scholarly journals 2165 Vesicular secretion of suppressor of cytokine signaling 3 by alveolar macrophages is dysregulated in NSCLC patients and its provision inhibits epithelial cell transformation and tumor cell function

2018 ◽  
Vol 2 (S1) ◽  
pp. 36-36
Author(s):  
Jennifer Speth ◽  
Loka R. Penke ◽  
Joseph Bazzill ◽  
Douglas A. Arenberg ◽  
James J. Moon ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Tumor Cell ◽  
Alveolar Macrophages ◽  
Cell Transformation ◽  
Soft Agar ◽  
Cigarette Smoke Extract ◽  
Cytokine Signaling ◽  
Suppressor Of Cytokine Signaling ◽  
Induced Apoptosis ◽  
Nsclc Patients

OBJECTIVES/SPECIFIC AIMS: Insufficient endogenous expression of suppressor of cytokine signaling 3 (SOCS3) with subsequent over-activation of its target, the transcription factor STAT3, has been associated with tumorigenesis and cancer development in the lung and other organs. We have observed that a “backup” source of SOCS3 in the lung, namely that secreted in microvesicles (MVs) by alveolar macrophages, is reduced in the bronchoalveolar lavage fluid (BALF) of KRAS mutant mice harboring lung tumors. Here we sought to evaluate levels of SOCS3 in BALF of a cohort of non-small cell lung cancer (NSCLC) patients and to test the effects of vesicular SOCS3 administration on tumor cell transformation and function as potential therapeutic strategy. METHODS/STUDY POPULATION: In total, 22 BALF samples were obtained from healthy volunteers (n=11) as well as patients undergoing diagnostic bronchoscopies for suspected lung cancer (n=11). SOCS3 levels in the BALF were determined by ELISA after brief sonication to disrupt vesicles. In vitro experiments utilized the human adenocarcinoma cell line (A549) or human G12V mutant KRAS-expressing rat lung epithelial cells (RLE-G12V). Proliferation, Fas ligand (FasL)-induced apoptosis, and chemical transformation with N-methyl-N′-nitro-N-nitrosoguanidine (MNNG) or cigarette smoke extract (CSE) were assessed by CyQuant assay, annexin V staining, and soft agar assay, respectively. For SOCS3 rescue, epithelial cells were treated with natural alveolar macrophages-derived MVs (isolated via ultracentrifugation) or synthetic unilamellar liposomes containing human recombinant SOCS3 for at least 1 hour before assay. RESULTS/ANTICIPATED RESULTS: SOCS3 levels were significantly reduced in BALF samples of patients determined to have NSCLC as compared with healthy volunteers (186.6±26.74 vs. 395.6±74.31 pg/mL, p=0.015, n=11). Addition of exogenous SOCS3-containing liposomes had the capacity to significantly inhibit MNNG and cigarette smoke extract-induced transformation and colony formation in soft agar. Exogenous SOCS3 provided in liposomes or in natural MVs significantly induced apoptosis (both in the presence and absence of FasL) and inhibited basal proliferation of A549 cells. DISCUSSION/SIGNIFICANCE OF IMPACT: These data identified a novel dysregulation of immune surveillance in the form of decreased SOCS3 secretion in the tumor-bearing lung that may contribute to tumorigenesis via sustained STAT3 activation. Future studies will focus on the mechanism underlying this defect and whether rescuing SOCS3 secretion can inhibit cancer progression in vivo.

scholarly journals Corrigendum to “Down-regulation of HSP27 sensitizes TRAIL-resistant tumor cell to TRAIL-induced apoptosis” [Lung Cancer 68 (1) (2010) 27–38]

Lung Cancer ◽  
2020 ◽  
Vol 141 ◽  
pp. 119-120
Author(s):  
Hongqin Zhuang ◽  
Weiwei Jiang ◽  
Wei Cheng ◽  
Kui Qian ◽  
Wei Dong ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Tumor Cell ◽  
Down Regulation ◽  
Induced Apoptosis

scholarly journals Urinary exosomal long noncoding RNAs serve as biomarkers for early detection of non-small cell lung cancer

2021 ◽  
Author(s):  
Quan Lin ◽  
Danli Xie ◽  
Liangliang Pan ◽  
Yongliang Lou ◽  
Mengru Shi
Keyword(s):  
Lung Cancer ◽  
Tumor Cell ◽  
Small Cell Lung Cancer ◽  
Differential Expression ◽  
Kegg Pathway ◽  
Small Cell ◽  
Healthy Controls ◽  
Small Cell Lung ◽  
Pathway Analyses ◽  
Nsclc Patients

Objective: Increasing the efficiency of early diagnosis using noninvasive biomarkers is crucial for enhancing the survival rate of lung cancer patients. We explore the differential expression of non-small cell lung cancer (NSCLC) related lncRNAs in urinary exosomes in NSCLC patients and normal controls to diagnose lung cancer. Methods: A differential expression analysis between NSCLC patients and healthy controls was performed using microarrays. Gene ontology (GO) term and Kyoto encyclopedia of genes and genomes (KEGG) pathway analyses were used to predict potential functions of lncRNAs in NSCLC. QT-PCR was used to verify microarray results. Results: A total of 640 lncRNAs (70 up- and 570 down-regulated) were differentially expressed in NSCLC patients in comparison to healthy controls. Six lncRNAs were detected by QT-PCR. GO term and KEGG pathway analyses showed that differential lncRNAs were enriched in cellular component organization or biogenesis, as well as other biological processes and signaling pathways, such as the PI3K-AKT, FOXO, p53, and fatty acid biosynthesis. Conclusions: The differential lncRNAs in urinary exosomes are potential diagnostic biomarkers of NSCLC. The lncRNAs enriched in specific pathways may be associated with tumor cell proliferation, tumor cell apoptosis, and the cell cycle involved in the pathogenesis of NSCLC.

Activity of the Suppressor of Cytokine Signaling–3 Promoter in Human Non–Small-Cell Lung Cancer

2004 ◽  
Vol 5 (6) ◽  
pp. 366-370 ◽  
Author(s):  
Biao He ◽  
Liang You ◽  
Zhidong Xu ◽  
Julien Mazieres ◽  
Amie Y. Lee ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Cytokine Signaling ◽  
Small Cell Lung ◽  
Suppressor Of Cytokine Signaling

scholarly journals 365 Computational pathology delivers objective and quantitative PD-L1 expression analysis for enrichment of responders to durvalumab in non-small cell lung cancer (NSCLC)

2021 ◽  
Vol 9 (Suppl 3) ◽  
pp. A392-A393
Author(s):  
Guenter Schmidt ◽  
Ansh Kapil ◽  
Lina Meinecke ◽  
Farzad Sekhavati ◽  
Jan Lesniak ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Clinical Trial ◽  
Overall Survival ◽  
Tumor Cell ◽  
Tumor Cells ◽  
Visual Assessment ◽  
Small Cell ◽  
Small Cell Lung ◽  
Kaplan Meier ◽  
Nsclc Patients

BackgroundThe pathologist’s visual assessment of tumor proportion score (TPS) with 25% cutoff on PD-L1 stained tissue samples is an established method to select metastatic NSCLC patients that are likely to respond to an anti-PD-L1 monotherapy.1 However, manual scoring is often subject to subjectivity in human perception2 and there remains a critical need for more objective and quantitative methods to assess PD-L1 expression in immuno-oncology.MethodsWe used deep learning (DL) based image analysis (IA) to generate a novel PD-L1 Quantitative Continuous Score (QCS)3 in tumor cells. PD-L1 QCS consists of two DL models to first segment epithelial regions and second detect membranes, cytoplasm and nuclei of each tumor cell in PD-L1 immunohistochemically (IHC) stained tissue slides. The PD-L1 expression of each tumor cell compartment was estimated by the respective optical density (OD) of DAB, and tumor cells with a membrane OD greater than ODmin are considered as PD-L1-positive. A slide comprising at greater percentage of PD-L1-positive tumor cells than a cutoff value (CoV) is considered QCS-positive. The ODmin and CoV parameters were linked to patient overall survival (OS), by minimizing the Kaplan Meier log-rank p-values and keeping at least 50% prevalence in the QCS-positive subgroup.Fully supervised QCS-IA models were extensively trained using pathologists’ annotations and the performance was validated on unseen data to ensure its generalization and robustness.3 4 The QCS IA was locked and blindly applied on clinical trial data (NCT01693562, durvalumab-treated late-stage NSCLC cohort) without further refinement.ResultsData analytics techniques were used to determine optimal PD-L1 QCS parameters for the clinical trial cohort of N=162 late-stage NSCLC patients. A PD-L1 QCS algorithm (ODmin=8, CoV=57%) is able to stratify durvalumab-treated NSCLC patients at a higher prevalence and more significant log rank p-value (64%, p=0.0001) for OS (figure 1) compared to pathologist TPS (59%, p=0.01). Median OS times of (19.2 months vs 7.9 months) was observed in the QCS-positive vs negative subgroups, respectively. The box plots (figure 2) indicate an overall good agreement (72% concordance) of the fully automated QCS with the pathologists TPS, which quantitatively supports the positive visual assessment of the cell segmentation accuracy.Abstract 365 Figure 1Kaplan Meier (KM) curves for OS stratification. KM curves for Overall Survival (OS) stratification with (left) manual PD-L1 TPS score (25% cutoff), and (right) automated QCS (57% cutoff).Abstract 365 Figure 2QCS scores within TPS positive and negative groups. Box plot indicating percent positive cells (OD≥8) as measured by PD-L1 QCS within the PD-L1 high (red) and low (blue) groups as per pathologist assessment by TPS.ConclusionsThe novel Quantitative Continuous Scoring (QCS) provides an objective way of correlating a quantitative estimate of PD-L1 IHC expression on tumor cells with survival of durvalumab-treated NSCLC patients. This data establishes a first proof-of-concept demonstrating the potential utility of PD-L1 QCS towards precision medicine in immuno-oncology.ReferencesRebelatto M, et al. Development of a programmed cell death ligand-1 immunohistochemical assay validated for analysis of non-small cell lung cancer and head and neck squamous cell carcinoma. Diagnostic Pathology 2016.Tsao M S, et al. PD-L1 immunohistochemistry comparability study in real-life clinical samples: results of blueprint phase 2 project. Journal of Thoracic Oncology 2018.Gustavson M, et al. Novel approach to HER2 quantification: digital pathology coupled with AI-based image and data analysis delivers objective and quantitative HER2 expression analysis for enrichment of responders to trastuzumab deruxtecan (T-DXd; DS-8201), specifically in HER2-low patients. (2021) DOI: 10.1158/1538-7445.SABCS20-PD6-01Kapil A, et al. Domain adaptation-based deep learning for automated tumor cell (TC) scoring and survival analysis on PD-L1 stained tissue images. IEEE Transactions on Medical Imaging DOI: 10.1109/TMI.2021.3081396Ethics ApprovalClinical study NCT01693562, from which data in this report were obtained, was carried out in accordance with the Declaration of Helsinki and Good Clinical Practice guidelines. The study protocol, amendments, and participant informed consent document were approved by the appropriate institutional review boards.

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