Recommended audience(s):
Graduate-level trainees, clinical researchers, professionals in translational medicine, and those interested in biomarker-driven trial design and precision therapeutics.
Review prepared by:
Eleane Hamburger, Ph.D
Fountzilas et al. provide a comprehensive synthesis of the evolving landscape of clinical trial design in precision oncology, effectively framing the transition from histology-driven to biomarker-informed therapeutic strategies. A key strength lies in the structured overview of innovative designs particularly basket, umbrella, and platform trials, and their positioning within the limitations of conventional phase I-III frameworks. The inclusion of representative trials enhances translational relevance and reinforces the clinical momentum of biomarker-matched therapies.
The review also highlights emerging paradigms, including N-of-1 trials, real-world data integration, and artificial intelligence, reflecting the growing need for adaptive, patient-centered approaches in the context of tumor heterogeneity. The emphasis on multi-omic profiling further aligns with current efforts to refine therapeutic targeting across biological scales.
From a career and professional development perspective, this manuscript underscores the expanding skillset required in precision medicine. Competency in complex trial design, biomarker validation, and multi-omic data interpretation is increasingly essential, alongside interdisciplinary literacy in data science, critical appraisal of AI-driven tools, and awareness of regulatory considerations. For trainees, these skills directly support career readiness for roles in clinical trial design, biomarker-driven patient stratification, and translational research, reinforcing the importance of integrating experimental, clinical and quantitative training. This work has also strengthened my understanding of how these domains intersect within precision oncology and clinical trial development.
However, the review remains largely descriptive. Key challenges, such as biomarker reproducibility, risks of overfitting in small cohorts, and scalability of individualized therapies, are noted but not deeply examined. Similarly limited discussion of AI limitations (e.g., interpretability and bias) restricts critical engagement.
Overall, this work effectively captures the shift toward individualized oncology while highlighting the competencies required to translate these advances into practice.
