Prognostic factors versus predictive factors: Examples from a clinical trial of erlotinib

• Published in: Molecular oncology Vol. 1; no. 4; pp. 406 - 412
• Main Author: Clark, Gary M.
• Format: Journal Article
• Language: English
• Published: United States Elsevier B.V 01.04.2008; John Wiley & Sons, Inc; John Wiley and Sons Inc
• Subjects: Biomarkers; Cancer therapies; Carcinoma, Non-Small-Cell Lung - diagnosis; Carcinoma, Non-Small-Cell Lung - drug therapy; Carcinoma, Non-Small-Cell Lung - mortality; Carcinoma, Non-Small-Cell Lung - pathology; Cigarettes; Clinical trials; Confidence intervals; Drug Delivery Systems; Epidermal growth factor; Epidermal growth factor receptor; ErbB Receptors - antagonists & inhibitors; Erlotinib; Erlotinib Hydrochloride; Females; Gender; Histology; Humans; Hypothesis testing; Lung cancer; Males; Medical prognosis; Mutation; Non-small lung cancer; Patients; Predictive; Predictive Value of Tests; Prognosis; Prognostic; Quinazolines - therapeutic use; Randomized Controlled Trials as Topic; Response rates; Retrospective Studies; Smoking; Smoking - adverse effects; Erlotinib; Predictive; Histology; Non-small lung cancer; Prognostic; Epidermal growth factor receptor; Smoking
• ISSN: 1574-7891; 1878-0261
• DOI: 10.1016/j.molonc.2007.12.001

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.