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Chemokine network dysregulation in EGFR-mutant NSCLC: impact on tumor immunity and drug resistance
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EGFR-mutant non–small cell lung cancer (NSCLC) initially responds well to EGFR tyrosine kinase inhibitors (EGFR-TKIs), yet resistance is nearly inevitable and responses to immune checkpoint inhibitors (ICIs) remain limited. In a retrospective cohort of 57 patients with advanced EGFR-mutant NSCLC treated with nivolumab or pembrolizumab, the objective response rate was 12.3%, although responses were concentrated in a small subgroup with concurrent high PD-L1 expression and dense intratumoral CD8⁺ immune-cell infiltration. Emerging evidence links these outcomes to chemokine-network dysregulation that alters immune trafficking and spatial organization. Oncogenic EGFR signaling shifts chemokine networks away from effector T-cell recruitment and toward regulatory and myeloid-cell accumulation, thereby promoting immune exclusion and treatment adaptation. CXCL12–CXCR4 signaling, stromal barriers, and defective organization of tertiary lymphoid structures further reinforce immune exclusion and ICI resistance. Under EGFR-TKI pressure, chemokine programs evolve dynamically, permitting transient immune entry while sustaining drug tolerance, EMT-associated persistence, and myeloid-supported resistance. However, most proposed chemokine mechanisms remain supported predominantly by preclinical or clinical-correlative evidence, and their causal, temporal, and predictive relevance in patients remains unresolved. Defining these circuits may guide biomarker-driven combinations to convert immune-excluded tumors into immune-permissive states and to prolong disease control through rational TKI-, ICI-, and chemokine-targeted therapeutic strategies.

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Publication Date
Tue Sep 30 2014
Journal Name
J Clin Biomed Sci
Detection of EGFR Mutations in Bronchial Wash from Iraqi patients with nonsmall Cell Lung Cancer (NSCLC)
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Background: Non-small cell lung cancer (NSCLC) is caused of 85% of all lung cancers. Among the most important factors for lung tumor growth and proliferation are the tyrosine kinase receptors that coded by the epidermal growth factor recep-tor (EGFR) gene. Activation of EGFR ultimately leads to developing of lung cancer. The present study was undertaken with an objective to detect EGFR mutations in bronchial wash from Iraqi patients with NSCLC before treatment. Methods: DNA was extracted from bronchial wash samples collected from 50 patients with NSCLC by using a Qiamp DNA Mini Kit (Qiagen, Hilden, Germany). Then, EGFR mutations were determined by using real-time RCR combined with two technologies, Amplification Refractory Mutation System (

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Publication Date
Mon Sep 15 2014
Journal Name
Journal Of Clinical And Biomedical Sciences
Detection of EGFR Mutations in Bronchial Wash from Iraqi patients with nonsmall Cell Lung Cancer (NSCLC)
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Background: Non-small cell lung cancer (NSCLC) is caused of 85% of all lung cancers. Among the most important factors for lung tumor growth and proliferation are the tyrosine kinase receptors that coded by the epidermal growth factor recep-tor (EGFR) gene. Activation of EGFR ultimately leads to developing of lung cancer. The present study was undertaken with an objective to detect EGFR mutations in bronchial wash from Iraqi patients with NSCLC before treatment. Methods: DNA was extracted from bronchial wash samples collected from 50 patients with NSCLC by using a Qiamp DNA Mini Kit (Qiagen, Hilden, Germany). Then, EGFR mutations were determined by using real-time RCR combined with two technologies, Amplification Refractory Mutation System (

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Publication Date
Tue Aug 01 2023
Journal Name
Dna Repair
The interactions between DNA methylation machinery and long non-coding RNAs in tumor progression and drug resistance
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DNA methylation is one of the main epigenetic mechanisms in cancer development and progression. Aberrant DNA methylation of CpG islands within promoter regions contributes to the dysregulation of various tumor suppressors and oncogenes; this leads to the appearance of malignant features, including rapid proliferation, metastasis, stemness, and drug resistance. The discovery of two important protein families, DNA methyltransferases (DNMTs) and Ten-eleven translocation (TET) dioxygenases, respectively, which are responsible for deregulated transcription of genes that play pivotal roles in tumorigenesis, led to further understanding of DNA methylation-related pathways. But how these enzymes can target specific genes in different malignancies;

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Publication Date
Tue Jan 20 2026
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Comparative Histopathological Study of Ciprofloxacin Impact on Mus Musculus Renal Cortex Infected with Multi-drug Resistance and Susceptible Uropathogenic Escherichia Coli
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Urinary tract infections are mainly caused by uropathogenic Escherichia coli, which represent a significant global issue along with the rising of antibiotic resistance and treatment challenges. The aim of this study was to evaluate ciprofloxacin efficacy as a treatment in animal models following infection with multidrug-resistant UPEC and multidrug-susceptible UPEC and to determine the nephrotoxic effect of these antibiotics on the renal cortex. Up to 76 E. coli isolates were collected from UTI patients in Baghdad province, characterized by morphological and biochemical features, and confirmed using the Vitek-2 compact system. Mice were orally infected via gastric gavage with G33 using a bacterial load of 107 cells/ml, followed by p

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Publication Date
Mon Nov 24 2025
Journal Name
Baghdad Science Journal
Transformer Network on Global Self-Attention Mechanism for Brain Tumor Segmentation
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Transformers are a specific category of neural network design. Transformers often depend on extensive pre-training on a large scale and exhibit a notable degree of computational complexity. The disadvantage of using this method is a significant increase in computational complexity, which necessitates a significant commitment of time and computing resources in order to successfully work with these models. Transformer networks possess the desirable benefit of extracting distant characteristics effectively via their self-attention mechanism. In this paper, the Global Self-Attention Transformer module is applied to tackle these issues. The model is based on a segmentation problem called Brain-GS that works as a mechanism and encompasses

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Publication Date
Fri Nov 01 2024
Journal Name
Current Medicinal Chemistry
Synthesis, In Silico Prediction, and In Vitro Evaluation of Anti-tumor Activities of Novel 4'-Hydroxybiphenyl-4-carboxylic Acid Derivatives as EGFR Allosteric Site Inhibitors
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Introduction:

Allosteric inhibition of EGFR tyrosine kinase (TK) is currently among the most attractive approaches for designing and developing anti-cancer drugs to avoid chemoresistance exhibited by clinically approved ATP-competitive inhibitors. The current work aimed to synthesize new biphenyl-containing derivatives that were predicted to act as EGFR TK allosteric site inhibitors based on molecular docking studies.

Methods:

A new series of 4'-hydroxybiphenyl-4-carboxylic acid derivatives, including hydrazine-1-carbothioamide (S3-S6) and 1,2,4-triazole (S7-S10) derivatives, were synthesized and characterized using IR, 1HNMR, 13CNMR

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Publication Date
Mon Jun 01 2020
Journal Name
Comparative Immunology, Microbiology And Infectious Diseases
Drug resistance and virulence traits of Acinetobacter baumannii from Turkey and chicken raw meat
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Publication Date
Mon Mar 08 2021
Journal Name
Baghdad Science Journal
some Chemical effects on growth of azotobacter chroococcum wild-type and it's mutant-44
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the effecth of some chemicals on growth of two azotobacter chroococcum and aniline caused significant increase of growth

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Publication Date
Fri Sep 26 2025
Journal Name
Applied Data Science And Analysis
Deep Learning in Genomic Sequencing: Advanced Algorithms for HIV/AIDS Strain Prediction and Drug Resistance Analysis
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Genome sequencing has significantly improved the understanding of HIV and AIDS through accurate data on viral transmission, evolution and anti-therapeutic processes. Deep learning algorithms, like the Fined-Tuned Gradient Descent Fused Multi-Kernal Convolutional Neural Network (FGD-MCNN), can predict strain behaviour and evaluate complex patterns. Using genotypic-phenotypic data obtained from the Stanford University HIV Drug Resistance Database, the FGD-MCNN created three files covering various antiretroviral medications for HIV predictions and drug resistance. These files include PIs, NRTIs and NNRTIs. FGD-MCNNs classify genetic sequences as vulnerable or resistant to antiretroviral drugs by analyzing chromosomal information and id

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Publication Date
Mon Jan 01 2024
Journal Name
Baghdad Science Journal
Artificial Neural Network and Latent Semantic Analysis for Adverse Drug Reaction Detection
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Adverse drug reactions (ADR) are important information for verifying the view of the patient on a particular drug. Regular user comments and reviews have been considered during the data collection process to extract ADR mentions, when the user reported a side effect after taking a specific medication. In the literature, most researchers focused on machine learning techniques to detect ADR. These methods train the classification model using annotated medical review data. Yet, there are still many challenging issues that face ADR extraction, especially the accuracy of detection. The main aim of this study is to propose LSA with ANN classifiers for ADR detection. The findings show the effectiveness of utilizing LSA with ANN in extracting AD

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