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Abed Agbarya on Muck Rack

Abed Agbarya

As seen in: Nature, MDPI, BioRxiv, The Lancet
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Articles

Sequencing-free On-chip detection of lung cancer by fluorescent enzymatic profiling of cfDNA methylation

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By Abed Agbarya, Yael Michaeli, Assaf Grunwald, Lynne Itelson, Suheil Artul, Rasha Khoury, Michael Peled, Yuval Ebenstein, Noa Gilat, Jasline Deek, Sivan Yogev
| Nature Verified
Abstract We present a highly sensitive, low-cost approach for detecting lung cancer and monitoring response to therapy, based on sequencing-free detection of methylation biomarkers in cell-free DNA. After bisulfite treatment and PCR amplification of cell-free DNA, an engineered methyltransferase is used to fluorescently label all originally methylated CpG sites. Labeled cell-free DNA is then analyzed on a standard hybridization microarray.
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Social Network Clustering Analysis for Detection Associated Genetic Co-Mutations in Patients with Actionable Driver Mutations in NSCLC

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By Abed Agbarya, Haitham Nasrallah, Kamel Mhameed
| preprints.org
You are already at the latest version Non–small cell lung cancer (NSCLC) exhibits genomic heterogeneity that affects tumor immunogenicity and PD-L1 expression. Patient clustering based on shared mutational profiles using social network analysis (SNA) has been narrowly explored.
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Investigation of the Prevalence of Associated Genetic Mutations (Co-Mutations) in Patients with Actionable Driver Mutations in Lung Cancer: A Retrospective Study

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By Abed Agbarya, Walid Shalata, Edmond Sabo, Leonard Saiegh
| preprints.org
Background/Objectives: Lung cancer remains the leading cause of cancer-related mortality globally. Approximately 45% of these tumors harbor oncogenic mutations that drive carcinogenesis and are amenable to targeted therapies. Other predictive biomarkers— e.g., PD-L1, TMB, and MSI—play a crucial role in patients’ management. This study aims to investigate the existence of mutation clusters (co-mutations) and evaluate the correlation of these clusters with various clinical and laboratory parameters.
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