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A deep learning human-AI pipeline cross-references PubMed, GEO, DisGeNET, TCGA, and MICAD with RNAseq from over 17,000 tissues to identify targets and leads across cancers, enabling refined targeted theranostics for diagnosis, understanding, treatment, and monitoring.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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\u003Cbr>[https://doi.org/10.1038/s41467-024-55291-x](https://doi.org/10.1038/s41467-024-55291-x) |\n| --- | --- | --- | --- |\n| The Theranostic Genome |  |  |  |\n| Received: 25 January 2024\u003Cbr>Accepted: 5 December 2024 Check for updates | Xiaoying Xu 1,8, Pablo Jané 2,3,8, Vincent Taelman1,8, Eduardo Jané 4, Rebecca A. Dumont1, Yonathan Garama1, Francisco Kim 1, María del Val Gómez5, Karim Gariani6 & Martin A. Walter 1,7 \u003Cbr>Theranostic drugs represent an emerging path to deliver on the promise of precision medicine. However, bottlenecks remain in characterizing theranostic targets, identifying theranostic lead compounds, and tailoring theranostic drugs. To overcome these bottlenecks, we present the Theranostic Genome, the part of the human genome whose expression can be utilized to combine therapeutic and diagnostic applications. Using a deep learning-based hybrid human-AI pipeline that cross-references PubMed, the Gene Expression Omnibus, DisGeNET, The Cancer Genome Atlas and the NIH Molecular Imaging and Contrast Agent Database, we bridge individual genes in human cancers with respective theranostic compounds. Cross-referencing the Theranostic Genome with RNAseq data from over 17’000 human tissues identifiestheranostic targets and lead compounds for various human cancers, and allows tailoring targeted theranostics to relevant cancer subpopulations. We expect the Theranostic Genome to facilitate the development of new targeted theranostics to better diagnose, understand, treat, and monitor a variety of human cancers. |  |  |\n| Artificial intelligence is anticipated to revolutionize every single stage of drug development1. Among others, machine learning approaches have been used for streamlining of drug screening2, de novo drug development3, drug binding prediction4, drug interaction prediction5, and late-stage drug diversification6. Moreover, a deep learning framework for high-throughput mechanism-driven compound screening has recently facilitated drug repurposing7. Based on these recent developments, machine learning is anticipated to equally facilitate the development, refinement and clinical implementation of theranostic drugs.\u003Cbr>Theranostic drugs represent a new paradigm for precision oncology8. They are typically based on radiopharmaceuticals, and are designed to identify all tumour locations, assess the expression oftheir target molecules in these locations, provide predictive and prognostic information, treat the disease, and monitor the response to treatment9. The first clinically relevant theranostic agent used was radioiodine10,11, which for the first time facilitated the detection, |  | phenotyping, treatment, and monitoring of thyroid cancers with a single compound. This principal was subsequently utilized for the development of DOTATATE (DOTA-0-tyr3-octreotate) for neuroendocrine tumours12,13 and PSMA (prostate-specific membrane antigen) ligands for prostate cancers14,15.\u003Cbr>The clinical translation of additional such theranostic agents would undoubtedly represent a major advance for the field of precision oncology. However, several key knowledge gaps exist, which impede translation, including the current catalogue of all available radiotracers with theranostic potential and the molecular targets of these tracers, as well as the expression and relevance of these targets across the spectrum of human cancers.\u003Cbr>Herein, we seek to close these knowledge gaps by outlining the complete spectrum of gene products that can be targeted with radiotheranostics. Thereby, we describe the Theranostic Genome, the part of the human genome whose expression can be utilized for combined therapeutic and diagnostic applications. Using gene |  |\n\n1University of Lucerne, Lucerne, LU, Switzerland. 2University of Geneva, Geneva, GE, Switzerland. 3Nuclear Medicine and Molecular Imaging Division, Geneva University Hospitals, Geneva, GE, Switzerland. 4Departamento de Matemática Aplicada a la Ingeniería Aeroespacial - ETSIAE, Univ","cbCaiqBu1j6dAB3s","https://ap.wps.com/l/cbCaiqBu1j6dAB3s","pdf",2755195,12,"English","# Results\n## Data Pipeline","[{\"question\":\"What problem does the Theranostic Genome aim to solve?\",\"answer\":\"It addresses bottlenecks in characterizing theranostic targets, identifying lead compounds, and tailoring theranostic drugs to relevant cancer subpopulations.\"},{\"question\":\"How is the Theranostic Genome defined in the study?\",\"answer\":\"It is defined as the part of the human genome whose expression can be used to combine therapeutic and diagnostic applications.\"},{\"question\":\"What data sources and computational methods are used to identify targets and compounds?\",\"answer\":\"A deep learning-based hybrid human-AI pipeline cross-references PubMed, GEO, DisGeNET, TCGA, and MICAD, and integrates RNAseq tissue data to pinpoint theranostic targets and lead compounds.\"}]","The Theranostic Genome - Article - Precision oncology theranostic targets | PDF",1789985412]