Connecting patients to rare disease trials through scalable genetic testing infrastructure

At Seqera Sessions London 2026, Dr. Katie Barnes, Head of Clinical Genetics at Sano Genetics, outlined a practical challenge facing the field: how to move from fragmented patient identification and testing processes to scalable systems that can support modern clinical trials. Her talk focused on the clinical and bioinformatics infrastructure required to make that shift possible.

The persistent bottlenecks in rare disease trials

Katie highlighted two constraints that continue to shape trial design and timelines.

First, identifying eligible patients remains difficult. Rare and ultra rare populations require highly targeted approaches, often across geographies and fragmented data sources. Second, access to genetic testing is still uneven. Barriers related to cost, awareness, and logistics limit both diagnosis and trial readiness.

These challenges reduce the effective recruitment pool and introduce delays early in the trial lifecycle.

Why the current model creates inefficiencies

Recruitment, testing, and data analysis are typically managed through separate vendors, timelines, and datasets. Each transition between steps introduces additional coordination, data handling, and operational overhead.

Clinical teams often need to manage multiple systems and processes to move patients from identification through to confirmed eligibility and enrollment.

Katie’s perspective focused on addressing this at the system level by structuring these components as part of an integrated workflow.

Clinical genetics infrastructure as the unifying layer

Katie’s core argument was that improving recruitment requires investment in clinical genetics infrastructure, not just outreach.

This includes the ability to:

• Deliver genetic testing at scale
• Integrate testing with clinical workflows and trial protocols
• Support patients through counselling, consent, and result return

Platforms like Sano’s support end-to-end workflows, from pre-screening through to testing, reporting, and ongoing engagement within a single system. This integration reduces operational burden and improves data consistency across studies.

Scaling genetic testing beyond the clinic

A key enabler is the shift toward distributed and at-home genetic testing models.

Katie described how Sano’s infrastructure supports:

• Whole genome sequencing, exome sequencing, and genotyping through certified lab networks
• Remote sample collection and logistics
• Integration with clinical sites for biomarker validation
• Centralized data generation, analysis, and reporting

This approach expands access to testing and allows trials to reach patients who would otherwise be excluded due to geography or healthcare system constraints.

The role of reproducible bioinformatics pipelines

As testing scales, so does data complexity. Katie emphasized that robust bioinformatics pipelines are essential to maintain clinical grade outputs.

Key requirements include:

• Reproducibility: consistent outputs from the same inputs across environments
• Accuracy and quality control: reliable processing that reflects true biological signal
• Traceability: full visibility into inputs, parameters, and pipeline versions
• Scalability: efficiency across local, high performance computing (HPC), and cloud systems

She emphasized how raw data from multiple sources must be standardized, processed, and interpreted through unified workflows to generate clinically usable reports. Standardization reduces variability and supports regulatory and clinical requirements.

From pipeline complexity to operational efficiency

Katie also addressed the growing complexity of genomic data processing. As projects span multiple platforms and assay types, workflows can become fragmented and difficult to manage.

The approach presented focuses on consolidating these inputs into modularized, config-driven pipelines run through a centralized system that produce consistent variant level data and reports.

At Sano, this had a measurable impact:

• Up to 9x faster time from lab to report
• More than 250 hours saved in a 1,000 sample study

These gains translate directly into faster trial timelines and more efficient study operations.

Implications for clinical trial design

The broader takeaway from the session is that patient recruitment, genetic testing, and data processing are no longer separate challenges. They are components of a single system that must be designed for scale from the outset.

For clinical genetics teams, this means prioritizing:

• Integrated testing and data infrastructure
• Reproducible and auditable pipelines
• Patient-centric workflows that support access and retention

As trials become more genetically stratified, these capabilities will define how quickly and effectively new therapies reach patients.