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Updated: Jun 29, 2026
Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
Published on: June 21, 2018
The use of genomics in clinical trial design
1Biometric Research Branch, National Cancer Institute, 9000 Rockville Pike, Bethesda, MD 20892, USA. rsimon@nih.gov
Abstract:
Many cancer treatments benefit only a minority of patients who receive them. This results in an enormous burden on patients and on the health care system. The problem will become even greater with the increasing use of molecularly targeted agents whose benefits are likely to be more selective unless the drug development process is modified to include co-development of companion diagnostics. Whole genome biotechnology and decreasing costs of genome sequencing make it increasingly possible to achieve an era of predictive medicine in oncology therapeutics. The challenges are numerous and substantial but are not primarily technological. They involve organizing publicly funded diagnostics of deregulated pathways, adopting new paradigms for drug development, and developing incentives for industry to incur the complexity and expense of co-development of drugs and companion diagnostics. This article reviews some designs for phase III clinical trials that may facilitate movement to a more predictive oncology.
Insights
Predictive oncology aims to improve cancer treatment efficacy by developing companion diagnostics alongside targeted therapies. This approach enhances patient outcomes and reduces healthcare burdens by ensuring treatments benefit the right patients.
Area of Science:
- Oncology
- Biotechnology
- Genomics
Background:
- Many cancer treatments benefit only a small patient subset, leading to significant patient and healthcare system burdens.
- The rise of molecularly targeted agents necessitates companion diagnostics for selective efficacy, requiring modifications in drug development.
- Advancements in whole genome biotechnology and decreasing sequencing costs enable predictive medicine in oncology.
Purpose of the Study:
- To review clinical trial designs that can facilitate the transition to predictive oncology.
- To address challenges in implementing predictive medicine, focusing on diagnostics, drug development paradigms, and industry incentives.
Main Methods:
- Review of existing and proposed phase III clinical trial designs.
- Analysis of challenges in integrating diagnostics with drug development.
- Discussion of economic and organizational factors influencing industry adoption.
Main Results:
- Current drug development models are insufficient for the era of predictive oncology.
- Technological feasibility for predictive medicine is largely established.
- Key challenges lie in organizing diagnostics, adopting new drug development paradigms, and incentivizing industry co-development.
Conclusions:
- A shift towards predictive oncology requires strategic changes in clinical trial design and drug development.
- Co-development of drugs and companion diagnostics is crucial for maximizing treatment benefits.
- Overcoming organizational and economic hurdles is essential for realizing the potential of predictive medicine in cancer care.
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