title: Derivation of cancer diagnostic and prognostic signatures from gene expression data.
abstract: The ability to compare genome-wide expression profiles in human tissue samples has the potential to add an invaluable molecular pathology aspect to the detection and evaluation of multiple diseases. Applications include initial diagnosis, evaluation of disease subtype, monitoring of response to therapy and the prediction of disease recurrence. The derivation of molecular signatures that can predict tumor recurrence in breast cancer has been a particularly intense area of investigation and a number of studies have shown that molecular signatures can outperform currently used clinicopathologic factors in predicting relapse in this disease. However, many of these predictive models have been derived using relatively simple computational algorithms and whether these models are at a stage of development worthy of large-cohort clinical trial validation is currently a subject of debate. In this review, we focus on the derivation of optimal molecular signatures from high-dimensional data and discuss some of the expected future developments in the field.
authors.affiliation: M. D. Anderson Cancer Center Orlando, Cancer Research Institute, 6900 Lake Nona Blvd, Orlando, FL 32827, USA. steven.goodison@orlandohealth.com
authors.forename: Steve
authors.initials: S
authors.lastname: Goodison
authors.forename: Yijun
authors.initials: Y
authors.lastname: Sun
authors.forename: Virginia
authors.initials: V
authors.lastname: Urquidi
mesh_terms: D001943:Breast Neoplasms / Q000175:diagnosis / Q000235:genetics; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D008297:Male; D009369:Neoplasms / Q000175:diagnosis* / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis; D011379:Prognosis; D011471:Prostatic Neoplasms / Q000175:diagnosis / Q000235:genetics