Combination Therapy of PPARγ Ligands and Inhibitors of Arachidonic Acid in Lung Cancer

Lung cancer is the leading cause of cancer death in the United States and five-year survival remains low. Numerous studies have shown that chronic inflammation may lead to progression of carcinogenesis. As a result of inflammatory stimulation, arachidonic acid (AA) metabolism produces proliferation mediators through complex and dynamic interactions of the products of the LOX/COX enzymes. One important mediator in the activation of the AA pathways is the nuclear protein PPARγ. Targeting LOX/COX enzymes and inducing activation of PPARγ have resulted in significant reduction of cell growth in lung cancer cell lines. However, specific COX-inhibitors have been correlated with an increased cardiovascular risk. Clinical applications are still being explored with a novel generation of dual LOX/COX inhibitors. PPARγ activation through synthetic ligands (TZDs) has revealed a great mechanistic complexity since effects are produced through PPARγ-dependent and -independent mechanisms. Furthermore, PPARγ could also be involved in regulation of COX-2. Overexpression of PPARγ has reported to play a role in control of invasion and differentiation. Exploring the function of PPARγ, in this new context, may provide a better mechanistic model of its role in cancer and give an opportunity to design a more efficient therapeutic approach in combination with LOX/COX inhibitors

Cytokines and Growth Factors Stimulate Hyaluronan Production: Role of Hyaluronan in Epithelial to Mesenchymal-Like Transition in Non-Small Cell Lung Cancer

In this study, we investigated the role of hyaluronan (HA) in non-small cell lung cancer (NSCLC) since close association between HA level and malignancy has been reported. HA is an abundant extracellular matrix component and its synthesis is regulated by growth factors and cytokines that include epidermal growth factor (EGF) and interleukin-1β (IL-1β). We showed that treatment with recombinant EGF and IL-1β, alone or in combination with TGF-β, was able to stimulate HA production in lung adenocarcinoma cell line A549. TGF-β/IL-1β treatment induced epithelial to mesenchymal-like phenotype transition (EMT), changing cell morphology and expression of vimentin and E-cadherin. We also overexpressed hyaluronan synthase-3 (HAS3) in epithelial lung adenocarcinoma cell line H358, resulting in induced HA expression, EMT phenotype, enhanced MMP9 and MMP2 activities and increased invasion. Furthermore, adding exogenous HA to A549 cells and inducing HA H358 cells resulted in increased resistance to epidermal growth factor receptor (EGFR) inhibitor, Iressa. Together, these results suggest that elevated HA production is able to induce EMT and increase resistance to Iressa in NSCLC. Therefore, regulation of HA level in NSCLC may be a new target for therapeutic intervention

Expression of heterogeneous nuclear ribonucleoprotein A2/B1 changes with critical stages of mammalian lung development

Recent reports have demostrated a link between expression of members of the family of heterogeneous nuclear ribonucleoproteins (hnRNPs) and cancer. Overexpression of hnRNP A2/B1 correlated with the eventual development of lung cancer in three different clinical cohorts. We have studied the expression of hnRNP A2/B1 messenger RNA (mRNA) and protein during mammalian development. The expression of hnRNP A2/B1 mRNA and protein are parallel but change dynamically during critical periods in mouse pulmonary development. hnRNP A2/B1 is first detected in the lung in the early pseudoglandular period, peaks at the beginning of the canalicular period, and remains high during the saccular (alveolar) period. In mouse and rat, hnRNP A2/B1 expression is first evident in the earliest lung buds. As lung development progresses, the cuboidal epithelial cells of the distal primitive alveoli show high levels of the ribonucleoprotein, which is almost undetectable in the proximal conducting airways. The expression of hnRNP A2/ B1 is restricted in mature lung. Similar dynamic pattern of expression through lung development was also found in rat and human lung. Upregulated expression of hnRNP A2/B1 at critical periods of lung development was comparable to the level of expression found in lung cancers and preneoplastic lesions and is consistent with hnRNP A2/B1 overexpression playing an oncodevelopmental role

The International Association for the Study of Lung Cancer Early Lung Imaging Confederation.

PurposeTo improve outcomes for lung cancer through low-dose computed tomography (LDCT) early lung cancer detection. The International Association for the Study of Lung Cancer is developing the Early Lung Imaging Confederation (ELIC) to serve as an open-source, international, universally accessible environment to analyze large collections of quality-controlled LDCT images and associated biomedical data for research and routine screening care.MethodsELIC is an international confederation that allows access to efficiently analyze large numbers of high-quality computed tomography (CT) images with associated de-identified clinical information without moving primary imaging/clinical or imaging data from its local or regional site of origin. Rather, ELIC uses a cloud-based infrastructure to distribute analysis tools to the local site of the stored imaging and clinical data, thereby allowing for research and quality studies to proceed in a vendor-neutral, collaborative environment. ELIC's hub-and-spoke architecture will be deployed to permit analysis of CT images and associated data in a secure environment, without any requirement to reveal the data itself (ie, privacy protecting). Identifiable data remain under local control, so the resulting environment complies with national regulations and mitigates against privacy or data disclosure risk.ResultsThe goal of pilot experiments is to connect image collections of LDCT scans that can be accurately analyzed in a fashion to support a global network using methodologies that can be readily scaled to accrued databases of sufficient size to develop and validate robust quantitative imaging tools.ConclusionThis initiative can rapidly accelerate improvements to the multidisciplinary management of early, curable lung cancer and other major thoracic diseases (eg, coronary artery disease and chronic obstructive pulmonary disease) visualized on a screening LDCT scan. The addition of a facile, quantitative CT scanner image quality conformance process is a unique step toward improving the reliability of clinical decision support with CT screening worldwide

The IASLC Early Lung Imaging Confederation (ELIC) Open-Source Deep Learning and Quantitative Measurement Initiative.

BackgroundWith global adoption of CT lung cancer screening, there is increasing interest to use artificial intelligence (AI) deep learning methods to improve the clinical management process. To enable AI research using an open source, cloud-based, globally distributed, screening CT imaging dataset and computational environment that are compliant with the most stringent international privacy regulations that also protects the intellectual properties of researchers, the International Association of the Study of Lung Cancer (IASLC) sponsored development of the Early Lung Imaging Confederation (ELIC) resource in 2018. The objective of this report is to describe the updated capabilities of ELIC and illustrate how this resource can be utilized for clinically relevant AI research.MethodsIn this second Phase of the initiative, metadata and screening CT scans from two time points were collected from 100 screening participants in seven countries. An automated deep learning AI lung segmentation algorithm, automated quantitative emphysema metrics, and a quantitative lung nodule volume measurement algorithm were run on these scans.ResultsA total of 1,394 CTs were collected from 697 participants. The LAV950 quantitative emphysema metric was found to be potentially useful in distinguishing lung cancer from benign cases using a combined slice thickness ≥ 2.5 mm. Lung nodule volume change measurements had better sensitivity and specificity for classifying malignant from benign lung nodules when applied to solid lung nodules from high quality CT scans.ConclusionThese initial experiments demonstrated that ELIC can support deep learning AI and quantitative imaging analyses on diverse and globally distributed cloud-based datasets