and Omic Data
Case Study Highlight
Our client faced challenges with siloed multi-modal data, requiring integration for effective discovery use. Leveraging our domain expertise, we employed our cloud-based technology designed for managing complex datasets, along with our proficiency in AI-driven multi-modal analysis. This approach enabled their team to fully utilize their data for drug and biomarker discovery, leading to the identification of novel, clinically significant patient subgroups.
Our client is a leading innovator in oncology therapeutics, renowned for their dynamic development pipeline dedicated to targeted cancer therapies. With a specialized focus on creating new treatments for various solid tumors, a major strategy now involves the fusion of imaging and multi-omics data. This approach would significantly enhance the understanding of cancer biology, refine treatment precision, and offer tailored therapies suited to individual patient profiles. Additionally, these innovative methods hold the potential to advance early cancer detection and implement preventive strategies, marking a significant contribution to the evolution of cancer care.
Our client conducted an internal assessment that revealed the inefficiencies, high costs, and slow pace of building in-house data solutions. Challenges included complex data management, time-intensive data analysis, resource-heavy processes, scalability issues, and risks of human error. 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:
- Multi-Modal Data Silos: Integrating diverse data types, such as genomic (RNA-Seq), proteomic (mass spec), and immunohistochemistry (IHC) imaging data, into a cohesive and interpretable format is a formidable task. Ensuring these data streams are compatible and can be analyzed together to yield meaningful insights requires advanced computational tools and expertise not available to the client.
- Identifying Effective Drug Targets: Within the intricate landscape of oncology, identifying drug targets was challenging without comprehensive insights into molecular and anatomical characteristics.
- Resource Intensiveness: Integrating multi-modal data requires significant computational resources and expertise in bioinformatics and data science. Our client lacked the necessary infrastructure and technical know-how, making it difficult to leverage the available data fully.
- Interpretation Challenges: Even when data from different modalities are brought together, interpreting this integrated dataset can be challenging. It requires a deep understanding of both the biological implications and the technical aspects of the data, necessitating a multidisciplinary approach.
- Scalability: As research progresses, the data generated can grow exponentially. Ensuring that the data integration processes are scalable and can handle increasing volumes of data is a significant challenge.
- Delayed Decision-Making: The absence of integrated data hindered timely decision-making, causing prolonged development timelines and increased resource expenditure.
- Collaboration Challenges: Working effectively with a range of partners presented challenges in aligning goals and coordinating activities across teams.
Sonrai's Integrated Approach:
Sonrai stood out as the ideal partner with our specialization in AI-driven precision medicine and expertise in integrating multi-modal data. Sonrai Discovery’s advanced AI algorithms and machine learning capabilities are specifically tailored for oncology research. Sonrai’s approach, emphasizing customized solutions and a collaborative mindset, provided scalable, efficient, and accurate data management. This alignment with the client’s needs positioned Sonrai as a strategic partner to enhance the drug development process significantly.
In partnering with Sonrai, our client immediately wished to focus on the identification of new effective drug targets.
- Multi-Modal Integration: Using Sonrai Discovery, our client first addressed the challenge of complex data management by quickly integrating their diverse data types, a very specialized capability. This included consolidating IHC images from a previous trial, protein expression data, clinical data and Next-Generation Sequencing (NGS) data into a cohesive framework.
- Advanced Analytics and Correlation: Sonrai Discovery transformed how the client interpreted and performed cross-correlation analysis between molecular profiles and imaging data, facilitating the identification of correlations and patterns that would not have been possible. Explore more in Sonrai’s AI biomarker discovery workflow.
- Enhanced Biomarker Management: As part of Sonrai’s comprehensive strategy in assisting the client, the incorporation of a biomarker management library played a pivotal role. This tool was instrumental in examining the expression of target genes across a spectrum of disease indications, significantly enhancing the client’s strategic approach to drug development.
- Dynamic Cohort Exploration: The platform enabled intricate analyses without coding expertise, allowing the team to explore vast data types and construct cohorts for in-depth study.
- Scalable Cloud Infrastructure: Sonrai’s cloud-based infrastructure allowed for efficient analysis and collaboration across teams, facilitating data access and reducing reliance on in-house IT resources.
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Results and Impact:
Sonrai’s partnership has set the stage for transformative developments in the client’s oncology research. The client now has a better understanding of their drug targets and most importantly they’re picking the right patients for their drug.
- Identifying New Drug Targets: By leveraging Sonrai’s integrated data approach, the client successfully identified effective drug targets to explore in the lab that were previously unavailable because of siloed data.
- Panel of Biomarkers Identification: A panel of candidate biomarkers of response was identified which could bested as secondary endpoints in pre-clinical models and potentially in future clinical trials.
- Reproducibility: The use of Sonrai’s tools improved the reproducibility of findings, a crucial aspect in advancing reliable cancer research.
- Precision in Treatment and Patient Selection: The integration approach led to a deeper understanding of disease mechanisms, enabling the tailoring of treatments with unprecedented precision and improving patient selection for clinical trials.
- New biology: The use of Sonrai’s tools led to a better understanding of new drug’s mechanism of action. The ability to call on pathway databases directly from the platform led to fast biological insights
See an example of multi-omic analysis performed on the Sonrai Discovery platform in the form of this Weaver Plot created by the Sonrai Data Science team.