Planning and Conducting a Pharmacogenetics Association Study

Attempted replication of pharmacogenetic association of variants in PPP1R14C and CCDC148 with aromatase inhibitor-induced musculoskeletal symptoms

Third-generation aromatase inhibitors (AI) are the standard treatment for patients with hormone receptor positive (HR+) breast cancer. While effective, AI can lead to severe adverse events, including AI-induced musculoskeletal syndrome (AIMSS). Genetic predictors of AIMSS have the potential to personalize AI treatment and improve outcomes. We attempted to replicate results from a previous genome-wide association study that found a lower risk of AIMSS in patients carrying PPP1R14C rs912571 and a higher risk in patients carrying CCDC148 rs79048288. AIMSS data were collected prospectively from patients with HR+ breast cancer prior to starting and after 3 and 6 months of adjuvant AI via the Patient-Reported Outcome Measurement Information System and Functional Assessment of Cancer Therapy-Endocrine Symptom. Germline genotypes for PPP1R14C rs912571 and CCDC148 rs79048288 were tested for a similar association with AIMSS as previously reported via $2 tests. Of the 143 patients with AIMSS and genetics data were included in the analysis. There was no association identified between PPP1R14C rs912571 and AIMSS risk ( P  > 0.05). Patients carrying CCDC148 rs79048288 variant alleles had lower AIMSS incidence in a secondary analysis ( P  = 0.04); however, this was in the opposite direction of the previous finding. The study did not replicate previously reported associations with AIMSS risk for genetic variants in PPP1R14C and CCDC148 and AIMSS risk. Further research is needed to discover and validate genetic predictors of AIMSS that can be used to personalize treatment in patients with HR+ breast cancer.

Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges

Over the past two decades, Next-Generation Sequencing (NGS) has revolutionized the approach to cancer research. Applications of NGS include the identification of tumor specific alterations that can influence tumor pathobiology and also impact diagnosis, prognosis and therapeutic options. Pharmacogenomics (PGx) studies the role of inheritance of individual genetic patterns in drug response and has taken advantage of NGS technology as it provides access to high-throughput data that can, however, be difficult to manage. Machine learning (ML) has recently been used in the life sciences to discover hidden patterns from complex NGS data and to solve various PGx problems. In this review, we provide a comprehensive overview of the NGS approaches that can be employed and the different PGx studies implicating the use of NGS data. We also provide an excursus of the ML algorithms that can exert a role as fundamental strategies in the PGx field to improve personalized medicine in cancer.

Tutorial: Statistical Analysis and Reporting of Clinical Pharmacokinetic Studies

Pharmacokinetics is the cornerstone of understanding drug absorption, distribution, metabolism, and elimination. It is also the key to describing variability in drug response caused by drug‐drug interactions (DDIs), pharmacogenetics, impaired kidney and liver function, etc. This tutorial aims to provide a guideline and step‐by‐step tutorial on essential considerations when designing clinical pharmacokinetic studies and reporting results. This includes a comprehensive guide on how to conduct the statistical analysis and a complete code for the statistical software R. As an example, we created a mock dataset simulating a clinical pharmacokinetic DDI study with 12 subjects who were administered 2 mg oral midazolam with and without an inducer of cytochrome P450 3A. We provide a step‐by‐step guide to the statistical analysis of this clinical pharmacokinetic study, including sample size/power calculation, descriptive statistics, noncompartmental analyses, and hypothesis testing. The different analyses and parameters are described in detail, and we provide a complete R code ready to use in supplementary files. Finally, we discuss important considerations when designing and reporting clinical pharmacokinetic studies. The scope of this tutorial is not limited to DDI studies, and with minor adjustments, it applies to all types of clinical pharmacokinetic studies. This work was done by early career researchers for early career researchers. We hope this tutorial may help early career researchers when getting started on their own pharmacokinetic studies. We encourage you to use this as an inspiration and starting point and continuously evolve your statistical skills.