Events

Englander Institute for Precision Medicine – AI Clinic Join us for our first bi-weekly AI Clinic meeting on Wednesday, January 14th at 2:00pm ET. AI Clinic Info: These sessions 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. All skill levels are welcome, from beginners to advanced users. 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 (preferably two to lead each session) who demonstrate expertise in coding and experience with AI tools such as GPT-5/Codex, Claude Sonnet 4.5/Claude Code, and Gemini 2.5 Pro. We are also seeking participants who are looking for troubleshooting guidance. Reach out to Victoria Cummings (vjc4001@med.cornell.edu) should you have any questions or inquiries.

Precision Medicine Research Conference Thursday, January 15, 2026 2-3PM Belfer Research Building, Room 1401 and via Zoom "Unraveling Connections Between Therapy and Immune Dynamics" Presented by: Taha Merghoub, PhD Margaret and German Sokol Professor in Oncology Research, Professor of Pharmacology, Professor of Immunology Research in Medicine, Deputy Director, Meyer Cancer Center Weill Cornell Medicine * This activity has been approved for AMA PRA Category 1 Credit(s)™*

Englander Institute for Precision Medicine Seminar Series “From Organoids to Algorithms: Translational Platforms for Precision Oncology in Solid Tumors” Presented by Marianna Kruithof-de Julio, Ph.D. Professor of Experimental Urology, Director, Cancer Translational Research Program, Director, Organoid CORE, University of Bern, CH. Biography: Marianna Kruithof-de Julio is a Professor of Experimental Urology and Director of the Cancer Translational Research Program at the University of Bern. She leads the Urology Research Laboratory and the Organoid Core Facility, developing patient-derived organoid and organ-on-chip models to better understand cancer and support therapy decisions. Her work focuses on prostate, bladder, renal, and pancreatic cancers, combining advanced technologies such as spatial transcriptomics and AI-driven histopathology with functional drug screening. In addition to her research, she serves as Editor-in-Chief of Gene and is Founder of OnconiX, committed to translating science into practical solutions that improve patient care. Abstract: The complexity of solid tumors such as pancreatic ductal adenocarcinoma (PDAC) and bladder cancer (BLCa) demands integrative approaches that combine biological fidelity with computational power. Here we outline a multi-modal translational framework that leverages patient-derived organoids, spatial transcriptomics, microfluidic platforms, and artificial intelligence to accelerate personalized cancer therapy development. Feasibility trials in PDAC have demonstrated the successful acquisition of high-quality biopsies for downstream applications, including organoid generation and spatial profiling. These models recapitulate key histopathological and molecular features, enabling functional drug screening and predictive modeling. AI-based classifiers trained on transcriptomic data further stratify tumors by therapeutic response, even in cases where organoid derivation is not feasible. Complementing this, the iBloC (immune Bladder-on-Chip) platform introduces a microfluidic system tailored for bladder cancer, simulating tumor-immune interactions under physiologically relevant conditions. This chip-based model supports dynamic drug testing and real-time molecular analysis, bridging the gap between preclinical research and clinical application. Together, these platforms represent a scalable and clinically relevant pipeline for precision oncology, integrating experimental and computational tools to guide individualized treatment strategies.