Pharmacogenetics- and pharmacogenomics-based rational clinical trial designs in oncology

Multigene Copy Number Alteration Risk Score Biomarker-Based Enrichment Study Designs in Metastatic Castrate-Resistant Prostate Cancer

PURPOSE

A composite multigene risk score derived from tumor-biology alterations specific to metastatic castrate-resistant prostate cancer (mCRPC) state was evaluated as a classifier to design biomarker-based enrichment clinical trials.

METHODS

A plasma cell-free DNA copy number alteration risk score based on alterations in 24 genes was simulated to develop a biomarker classifier-based clinical trial design enriched for high-risk patients to detect a survival advantage of a novel treatment (hazard ratio of 0.70 with 80% power). We determined the design trade-offs between the number of patients screened and enrolled when varying the type of patients to enrich and the extent of enrichment needed.

RESULTS

For a 2-year overall survival end point in mCRPC state, fully enriching patients with mCRPC having a high-risk score of 3 or more (the 95th percentile of a range of risk scores in patients with mCRPC) was determined to require screening to a maximum of 4,149 patients to enroll 259 patients for the targeted effect size. A nonenriched trial was determined to require enrolling 689 patients to be equivalently powered. We identified a pragmatic alternative, which is to enrich patients with mCRPC with a risk score of 1 or more (the 67th percentile) and an enrichment fraction of 0.25. This would require screening 658 patients to enroll 584 patients, and it maximizes the ability to detect a difference in treatment effect by risk score.

CONCLUSION

A plasma multi-CNA risk score classifier can feasibly be leveraged to design an enrichment trial in mCRPC. Enriching 25% of patients screened with a risk score >1 was observed to be optimal for obtaining an adequately powered, biomarker-based mCRPC-enriched clinical trial.

Pharmacogenetic biomarkers for predicting drug response

Drug response shows significant interpatient variability and evidence that genetics influences outcome of drug therapy has been known for more than five decades. However, the translation of this knowledge to clinical practice remains slow. Using examples from clinical practice six considerations about the implementation of pharmacogenetics (PGx) into routine care are discussed: the need for PGx biomarkers; the sources of genetic variability in drug response; the amount of variability explained by PGx; whether PGx test results are actionable; the level of evidence needed for implementation of PGx and the sources of information regarding interpretation of PGx data.