SMARTCORE Technology: Using AI and Patient Tissue to Identify Potential Cancer Therapies for Ultra-rare Cancers

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Project Summary Our proposal presents a new approach, called SmartCore, to tackle the challenge of finding practical solutions for ultra-rare cancers. We aim to use an AI-driven drug screening platform designed to test primary human tumor tissue. Our strategy is to identify and repurpose drugs that exhibit activity against the cancer of interest. We circumvent the latency of developing organoids or patient-derived xenograft models for drug screening by utilizing intact tumors as organotypic cultures. Using a machine-learning algorithm, we can predict tumor sensitivity to a panel of ~4,000 drugs, including ~1800 FDA-approved drugs, by inputting responses to a set of computationally selected 254 compounds. Our long-term goal is to create a robust drug and target discovery method for individual cancers, irrespective of rarity, that directly links to clinical application, thereby addressing a critical ‘bench-to-bedside’ gap in personalized and precision oncology. To realize our goal, we intend to expand the capability of our platform to make use of clinical needle biopsies. This will vastly increase the utility of our SmartCore technology to analyze biopsy samples from any tumor that can be shipped to our processing facility within 24 hrs. This proposal will demonstrate the proof-of-concept of our SmartCore platform in an ultra-rare condition, fibrolamellar cancer of the liver, with two specific aims. Aim 1 is to discover therapeutics for fibrolamellar cancer using AI-based chemical screening. Here, we will establish technical parameters for optimal performance of our AI-based screening platform using independent FLC cohorts obtained from multiple sources, validate the top ‘hits’ using established patient-derived xenograft models from three independent human FLCs, and deduce signaling networks and proteins targeted by the candidate drugs, highlighting molecular pathways important for the survival of FLC. Aim 2 is to develop an AI-based chemical screening approach using needle biopsies. To broaden the impact of our technology, we will modify our AI-based screening to accommodate 18- gauge core needle biopsies routinely performed in clinical practice. We will curate a set of <40 drugs for FLC- specific testing as the basis for drug prediction using our deep neural network algorithm, test the accuracy of this approach by comparing needle biopsies with larger tissue slices in our FLC population and optimize pre-analytic conditions to yield reproducible results. If successful, our technology will make use of needle core biopsies, be agnostic to the underlying molecular derangement, screen a large collection of compounds using deep neural network algorithms, and require a short turnaround time of one week from start to finish; all of which are attributes of an ideal test that overcomes the Achilles heel of personalized oncology.