adaptive parameter optimization for stable ctDNA-based MRD detection in heterogeneous samples.

MOTIVATION: Minimal residual disease (MRD) as critical biomarker for cancer prognosis and management plays a crucial role in improving patient outcomes. However, detecting MRD via next-generation sequencing-based circulating tumor DNA variant calling remains unstable due to the extremely low variant allele frequency and significant inter- and intra-sample heterogeneity. Although parameter optimization can theoretically enhance the detection performance of variants, achieving stable MRD detection remains challenging due to three key factors: (i) the necessity for individualized parameter tuning across numerous heterogeneous genomic intervals within each sample, (ii) the tightly interdependent parameter requirements across different stages of variant detection workflows, and (iii) the limitations of current automated parameter optimization methods. RESULTS: In this study, we propose MRDagent, a novel variant detection tool designed specifically for MRD detection. MRDagent incorporates an iterative and self-adaptive optimization framework capable of handling unknown objectives, varying constraints, and highly coupled parameters across stages. A key innovation of MRDagent is the integration of a convolutional neural network-based meta-model, trained on historical data to enable rapid parameter prediction. This significantly enhances computational efficiency and generalization performance. Extensive evaluations on simulated and real-world datasets demonstrate MRDagent's superior and stable performance, providing an efficient, reliable solution for MRD detection in clinical and high-throughput research applications. AVAILABILITY AND IMPLEMENTATION: MRDagent is freely available at https://github.com/aAT0047/MRDagent.git. The corresponding dataset and software archive are available at Zenodo: https://doi.org/10.5281/zenodo.15458496.

Circulating Tumor DNA↗

PubMed2025

THE CAUSAL ASSOCIATION OF CARDIOMETABOLIC DISEASES AND SEPSIS-RELATED OUTCOMES: A MENDELIAN RANDOMIZATION AND POPULATION STUDY.

Objective: The causality between cardiometabolic disease (CMD) and sepsis has remained largely unknown. To elucidate this, we conducted a Mendelian randomization (MR) and population study. Methods: First, we used univariable and multivariable MR analyses to investigate causal associations between CMD and sepsis-related outcomes. We obtained genome-wide association study summary from both the MRC Integrative Epidemiology Unit and the FinnGen consortium. Subsequently, a two-step mediation MR analysis was performed to explore mediators. Afterward, we conducted an observational study using the Medical Information Mart for Intensive Care IV database, in which multivariable logistic regression models were utilized to examine the relationship between CMD and sepsis-related outcomes. Results: In the MR study, type 2 diabetes mellitus (OR = 1.058, 95% CI = 1.017-1.100, P = 0.005), obesity (OR = 1.113, 95% CI = 1.057-1.172, P < 0.001), and heart failure (HF) (OR = 1.178, 95% CI = 1.063-1.305, P = 0.002) were independently causally related to sepsis. Obesity (OR = 1.215, 95% CI = 1.027-1.437, P = 0.023) and HF (OR = 1.494, 95% CI = 1.080-2.065, P = 0.015) also showed independent causal associations with sepsis critical care admission. Mediation MR analysis identified 23 blood metabolites potentially causally linked to sepsis ( P < 0.05), yet none mediated the relationship between CMD and sepsis. In the observational study, we found associations between sepsis and several conditions including type 2 diabetes mellitus, obesity, hypertension, stroke, HF, and hyperlipidemia after adjusting for confounding factors. Moreover, hypertension, stroke, HF, coronary artery disease, and hyperlipidemia were linked to sepsis critical care admission. Conclusion: This study has, for the first time, revealed indicative evidence of a causal relationship between CMD and sepsis through observational and genetic evidence. Taken together, clinical attention to sepsis may be warranted among patients with CMD.

Humans↗