Strategies to design clinical studies to identify predictive biomarkers in cancer research.
Perez-Gracia JL., Sanmamed MF., Bosch A., Patiño-Garcia A., Schalper KA., Segura V., Bellmunt J., Tabernero J., Sweeney CJ., Choueiri TK., Martín M., Fusco JP., Rodriguez-Ruiz ME., Calvo A., Prior C., Paz-Ares L., Pio R., Gonzalez-Billalabeitia E., Gonzalez Hernandez A., Páez D., Piulats JM., Gurpide A., Andueza M., de Velasco G., Pazo R., Grande E., Nicolas P., Abad-Santos F., Garcia-Donas J., Castellano D., Pajares MJ., Suarez C., Colomer R., Montuenga LM., Melero I.
The discovery of reliable biomarkers to predict efficacy and toxicity of anticancer drugs remains one of the key challenges in cancer research. Despite its relevance, no efficient study designs to identify promising candidate biomarkers have been established. This has led to the proliferation of a myriad of exploratory studies using dissimilar strategies, most of which fail to identify any promising targets and are seldom validated. The lack of a proper methodology also determines that many anti-cancer drugs are developed below their potential, due to failure to identify predictive biomarkers. While some drugs will be systematically administered to many patients who will not benefit from them, leading to unnecessary toxicities and costs, others will never reach registration due to our inability to identify the specific patient population in which they are active. Despite these drawbacks, a limited number of outstanding predictive biomarkers have been successfully identified and validated, and have changed the standard practice of oncology. In this manuscript, a multidisciplinary panel reviews how those key biomarkers were identified and, based on those experiences, proposes a methodological framework-the DESIGN guidelines-to standardize the clinical design of biomarker identification studies and to develop future research in this pivotal field.