scholarly journals In-Silico Analysis of The Correlation Between PD-L1 and Pro-Inflammatory Type Interleukins and The Distribution of Their Potential Primary Sources in KRAS-Mutated Non-Small Cell Lung Carcinoma

Acta Medica ◽  
2021 ◽  
pp. 1-8
Author(s):  
Nese Unver
Keyword(s):  
Lung Cancer ◽  
Dendritic Cells ◽  
Cancer Cells ◽  
Immune Cells ◽  
Small Cell ◽  
Interleukin 12 ◽  
Small Cell Lung ◽  
Programmed Death Ligand 1 ◽  
Programmed Death ◽  
Interleukin 23

Objective: Non-Small Cell Lung Cancer has a high incidence and great clinical importance as the cancer subtype with the highest mortality. It is necessary to investigate cytokines associated with the Programmed death-ligand 1, one of the immunotherapeutic target molecules, in KRas mutant lung cancer cells. Materials and Methods: In this study, the expression of Programmed death-ligand 1 as well as pro-inflammatory interleukins was evaluated in 44 lung cancer cell lines harboring KRas mutations and RNAseq expression data of lung adenocarcinoma patients and correlation analyses were performed. Macrophages and dendritic cells, the major immune cells associated with Interleukin-1, Interleukin-6, Interleukin-12 and Interleukin-23, were also evaluated. Results: In KRas mutant lung cancer cells and lung adenocarcinoma tissues, expression of cytokines Interleukin-1A, Interleukin-6, Interleukin-12 and Interleukin-23 showed a positive correlation with Programmed death-ligand 1 expression (p≤0.05). The quantity of M1 macrophages and dendritic cells, both of which are cytokine-producing immune cells, is less in KRas mutant lung cancer tissues than non-mutants. Conclusion: Detailed studies in clinical samples, especially in blood, primary, and metastatic tissues, will help to create and validate cytokine panels that can be used in therapeutic targeting of KRas mutant subtype lung cancer with high Programmed death-ligand 1 expression.

scholarly journals Osimertinib (AZD9291) decreases programmed death ligand-1 in EGFR-mutated non-small cell lung cancer cells

2017 ◽  
Vol 38 (11) ◽  
pp. 1512-1520 ◽  
Author(s):  
Xiao-Ming Jiang ◽  
Yu-Lian Xu ◽  
Mu-Yang Huang ◽  
Le-Le Zhang ◽  
Min-Xia Su ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cancer Cells ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Lung Cancer Cells ◽  
Small Cell Lung ◽  
Programmed Death Ligand 1 ◽  
Programmed Death ◽  
Egfr Mutated

scholarly journals 38 Clinical validation of an image analysis assay for determining programmed death-ligand 1 (22C3) in non-small cell lung cancer

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A39-A39
Author(s):  
Roberto Gianani ◽  
Will Paces ◽  
Elliott Ergon ◽  
Kristin Shotts ◽  
Vitria Adisetiyo ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Image Analysis ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Clinical Validation ◽  
Small Cell Lung ◽  
Programmed Death Ligand 1 ◽  
Programmed Death

BackgroundDetermination of programmed death-ligand 1 (PD-L1) level in tumor by immunohistochemistry (IHC) is widely used to predict response to check point inhibitor therapy. In particular, the Dako PD-L1 (22C3) antibody is a common companion diagnostic to the monoclonal antibody drug Keytruda® (pembrolizumab) in non-small cell lung cancer (NSCLC).1 However, for the practicing pathologist, interpretation of the PD-L1 (22C3) assay is cumbersome and time consuming. Manual pathologist scoring also suffers from poor intra- and inter-pathologist precision, particularly around the cut-off point.2 In this clinical validation study, we developed an image analysis (IA) based solution to accurately and precisely score digital images obtained from PD-L1 stained NSCLC tissues for making clinical enrollment decisions.Methods10 NSCLC tissue samples were purchased from a qualified vendor and IHC stained for PD-L1; 4 of these samples had serial sections stained on two separate days. Stained slides were scanned at 20X magnification and analyzed using Flagship Biosciences’ IA solutions that quantify PD-L1 expression and separate tumor and stromal compartments. Resulting image markups of cell detection and PD-L1 expression were reviewed by an MD pathologist for acceptance. PD-L1 staining was evaluated by digital IA in the sample’s tumor compartment for Total Proportion Score (TPS,%). Assay specificity was defined by ≥ 90% of the tissue cohort exhibiting appropriate cell recognition (≥ 90% cells correctly recognized as determined by the pathologist), with ≤ 10% false positive rate for staining classification. Sensitivity was defined by ≥ 90% of the cohort exhibiting appropriate cell identification (≥ 90% cells correctly identified), with ≤ 10% false negative rate for staining classification. Accuracy was defined by the combination of sensitivity and specificity and precision was defined by concordance of the binned TPS (<1%, ≥ 1%, ≥ 50%) in ≥ 80% of the samples stained on multiple days.ResultsThe preliminary results show that IA can yield high analytical sensitivity, specificity, accuracy, and precision in the determination of the PD-L1 score. 100% of the tissue cohort met criteria for analytical specificity, sensitivity, and accuracy and 100% of the samples stained on multiple days met the precision criteria.ConclusionsThis data demonstrates the feasibility of an IA approach as applied to PD-L1 (22C3) scoring. Ongoing experiments include application of the developed 22C3 algorithm on a separate cohort of 20 NSCLC samples to determine the correlation of digital scoring and scoring obtained by three pathologists. Additionally, we will evaluate the precision obtained by digital scoring in relation to the intra- and inter-pathologist concordance.ReferencesIncorvaia L, Fanale D, Badalamenti G, et al. Programmed death ligand 1 (PD-L1) as a predictive biomarker for pembrolizumab therapy in patients with advanced non-small-cell lung cancer (NSCLC). Adv Ther 2019;36:2600–2617.Rimm DL, Han G, Taube JM, et al. A prospective, multi-institutional, pathologist-based assessment of 4 immunohistochemistry assays for PD-L1 expression in non–small cell lung cancer. JAMA Oncol 2017;3:1051–1058.

scholarly journals Natural Killer Cells and Dendritic Cells: Expanding Clinical Relevance in the Non-Small Cell Lung Cancer (NSCLC) Tumor Microenvironment

Cancers ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 4037
Author(s):  
Pankaj Ahluwalia ◽  
Meenakshi Ahluwalia ◽  
Ashis K. Mondal ◽  
Nikhil S. Sahajpal ◽  
Vamsi Kota ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Dendritic Cells ◽  
Tumor Microenvironment ◽  
Small Cell Lung Cancer ◽  
Nk Cells ◽  
Natural Killer ◽  
Cell Lung Cancer ◽  
Immune Cells ◽  
Small Cell ◽  
Small Cell Lung

Non-small cell lung cancer (NSCLC) is a major subtype of lung cancer that accounts for almost 85% of lung cancer cases worldwide. Although recent advances in chemotherapy, radiotherapy, and immunotherapy have helped in the clinical management of these patients, the survival rate in advanced stages remains dismal. Furthermore, there is a critical lack of accurate prognostic and stratification markers for emerging immunotherapies. To harness immune response modalities for therapeutic benefits, a detailed understanding of the immune cells in the complex tumor microenvironment (TME) is required. Among the diverse immune cells, natural killer (NK cells) and dendritic cells (DCs) have generated tremendous interest in the scientific community. NK cells play a critical role in tumor immunosurveillance by directly killing malignant cells. DCs link innate and adaptive immune systems by cross-presenting the antigens to T cells. The presence of an immunosuppressive milieu in tumors can lead to inactivation and poor functioning of NK cells and DCs, which results in an adverse outcome for many cancer patients, including those with NSCLC. Recently, clinical intervention using modified NK cells and DCs have shown encouraging response in advanced NSCLC patients. Herein, we will discuss prognostic and predictive aspects of NK cells and DC cells with an emphasis on NSCLC. Additionally, the discussion will extend to potential strategies that seek to enhance the anti-tumor functionality of NK cells and DCs.

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