Reassessing the classification of rare cancer types
Historically, research on rare cancers centered around tumor types with exceptionally low incidence rates, fewer than 6 in 100,000 people. Advances in tumor biology and deeper insights into the complexity of cancer have transformed this thinking. Today, common cancers are increasingly defined by their genetic profiles, enabling the identification of distinct sub-groups that are also classified as rare. These sub-groups, marked by rare genetic mutations, are unlocking new possibilities for targeted drug development based on biomarker status.
Role of biomarkers
The goal is to tailor treatments to individual patients by leveraging their molecular profiles and to pinpoint those most likely to benefit from the therapy. Biomarkers play a crucial role in this process and can be characterized into several types based on their function and application.
- Diagnostic biomarkers: These help in detecting or confirming the presence of a disease or medical condition. For example, prostate-specific antigen (PSA) is used to support the diagnosis of prostate cancer.
- Prognostic biomarkers: These predict the likely progression or outcome of a disease. For instance, Ki-67 is used in cancer prognosis to assess tumor aggressiveness.
- Predictive biomarkers: These indicate the likelihood of a patient responding to a specific treatment. For example, testing for BRAF mutations can predict the effectiveness of targeted therapies in melanoma.
- Susceptibility/Risk biomarkers: These identify individuals at risk of developing a disease, such as the BRCA1 and BRCA2 genetic markers for breast cancer.
The success of numerous biomarker-driven drug approvals underscores the transformative potential of personalized medicine. A prominent example is trastuzumab (Herceptin), used for HER2-positive breast cancer, where HER2 overexpression serves as a predictor of patient response to treatment. Likewise, PD-L1 expression levels play a crucial role in determining the use of immune checkpoint inhibitors, such as pembrolizumab, for cancer therapies. These cases illustrate how biomarkers not only enhance the precision of treatments but also expedite drug approval processes by showcasing clear therapeutic advantages within specific patient groups.
Identifying rare patient populations
Precision medicine requires targeting highly specific patient populations, guided by tumor-specific biomarkers. However, global access to genetic testing remains uneven, creating barriers to patient inclusion worldwide. Recruiting participants for rare biomarker-driven trials demands meticulous planning to ensure timely enrollment of the appropriate populations. Without such planning, trials risk early termination due to delays and rising costs.
Recommended strategies for successful patient recruitment:
Conduct comprehensive feasibility assessments to evaluate biomarker prevalence, ethnic diversity, and environmental influences. Identify regions where eligible patients are most likely to be found.
Expand geographical scope by opening more regions, countries, and sites to increase access to the target population.
Evaluate site capabilities to identify suitable patients through referral networks and robust patient database screening.
Implement biomarker pre-screening to efficiently identify candidates who express the required biomarker before completing all other screening assessments. This approach minimizes unnecessary procedures, reducing both costs and burden to patients and sites.
Engage patient advocacy groups to raise awareness and promote trial participation directly to potential patients.
Challenges for drug developers
Drug developers working on biomarker-driven trials, especially in oncology, face a range of scientific, operational, and regulatory challenges.
Scientific challenges
- Biomarker identification and validation: Finding biomarkers that are truly predictive or prognostic is complex. Many biomarkers lack robust validation across diverse patient populations and tumor types leading to uncertainty about whether a biomarker is a driver of disease or merely associated with it.
- Tumor heterogeneity: Intratumoral and interpatient variability can lead to inconsistent biomarker expression. This complicates patient selection and can result in misleading trial outcomes.
- Resistance mechanisms: Even when a biomarker predicts initial response, resistance often develops. Biomarkers for acquired resistance are harder to identify and validate.
Operational and trial design challenges:
- Sample collection and quality: High-quality biopsies or liquid biopsies are needed for biomarker testing. Tumor sampling can be invasive, and sample quality affects test reliability.
- Adaptive trial designs: Biomarker-driven trials often require complex, adaptive designs. These designs demand early involvement of statisticians and data scientists to manage multiple endpoints and eligibility criteria.
Data and analytical challenges
- Data interpretation: Genomic profiling generates vast data, making it difficult to distinguish actionable mutations from noise. Determining the clinical relevance of molecular alterations is not always straightforward.
- Statistical power: Small sample sizes in biomarker-defined groups can limit statistical power. Traditional metrics like p-values may not be meaningful in early-phase trials.
Regulatory and commercial challenges
- Regulatory uncertainty: Regulatory pathways for biomarker-based therapies and companion diagnostics are still evolving. Co-development of drugs and diagnostics adds complexity to approval processes.
- Reimbursement and market access: Payers may be reluctant to reimburse expensive targeted therapies for small populations. Demonstrating cost-effectiveness is harder when the patient pool is limited.
In conclusion
More than half of current oncology trials incorporate biomarkers for patient selection, treatment, stratification, or endpoint analysis with biomarker-driven trials being especially common in areas such as rare tumor types, lung cancer and breast cancer. Although significant challenges remain, there have been plenty of success stories highlighting how precision medicine is transforming cancer treatment.
