Systems Immunology Analysis Reveals an Immunomodulatory Effect of Snail-p53 Binding on Neutrophil- and T Cell-Mediated Immunity in KRAS Mutant Non-Small Cell Lung Cancer

Hammoudeh, Sarah; Venkatachalam, Thenmozhi; Ansari, Abdul Wahid; Bendardaf, Riyad; Hamid, Qutayba; Rahmani, Mohamed; Hamoudi, Rifat

doi:10.3389/fimmu.2020.569671

Cited by 6 publications

(4 citation statements)

References 53 publications

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“…One ng of RNA of each sample was analyzed using targeted whole RNA-seq with AmpliSeq whole transcriptome on S5 system (Thermo Fisher Scientific), and RNA-seq data were analyzed using the Ion Torrent Software Suite version 5.4. Alignment was carried out using the Torrent Mapping Alignment Program (TMAP), as described in ( 23 ).…”

Section: Methodsmentioning

confidence: 99%

Identifying Immunological and Clinical Predictors of COVID-19 Severity and Sequelae by Mathematical Modeling

Elemam¹,

Hammoudeh²,

Salameh³

et al. 2022

Front. Immunol.

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Since its emergence as a pandemic in March 2020, coronavirus disease (COVID-19) outcome has been explored via several predictive models, using specific clinical or biochemical parameters. In the current study, we developed an integrative non-linear predictive model of COVID-19 outcome, using clinical, biochemical, immunological, and radiological data of patients with different disease severities. Initially, the immunological signature of the disease was investigated through transcriptomics analysis of nasopharyngeal swab samples of patients with different COVID-19 severity versus control subjects (exploratory cohort, n=61), identifying significant differential expression of several cytokines. Accordingly, 24 cytokines were validated using a multiplex assay in the serum of COVID-19 patients and control subjects (validation cohort, n=77). Predictors of severity were Interleukin (IL)-10, Programmed Death-Ligand-1 (PDL-1), Tumor necrosis factors-α, absolute neutrophil count, C-reactive protein, lactate dehydrogenase, blood urea nitrogen, and ferritin; with high predictive efficacy (AUC=0.93 and 0.98 using ROC analysis of the predictive capacity of cytokines and biochemical markers, respectively). Increased IL-6 and granzyme B were found to predict liver injury in COVID-19 patients, whereas interferon-gamma (IFN-γ), IL-1 receptor-a (IL-1Ra) and PD-L1 were predictors of remarkable radiological findings. The model revealed consistent elevation of IL-15 and IL-10 in severe cases. Combining basic biochemical and radiological investigations with a limited number of curated cytokines will likely attain accurate predictive value in COVID-19. The model-derived cytokines highlight critical pathways in the pathophysiology of the COVID-19 with insight towards potential therapeutic targets. Our modeling methodology can be implemented using new datasets to identify key players and predict outcomes in new variants of COVID-19.

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Section: Methodsmentioning

confidence: 99%

Identifying Immunological and Clinical Predictors of COVID-19 Severity and Sequelae by Mathematical Modeling

Elemam¹,

Hammoudeh²,

Salameh³

et al. 2022

Front. Immunol.

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“…The data obtained using lung mesothelioma also showed that blocking the Snail-p53 association with a small molecule, GN25, led to p53 activation ( Cho et al, 2015 ). Also, inhibition of the Snail-p53 axis by GN25 in A549 cells changed the expression profile of genes associated with immunoregulation, increased the expression of IL8, CXCL2, and CD81 ( Hammoudeh et al, 2020 ). Inhibition of p53 through the interaction with Snail occurs in tumors that contain K-Ras mutations.…”

Section: P53-mediated Effects On Emt At the Protein Stability Levelmentioning

confidence: 99%

Opposing Roles of Wild-type and Mutant p53 in the Process of Epithelial to Mesenchymal Transition

Semenov

Daks

Fedorova

et al. 2022

Front. Mol. Biosci.

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The central role of an aberrantly activated EMT program in defining the critical features of aggressive carcinomas is well documented and includes cell plasticity, metastatic dissemination, drug resistance, and cancer stem cell-like phenotypes. The p53 tumor suppressor is critical for leashing off all the features mentioned above. On the molecular level, the suppression of these effects is exerted by p53 via regulation of its target genes, whose products are involved in cell cycle, apoptosis, autophagy, DNA repair, and interactions with immune cells. Importantly, a set of specific mutations in the TP53 gene (named Gain-of-Function mutations) converts this tumor suppressor into an oncogene. In this review, we attempted to contrast different regulatory roles of wild-type and mutant p53 in the multi-faceted process of EMT.

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“…Chemotherapy resistance is a major barrier for breast cancer treatment, and strategies to circumvent resistance are required [58]. Tumors evade the immune system by inducing in ammation [59]. In the early stages of tumor development, the immune response exerts anti-tumor activity, whereas, in the advanced stages, the tumor establishes mechanisms to evade the immune response, thus eliciting a chronic in ammation process that shows a pro-tumor effect [60].…”

Section: Discussionmentioning

confidence: 99%

Machine learning revealed inflammatory features and a novel risk score-based classification with appealing implications in discriminating the prognosis, immunotherapy and chemotherapy

Bai

2022

Preprint

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Breast cancer is the most common and ranks the second cause of related cancer-related death in women. However, the relationship between inflammation-related gene signatures and the prognosis of breast cancer remains elusive. We constructed inflammation related gene features to evaluate the prognosis, immunotherapy, inflammatory response and chemotherapy of breast cancer patients, including 4073 breast cancer patients (TCGA: 1104, GEO: 2969). ● Through univariate Cox regression, LASSO, stepwise regression, and multiple Cox regression analyses, we found that the signatures of inflammatory genes (including 12 genes) can be used to classify breast cancer patients. Patients in the higher risk score group had a poorer prognosis, often accompanied by a higher abundance of macrophages and a lower abundance of lymphocytes, suggesting that inflammation was present in high-risk individuals. ● Tumor mutational burden (TMB) and drug sensitivity analysis showed that PD 0332991, ROSCOVITINE has a higher drug sensitivity to the treatment of low-risk inflammatory breast cancer, while it has a higher drug sensitivity to high-risk patients than carlumide and imatinib. ● Based on risk scores and clinical data, we use training sets and test sets to build nomographs that can be used to calculate patient survival. Our study provides not only insights into the identification of breast cancer patients with poor prognoses, but also treatment strategiesfor breast cancer patients.

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Systems Immunology Analysis Reveals an Immunomodulatory Effect of Snail-p53 Binding on Neutrophil- and T Cell-Mediated Immunity in KRAS Mutant Non-Small Cell Lung Cancer

Cited by 6 publications

References 53 publications

Identifying Immunological and Clinical Predictors of COVID-19 Severity and Sequelae by Mathematical Modeling

Identifying Immunological and Clinical Predictors of COVID-19 Severity and Sequelae by Mathematical Modeling

Opposing Roles of Wild-type and Mutant p53 in the Process of Epithelial to Mesenchymal Transition

Machine learning revealed inflammatory features and a novel risk score-based classification with appealing implications in discriminating the prognosis, immunotherapy and chemotherapy

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