TAIPEI, Taiwan -- Relapse-free and overall survival for non-small-cell lung cancer can be predicted by a molecular signature with five specific genes, researchers here reported.
TAIPEI, Taiwan, Jan. 3 -- Relapse-free and overall survival for non-small-cell lung cancer (NSCLC) can be predicted by a molecular signature with five specific genes, researchers here reported.
The current staging system for non-small-cell lung cancer is inadequate for predicting treatment outcomes, whereas molecular methods appear valuable in estimating prognosis and in determining therapy, Pan-Chyr Yang, M.D., Ph.D., of the National Taiwan University Hospital, and colleagues, wrote in the Jan. 4 issue of the New England Journal of Medicine.
In a gene profiling study, the researchers used computer-generated random numbers to assign 185 frozen specimens for microarray analysis, real-time reverse-transcriptase polymerase chain reaction (RT-PCR) analysis, or both.
Next, they studied gene expression in frozen specimens of lung cancer tissue from 125 randomly selected patients who had undergone surgical resection for non-small-cell lung cancer between 1999 and 2003. The specimens were evaluated for the level of expression and for survival.
The specimens represented a mixture of tumor types and stages. Of the 125 specimens, 60 were adenocarcinomas, 52 were squamous-cell carcinomas, and 13 were other types.
Of an original 672 genes associated with invasive activity, the researchers identified 16 that correlated with increased survival (four) or decreased (12). From this, they developed a score that discriminated between high and low risk of death or recurrence.
To develop a gene-expression model to predict treatment outcome, they selected five genes for RT-PCR and decision-tree analysis. They found that the presence of the five-gene signature was an independent predictor of relapse-free and overall survival.
The median follow-up of the 101 patients was 20 months. The patients with a high-risk gene signature had a shorter median overall survival than the patients with a low-risk gene signature (20 months vs. 40 months, P
In an accompanying editorial, Roy Herbst, M.D., Ph.D., and Scott Lippman, M.D., both of the University of Texas M.D. Anderson Cancer Center in Houston, took the long view, commenting on the study but also outlining the future of molecular profiling.
The study by Dr. Yang and colleagues produced "robust, promising results," they wrote, but it also raised several questions.
Given that tumors are heterogeneous and that the biopsy specimens were not microdissected, the analysis could have underestimated or overestimated the importance of invasion-related genes, which can vary in expression throughout a tumor. Molecular epidemiologic, stromal, and vascular factors are critical to the metastatic process, and these elements were not specifically analyzed in this study.
Furthermore, they said, the statistical design can also influence the selection of a signature, and the choice of the cutoff expression levels surely influenced how the original 672 genes were selected. The relationship of the signature genes to metastasis may be "contributory or possibly only associative, and for this reason, proteomics will probably be useful in evaluating this question of cause and effect," they wrote.
This report reflects the maturation of the first phase of lung-cancer genomics, which has been based on stored tissue and clinical charts. "The field is now poised to begin its next phase," they said. Needed are prospective trials of adjuvant chemotherapy in patients with early lung cancer who are selected because they have a high risk of relapse or metastasis according to the molecular signature identified in this study or by others.
Looking to the future, the editorial writers suggested that cancer genomics will expand in two areas: molecular profiles associated with response or resistance to particular standard or novel therapies, and clinical trials based on molecular profiles that indicate a benefit from new or standard agents.
This new phase of target profiling and agent-specific profiling will probably require an algorithm that would include genomic, proteomic, clinical, and imaging factors, they said.
"The profiles, we predict, will be used for the development of novel drugs. Patients with early-stage cancers will be assigned to particular drugs on the basis of the molecular characteristics of the tumors. Then the development of drugs for the treatment of lung cancer will be focused on personalized therapy," they concluded.
Harn-Jing Terng, Ph.D., of the Taiwan group reported being an employee of Advpharma, Tapei Hsien, Taiwan, which also provided grant support for this study.