The Use of Genomics in Clinical Trial Design

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 codevelopment 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 codevelopment of drugs and companion diagnostics. This article reviews some designs for phase III clinical trials that may facilitate movement to a more predictive oncology.

Comparison of Statistical Analysis Plans in Randomize-All Phase III Trials with a Predictive Biomarker

“…However, it is more common that at the initiation of phase III trials, there are no compelling biologic or early trial data for a candidate predictive biomarker regarding its capability to predict treatment effects or there is uncertainty about a cutoff point of an analytically validated predictive assay. In such situations, it is generally reasonable to include all patients as eligible for randomization, as done in traditional clinical trials, but to plan for prospective subset analysis based on the predictive biomarker with a control of the study-wise type I error rate at the level a, for example, a ¼ 2.5% at a one-sided level, under the global null hypothesis of no treatment effects for any patients (5)(6)(7)(8)(9)(10)(11)(12)(13)(14). For such statistical analysis plans that allow this, we can identify three approaches: fixed-sequence, fallback, and treatment-by-biomarker interaction approaches (see Fig.…”

“…If this is significant, treatment efficacy for a subset of the rest patients (fixed-sequence-1; ref. 7) or for the overall population (fixed-sequence-2; ref. 10) is tested using the same significance level a.…”

“…The treatment-by-biomarker interaction approaches involve deciding whether to compare treatments overall or within the biomarker-based subsets based on a preliminary test of interaction of treatment and biomarker (6,7). For example, in the MARVEL trial (17) to compare erlotinib and pemetrexed as second-line treatment for non-small cell lung cancer, the analysis was planned to be conducted separately in EGFR-positive and -negative patients, with the use of an interaction test on the difference in treatment effects between the two subsets of patients.…”

Biochips: Bright Future in Clinical Dx?

“…In drug development biomarker applications also comprise patient stratification to identify subjects to be enrolled in studies and for improved design of clinical trials [1] thus reducing effort, expenses and time. While this will raise success rates in treatment, speed up drug development and bring the appropriate therapy to those subjects benefiting most of it, targeting will at the same time reduce the total number of administrations.…”

“…Issues to be addressed comprise, in particular with regard to the high-density biochip formats used in the screening phase, reproducible/automated preanalytics, standardisation, normalisation, statistics and extensive clinical validation of identified biomarker candidates. [1,2] The microarray quality control [4] came to the conclusion that microarray results, i.e. differentially expressed genes, are reproducible and reliable.…”