PODS-PACK
Precision Oncology Decision Support – Protein AI Companion Knowledge
For many cancer patients, doctors use genetic tests to match them with targeted treatments. But what happens when those tests don’t reveal any options? This is a major challenge, especially for people with rare cancers. This research project is working to change that by looking beyond genetics and into something just as important—proteins.
Proteins play a crucial role in how cancer develops and responds to treatment. By analyzing unique protein patterns within a patient’s tumor, this research team aims to identify new treatment opportunities—even in cases where no genetic markers are present. Leveraging cutting-edge data analysis, artificial intelligence (AI), and vast medical databases, the team is developing a comprehensive tumor profiling approach that prioritizes proteins while integrating genetic and clinical data to advance precision oncology. This approach serves as a protein-informed digital learning companion, empowering clinicians with deeper insights into treatment options that were previously inaccessible. Importantly, existing protein test results, while preferable, are not required, as the system will learn from others and augment available genetic and clinical data with inferred protein insights, broadening access to personalized cancer care.
This research project is pioneering a shift from genomic- to proteomic-cancer targetable treatments, expanding the reach of precision medicine to provide treatment options for even the most complex cases. A key component of this work is the development of a human-mediated, AI-generated corpus of hypothesized drug-protein target relationships and testing designs, serving as a foundational resource for AI-enabled cancer clinical care. By doing so, this corpus will establish guidelines and protocols for AI-assisted precision oncology. Through this approach, the project lays the groundwork for scalable, evidence-based AI applications in cancer treatment selection and response prediction.
Team Members
MD Anderson
Ecaterina Dumbrava, Assistant Professor
Investigational Cancer Therapeutics
Samir Hanash, Professor
Clinical Cancer Prevention
Brian Iorgulescu, Assistant Professor
Hematopathology
Ehsan Irajizad, Assistant Professor
Biostatistics
Anil Korkut, Associate Professor
Bioinformatics & Computational Biology
Funda Meric-Bernstam, Professor and Chair
Investigational Cancer Therapeutics
Jody Vykoukal, Research Group Leader
McCombs Institute for the Early Detection and Treatment of Cancer
UT Austin
Jeanne Kowalski-Muegge, Professor
Co-Program Leader of Quantitative Oncology, Livestrong Cancer Institutes Oncology, Dell Medical School
Kyaw Aung, Assistant Professor
Oncology, Dell Medical School
Ying Ding, Professor
School of Information
Adam Klivans, Professor
Computer Science, College of Natural Sciences
Annalee Nguyen, Research Assistant Professor
Chemical Engineering, Cockrell School of Engineering
Carla Vandenberg, Associate Professor
College of Pharmacy
Yan Zhang, Professor
School of Information
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