Strategies to design clinical studies to identify predictive biomarkers in cancer research
(2017) In Cancer Treatment Reviews 53. p.79-97- 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... (More)
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.
(Less)
- author
- organization
- publishing date
- 2017-02-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Biomarkers, Clinical trial design, Extreme phenotypes, Mutation, Rearrangement
- in
- Cancer Treatment Reviews
- volume
- 53
- pages
- 79 - 97
- publisher
- Elsevier
- external identifiers
-
- pmid:28088073
- wos:000394075400009
- scopus:85009135202
- ISSN
- 0305-7372
- DOI
- 10.1016/j.ctrv.2016.12.005
- language
- English
- LU publication?
- yes
- id
- d6c3669d-f7c3-4e6e-bceb-ef0be5a05b8b
- date added to LUP
- 2017-02-03 15:15:24
- date last changed
- 2025-01-12 20:45:11
@article{d6c3669d-f7c3-4e6e-bceb-ef0be5a05b8b,
abstract = {{<p>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.</p>}},
author = {{Perez-Gracia, Jose Luis and Sanmamed, Miguel F. and Bosch Campos, Ana and Patiño-Garcia, Ana and Schalper, Kurt A. and Segura, Victor and Bellmunt, Joaquim and Tabernero, Josep and Sweeney, Christopher J. and Choueiri, Toni K. and Martín, Miguel and Fusco, Juan Pablo and Rodriguez-Ruiz, Maria Esperanza and Calvo, Alfonso and Prior, Celia and Paz-Ares, Luis and Pio, Ruben and Gonzalez-Billalabeitia, Enrique and Gonzalez Hernandez, Alvaro and Páez, David and Piulats, Jose María and Gurpide, Alfonso and Andueza, Mapi and Velasco, Guillermo and Pazo, Roberto and Grande, Enrique and Nicolas, Pilar and Abad-Santos, Francisco and Garcia-Donas, Jesus and Castellano, Daniel and Pajares, María J. and Suarez, Cristina and Colomer, Ramon and Montuenga, Luis M. and Melero, Ignacio}},
issn = {{0305-7372}},
keywords = {{Biomarkers; Clinical trial design; Extreme phenotypes; Mutation; Rearrangement}},
language = {{eng}},
month = {{02}},
pages = {{79--97}},
publisher = {{Elsevier}},
series = {{Cancer Treatment Reviews}},
title = {{Strategies to design clinical studies to identify predictive biomarkers in cancer research}},
url = {{http://dx.doi.org/10.1016/j.ctrv.2016.12.005}},
doi = {{10.1016/j.ctrv.2016.12.005}},
volume = {{53}},
year = {{2017}},
}