Adaptive Signature Design- review of the biomarker guided adaptive phase –III controlled design

Abstract

Genomics having a profound impact on oncology drug development necessitates the use of genomic signatures for therapeutic strategy and emerging medicine proposals. Since its advent in the arena of clinical trials biomarker-related predictive methods for the identification and selection of patient subgroups, with optimal treatment response, are widely used. Genetic signatures which are accountable for the differential response to treatments are experimentally recognizable and analytically validated in phase II stage of clinical trials. The availability of robust and validated biomarkers in phase III is limited. Hence, the development of a clinical trial design without the availability of biomarker identity for treatment-sensitive patients becomes indispensable. Adaptive Signature Design (ASD) is a design procedure of developing and validating a predictive classifier (diagnostic testing strategy) when the signature of subjects responding differentially to treatment is remote in the context of the study. This review provides a detailed methodology and statistical background of this pioneering design developed by Freidlin and Simon (2005). In addition, it concentrates on the advances in ASD regarding statistical issues such as predictive assay identification, classification techniques, statistical methods, subgroup search, choice of differentially expressed genes, and multiplicity correction. The statistical methodology behind the design is explained with the intent of building the ground steps for future research approachable, especially for beginning researchers. Most of the existing research articles give a microcosmic view of the design and lack in describing the details behind the methodology. This study covers those details and marks the novelty of our research.

BZW1 is a prognostic and immunological biomarker in pancreatic adenocarcinoma

“…[26] The study by Bhattacharyya et al describes in detail the biomarker-guided adaptive phase III clinical trials, where first we need to identify significant genes among a large number of evaluated differential genes by high-throughput screening methods; second, estimate the gene-treatment interaction effect; and lastly, utilize an unknown-gene adaptive signature design to assess the level of significance in selecting differentially expressed genes by Bonferroni adjustment and false discovery rate. [27] BZW1, a member of the bZIP transcription factor superfamily, is involved in the progression of multiple tumors. Shi et al [28] found that highly expressed BZW1 can significantly promote the proliferation of prostate cancer cells by regulating the TGF-β1/Smad pathway.…”

Adding flexibility to clinical trial designs: an example-based guide to the practical use of adaptive designs

“…Beyond being structured to allow formal analysis of sub-groups, there is also a large ethical benefit to these designs, exposing as few patients as possible to treatments from which they may not receive a benefit. Should no such pre-defined biomarkers be available, one might consider adaptive signature designs [168,169], also referred to as biomarker adaptive designs [149,170,171].…”

Adding flexibility to clinical trial designs: an example-based guide to the practical use of adaptive designs

“…Beyond being structured to allow formal analysis of sub-groups there is also a large ethical benefit to these designs; exposing as few patients as possible to treatments from which they may not receive a benefit. Should no such pre-defined biomarkers be available one might consider adaptive signature designs [168,169], also referred to as biomarker adaptive designs [150,170,171]. They aim to identify and use predictive biomarkers [170] during the trial.…”