SC05: Precision Medicine and Companion Diagnostics: Statistical and Design Considerations

Biomarkers are frequently included in medical research due to their vital role in the health care sector, particularly in drug discovery. Throughout a drug's lifecycle, biomarkers enhance disease understanding, help identify effective compounds for treatment, pinpoint target populations, and accelerate clinical trials, thus supporting precision medicine and health. Precision medicine, often termed "personalized medicine," is an emerging field poised to transform health care by customizing treatments to individual patients. Traditional medicine has typically used a "one-size-fits-all" approach, treating patients with similar symptoms or conditions uniformly. This method overlooks genetic and biological differences, known as biomarkers, resulting in varied treatment responses. The rise of precision medicine is fueled by advancements in omics, bioinformatics, and data analytics, enabling researchers to explore individuals' unique biological profiles through biomarkers. We are in the era of precision medicine with the FDA announcement of this initiative in 2016 and a policy initiative in 2018 to accelerate personalized medicine. A few early examples are the EGFR biomarker status in the tumors of patients--it was used for the approval of Vizimpro (dacomitinib) to treat advanced, non-small cell lung cancer (NSCLC)--and BRAF biomarker status in the tumors of patients for approval of Mektovi (binimetinib) to treat metastatic melanoma. Since, there has been an increase each year of personalized medicine approvals. Such an initiative has earmarked the need for the designs and statistical approaches for biomarker-driven trials. In personalized medicine and clinical trials, biomarkers are particularly significant because many treatments do not benefit the average patient. Precision medicine is an evolving area in the medical field and relies on biomarkers to make patient-enrichment decisions. Biomarkers provide guidance in finding the right patient subpopulation to treat, thereby providing drug development direction for the pharmaceutical industry. By categorizing patients based on their biomarker status, researchers can identify those most likely to benefit from a specific treatment, thus enhancing trial success. Selecting the right patient population for clinical trials hinges on identifying the most effective biomarkers and determining the appropriate cut-off thresholds for continuous biomarkers. For continuous biomarkers, choosing the optimal cut-off is essential to classify individuals as biomarker-positive or biomarker-negative. Many statistical issues arise with biomarkers, such as missing data, combining markers, and high-dimensionality. Biomarker discovery is moving at a rapid pace, and appropriate statistical methods and designs need to keep up with the demand of industry, scientific objectives, technological advancements, and regulatory agencies' requests for personalized medicine. Biomarkers have thus become central not only to patient stratification/enrichment and trial optimization but also to the regulatory landscape that supports these innovations. To integrate a biomarker into clinical decision-making, health agencies may require a corresponding diagnostic test to identify which patients should receive a specific therapy. This need has led to the emergence of companion diagnostics (CDx)--an innovation that synchronizes diagnostics with drug development, transforming the processes for developing and approving precision medicine therapies. The concept of CDx was introduced in 1998 when the US Food and Drug Administration approved HercepTest, an immunohistochemical (IHC) assay to determine HER2 protein expression for patients receiving trastuzumab (Herceptin). Since then, CDx has rapidly evolved alongside precision medicine, prompting global health agencies--including the FDA, the European Medicines Agency (EMA), Japan's Pharmaceuticals and Medical Devices Agency (PMDA), and China's National Medical Products Administration (NMPA)--to issue guidelines and frameworks that increasingly emphasize co-development, co-validation, and co-approval of therapeutic products and their corresponding CDx. With increasingly complex biomarkers and heterogeneous patient populations, statistical challenges have grown in step with this evolution. The design, validation, and interpretation of CDx studies demand robust statistical methods for the assessment of clinical utility. As a result, statisticians need to engage in careful consideration of the study design of the CDx. Often, limited data is used to inform a decision to be taken forward to Phase 3 trials. It is essential to have more efficient designs that can make better-informed decisions going from early to late phase trials. In the pharmaceutical industry, identifying the patient population that will benefit from a treatment is essential. This process requires the use of statistical methods and appropriate study designs to guide strategic decisions, which are vital for advancing the portfolio and facilitating regulatory interactions. This short course will focus on precision medicine and companion diagnostics. The first half will provide the audience with a concise understanding of precision medicine and the statistical and design considerations to find a predictive biomarker for patient stratification and enrichment. Key issues, evolution of the statistical field, and new statistical and design approaches around precision medicine and biomarkers will be covered so participants will be more knowledgeable and successful in their study team contributions. The second half of the short course aims to equip clinical statisticians with a comprehensive understanding of the statistical principles and methodologies that underpin successful CDx development. Participants will gain insights into fundamental design elements, key analytical strategies, and emerging advanced methods, thereby strengthening their capacity to navigate the evolving regulatory landscape and successfully contribute to CDx study teams.