Researchers from the Mayo Clinic Cancer Center have identified two non-cell type-specific gene expression biomarker profiles that offer improved survival prediction for non-small-cell lung cancer.
Gene expression profiling allows detection of a tumour's molecular signature and can lead to better sub-classification of patients.
In 2006, Mayo Clinic Cancer Center researchers reported that gene expression profiling methods were not better than existing conventional methods of assessing survival in lung cancer patients. They based that conclusion on their evaluation of literature published since this promising technology started about 10 years ago.
"While we have seen a number of exciting results from gene expression profiling in lung cancer outcome prediction, none provided value-added, unique information," says Zhifu Sun, M.D., Mayo Clinic cancer researcher and the study's lead author.
"By saying that, we mean that none of the previously identified genetic biomarkers provided more information than conventional methods, and their clinical applications were limited. Our goal was to identify a biomarker or panels of markers that would add to our current knowledge of patient management, allowing much more refined prognostic sub-classification, and eventually leading to better personalized treatment options," he added.
Before this study's findings, gene expression profiling in lung cancer provided no additional information than that gleaned from conventional methods that factor in age, gender, stage, cell type and tumour grade, performance status or treatment. The studies did not consider these factors in marker selection; therefore, molecular markers or signatures identified were often surrogates of what the research community already knew.
Dr. Sun and the Mayo research team took a different approach. They looked at adenocarcinoma and squamous cell carcinoma separately and first selected two nonoverlapping sets of survival-related gene signatures from two large microarray datasets.
This was done by adjusting for the conventional predictors. The signatures were then evaluated in two large independent datasets of non-small-cell lung cancer. Each signature was evaluated in the same cell type it was derived from, and also validated for survival prediction in other cell types.
The team found two survival-related 50-gene signatures, one for each of the two cancer types. These were non-overlapping and largely unique in gene content compared to previously identified predictive gene expression signatures for lung cancer developed by other researchers.
When the two signatures were evaluated in two independent sets of non-small-cell lung cancer patients, the research team found that the adenocarcinoma gene expression profile was able to predict survival for both adenocarcinoma and squamous cell carcinoma patients. Conversely, the squamous cell carcinoma profile could also predict for adenocarcinoma, although interestingly, not as well for squamous cell carcinoma.
However, both sets of 50 genes were able to provide significantly improved survival prediction for non-small-cell lung cancers independent of other traditional variables. These genes were most likely related to tumour aggressiveness regardless of histologic features or stage.
Dr. Sun and his fellow researchers caution that their research will need to be further validated with additional studies, but they are hopeful that the results will lead to improved outcome prediction and treatment selection for lung cancer patients. They say the next step is to discover the underlying mechanisms for some or all of the 50 genes in the profiles.
The findings will be published in the Feb. 20 issue of the Journal of Clinical Oncology.