| Literature DB >> 28088073 |
Jose Luis Perez-Gracia1, Miguel F Sanmamed2, Ana Bosch3, Ana Patiño-Garcia4, Kurt A Schalper5, Victor Segura6, Joaquim Bellmunt7, Josep Tabernero8, Christopher J Sweeney7, Toni K Choueiri7, Miguel Martín9, Juan Pablo Fusco10, Maria Esperanza Rodriguez-Ruiz11, Alfonso Calvo12, Celia Prior13, Luis Paz-Ares14, Ruben Pio15, Enrique Gonzalez-Billalabeitia16, Alvaro Gonzalez Hernandez17, David Páez18, Jose María Piulats19, Alfonso Gurpide20, Mapi Andueza10, Guillermo de Velasco7, Roberto Pazo21, Enrique Grande22, Pilar Nicolas23, Francisco Abad-Santos24, Jesus Garcia-Donas25, Daniel Castellano14, María J Pajares26, Cristina Suarez8, Ramon Colomer27, Luis M Montuenga26, Ignacio Melero11.
Abstract
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.Entities:
Keywords: Biomarkers; Clinical trial design; Extreme phenotypes; Mutation; Rearrangement
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Year: 2016 PMID: 28088073 DOI: 10.1016/j.ctrv.2016.12.005
Source DB: PubMed Journal: Cancer Treat Rev ISSN: 0305-7372 Impact factor: 12.111