title: Genomic prognostic models in early-stage lung cancer.
abstract: Patients with early-stage lung cancer demonstrate significant recurrence rates and lower-than-expected survival rates after surgical resection, indicating that our current staging methods do not adequately predict outcome. Since the last revision of the TNM staging system, a number of genomic models have been proposed which more accurately predict prognosis in patients with early-stage lung cancer. A variety of prognostic genomic models based on gene-expression profiling and quantitative polymerase chain reaction (PCR) are able to stratify patients with early-stage lung cancer into high- and low-risk groups with respect to disease-free and overall survival. In the future, clinical application of these models may ultimately dictate both the use of adjuvant therapy as well as the choice of surgical procedure in patients with early-stage lung cancer. An effort to develop a robust genomic model for use in the clinical setting should be prompted by encouraging results obtained by the use of a quantitative PCR-based genomic signature in the field of breast oncology.
authors.affiliation: Department of Surgery, University of California San Francisco Cancer Center, CA 94143-0128, USA. johannes.kratz@ucsfmedctr.org
authors.forename: Johannes R
authors.initials: JR
authors.lastname: Kratz
authors.forename: David M
authors.initials: DM
authors.lastname: Jablons
mesh_terms: D020869:Gene Expression Profiling; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics* / Q000401:mortality / Q000473:pathology; D009367:Neoplasm Staging; D020411:Oligonucleotide Array Sequence Analysis; D011379:Prognosis; D020133:Reverse Transcriptase Polymerase Chain Reaction