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Cancer Treat Rev. 2017 Feb;53:79-97. doi: 10.1016/j.ctrv.2016.12.005. Epub 2016 Dec 30.

Strategies to design clinical studies to identify predictive biomarkers in cancer research.

Author information

1
Department of Oncology, University Clinic of Navarra, Pamplona, Spain; Health Research Institute of Navarra (IDISNA), Pamplona, Spain. Electronic address: jlgracia@unav.es.
2
Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA.
3
Division of Oncology and Pathology Department of Clinical Sciences, Lund University, Sweden.
4
Department of Pediatrics and CIMA LAB Diagnostics, University Clinic of Navarra, Pamplona, Spain; Health Research Institute of Navarra (IDISNA), Pamplona, Spain.
5
Department of Pathology, Yale School of Medicine, New Haven, CT, USA.
6
IDISNA and Bioinformatics Unit, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Navarra, Spain.
7
Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA.
8
Department of Medical Oncology, Vall d'Hebron University Hospital and Institute of Oncology (VHIO), Universitat Autònoma de Barcelona, Barcelona, Spain.
9
Instituto de Investigación Sanitaria Gregorio Marañón, Universidad Complutense, Madrid, Spain.
10
Department of Oncology, University Clinic of Navarra, Pamplona, Spain.
11
Department of Oncology, University Clinic of Navarra, Pamplona, Spain; Health Research Institute of Navarra (IDISNA), Pamplona, Spain; Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain.
12
Health Research Institute of Navarra (IDISNA), Pamplona, Spain; Department of Histology and Pathology, School of Medicine, University of Navarra, Pamplona, Navarra, Spain.
13
Department of Gene Therapy and Regulation of Gene Expression, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain.
14
Department of Medical Oncology, Hospital Universitario 12 de Octubre, Madrid, Spain.
15
Health Research Institute of Navarra (IDISNA), Pamplona, Spain; Program in Solid Tumors and Biomarkers, CIMA, University of Navarra, Spain.
16
Department of Hematology and Medical Oncology, Hospital Universitario Morales Meseguer, Universidad Católica San Antonio de Murcia, Murcia, Spain.
17
Department of Biochemistry, University Clinic of Navarra, Pamplona, Spain.
18
Department of Medical Oncology, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain.
19
Department of Medical Oncology, Institut Català d'Oncologia, Barcelona, Spain.
20
Department of Oncology, University Clinic of Navarra, Pamplona, Spain; Health Research Institute of Navarra (IDISNA), Pamplona, Spain.
21
Department of Medical Oncology, Hospital Universitario Miguel Servet, Zaragoza, Spain.
22
Department of Medical Oncology, Hospital Universitario Ramón y Cajal, Madrid, Spain.
23
Chair in Law and the Human Genome, University of the Basque Country, Bizkaia, Spain.
24
Clinical Pharmacology Service, Hospital Universitario de la Princesa, Instituto Teófilo Hernando, University Autónoma de Madrid (UAM), Instituto de Investigación Sanitaria la Princesa (IP), Madrid, Spain.
25
Department of Medical Oncology, HM Hospitales - Centro Integral Oncológico HM Clara Campal, Madrid, Spain.
26
Health Research Institute of Navarra (IDISNA), Pamplona, Spain; Department of Histology and Pathology, School of Medicine, University of Navarra, Pamplona, Navarra, Spain; Program in Solid Tumors and Biomarkers, CIMA, University of Navarra, Spain.
27
Department of Oncology, Hospital Universitario de la Princesa, Spain.

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.

KEYWORDS:

Biomarkers; Clinical trial design; Extreme phenotypes; Mutation; Rearrangement

PMID:
28088073
DOI:
10.1016/j.ctrv.2016.12.005
[Indexed for MEDLINE]
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