Studying the immune landscape in lung cancer models: choosing the right experimental tools

Development of syngeneic murine cell lines for use in immunocompetent orthotopic lung cancer models

BACKGROUND

Immunocompetent animal models are required to study tumor-host interactions, immunotherapy, and immunotherapeutic combinations, however the currently available immunocompetent lung cancer models have substantial limitations. While orthotopic models potentially help fill this gap, the utility of these models has been limited by the very small number of murine lung cancer cell lines capable of forming orthotopic tumors in immunocompetent C57BL/6 hosts.

METHODS

Primary lung tumors with specific genetic alterations were created in C57BL/6 background mice. These tumors were then passaged through other animals to increase tumorigenicity and select for the ability to grow in a non-self animal. Once tumors demonstrated growth in a non-self host, cell lines were established. Successful cell lines were evaluated for the ability to produce orthotopic lung tumors in immunocompetent hosts.

RESULTS

We produced six murine lung cancer lines capable of orthotopic lung tumor formation in immunocompetent C57BL/6 animals. These lines demonstrate the expected genetic alterations based on their primary tumor genetics.

CONCLUSIONS

These novel cell lines will be useful for evaluating tumor-host interactions, the impact of specific oncogenic alterations on the tumor microenvironment, and immunotherapeutic approaches. This method of generating murine lines capable of orthotopic growth can likely be applied to other tumors and will broaden the applicability of pre-clinical testing of immunotherapeutic treatment regimens.

Analysis of expression differences of immune genes in non-small cell lung cancer based on TCGA and ImmPort data sets and the application of a prognostic model

Background

There has been little investigation carried out into the activity of immune-related genes in the prognosis of non-small cell lung cancer (NSCLC). Our study set out to analyze the correlation between the differential expression of immune genes and NSCLC prognosis by screening the differential expression of immune genes. Based on the immune genes identified, we aimed to construct a prognostic risk model and explore some novel molecules which have predictive potential for therapeutic effect and prognosis in lung cancer.

Methods

Immune gene transcriptome data and clinical data of NSCLC samples were extracted from TCGA database, and transcription factors in the ImmPort dataset were obtained. The data were divided into two groups: normal tissues and tumor tissues. The expression levels of immune genes were compared using the edgeR algorithm, and then differential expression analysis was performed. The survival analysis was carried out by combining differential immune genes with clinical survival time, so that the immune genes influencing the prognosis of NSCLC could be determined. A risk score was calculated based on the expression levels of the immune genes related to the prognosis of NSCLC and their corresponding coefficients to construct a prognostic risk model. This model was used to calculate patient risk scores and perform clinical correlation analysis. The selected molecules were further verified by clinical samples.

Results

By comparing NSCLC tissues with normal tissues, a total of 6,778 differentially expressed genes were found (P<0.05), of which 490 were differential immune-related genes. Survival analysis determined 28 differential immune genes to be associated with prognosis (P<0.05). We calculated the patient risk value based on the immune gene prognosis model. The survival curve was drawn according to the patient risk score and showed that the survival prognosis was significantly different for the high-risk and the low-risk groups (P<0.05). The area under the receiver operating characteristic (ROC) curve (AUC) was 0.723, which represented a relatively high true-positive rate. All of the results proved the reliability of our immune gene risk prognostic model. After drawing the risk curve, S100A16, IGKV4, S100P, ANGPTL4, SEMA4B, and LGR4 were found to be the high-risk immune genes in NSCLC. Clinical correlation analysis of survival-related differential immune genes revealed that in patients with lymph node metastasis, ANGPTL4 was positively correlated with T stage, S100a16 and SEMA4B were upregulated, and VIPR1 was downregulated. Further analysis revealed that VIPR1 was decreased in metastatic lung cancer compared to non-metastatic lung cancer. Furthermore, the real-time PCR detection of the clinical samples showed that S100A16 expression in lung cancer was increased, while VIPR1 expression in lung cancer was downregulated, which was consistent with the results of our bioinformatics analysis.

Conclusions

Based on big data from the TCGA and ImmPort databases, our study analyzed the relationship between differential expression of immune-related genes and clinical data, and constructed a prognostic model based on the immune genes identified. Two novel molecules, S100A16 and VIPR1, were verified to possibly have significant biological function in NSCLC. Our research may provide us with new insight into the immune genes by which the malignant biological behavior of NSCLC is mediated.