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.
Full text
https://www.xiahepublishing.com/2835-3315/CSP-2023-00034
The study was recently published in the Cancer Screening and Prevention.
Cancer Screening and Prevention (CSP) publishes high-quality research and review articles related to cancer screening and prevention. It aims to provide a platform for studies that develop innovative and creative strategies and precise models for screening, early detection, and prevention of various cancers. Studies on the integration of precision cancer prevention multiomics where cancer screening, early detection and prevention regimens can precisely reflect the risk of cancer from dissected genomic and environmental parameters are particularly welcome.
