Integrating Imaging and Omic Data

Case study highlights

Our client faced challenges with siloed multi-modal data, requiring integration for effective discovery use. Leveraging our domain expertise, we employed cloud-based technology designed for managing complex datasets alongside our proficiency in AI-driven multi-modal analysis. This approach enabled their team to fully use their data for drug and biomarker discovery, supporting the identification of novel, clinically relevant patient subgroups.

The company

Our client is a leading innovator in oncology therapeutics, with a dynamic development pipeline dedicated to targeted cancer therapies. With a specialised focus on new treatments for solid tumours, a major strategy involves fusing imaging and multi-omics data — an approach intended to enhance the understanding of cancer biology, refine treatment precision, and offer tailored therapies suited to individual patient profiles, while also advancing early cancer detection.

The challenge

Our client conducted an internal assessment that revealed the inefficiencies, high costs and slow pace of building in-house data solutions. These challenges underscored the need for a sophisticated AI partnership to keep pace with rapid advancements in precision medicine. Some of the challenges identified included:

• Data integration complexity. Combining genomic (RNA-Seq), proteomic (mass spectrometry) and IHC imaging data into a unified format was technically demanding and beyond the client's internal capabilities.
• Target identification difficulty. Identifying viable drug targets was hindered by limited molecular and anatomical insights.
• Resource constraints. Integrating multi-modal data required computational power and bioinformatics expertise the client lacked.
• Interpretation barriers. Interpreting integrated data required deep biological and technical knowledge, necessitating a multidisciplinary approach.
• Scalability issues. Growing data volumes posed challenges to maintaining scalable integration processes.
• Delayed decisions. Lack of integrated data slowed decision-making and extended development timelines and costs.
• Collaboration hurdles. Coordinating with multiple partners was difficult due to misaligned goals and poor activity coordination.

Sonrai's approach

Sonrai offered AI-driven precision medicine expertise and advanced multi-modal data integration. Tailored AI tools for oncology, combined with a collaborative, solution-focused approach, enabled scalable, efficient and accurate data management, aligning with the client's drug development needs. In partnering with Sonrai, the client's immediate focus was identifying new, effective drug targets.

• Multi-modal integration. Using Sonrai Discovery, the client rapidly unified IHC images, protein expression, clinical and NGS data, overcoming complex data management challenges with specialised integration capabilities.
• Advanced analytics and correlation. Sonrai Discovery enabled the client to uncover hidden patterns by linking molecular profiles with imaging data. See Sonrai's AI biomarker discovery workflow for a worked example of this kind of analysis.
• Enhanced biomarker management. Sonrai's biomarker management capability allowed the client to track target gene expression across multiple disease areas, strengthening their drug development strategy.
• Dynamic cohort exploration. The platform allowed non-coders to explore diverse data and build study cohorts through advanced, code-optional analysis tools.
• Scalable cloud infrastructure. Sonrai's cloud platform enabled efficient analysis, seamless team collaboration, and reduced dependence on internal IT resources.

Results and impact

Sonrai's partnership set the stage for transformative developments in the client's oncology research. The client gained a clearer understanding of their drug targets and, importantly, a sharper view of which patient populations were most relevant to their drug candidates.

• Accelerating multi-modal drug discovery and biomarker development. Working with Sonrai, the client advanced its drug discovery efforts using Sonrai's AI-driven analytics platform and integrated data ecosystem.
• Accelerated target identification and validation. Sonrai's platform expedited the identification of novel drug targets by efficiently analysing complex patterns across multi-modal data, including omics, clinical and imaging datasets. Validation workflows incorporating cross-validation and independent datasets strengthened confidence in the reliability and reproducibility of selected targets.
• Panel of biomarkers identification. A panel of candidate biomarkers of response was identified, which could be used as secondary endpoints in pre-clinical models and potentially in future clinical trials.
• Breakthrough insights through data integration. By integrating siloed imaging and omics data, Sonrai helped unlock a deeper understanding of disease mechanisms and drug action, supporting identification of new therapeutic targets and candidate response-biomarker panels previously obscured by fragmented data.
• Informed patient stratification. The platform's interpretive power within biological and clinical contexts supported more informed hypotheses on treatment strategy and patient selection criteria for future clinical trials.
• Faster translation to clinical applications. The streamlined, reproducible process supported quicker translation of early-stage discoveries into clinical development planning, reducing time to internal milestone decisions.
• Optimised collaboration and resource efficiency. Sonrai's cloud-native infrastructure and collaboration features supported scalable compute power and communication among interdisciplinary teams, letting the client focus R&D efforts more strategically while reducing operational burden.