Room: Sorrell Room 1005

• Chair: Ludmil Alexandrov, PhD, University of California, San Diego
• Co-chair: Cecilia Yeung, MD, Fred Hutchinson Cancer Center
• Long Cai, PhD, California Institute of Technology
• DMCC Statistician: Yingye Zheng, PhD, Fred Hutchinson Cancer Center
• DMCC Staff: Jackie Dahlgren, Fred Hutchinson Cancer Center
• NCI PD: Indu Kohaar, PhD, National Cancer Institute
• NCI PD: Sidney Fu, MD, National Cancer Institute

Agenda

Suggested Discussion Points:

1. Spatiomics in Early Cancer Detection:

• Advancements in spatially resolved omics technologies for detecting early-stage cancer and their potential clinical applications.
• Case studies demonstrating the utility of spatiomics in identifying early pathological changes and refining early detection methods.
• Overcoming challenges in detecting subtle spatial heterogeneities in early cancer lesions, with a focus on improving resolution and sensitivity.
• The integration of spatiomics with traditional imaging and histopathology to enhance early detection accuracy.

2. Emerging Biomarkers from Spatiomics:

• Techniques for integrating multi-omics data (genomics, proteomics, transcriptomics) with spatial information to identify novel early cancer biomarkers.
• Development of predictive models combining spatiomics and multi-omics data for early cancer detection.
• Use of single-cell spatiomics to explore the tumor microenvironment and identify early indicators of cancer development.

3. Role of Digital Pathology in Biomarker Detection and Discovery:

• Validation of digital pathology platforms for integration with spatiomics and the development of reference sets to advance research.
• Application of AI, machine learning, and cross-validation in identifying patterns within pathology images and spatiomic analyses for novel biomarker discovery.
• Potential for advanced AI in digital pathology to substitute current molecular tests and future spatiomics approaches.

4. Clinical Applications and EDRN’s Role:

• Strategies for promoting the integration of digital pathology data in ongoing EDRN studies and expanding its clinical applications.
• Translating spatiomics and digital pathology findings into clinical practice: Opportunities and barriers.
• Leveraging digital pathology and spatiomics for personalized medicine in early cancer detection.

5. Data Standardization and Interoperability:

• Establishing guidelines for data standardization in spatiomics and digital pathology to ensure consistent and reliable results.
• Solutions for ensuring interoperability between various digital pathology platforms and spatiomics technologies to streamline biomarker discovery.
• Developing robust data-sharing frameworks to foster collaboration across institutions and improve reproducibility.

6. Identifying Key Research Gaps and Opportunities for Future Collaboration:

• Mapping the current landscape of spatiomics and digital pathology research to identify unmet needs and knowledge gaps.
• Prioritizing research areas that could benefit from collaborative efforts between academic institutions, industry, and clinical stakeholders.
• Exploring funding opportunities and partnerships to advance the field of spatiomics and digital pathology in early cancer detection.