Genomic signatures individualize therapeutic decisions in non-small-cell lung cancer.
Gene expression signatures have been developed in an effort to dissect the biologic phenotypes of malignancies. These signatures have tremendous power to identify new cancer subtypes and to predict clinical outcomes based on patterns of gene expression. Expression profiles specific to a phenotype can be derived from in vitro data, as well as from patient cohorts with clinically relevant outcomes. In addition to predicting outcomes in non-small-cell lung cancer (NSCLC), similar techniques have been used to develop gene expression signatures that predict sensitivity or resistance to specific chemotherapeutic agents. Additionally, expression data have been used to identify oncogenic pathway deregulation to help direct the use of targeted agents. Used in combination, it is likely that gene expression signatures will help assess prognosis and may also be of value in guiding the use of cytotoxic and targeted therapy in NSCLC. Clinical trials are ongoing to validate these predictive gene expression signatures in a prospective manner.
Anguiano
Ariel
A
Institute for Genome Sciences and Policy, Division of Medical Oncology, Box 3382, 101 Science Drive, Duke University, Durham, NC 27708, USA.
Potti
Anil
A
D002289:Carcinoma, Non-Small-Cell Lung / Q000175:diagnosis / Q000235:genetics / Q000628:therapy; D002986:Clinical Trials as Topic; D004273:DNA, Neoplasm / Q000032:analysis*; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D016678:Genome; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D010641:Phenotype; D011237:Predictive Value of Tests; D011379:Prognosis
D004273:DNA, Neoplasm