Events

Englander Institute for Precision Medicine – AI Clinic Join us for our AI Clinic meeting on Wednesday, August 12th at 2:00pm ET. (note: more information will be covered outside of weekly topics) Topic of Discussion for this week “Running Agentic Workflows Across OMOP, BigQuery, and Genomics” Session Leader: Hunter Gaudio, B.Sc. Ph.D. Student Biomedical Engineering, Cornell University AI Clinic Info: EIPM's bi-weekly sessions (Wednesdays at 2pm-3pm) aim to enhance how we use LLMs for complex tasks, including coding. They will serve as a collaborative space for hands-on troubleshooting, knowledge sharing, and continuous learning. Who is this for? This clinic is designed for our entire scientific and clinical community, with a special emphasis on trainees (students, postdocs, residents and fellows). It is the perfect forum for: Researchers and Scientists looking to apply LLMs to data analysis, coding challenges, or manuscript writing.Clinicians interested in exploring how AI tools can securely optimize research and workflows.Trainees seeking a supportive, hands-on environment to learn and get guidance on applying advanced LLMs to their specific projects.What participants can expect from these sessions: Collaborate with our most LLM-proficient team members (“Super-Users”) to troubleshoot and refine their work.Present real challenges they are encountering (e.g., coding issues, complex prompts, workflow optimization).Potentially share “micro-presentations” when someone discovers a new model, feature, or technique.Our goals are to: Build internal expertise and share best practices. Help participants overcome real technical challenges, and create a community of practice around practical AI use. We are currently seeking Super-Users who demonstrate expertise in coding and experience with AI tools such as Chat GPT 5.2, Gemini 3.0, & Claude 4.5 Opus. We are also seeking participants who are looking for troubleshooting guidance. All skill levels are welcome, from beginners to advanced users. Reach out to Victoria Cummings (vjc4001@med.cornell.edu) should you have any questions or inquiries.

Englander Institute for Precision Medicine Seminar Series “Precision High-throughput Drug Screening in 3D Glioma Cerebral Organoids” Presented By: Stefano Cirigliano, Ph.D. Director, Glioblastoma Cerebral Organoid Core Assistant Professor of Research in Neuroscience “Identifying Selective Therapeutic Combinations for Ovarian Cancer High Throughput Drug Screening and Multi-Omic Integration” Presented By: Benjamin D. Hopkins, Ph.D. Director, Ex Vivo Models, Englander Institute for Precision Medicine Assistant Professor, Research in Systems and Computational Biomedicine Weill Cornell Medicine Abstract for “Precision High-throughput Drug Screening in 3D Glioma Cerebral Organoids” with Stefano Cirigliano, Ph.D. Glioblastoma multiforme (GBM), the most common primary malignant brain tumor in adults, remains incurable despite extensive research and clinical efforts. The use of preclinical models that fail to accurately replicate the biological complexity of human disease for drug screening has been suggested as a contributing factor to our inability to identify clinically effective drugs for GBM. Our lab has developed the cerebral organoid glioma (GLICO) model, a co-culture of human cerebral organoids and patient-derived glioma stem cells (GSCs). The GLICO model better recapitulates the human GBM microenvironment and intratumor heterogeneity, making it a promising model for improved drug screening. The use of the GLICO model as a novel patient-specific drug screening assay will rely on developing tools to precisely assess tumor cell viability in real-time and at multiple time points within a normal cerebral organoid (CO) without disrupting the organoid. Traditional in vitro tumor organoid viability assays, however, fail to distinguish between cell populations and are endpoint-destructive, preventing analysis of tumor response over time. To address these limitations, we present a novel, non-destructive, and automatable luminescence-based tumor tracking system compatible with both cell-autonomous and co-culture 3D tumor models. Our system detected drug responses in both GSCs and GLICOs with high reproducibility across manual and high-throughput settings. Notably, GSCs exhibited differential responses to the same treatments in GSCs versus GLICOs, underscoring the significance of integrating more physiologically relevant 3D organoid models into drug screening. Our platform provides a novel, sensitive, and robust tumor tracking platform, enabling real-time, non-destructive measurement of GSC viability within the clinically relevant GLICO model, improving the predictive value of drug screening and precision medicine for GBM. Abstract for “Identifying Selective Therapeutic Combinations for Ovarian Cancer High Throughput Drug Screening and Multi-Omic Integration” with Benjamin D. Hopkins, Ph.D. Ovarian cancer remains a therapeutic challenge due to rapid development of treatment resistance andlimited alternative options when standard-of-care therapies fail. High-throughput drug screening provides a powerful approach to systematically identify novel agents with selective activity against tumor cells. Our work leverages functional drug-response profiling across a genetically diverse panel of patient-derived organoid models to identify tumor-selective agents while simultaneously assessing potential off-target toxicity. Using the Kinome Library, we comprehensively screen for functional kinase dependencies across these diverse models, revealing that ovarian cancers specific dependencies. To develop rational multi-agent combinations, we integrate ReFLEX multi-omic analysis to map drug-induced adaptive signaling and identify complementary therapeutic regimens. By combining functional screening with multi-omic insights, we construct evidence-based drug combinations targeting interconnected pathways, maximizing efficacy while aiming to minimize toxicity and resistance emergence. This integrated screening approach establishes a scalable platform for discovering selective therapeutics and rationally designing combination regimens.