Although immune checkpoint inhibitors (ICIs) have produced remarkable responses in non-small cell lung cancer (NSCLC) patients, receivers still have a relatively low response rate. Initial response assessment by conventional imaging and evaluation criteria is often unable to identify whether patients can achieve durable clinical benefit from ICIs. Overall, there are sparse effective biomarkers identified to screen NSCLC patients responding to this therapy. A lot of studies have reported that patients with specific gene mutations may benefit from or resist to immunotherapy. However, the single gene mutation may be not effective enough to predict the benefit from immunotherapy for patients. With the advancement in sequencing technology, further studies indicate that many mutations often co-occur and suggest a drastic transformation of tumour microenvironment phenotype. Moreover, co-mutation events have been reported to synergise to activate or suppress signalling pathways of anti-tumour immune response, which also indicates a potential target for combining intervention. Thus, the different mutation profile (especially co-mutation) of patients may be an important concern for predicting or promoting the efficacy of ICIs. However, there is a lack of comprehensive knowledge of this field until now. Therefore, in this study, we reviewed and elaborated the value of cancer mutation profile in predicting the efficacy of immunotherapy and analysed the underlying mechanisms, to provide an alternative way for screening dominant groups, and thereby, optimising individualised therapy for NSCLC patients.

Purpose: Radiation pneumonitis (RP) frequently occurs during a treatment course of chest radiotherapy, which significantly reduces the clinical outcome and efficacy of radiotherapy. The ability to easily predict RP before radiotherapy would allow this disease to be avoided.Methods and Materials: This study recruited 48 lung cancer patients requiring chest radiotherapy. For each participant, RNA sequencing (RNA-Seq) was performed on a peripheral blood sample before radiotherapy. The RNA-Seq data was then integrated into a genome-scale flux analysis to develop an RP scoring system for predicting the probability of occurrence of RP. Meanwhile, the clinical information and radiation dosimetric parameters of this cohort were collected for analysis of any statistical associations between these parameters and RP. A non-parametric rank sum test showed no significant difference between the predicted results from the RP score system and the clinically observed occurrence of RP in this cohort.Results: The results of the univariant analysis suggested that the tumor stage, exposure dose, and bilateral lung dose of V5 and V20 were significantly associated with the occurrence of RP. The results of the multivariant analysis suggested that the exposure doses of V5 and V20 were independent risk factors associated with RP and a level of RP ≥ 2, respectively. Thus, our results indicate that our RP scoring system could be applied to accurately predict the risk of RP before radiotherapy because the scores were highly consistent with the clinically observed occurrence of RP.Conclusion: Compared with the standard statistical methods, this genome-scale flux-based scoring system is more accurate, straightforward, and economical, and could therefore be of great significance when making clinical decisions for chest radiotherapy.