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    GenomOncology: Transforming Clinical Trial Matching Through AI With Ian Maurer and Waqas Haque, MD, MPH

    Expert Perspectives · Waqas Haque, MD, MPH · 2025-03-19

    In this episode of the Exploring AI in Oncology podcast, Waqas Haque, MD, MPH, a Hematology/Oncology Fellow at the University of Chicago, speaks with Ian Maurer, Chief Technology Officer at GenomOncology. Their discussion explored how GenomOncology’s precision oncology platform is transforming clinical trial matching, addressing barriers to access, and leveraging artificial intelligence (AI) to improve outcomes for patients and providers.

    The Evolution of GenomOncology

    GenomOncology, founded in 2012, initially focused on molecular pathology. The company developed a “molecular pathology workbench” to generate pathology reports from sequencing data. Over time, this evolved into a comprehensive precision oncology platform powered by a knowledge graph. Mr. Maurer described the platform as “the knowledge of oncology, specifically precision oncology, codified in nodes and edges.” This knowledge graph enables the automatic matching of patients to clinical trials, drawing from a curated database of over 14,000 trials.

    Tackling the Complexity of Clinical Trial Matching

    One of the most significant challenges in oncology is matching patients to appropriate clinical trials. As Mr. Maurer noted, only 2% to 3% of trials achieve full enrollment due to barriers such as complex criteria, geographic limitations, and institutional preferences. GenomOncology’s platform addresses these challenges by codifying trial data, including molecular biomarkers, disease ontologies, and clinical eligibility criteria.

    The platform uses symbolic AI to perform large-scale reasoning and generate high-quality trial matches. Mr. Maurer emphasized the importance of reducing false positives, explaining, “We provide clinical trial navigators and nurses with high-quality candidates, allowing humans to make the final screening decisions.” Additionally, the integration of large language models (LLMs) enhances the platform’s ability to process unstructured data, such as patient records in PDF format, using optical character recognition (OCR) and natural language processing (NLP).

    Overcoming Barriers to Trial Access

    GenomOncology has partnered with organizations like the Pancreatic Cancer Action Network to improve trial matching. In one collaboration, biomarker-driven trial matching increased by over 300%. Mr. Maurer highlighted the importance of addressing geospatial and institutional barriers, as well as integrating with electronic health records (EHRs) and clinical trial management systems (CTMS). For example, their partnership with Meditech led to a successful deployment at Frederick Health, significantly increasing trial enrollment.

    Mr. Maurer also discussed the potential of LLMs to further enhance trial access. He envisions a future where providers can “chat” with trials, asking questions about eligibility and receiving detailed, transparent answers. This approach would combine GenomOncology’s precise filtering with the conversational capabilities of LLMs, making trial selection more efficient.

    The Future of AI in Oncology

    Looking ahead, Mr. Maurer expressed optimism about the advancements in AI, particularly LLMs and multimodal models. He believes these tools will help healthcare professionals manage the growing complexity of oncology data. “We’re swimming in data, trying to do the best for patients, and we’re burdened by too much work,” he said. GenomOncology aims to alleviate this burden by providing tools that integrate seamlessly into clinical workflows.

    Key Takeaways

    • Precision Oncology Platform: GenomOncology’s knowledge graph enables efficient and accurate patient-to-trial matching, reducing false positives and improving outcomes
    • Integration with EHRs: Embedding the platform into EHRs, as seen with Meditech, simplifies trial access for providers and increases enrollment
    • Role of AI: Technologies like symbolic AI, LLMs, OCR, and NLP enhance data processing and trial matching capabilities
    • Future Outlook: AI advancements will continue to address the growing complexity of oncology data, empowering providers to focus on patient care

    About Dr. Haque and Mr. Maurer

    Waqas Haque, MD, MPH, is a Hematology/Oncology Fellow at the University of Chicago. He recently completed his Internal Medicine Residency at New York University (NYU) in a Clinical Investigator Track. Dr. Haque has balanced his patient care work with a variety of research projects. During his fellowship training at the University of Chicago, he plans to further his work in innovative clinical trial design, value-based care delivery to cancer patients, and clinical investigation.

    Ian Maurer is the Chief Technology Officer at GenomOncology, where he leads the development of innovative precision oncology solutions. He focuses on leveraging advanced technologies, including AI and knowledge graphs, to improve clinical trial matching and streamline oncology workflows. Mr. Maurer is dedicated to enhancing access to clinical trials and empowering healthcare providers with cutting-edge tools to deliver personalized cancer care.