PRMT5 Inhibition Promotes PD-L1 Expression and Immuno-Resistance in Lung Cancer

Protein arginine transferase 5 (PRMT5) has been implicated as an important modulator of tumorigenesis as it promotes tumor cell proliferation, invasion, and metastasis. Studies have largely focused on PRMT5 regulating intrinsic changes in tumors; however, the effects of PRMT5 on the tumor microenvironment and particularly immune cells are largely unknown. Here we found that targeting PRMT5 by genetic or pharmacological inhibition reduced lung tumor progression in immunocompromised mice; however, the effects were weakened in immunocompetent mice. PRMT5 inhibition not only decreased tumor cell survival but also increased the tumor cell expression of CD274 in vitro and in vivo, which activated the PD1/PD-L1 axis and eliminated CD8+T cell antitumor immunity. Mechanistically, PRMT5 regulated CD274 gene expression through symmetric dimethylation of histone H4R3, increased deposition of H3R4me2s on CD274 promoter loci, and inhibition of CD274 gene expression. Targeting PRMT5 reduced this inhibitory effect and promoted CD274 expression in lung cancer. However, PRMT5 inhibitors represent a double-edged sword as they may selectively kill cancer cells but may also disrupt the antitumor immune response. The combination of PRMT5 inhibition and ani-PD-L1 therapy resulted in an increase in the number and enhanced the function of tumor-infiltrating T cells. Our findings address an unmet clinical need in which combining PRMT5 inhibition with anti-PD-L1 therapy could be a promising strategy for lung cancer treatment.

Dexmedetomidine Associated Lung Cancer Proliferation and Tumor Growth is Prevented by β-Caryophyllene: Role of AMPK Activation and PGC-1α/TFAM Over Expression

Background: Dexmedetomidine has been reported to induce anti-apoptotic effects and metastatic progression in lung cancer. In the current investigation, the effect of β-Caryophyllene on dexmedetomidine induced cell proliferation and apoptosis of lung cancer cells and tumor growth in mice was studied. Methods: A549 cell line was cultured with either dexmedetomidine alone or together with β-Caryophyllene for 24 h and analysed for cell proliferation with MTT assay. ELISA based kit was used to determine apoptotic DNA fragmentation. Western blotting was used to determine expression levels of target proteins. The induction of experimental lung tumor in rat model was achieved through the injection of A549 tumor cells subcutaneously into the middle left side of the mice after anesthetization with pentobarbital (35 mg/kg) at 2.8 × 106 cells in 400 μl of PBS. Result: We found that β-Caryophyllene exerts the anti-proliferative effects on A549 cells. Furthermore, β-Caryophyllene significantly prevents apoptotic cell death and causes up-regulation of PGC-1α and TFAM compared to dexmedetomidine treated cells. We observed that β-Caryophyllene suppressed tumor development in mice significantly compared to dexmedetomidine treated group without changing body weight.

Inhibition of Rac1 attenuates radiation-induced lung injury while suppresses lung tumor in mice

The lung is one of the most sensitive tissues to ionizing radiation, thus, radiation-induced lung injury (RILI) stays a key dose-limiting factor of thoracic radiotherapy. However, there is still little progress in the effective treatment of RILI. Ras-related C3 botulinum toxin substrate1, Rac1, is a small guanosine triphosphatases involved in oxidative stress and apoptosis. Thus, Rac1 may be an important molecule that mediates radiation damage, inhibition of which may produce a protective effect on RILI. By establishing a mouse model of radiation-induced lung injury and orthotopic lung tumor-bearing mouse model, we detected the role of Rac1 inhibition in the protection of RILI and suppression of lung tumor. The results showed that ionizing radiation induces the nuclear translocation of Rac1, the latter then promotes nuclear translocation of P53 and prolongs the residence time of p53 in the nucleus, thereby promoting the transcription of Trp53inp1 which mediates p53-dependent apoptosis. Inhibition of Rac1 significantly reduce the apoptosis of normal lung epithelial cells, thereby effectively alleviating RILI. On the other hand, inhibition of Rac1 could also significantly inhibit the growth of lung tumor, increase the radiation sensitivity of tumor cells. These differential effects of Rac1 inhibition were related to the mutation and overexpression of Rac1 in tumor cells.

Lung tumor MHCII immunity depends on in situ antigen presentation by fibroblasts

A key unknown of the functional space in tumor immunity is whether CD4 T cells depend on intratumoral MHCII cancer antigen recognition. MHCII-expressing, antigen-presenting cancer-associated fibroblasts (apCAFs) have been found in breast and pancreatic tumors and are considered to be immunosuppressive. This analysis shows that antigen-presenting fibroblasts are frequent in human lung non-small cell carcinomas, where they seem to actively promote rather than suppress MHCII immunity. Lung apCAFs directly activated the TCRs of effector CD4 T cells and at the same time produced C1q, which acted on T cell C1qbp to rescue them from apoptosis. Fibroblast-specific MHCII or C1q deletion impaired CD4 T cell immunity and accelerated tumor growth, while inducing C1qbp in adoptively transferred CD4 T cells expanded their numbers and reduced tumors. Collectively, we have characterized in the lungs a subset of antigen-presenting fibroblasts with tumor-suppressive properties and propose that cancer immunotherapies might be strongly dependent on in situ MHCII antigen presentation.

Targeting HSP90 Inhibits Proliferation and Induces Apoptosis Through AKT1/ERK Pathway in Lung Cancer

Lung cancer is one of the most common malignant cancers worldwide. Searching for specific cancer targets and developing efficient therapies with lower toxicity is urgently needed. HPS90 is a key chaperon protein that has multiple client proteins involved in the development of cancer. In this study, we investigated the transcriptional levels of HSP90 isoforms in cancerous and normal tissues of lung cancer patients in multiple datasets. The higher expression of HSP90AA1 in cancer tissues correlated with poorer overall survival was observed. The higher levels of transcription and expression of HSP90AA1 and the activity of AKT1/ERK pathways were confirmed in lung cancer patient tissues. In both human and mouse lung cancer cell lines, knocking down HSP90AA1 promoted cell apoptosis through the inhibition of the pro-survival effect of AKT1 by decreasing the phosphorylation of itself and its downstream factors of mTOR and BAD, as well as downregulating Mcl1, Bcl-xl, and Survivin. The knockdown also suppressed lung cancer cell proliferation by inhibiting ERK activation and downregulating CyclinD1 expression. The treatment of 17-DMAG, an HSP90 inhibitor, recaptured these effects in vitro and inhibited tumor cell growth, and induced apoptosis without obvious side effects in lung tumor xenograft mouse models. This study suggests that targeting HSP90 by 17-DMAG could be a potential therapy for the treatment of lung cancer.

Cloud-Based Lung Tumor Detection and Stage Classification Using Deep Learning Techniques

Artificial intelligence (AI), Internet of Things (IoT), and the cloud computing have recently become widely used in the healthcare sector, which aid in better decision-making for a radiologist. PET imaging or positron emission tomography is one of the most reliable approaches for a radiologist to diagnosing many cancers, including lung tumor. In this work, we proposed stage classification of lung tumor which is a more challenging task in computer-aided diagnosis. As a result, a modified computer-aided diagnosis is being considered as a way to reduce the heavy workloads and second opinion to radiologists. In this paper, we present a strategy for classifying and validating different stages of lung tumor progression, as well as a deep neural model and data collection using cloud system for categorizing phases of pulmonary illness. The proposed system presents a Cloud-based Lung Tumor Detector and Stage Classifier (Cloud-LTDSC) as a hybrid technique for PET/CT images. The proposed Cloud-LTDSC initially developed the active contour model as lung tumor segmentation, and multilayer convolutional neural network (M-CNN) for classifying different stages of lung cancer has been modelled and validated with standard benchmark images. The performance of the presented technique is evaluated using a benchmark image LIDC-IDRI dataset of 50 low doses and also utilized the lung CT DICOM images. Compared with existing techniques in the literature, our proposed method achieved good result for the performance metrics accuracy, recall, and precision evaluated. Under numerous aspects, our proposed approach produces superior outcomes on all of the applied dataset images. Furthermore, the experimental result achieves an average lung tumor stage classification accuracy of 97%-99.1% and an average of 98.6% which is significantly higher than the other existing techniques.

Study on Motion Management of Lung Cancer Treated by CyberKnife: Based on Tumor Position

Purpose: We investigated the movement characteristics of lung cancers and the clinical accuracy of tracking lung tumors with Synchrony Respiratory Tracking System (SRTs) during the CyberKnife treatment. We also explored the influencing factors of accuracy. These data provided the appropriate expansion margins of patients with different respiratory characteristics, which was helpful to realize the personalized design of treatment plans of CyberKnife. Methods and Materials: 73 patients with lung cancer treated with CyberKnife SRTs were selected retrospectively for this study. The patient's age, gender, respiratory characteristics and tumor datas (tumor size, anatomical position and geometric position) were recorded. During treatment, the deviation was checked every 45 s and compensated by the synchronous respiratory tracking system.Results: The total mean motion amplitudes and standard deviations of lung tumors in superior-inferior (SI), left-right (LR), and anterior-posterior (AP) directions were 4.15 ± 3.47 mm, 3.98 ± 3.21 mm and 3.79 ± 2.73 mm, respectively. The overall mean correlation errors and standard deviations were 0.86 ± 0.45 mm, 1.04 ± 0.76 mm and 0.70 ± 0.47 mm, respectively. The overall mean prediction errors and standard deviations were 0.18 ± 0.17 mm, 0.35 ± 0.39 mm and 0.35 ± 0.42 mm, respectively. The correlation errors of LR direction were less correlated with the geometric position of the tumor (r = 0.38), and not correlated with the anatomical position of the tumor (r < 0.3). The prediction errors were moderately correlated with the respiratory amplitude (r = 0.588), and less correlated with the baseline drift and the motion amplitude of the tumor (r = 0.407 and 0.365, respectively).Conclusions: The patient’s respiratory amplitude, the tumor motion amplitude, the tumor baseline drift and geometric position were the main factors affecting the tracking accuracy. Tumors at different geometric positions should be treated differently to ensure sufficient dose coverage of the lung tumor target.