Review: Cancer and neurodevelopmental disorders: multi-scale reasoning and computational guide

Date

2024-01-01

Author

Nussinov, Ruth
Yavuz, Bengi Ruken
Demirel, Habibe Cansu
Arici, M. Kaan
Jang, Hyunbum
Tunçbağ, Nurcan

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The connection and causality between cancer and neurodevelopmental disorders have been puzzling. How can the same cellular pathways, proteins, and mutations lead to pathologies with vastly different clinical presentations? And why do individuals with neurodevelopmental disorders, such as autism and schizophrenia, face higher chances of cancer emerging throughout their lifetime? Our broad review emphasizes the multi-scale aspect of this type of reasoning. As these examples demonstrate, rather than focusing on a specific organ system or disease, we aim at the new understanding that can be gained. Within this framework, our review calls attention to computational strategies which can be powerful in discovering connections, causalities, predicting clinical outcomes, and are vital for drug discovery. Thus, rather than centering on the clinical features, we draw on the rapidly increasing data on the molecular level, including mutations, isoforms, three-dimensional structures, and expression levels of the respective disease-associated genes. Their integrated analysis, together with chromatin states, can delineate how, despite being connected, neurodevelopmental disorders and cancer differ, and how the same mutations can lead to different clinical symptoms. Here, we seek to uncover the emerging connection between cancer, including pediatric tumors, and neurodevelopmental disorders, and the tantalizing questions that this connection raises.

Subject Keywords

artificial intelligence, chromatin, comorbidity, machine learning, mutations, pediatric tumors, protein-protein interaction (PPI) networks, transcriptomics

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85198513966&origin=inward
https://hdl.handle.net/11511/110197

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Graduate School of Informatics, Article