In oncology diagnostics, selecting between biobank specimens and prospective clinical trials for assay validation is not merely a methodological choice—it’s a strategic imperative. As regulatory standards tighten and payers demand robust evidence of clinical utility, developers of liquid biopsies, ctDNA assays, and multi-cancer early detection (MCED) tests face increasing pressure to generate high-quality data efficiently.

The decision to use biobank samples or conduct prospective clinical trials in oncology diagnostics depends on the test's intended use, the type of cancer, and the stage of development. Each approach offers distinct advantages and limitations.

Where Biobanks Add Value

Biobanks can accelerate early development by enabling rapid access to relevant samples. Utilizing biobanks provides value especially in:

• Late-stage cancer sample testing: Commonly stored in academic and commercial biobanks, these samples are valuable for analytical validation, signal detection, and establishing assay sensitivity and specificity. The Cancer Genome Atlas (TCGA), for example, includes thousands of annotated samples across diverse cancer types.
• Tissue–liquid biopsy concordance: Matched FFPE tissue and plasma samples can help validate liquid biopsy performance against traditional histopathology. Biobanks such as those under BBMRI-ERIC and the NCI Cancer Diagnosis Program offer these critical matched sets.
• Rare cancer inclusion: For cancers with low prevalence or limited patient populations (e.g., pediatric cancers, adrenal tumors), biobank samples provide a practical path to assay development or early feasibility testing.
• Retrospective case-control designs: Cost-effective and efficient, these initial studies can compare biomarker expression between cancer-positive and cancer-negative samples.
• Biomarker and algorithm development: Biobanks with deeply phenotyped specimens, such as the UK Biobank, are invaluable for building and refining multi-marker models and diagnostic algorithms, especially those powered by machine learning or AI.

When Biobanks Fall Short in Oncology Diagnostics

Despite their value in e.g., rare cancers and analytical validation, biobanks have limitations, especially when it comes to demonstrating real-world clinical performance. For oncology diagnostics aiming for early detection, population screening, or regulatory approval via PMA or De Novo pathways, relying solely on biobank samples is often insufficient. These gaps emerge in the transition from feasibility to pivotal studies, where prospective data becomes essential to secure payer confidence, clinician adoption, and FDA clearance:

• Lack of early-stage or asymptomatic samples: Most biobanks underrepresent cancers at stage I or II, which are critical for demonstrating performance in early detection assays. Developers of MCED tests face this barrier acutely.
• Missing longitudinal and outcomes data: Many samples lack full clinical annotation, including follow-up and treatment response. This limits their use in demonstrating clinical validity and especially clinical utility.
• Limited applicability to real-world settings: Biobank samples often come from academic centers, not the primary care or screening environments where many new diagnostics are intended for use. This can lead to spectrum bias and overestimated test performance.
• Inconsistent collection and handling protocols: Pre-analytical variability across sites: in sample storage, processing times, or freeze-thaw cycles can introduce skew sample integrity, especially for liquid biopsy targets like ctDNA
• Regulatory insufficiency: For pivotal studies, the FDA typically requires prospective data to assess real-world performance, usability, and clinical benefit.

Why Prospective Trials Remain Essential

Regulatory Approval

For oncology diagnostics intended for commercial distribution, the FDA often requires prospective clinical validation, particularly when:

• Submitting for De Novo classification of novel technologies
• Seeking PMA approval for high-risk diagnostics
• Demonstrating performance in an intended use population (e.g., screening asymptomatic individuals)

Clinical Utility Demonstration

Payers and clinicians demand evidence that a test meaningfully impacts patient management:

• Early detection tests must demonstrate positive predictive value in low-prevalence populations
• Companion diagnostics require correlation with therapeutic response in real-world treatment settings
• Monitoring assays should link test results to clinical decisions or patient outcomes

Operational Realities

Clinical trials provide data on:

• Usability in actual workflows (e.g., blood draw timing, turnaround time)
• Environmental robustness (e.g., shipping stability, site variability)
• User error rates in home or point-of-care applications

Optimizing Cost and Timeline in Diagnostic Development

The choice between biobank studies and clinical trials doesn’t only concern evidence generation, it also comes down to time, cost, and strategic fit. In oncology, where many diagnostics target low-prevalence populations or early-stage disease, these tradeoffs become especially pronounced.

Biobank-based studies offer rapid access to annotated specimens, many of which already have known biomarker status. This eliminates the need for site selection, patient recruitment, and trial monitoring, dramatically reducing time and cost. These studies are ideal for assay prototyping, early feasibility testing, and refining analytical parameters before clinical deployment.

In contrast, prospective clinical trials require higher upfront investment, particularly when targeting rare indications or asymptomatic populations where recruitment is slow. Timelines are extended due to regulatory approvals, site initiation, and participant follow-up. However, these trials provide the gold-standard data to demonstrate clinical validity, real-world utility, and support regulatory clearance, reimbursement, and physician adoption.

Adopting a hybrid development model is an increasingly common strategy among oncology diagnostic developers seeking to balance speed, cost, and evidentiary rigor. A typical study might involve:

• Biobank specimens for analytical validation, biomarker discovery, and early signal detection
• Focused clinical trials for targeted, prospective validation in intended use populations
• Post-market evidence to expand indications or support health economic claims

This hybrid approach enables faster initial progress while ensuring that the test ultimately meets the high evidentiary standards required for commercial success.

Case Study: How Freenome Balanced Biobank and Clinical Trial Evidence

Freenome's multiomics blood test development exemplifies the strategic hybrid validation approach. For early signal detection and algorithm development, the company leveraged plasma samples from commercial biobanks and academic studies, enabling rapid iteration on their platform that combines tumor DNA with immune biomarkers.

Using retrospective samples, Freenome demonstrated over 80% sensitivity in early feasibility studies, providing proof-of-concept without the substantial cost of prospective recruitment. However, to demonstrate real-world clinical utility and support FDA approval, Freenome launched PREEMPT CRC—one of the largest prospective studies in early cancer detection, enrolling 48,995 participants across over 200 sites. This prospective validation delivered the performance data needed for regulatory submission (79.2% sensitivity at 91.5% specificity) while demonstrating usability in the intended screening population. Freenome’s strategy exemplifies how combining retrospective and prospective data can streamline development while satisfying regulatory demands.

Meeting Regulatory and Payer Expectations: Beyond Biobanks

Biobank data is invaluable for early-stage assay development—supporting analytical validation, feasibility testing, and biomarker discovery. But when it comes to regulatory approval and reimbursement, it often falls short.

In the US, the FDA’s Final Rule on Laboratory Developed Tests (LDTs), finalized in 2024, significantly expands oversight, particularly for high-risk diagnostics like those in oncology. The rule emphasizes the necessity of prospective clinical data to demonstrate safety, effectiveness, and real-world utility beyond initial validation.

Similarly, under the EU's In Vitro Diagnostic Regulation (IVDR), most Class C and D diagnostics require prospective performance studies. While biobank studies may contribute important analytical and risk information, they are generally insufficient to support pivotal claims on their own.

From a reimbursement perspective, both public and private payers increasingly demand real-world clinical utility evidence. It is no longer enough to demonstrate biomarker detection: payers expect proof that the test positively influences clinical decision-making, treatment decisions, or patient outcomes. Because biobank specimens typically lack detailed longitudinal outcomes and real-world clinical context, they fall short in addressing these reimbursement requirements.

Integrating Biobank and Trial Data for Long-Term Success

For oncology diagnostics, biobank specimens are a powerful tool for early validation, feasibility studies, and biomarker exploration. But for regulatory approval, payer acceptance, and commercial success, especially in early detection and personalized therapy applications, prospective clinical trials are essential.

The most successful developers leverage both strategies, using biobanks to accelerate early development and reduce risk, then transitioning to focused clinical trials to generate the rigorous evidence for regulatory and market access.

The future of diagnostics is fast, precise, and patient-focused. At Lindus Health, we partner with you throughout your oncology diagnostics journey from start to finish. Ready to accelerate your innovation to market? Get started with our team today.