Finding the (biomarker-defined) subgroup of patients who benefit from a novel therapy: No time for a game of hide and seek

Bayesian Solutions for Assessing Differential Effects in Biomarker Positive and Negative Subgroups

The number of clinical trials that include a binary biomarker in design and analysis has risen due to the advent of personalised medicine. This presents challenges for medical decision makers because a drug may confer a stronger effect in the biomarker positive group, and so be approved either in this subgroup alone or in the all-comer population. We develop and evaluate Bayesian methods that can be used to assess this. All our methods are based on the same statistical model for the observed data but we propose different prior specifications to express differing degrees of knowledge about the extent to which the treatment may be more effective in one subgroup than the other. We illustrate our methods using some real examples. We also show how our methodology is useful when designing trials where the size of the biomarker negative subgroup is to be determined. We conclude that our Bayesian framework is a natural tool for making decisions, for example, whether to recommend using the treatment in the biomarker negative subgroup where the treatment is less likely to be efficacious, or determining the number of biomarker positive and negative patients to include when designing a trial.

Optimized biomarker evaluation and molecular testing in the era of breast cancer precision medicine

Ground breaking advances in medicine, driven in part by major technologic developments in molecular biology have led us to a new model for cancer care that has been termed personalized, or precision medicine. Precision medicine is a model for making medical decisions that employs an innovative clinical approach and advanced tumor testing methods that are tailored to understanding an individual patient's tumor biology and the molecular drivers of their disease. This medical model includes a combination of diagnostic testing and specific treatment options that can be offered to patients at presentation and in theory throughout the course of their disease as new mutations arise with the development of disease recurrence. Although the precision medicine model offers incredible potential to transform cancer care, these advances are only meaningful when they reach the correct patients. The evolving paradigm of precision medicine is changing the practice of pathology, and the pathology community needs to be mindful of these changes because every tissue specimen represents a patient's life, and those patients are depending on the pathology community to handle their tissue correctly. The diagnostic tests performed in the pathology laboratory for precision medicine are increasingly complex, and pathologists along with the entire laboratory and clinical communities need to take steps to ensure that the right diagnosis is given to the right patient to inform the right treatment options, at the right time, along every step of the continuum of care for cancer patients. While hormone receptors and human epidermal growth factor receptor 2 (HER2) overexpression and/or amplification have been the mainstay for risk-stratification, and treatment decision making in breast cancer since the early 2000's, the seminal work on gene expression by Perou and colleagues in the early 2000's opened the door for molecular testing in the prognostic and predictive assessment of breast cancer. Molecular testing is now part of the standard of care in the precision medicine model for breast cancer care. In this article, the reader will gain a better understanding of how the lack of standardization of pre-analytic factors has the potential to negatively impact the quality of the tissue specimen for downstream biomarker and molecular testing, which ultimately can negatively affect patient care. The reader will also gain insight into the current climate surrounding molecular testing in breast cancer.

Biomarker-Driven Oncology Trial Design and Subgroup Characterization: Challenges and Potential Solutions

In oncology drug development, using biomarkers to select a study population more likely to benefit from a therapeutic effect is critical to increase the efficiency of a clinical trial in demonstrating effectiveness. This perspective delves into therapeutic product approvals that were tested in pivotal trials with all-comers populations, but ultimately received US Food and Drug Administration approval for use within specific patient subgroups identified by biomarkers. Despite initial designs for efficacy and safety assessments in overall populations, a favorable benefit-risk assessment was primarily established in biomarker-positive subgroups. Analyzing these cases, we summarize key considerations pivotal to totality of evidence for regulatory benefit-risk assessments for biomarker-defined subgroup versus all-comers approvals, including biological and clinical rationales, biomarker prevalence, safety data, overall trial design, and subgroup efficacy characterization. Furthermore, a decision tree is proposed to guide optimal clinical trial design, delineating between patient enrichment and stratification, accounting for key factors including biological and clinical rationale, marker type (discreate or continuous), prevalence, assay readiness, and turnaround times for marker assessment. Finally, a recommended approach for subgroup characterization involves prespecifying magnitude of improvement that would be considered clinically meaningful in the biomarker-negative subgroup, which can be supplemented with methodologies such as Bayesian to incorporate evidence from similar studies when available. In summary, this perspective underscores the importance of clinical trial innovations, statistical methodologies and regulatory considerations, to optimize biomarker-driven drug development for patients with cancer.

Lessons learned from postmarketing withdrawals of expedited approvals for oncology drug indications

In the past decade, there have been a record number of oncology therapy approvals by the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA). Besides the EMA's conditional marketing authorisation programme and the FDA's Accelerated Approval Program, we observe a tendency towards fast approval for exploratory studies with non-randomised, uncontrolled designs and surrogate endpoints. This issue raises concerns about the robustness and effectiveness of accepted treatments, leaving patients and health-care professionals in a state of uncertainty. A substantial number of accelerated approvals have recently been withdrawn in the USA, with some still authorised in Europe, emphasising discrepancies in regulatory standards that affect both patients and society as a whole. We highlight examples of drugs, authorised on the basis of surrogate endpoints, that were later withdrawn due to an absence of overall survival benefit. Our findings address the challenges and consequences of accelerated approval pathways in oncology. In conclusion, this Policy Review calls for regulatory bodies to better align their procedures and insist on robust evidence, preferably through unbiased randomised controlled trials. Drug approval processes should prioritise patient benefit, overall survival, and quality of life to minimise risks and uncertainties for patients.