TY - JOUR
T1 - Strategies to design clinical studies to identify predictive biomarkers in cancer research
AU - Perez-Gracia, Jose Luis
AU - Sanmamed, Miguel F.
AU - Bosch Campos, Ana
AU - Patiño-Garcia, Ana
AU - Schalper, Kurt A.
AU - Segura, Victor
AU - Bellmunt, Joaquim
AU - Tabernero, Josep
AU - Sweeney, Christopher J.
AU - Choueiri, Toni K.
AU - Martín, Miguel
AU - Fusco, Juan Pablo
AU - Rodriguez-Ruiz, Maria Esperanza
AU - Calvo, Alfonso
AU - Prior, Celia
AU - Paz-Ares, Luis
AU - Pio, Ruben
AU - Gonzalez-Billalabeitia, Enrique
AU - Gonzalez Hernandez, Alvaro
AU - Páez, David
AU - Piulats, Jose María
AU - Gurpide, Alfonso
AU - Andueza, Mapi
AU - Velasco, Guillermo
AU - Pazo, Roberto
AU - Grande, Enrique
AU - Nicolas, Pilar
AU - Abad-Santos, Francisco
AU - Garcia-Donas, Jesus
AU - Castellano, Daniel
AU - Pajares, María J.
AU - Suarez, Cristina
AU - Colomer, Ramon
AU - Montuenga, Luis M.
AU - Melero, Ignacio
PY - 2017/2/1
Y1 - 2017/2/1
N2 - 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.
AB - 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.
KW - Biomarkers
KW - Clinical trial design
KW - Extreme phenotypes
KW - Mutation
KW - Rearrangement
UR - http://www.scopus.com/inward/record.url?scp=85009135202&partnerID=8YFLogxK
U2 - 10.1016/j.ctrv.2016.12.005
DO - 10.1016/j.ctrv.2016.12.005
M3 - Review article
C2 - 28088073
AN - SCOPUS:85009135202
SN - 0305-7372
VL - 53
SP - 79
EP - 97
JO - Cancer Treatment Reviews
JF - Cancer Treatment Reviews
ER -