Multi-Modal Biomarker Discovery
Case Study Highlights
In this case study we showcase how Sonrai revolutionized biomarker discovery by creating a unified data ecosystem that integrates various data types – RNA-Seq, methylation, mass spectrometry data, histopathology images, and clinical records – accelerating analysis and uncovering new insights.
An innovative US-based diagnostic company specializing in early detection of breast cancer is aiming to revolutionize early disease diagnosis by leveraging diverse data sources. They were looking to integrate transcriptomic, epigenomic, proteomic, imaging, and clinical data for a more comprehensive analysis to help drive biomarker discovery.
Our client aimed to enhance diagnostic test sensitivity and specificity, and patient stratification for early-stage breast cancer. Through collaboration and innovative data analysis of multi-modal data, the company successfully identified biomarkers and enhanced performance of their new diagnostic test.
- Data Format Discrepancies: Varied data formats across transcriptomic (RNA-Seq), epigenomic (methylation data), proteomic (mass spectrometry), imaging (H&E-stained histological images), and clinical datasets hindered unified analysis and limited insights for biomarker discovery and personalized treatment development.
- Siloed Data Storage: Data from different sources stored in separate databases impeded comprehensive cross-referencing and unified analysis.
- Manual Data Harmonization: Standardizing formats and aligning data structures manually was time-intensive and error-prone.
- Independent Data Analysis: Analyzing data types independently with specialized tools restricted holistic insights, crucial for integrated oncology diagnostics. The limited cross-modal analysis constrained the depth and efficiency of combining insights from different data types.
- Complex Data Merging: Integrating diverse datasets was challenging, affecting biomarker discovery efficiency and personalized treatments’ development.
- Collaboration Barriers: Sharing and integrating data across teams or institutions was complicated, hindering collaborative research.
- Integration into Clinical Practice: Translating research findings into clinical decisions was slow due to the lack of streamlined integration.
- Compliance and Security Issues: Disparate data storage and handling raised concerns for data privacy, security, and regulatory compliance.
- Scaling Costs: The company faced high costs in scaling its operations, exacerbated by the lack of cloud-based solutions.
The client chose Sonrai because we are the only company that can offer imaging and omics integration as part of our product offering. In addition, our comprehensive data analysis platform is fully cloud-based, allowing for easy scaling across multiple teams.
Through the partnership with Sonrai, the diagnostic company embarked on a transformative journey to harness the power of integrated multi-modal data analysis, achieving the following advantages:
- Sonrai facilitated the creation of a unified data ecosystem, integrating RNA-Seq, methylation and mass spectrometry data with histopathology images, and clinical records into a single, accessible platform.
- Utilizing proprietary data harmonization algorithms, Sonrai ensured seamless integration and alignment of diverse data formats and sources, mitigating challenges related to data interoperability and inconsistency.
- Sonrai’s data processing pipelines transformed and cleaned the data, making it analysis-ready and amenable to AI and machine learning.
- Leveraging advanced AI-driven analytics, Sonrai enabled in-depth analysis across the amalgamated datasets, allowing correlations and patterns to be identified for potential biomarkers crucial for oncology diagnostics.
- Through feature engineering and sophisticated data mining techniques, Sonrai identified correlations, patterns, and latent associations within the integrated data, unlocking potential biomarkers for early-stage breast cancer diagnosis.
- Sonrai’s robust data integration and machine learning capabilities assisted in discovering potential biomarkers that exhibited correlations across multiple data modalities, aiding in disease characterization and personalized treatment insights.
- Rigorous validation processes, including cross-validation and external validation using independent datasets, ensured the reliability and generalizability of identified biomarker candidates.
Integration of Imaging and Omics Data
- A standout feature of Sonrai Discovery is its ability to integrate imaging and omics data. This unique capability enables the viewing and annotating of whole slide images (WSI) and the deployment of computational AI algorithms for generating new data, thereby offering a cost-effective alternative to traditional pathologist-based analysis. Additionally, the ability to perform bioinformatics pipelines for processing the various omics data allows users to get the most out of their data. Sonrai’s integration with the Jupyter Notebook enables users to run their own code and utilize Sonrai’s extensive AI and machine learning tools.
Customization and Third-Party Tool Integration
- Sonrai’s platform allows for integrating third-party tools and the user’s proprietary applications. This flexibility facilitates a tailored approach to data analysis, ensuring the platform adapts to specific research needs and workflows.
- The platform enabled rapid and comprehensive insights, providing the company with actionable information for diagnostic test development.
- Interactive visualization tools and interpretability frameworks provide intuitive representations of complex data relationships, facilitating informed decision-making for clinicians and researchers.
Image 1. Heatmap visualizing RNA-Seq results.
Image 2. Heatmap to visualize DNA methylation profile.
Image 3. Volcano plot for protein analysis.
Image 4. H&E-stained histological image. Sonrai Discovery enables imaging data to be integrated with omics data depicted in Images 1-3.
This collaborative partnership with Sonrai empowered the emerging US-based diagnostic company to harness the potential of integrated multi-modal data analysis, enabling them to achieve significant advancements in the development of accurate oncology diagnostic tests, ultimately improving patient care and outcomes.
Efficient Biomarker Discovery
Sonrai’s integrated data analysis approach expedited the discovery of potential biomarkers by correlating transcriptomic, epigenomic, proteomic, imaging, and clinical data. This significantly accelerated the identification of promising candidates for improved diagnostics tests. It also enabled discovery of new biomarkers that would normally have been missed by doing independent data analysis.
Enhanced Diagnostic Precision
The unified insights derived from integrated multi-modal data analysis provided the diagnostic company with a comprehensive understanding of diseases, facilitating the development of highly accurate diagnostic tools tailored for oncology.
Cost-Efficiency and Timeliness
Sonrai’s approach optimized the biomarker discovery process, reducing both time and cost investments typically associated with traditional approaches, thereby streamlining the path from discovery to clinical application.
Empowering various teams in the company
The collaboration with Sonrai has not only advanced the company’s research but also positively influenced different facets of their organization, from clinical care to business strategy and regulatory compliance.
Data Scientists and Analysts
The collaboration with Sonrai empowered their bioinformaticians, data scientists and analysts with advanced tools and methodologies. This allowed for more efficient and comprehensive analysis of complex multi-modal datasets, amplifying their ability to derive meaningful insights at an accelerated pace. In addition, bioinformaticians were able to run their own code to gain insights.
For biomedical researchers, Sonrai Discovery accelerated biomarker discovery initiatives. The integration of diverse data sources and AI-driven analytics significantly expedited research efforts, enabling the identification of potential biomarkers across various modalities and fostering advancements in disease characterizations.
Clinical Researchers and Physicians
The insights derived from Sonrai’s analysis uncovered correlations and patterns across transcriptomic, epigenomic, proteomic, imaging, and clinical data, providing invaluable insights. These insights can potentially lead to more precise diagnostic tools, ultimately benefiting patient care outcomes.
Business Development and Strategy Leaders
The potential developments in innovative diagnostic tools have enhanced their competitive edge and opened new market opportunities, aligning with our strategic growth objectives.