IL-17C mediates the recruitment of tumor-associated neutrophils and lung tumor growth

Background:: Lung cancer remains the most common cause of cancer-related deaths in China and worldwide. Traditional surgery and chemotherapy do not offer an effective cure although gene therapy may be a promising future alter-native. Kallistatin (Kal) is an endogenous inhibitor of angiogenesis and tumorigenesis. Recombinant adeno-associated virus (rAAV) is considered the most promising vector for gene therapy of many diseases due to persistent and long-term transgen-ic expression. Objective:: The aim of this study was to investigate whether rAAV9-Kal inhibited NCI-H446 subcutaneous xenograft tumor growth in mice. Method:: The subcutaneous xenograft mode were induced by subcutaneous injection of 2×106 H446 cells into the dorsal skin of BALB/c nude mice. The mice were administered with ssrAAV9-Kal (single-stranded rAAV9) or dsrAAV9-Kal (double-stranded rAAV9）by intraperitoneal injection (I.P.). Tumor microvessel density (MVD) was examined by anti-CD34 stain-ing to evaluate tumor angiogenesis. Results:: Compared with the PBS (blank control) group, tumor growth in the high-dose ssrAAV9-Kal group was inhibited by 40% by day 49, and the MVD of tumor tissues was significantly decreased. Conclusion:: The results indicate that this therapeutic strategy is a promising approach for clinical cancer therapy and impli-cate rAAV9-Kal as a candidate for gene therapy of lung cancer.

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Kras-driven intratumoral heterogeneity triggers infiltration of M2 polarized macrophages via the circHIPK3/PTK2 immunosuppressive circuit

Scientific Reports ◽

10.1038/s41598-021-94671-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Theodora Katopodi ◽

Savvas Petanidis ◽

Kalliopi Domvri ◽

Paul Zarogoulidis ◽

Doxakis Anestakis ◽

...

Keyword(s):

Lung Cancer ◽

Tumor Growth ◽

Lung Tumor ◽

Quantitative Polymerase Chain Reaction ◽

Macrophage Polarization ◽

Metastatic Potential ◽

Intratumoral Heterogeneity ◽

M2 Macrophage

AbstractIntratumoral heterogeneity in lung cancer is essential for evasion of immune surveillance by tumor cells and establishment of immunosuppression. Gathering data reveal that circular RNAs (circRNAs), play a role in the pathogenesis and progression of lung cancer. Particularly Kras-driven circRNA signaling triggers infiltration of myeloid-associated tumor macrophages in lung tumor microenvironment thus establishing immune deregulation, and immunosuppression but the exact pathogenic mechanism is still unknown. In this study, we investigate the role of oncogenic Kras signaling in circRNA-related immunosuppression and its involvement in tumoral chemoresistance. The expression pattern of circRNAs HIPK3 and PTK2 was determined using quantitative polymerase chain reaction (qPCR) in lung cancer patient samples and cell lines. Apoptosis was analyzed by Annexin V/PI staining and FACS detection. M2 macrophage polarization and MDSC subset analysis (Gr1−/CD11b−, Gr1−/CD11b+) were determined by flow cytometry. Tumor growth and metastatic potential were determined in vivo in C57BL/6 mice. Findings reveal intra-epithelial CD163+/CD206+ M2 macrophages to drive Kras immunosuppressive chemoresistance through myeloid differentiation. In particular, monocytic MDSC subsets Gr1−/CD11b−, Gr1−/CD11b+ triggered an M2-dependent immune response, creating an immunosuppressive tumor-promoting network via circHIPK3/PTK2 enrichment. Specifically, upregulation of exosomal cicHIPK3/PTK2 expression prompted Kras-driven intratumoral heterogeneity and guided lymph node metastasis in C57BL/6 mice. Consequent co-inhibition of circPTK2/M2 macrophage signaling suppressed lung tumor growth along with metastatic potential and prolonged survival in vivo. Taken together, these results demonstrate the key role of myeloid-associated macrophages in sustaining lung immunosuppressive neoplasia through circRNA regulation and represent a potential therapeutic target for clinical intervention in metastatic lung cancer.

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Desipramine increased experimental lung tumor growth in stress-sensitive rats in the chronic mild stress model of depression

Pharmacological Reports ◽

10.1016/s1734-1140(10)71149-x ◽

2010 ◽

Vol 62 ◽

pp. 50

Author(s):

Marta Kubera ◽

Beata Grygier ◽

Danuta Wrona ◽

Piotr Gruca ◽

Zofia Rogóz ◽

...

Keyword(s):

Tumor Growth ◽

Lung Tumor ◽

Chronic Mild Stress ◽

Mild Stress ◽

Stress Model

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Systemic Platelet-activating Factor Receptor Activation Augments Experimental Lung Tumor Growth and Metastasis

Cancer Growth and Metastasis ◽

10.4137/cgm.s14501 ◽

2014 ◽

Vol 7 ◽

pp. CGM.S14501 ◽

Cited By ~ 15

Author(s):

Patrick C. Hackler ◽

Sarah Reuss ◽

Raymond L. Konger ◽

Jeffrey B. Travers ◽

Ravi P. Sahu

Keyword(s):

Lung Cancer ◽

Tumor Growth ◽

Cancer Progression ◽

Lung Tumor ◽

Platelet Activating Factor ◽

Receptor Activation ◽

Dependent Manner ◽

Melanoma Tumor ◽

Tumor Growth And Metastasis ◽

The Impact

Pro-oxidative stressors including cigarette smoke (CS) generate novel lipids with platelet-activated factor-receptor (PAF-R) agonistic activity mediate systemic immunosuppression, one of the most recognized events in promoting carcinogenesis. Our previous studies have established that these oxidized-PAF-R-agonists augment murine B16F10 melanoma tumor growth in a PAF-R-dependent manner because of its effects on host immunity. As CS generates PAF-R agonists, the current studies sought to determine the impact of PAF-R agonists on lung cancer growth and metastasis. Using the murine Lewis Lung Carcinoma (LLC1) model, we demonstrate that treatment of C57BL/6 mice with a PAF-R agonist augments tumor growth and lung metastasis in a PAF-R-dependent manner as these findings were not seen in PAF-R-deficient mice. Importantly, this effect was because of host rather than tumor cells PAF-R dependent as LLC1 cells do not express functional PAF-R. These findings indicate that experimental lung cancer progression can be modulated by the PAF system.

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Deep Learning Based Automated Orthotopic Lung Tumor Segmentation in Whole-Body Mouse CT-Scans

Cancers ◽

10.3390/cancers13184585 ◽

2021 ◽

Vol 13 (18) ◽

pp. 4585

Author(s):

Wouter R. P. H. van de Worp ◽

Brent van der Heyden ◽

Georgios Lappas ◽

Ardy van Helvoort ◽

Jan Theys ◽

...

Keyword(s):

Lung Cancer ◽

Deep Learning ◽

Tumor Growth ◽

Tumor Volume ◽

Learning Algorithm ◽

Lung Tumor ◽

Whole Body ◽

Ct Scans ◽

Therapeutic Effects ◽

Deep Learning Algorithm

Lung cancer is the leading cause of cancer related deaths worldwide. The development of orthotopic mouse models of lung cancer, which recapitulates the disease more realistically compared to the widely used subcutaneous tumor models, is expected to critically aid the development of novel therapies to battle lung cancer or related comorbidities such as cachexia. However, follow-up of tumor take, tumor growth and detection of therapeutic effects is difficult, time consuming and requires a vast number of animals in orthotopic models. Here, we describe a solution for the fully automatic segmentation and quantification of orthotopic lung tumor volume and mass in whole-body mouse computed tomography (CT) scans. The goal is to drastically enhance the efficiency of the research process by replacing time-consuming manual procedures with fast, automated ones. A deep learning algorithm was trained on 60 unique manually delineated lung tumors and evaluated by four-fold cross validation. Quantitative performance metrics demonstrated high accuracy and robustness of the deep learning algorithm for automated tumor volume analyses (mean dice similarity coefficient of 0.80), and superior processing time (69 times faster) compared to manual segmentation. Moreover, manual delineations of the tumor volume by three independent annotators was sensitive to bias in human interpretation while the algorithm was less vulnerable to bias. In addition, we showed that besides longitudinal quantification of tumor development, the deep learning algorithm can also be used in parallel with the previously published method for muscle mass quantification and to optimize the experimental design reducing the number of animals needed in preclinical studies. In conclusion, we implemented a method for fast and highly accurate tumor quantification with minimal operator involvement in data analysis. This deep learning algorithm provides a helpful tool for the noninvasive detection and analysis of tumor take, tumor growth and therapeutic effects in mouse orthotopic lung cancer models.

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