[Predictive Value of Tumor Mutation Burden in Immunotherapy for Lung Cancer]

Immune checkpoints and immunotherapy in non-small cell lung cancer: Novel study progression, challenges and solutions

Lung cancer is the most common type of cancer with the highest mortality rate worldwide. Non-small cell lung cancer (NSCLC) accounts for ~85% of the total number of lung cancer cases. In the past two decades, immunotherapy has become a more promising treatment method than traditional treatments (surgery, radiotherapy and chemotherapy). Immunotherapy has been shown to improve the survival rate of patients and to have a superior effect when controlling lung cancer than traditional therapy. However, only a small number of patients can benefit from immunotherapy, and not all patients who qualify experience long-term benefits. In the clinic, the objective response rate of programmed cell death protein 1 treatment without the prior screening of patients is only 15-20%. Immunotherapy is associated with both opportunities and challenges for patients with NSCLC. The current challenges of immunotherapy include the lack of accurate biomarkers, inevitable resistance and insufficient understanding of immune checkpoints. In previous years, several methods for overcoming the challenges posed by immunotherapy have been proposed, but combination therapy is the most suitable choice. A large number of studies have shown that the combination of drugs can significantly improve their efficacy, compared with monotherapy, and that some therapeutic combinations have been approved by the Food and Drug Administration for the treatment of NSCLC. Traditional Chinese medicine (TCM) is a traditional medical practice in China that can play an important role in immunotherapy. Most agents used in TCM originate from plants, and have the advantages of low toxicity and multiple targets. In addition, TCM includes a unique class of drugs that can improve autoimmunity. Therefore, TCM may be a promising treatment method for all types of cancer.

A six-long noncoding RNA model predicts prognosis in lung adenocarcinoma

Background

The incidence and mortality of lung cancer rank first among various malignant tumors. The lack of clear molecular classification and effective individualized treatment greatly limits the treatment benefits of patients. Long non-coding RNAs (lncRNAs) have been demonstrated widely involve in tumor progressing, and been proved easy to detect for occupying majority in transcriptome. However, less work focuses on studying the potency of lncRNAs as molecular typing and prognostic indicator in lung cancer.

Methods

Based on the 448 lung adenocarcinoma (LUAD) samples and the expression of 14,127 lncRNAs from the Cancer Genome Atlas (TCGA) database, we constructed a co-expression network using weighted gene co-expression network analysis. Then based on the feature module and the overall survival of patients, we constructed a risk score model through Cox proportional hazards regression and verified it with a validation cohort. Finally, according to the median of risk score, the function of this model was enriched.

Results

We identified a module containing 123 lncRNAs that is related with the prognosis of LUAD. Using univariate and multivariate Cox proportional hazards regression with lasso regression, six lncRNAs were identified to construct a risk score model. The calculation formula shown as follows: risk score = (−0.3057 × EXP_VIM-AS1) + (0.9678 × EXP_AC092811.1) + (1.0829 × EXP_NFIA-AS1) + (−0.3505 × EXP_AL035701.1) + (3.9378 × EXP_AC079336.4) + (−0.2810 × EXP_AL121790.2). Six-lncRNA model can be used as an independent prognostic indicator in LUAD (P<0.001) and the area under the 5-year receiver operating characteristic (ROC) curve is 0.715.

Conclusions

We developed a six-lncRNA model, which could be used for predicting prognosis and guiding medical treatment in LUAD patients.