Understand who will respond before you commit to trial decisions
We help clinical and translational teams identify responder populations, validate biomarkers, and generate reproducible evidence to support patient selection and trial design.
Reduce trial risk. Strengthen patient selection. Improve signal clarity.
The Stratification Challenge
Patient populations are heterogeneous, and without clear stratification, true treatment signals are diluted. Biomarker evidence is often inconsistent, making cohort definitions difficult to justify and increasing the risk of weak or inconclusive trial outcomes.
Who is truly driving response and who is not?
We analyse clinical outcomes alongside molecular data to investigate candidate responder subgroups and separate signal from background variability.
Which biomarkers are predictive and which are noise?
We evaluate biomarker signals across datasets and modalities, testing whether they consistently differentiate responders from non-responders.
How should cohorts be defined for confident trial design?
We define clinically meaningful cohorts grounded in reproducible evidence, supporting stronger patient selection.
What We Deliver
We deliver reproducible evidence that supports patient selection decisions, strengthens trial design, and helps teams move forward with confidence.
What you receive
Identify candidate responder subgroups by integrating clinical outcomes with molecular data, separating signal from background variability.
Evaluate candidate biomarkers across datasets and modalities. Where an independent dataset is available, findings can be assessed against it as external validation.
Define clinically meaningful cohorts grounded in biological and clinical evidence, enabling stronger patient selection strategies.
A report that connects findings to your question, with annotated figures, candidate priorities, analytical limitations and recommended next analyses.
Associations with treatment response do not, by themselves, establish predictive performance or a causal mechanism.
Built on Integrated Clinical and Molecular Data
Stratification decisions are only as strong as the data behind them. We integrate clinical and molecular datasets to ensure patient selection is supported by consistent, biologically grounded evidence.
Analyse response, survival, and endpoint data to understand how patient populations behave across treatment arms.
Assess spatial and phenotypic signals to uncover patterns linked to treatment response and disease context.
Identify gene expression and molecular signatures that differentiate responder and non-responder populations.
Combine clinical and molecular evidence to evaluate findings across datasets. Where an independent dataset is available, findings can be assessed against it as external validation.
Capture deeper biological resolution and evaluate signals across additional modalities.
How Teams Work With Sonrai
All analyses are performed inside the Sonrai Discovery platform within a secure Trusted Research Environment (TRE), where datasets remain governed and workflows remain fully reproducible.
Define the cohort or biomarker question
We work with your team to frame the stratification question, review the available clinical and molecular datasets, and agree on the evidence needed to define responder populations.
Analyse integrated molecular and clinical datasets
Our scientists integrate clinical outcomes, molecular biomarkers, imaging, and trial data inside Sonrai Discovery — running reproducible analyses that separate responders from non-responders with biological confidence.
Deliver evidence supporting patient selection
You receive clear, reproducible stratification outputs — cohort definitions, biomarker evidence, and decision-ready summaries that can be used internally and in clinical and regulatory discussions.
Cases and Worked Examples
Teams use Sonrai to turn complex, multi-modal data into clear, defensible evidence for patient stratification and clinical decision-making.
From early signal exploration to cohort definition, our work supports programme decisions as the evidence builds. Each example is labelled by type.
Explore All Case Studies →
Combines imaging and molecular data to look at spatial and biological differences relevant to patient selection.
A simulated example of an analytical approach. It does not show patient-selection performance in a real trial.
Discuss Patient Stratification
If you're defining responder populations or refining patient selection strategies, we help you turn complex clinical and molecular data into clear, defensible evidence for trial decisions.
Discuss Your ChallengesWhat to expect
We'll discuss your program, your datasets, and the stratification question you need to answer.
We'll outline how we'd integrate your data and define responder populations with biological confidence.
Clear, reproducible outputs your team can use to support patient selection and trial design decisions.
Questions about patient stratification and clinical trial design
Questions from clinical and translational teams about identifying responder populations, investigating stratification biomarkers, and generating reproducible evidence to support trial design and patient selection.
How does Sonrai help identify responder subgroups within a heterogeneous patient population?
Sonrai scientists work with your team to define the biological question and integrate clinical outcome data with molecular and imaging datasets to investigate patterns associated with treatment response. This work combines scientific expertise with Sonrai Discovery's analytical environment, enabling teams to analyse genomic, transcriptomic, imaging, and clinical data together rather than in isolation. The aim is to identify candidate subgroups and biomarkers for further evaluation. An association with response does not, by itself, show which patients will benefit.
What data types does Sonrai use for patient stratification?
Sonrai integrates clinical trial datasets, whole genome sequencing (WGS), RNA-seq, digital pathology, H&E imaging, single-cell sequencing, proteomics, and other molecular datasets within a single governed analytical environment. Analytical methods are selected based on the biological question and study design rather than applying a fixed workflow. Depending on the programme, imaging, molecular, and clinical features can be analysed together to investigate factors associated with treatment response and patient outcomes.
How does Sonrai address siloed patient data stored across different systems?
Siloed patient data is a common challenge across clinical and translational programmes. Sonrai uses automated cloud-based ETL workflows to bring together genomic data, clinical records, imaging data, and biomarker measurements within a unified analytical environment. Sonrai's scientists then work with these integrated datasets to ensure they are appropriately structured and quality checked before analysis begins. This enables cross-modal analyses that may be difficult to perform when data remains distributed across separate systems.
Can biomarker-led stratification support enrichment strategy and Phase III preparation?
Biomarker-led stratification can provide evidence that supports patient selection and enrichment strategies during clinical development. Sonrai helps teams investigate molecular, imaging, and clinical characteristics associated with treatment response and explore how these findings may inform future study design. The interpretation and application of these findings remain programme-specific and are considered alongside clinical, operational, and regulatory factors.
How does Sonrai ensure biomarker findings are reproducible and interpretable?
All analyses run within Sonrai's secure Trusted Research Environment using versioned datasets and traceable workflows, so results are traceable from raw data to finding. This means analyses can be reviewed, repeated, and independently assessed using the same underlying data and methods. Sonrai scientists also work alongside client teams to interpret findings within their biological and clinical context, helping ensure results are communicated clearly and supported by the underlying evidence.
What is the difference between prognostic and predictive biomarkers in patient stratification?
A prognostic biomarker provides information about likely patient outcomes regardless of treatment. A predictive biomarker is associated with response to a specific therapy or intervention. Distinguishing between the two requires careful study design, appropriate statistical analysis, and consideration of the biological context. Sonrai supports these investigations by integrating molecular, imaging, and clinical datasets within a reproducible analytical framework.
How does Sonrai handle complex multimodal data integration for stratification?
Patient stratification often requires information from multiple sources, including genomic data, clinical records, imaging data, and biomarker measurements. Sonrai Discovery brings these datasets together within a governed analytical environment using automated integration workflows. Sonrai's scientific team then reviews data quality, aligns modalities, and applies appropriate analytical methods before downstream analysis begins. This helps ensure that analyses are performed on consistent and well-characterised datasets.
How does Sonrai work alongside a client's existing scientific team?
Sonrai works as an extension of your team rather than as a black-box service. Engagements are collaborative, with Sonrai scientists working alongside translational, clinical, bioinformatics, and data science teams to define questions, review data, interpret findings, and discuss results within the context of the programme. The level of involvement can be adapted depending on internal expertise, resources, and project requirements.
What does a typical patient stratification engagement with Sonrai look like?
Engagements typically follow three stages. First, Sonrai's team works with stakeholders to define the biological question and review available datasets. Second, relevant molecular, imaging, and clinical data are integrated and analysed within Sonrai Discovery. Third, findings are delivered through reproducible outputs that may include subgroup analyses, biomarker hypotheses, cohort characterisations, visualisations, and supporting analytical evidence. Sonrai scientists remain involved throughout the process to provide scientific context and support interpretation of the results.