Gene cluster expression analysis for personalized lung cancer therapy

Xia & He Publishing Inc.May 23 2024

Background and objectives

About 30% of lung cancer patients are accessible to targeted therapy or immunotherapy based on the current criteria. In this study, a novel gene cluster expression analysis was introduced with a goal to potentially expand the treatments to more patients based on the proposed criteria.

Methods

Selected gene expression omnibus data sets were downloaded, normalized, and analyzed. A univariate recurrence prediction model was built based on the receiver operating characteristic, for which an optimal cutoff was determined to set abnormality status, called the gene cluster expression index (GCEI). Recurrence and survival risks were calculated and compared between two subgroups indexed by the GCEI. Moreover, a combinatory GCEI was also introduced and its performance was analyzed for combined multiple cluster statuses.

Results

The recurrence risks of the patient subgroups with abnormally expressed clusters with GCEI = 1 were much higher than for the corresponding normal subgroup with GCEI = 0. The higher risks ranged from 120–300% that of the corresponding lower-risk group.

Conclusions

Gene cluster expression index can be used to classify lung cancers with dramatically different recurrence risks and the recurrence risk (percentage) of the patient group with index 1 is typically 20% to 200% higher than the group with index 0. We expect that the higher risk group of index 1 may also be suitable for the corresponding targeted therapy or immunotherapy. Therefore, it may be used to guide targeted therapy or immunotherapy when the conventional companion tests give no recommendation. Nevertheless, this should be validated by clinical trials before it is applied in the clinical practice.