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.
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.
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.
Katie’s core argument was that improving recruitment requires investment in clinical genetics infrastructure, not just outreach.
This includes the ability to:
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.
A key enabler is the shift toward distributed and at-home genetic testing models.
Katie described how Sano’s infrastructure supports:
This approach expands access to testing and allows trials to reach patients who would otherwise be excluded due to geography or healthcare system constraints.
As testing scales, so does data complexity. Katie emphasized that robust bioinformatics pipelines are essential to maintain clinical grade outputs.
Key requirements include:
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.
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:
These gains translate directly into faster trial timelines and more efficient study operations.
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:
As trials become more genetically stratified, these capabilities will define how quickly and effectively new therapies reach patients.