Abstract 903: AURIGIN: A comprehensive single-cell OMICs atlas of human development and an AI/ML framework to classify and identify the drivers of tumor plasticity and altered cellular state
Authors: DeBartolo Joseph , Wilson Henry , Straley Kimberly S. , Bhatta Maulasri , Sharma Sambad , Sinicropi-Yao Sara , Neef James , Chan Betty , McRiner Andrew , Bittinger Mark , Antipov Laura , Yen Katharine E. , Graeber Thomas G. , Millan David S. 
Published: March 2024

Altered cellular state and plasticity have been increasingly identified as drivers of poor prognosis and treatment-resistance in cancer. Identifying therapeutics that correct altered cellular state and plasticity meets a broad need in the field of oncology, particularly in tumors resistant to standard-of-care therapy. Tumor plasticity and altered cellular states encompass many potential configurations, including dedifferentiated and transdifferentiated tumors, so classifying tumors based on their specific cell state profile is critical to identifying the gene drivers that are specific to that altered state. For that reason, we have developed AURIGIN - a platform that combines a comprehensive single-cell OMICs atlas of human development with an AI/ML framework that can classify cell state plasticity and identify the genes and pathways that drive those specific altered cellular states. AURIGIN is more comprehensive than previously disclosed single cell atlases in that it focuses and enriches the diverse stem, progenitor, and developmental states commonly involved in plastic cell states of cancer. The platform standardizes and integrates these states with multiple atlases of differentiated adult tissues to create a unified atlas of human development. In addition, the AI/ML paradigm of AURIGIN is embedded with developmental biology models that enable it to deconvolute state heterogeneity within tumor indications for precise identification of the most relevant gene targets for novel therapies. AURIGIN also unifies classified plasticity across tissue types by identifying altered cellular states that span multiple indications. Furthermore, we present the implementation of AURIGINDRIVE ML models that integrate pathway information, physical interaction datasets, and regulatory annotations to accurately predict the gene targets at the top of hierarchy of control of tumor plasticity. AURIGIN also enables and accelerates target validation by identifying models that map to the relevant classified plastic states. Similarly, AURIGIN defines clinically relevant patient selection and treatment efficacy biomarkers that are specifically defined from the classified plastic cellular state. We demonstrate the success of AURIGIN for therapeutic discovery. In sum, AURIGIN amplifies the value of multiOMIC tumor data in cellular state and plasticity target discovery by mapping the data into a developmental biology-informed ML framework.

Citation Format: Joseph DeBartolo, Henry Wilson, Kimberly S. Straley, Maulasri Bhatta, Sambad Sharma, Sara Sinicropi-Yao, James Neef, Betty Chan, Andrew McRiner, Mark Bittinger, Laura Antipov, Katharine E. Yen, Thomas G. Graeber, David S. Millan. AURIGIN: A comprehensive single-cell OMICs atlas of human development and an AI/ML framework to classify and identify the drivers of tumor plasticity and altered cellular state [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 903.