Cancer mutation profiles predict ICIs efficacy in patients with non-small cell lung cancer

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.

Automated summary

Although immune checkpoint inhibitors (ICIs) have shown remarkable efficacy in treating non-small cell lung cancer (NSCLC), it is challenging to accurately predict which patients will respond to ICIs using conventional criteria. Previous studies have suggested that specific gene mutations may play a role in immunotherapy response, but the predictive value of single gene mutations may be limited. With advances in sequencing technology, it has been discovered that multiple mutations often co-occur and can synergistically affect signaling pathways involved in anti-tumor immune responses. Therefore, the mutation profile, especially co-mutations, of patients may be an important consideration for predicting the efficacy of ICIs.