About 30% of lung cancer patients are accessible to targeted therapy or immunotherapy based on the current criteria.

In a study published in Cancer Screening and Prevention, a novel gene cluster expression analysis was introduced with a goal to potentially expand the treatments to more patients based on the proposed criteria.

Selected gene expression omnibus data sets were downloaded, normalised, and analysed.

A univariate recurrence prediction model was built based on the receiver operating characteristic, for which an optimal cut-off 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 analysed for combined multiple cluster statuses.

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.

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. It is expected 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.

Source: Xia & He Publishing Inc.