Prognostic factors versus predictive factors: Examples from a clinical trial of erlotinib.
It would be helpful to have factors that could identify patients who will, or will not, benefit from treatment with specific therapies. Ideally, these should be molecular-based factors. When results with molecular-based factors are disappointing, physicians often use clinical characteristics to make treatment decisions. Several characteristics have been suggested to predict sensitivity to epidermal growth factor receptor inhibitors in patients with non-small lung cancer, including gender, histology, smoking history. This report demonstrates that gender and histology are actually prognostic, rather than predictive factors. Before biomarkers or clinical characteristics are included in guidelines for selecting patients for specific treatments, it is imperative that the prognostic effects of these factors are distinguished from their ability to predict a differential clinical benefit from the specific treatment.
Clark
Gary M
GM
Biostatistics and Data Management, OSI Pharmaceuticals, Inc., 2860 Wilderness Place, Boulder, CO 80301, USA. gclark@osip.com
D015415:Biomarkers; D002289:Carcinoma, Non-Small-Cell Lung / Q000175:diagnosis* / Q000188:drug therapy* / Q000401:mortality / Q000473:pathology; D016503:Drug Delivery Systems; D066246:ErbB Receptors / Q000037:antagonists & inhibitors*; D000069347:Erlotinib Hydrochloride; D006801:Humans; D011237:Predictive Value of Tests; D011379:Prognosis; D011799:Quinazolines / Q000627:therapeutic use*; D016032:Randomized Controlled Trials as Topic; D012189:Retrospective Studies; D012907:Smoking / Q000009:adverse effects
D015415:Biomarkers; D011799:Quinazolines; D000069347:Erlotinib Hydrochloride; D066246:ErbB Receptors