Research Project: OMIC-NET PROJESİ: Duruma Özgü Çoklu Omik Veri Entegrasyonu için Ağ Tabanlı Çerçeve Yaklaşımın Geliştirilmesi
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Contributors
Funders
ID
TB.00669
Authors
Tunçbağ, Nurcan
Faculty Member
Publications
Unveiling hidden connections in omics data via pyPARAGON: an integrative hybrid approach for disease network construction
(Oxford University Press, 2024) Tunçbağ, Nurcan; Arici, Muslum Kaan; Department of Chemical and Biological Engineering; KUTTAM (Koç University Research Center for Translational Medicine); School of Medicine; Yes; College of Engineering; Research Center; SCHOOL OF MEDICINE
Network inference or reconstruction algorithms play an integral role in successfully analyzing and identifying causal relationships between omics hits for detecting dysregulated and altered signaling components in various contexts, encompassing disease states and drug perturbations. However, accurate representation of signaling networks and identification of context-specific interactions within sparse omics datasets in complex interactomes pose significant challenges in integrative approaches. To address these challenges, we present pyPARAGON (PAgeRAnk-flux on Graphlet-guided network for multi-Omic data integratioN), a novel tool that combines network propagation with graphlets. pyPARAGON enhances accuracy and minimizes the inclusion of nonspecific interactions in signaling networks by utilizing network rather than relying on pairwise connections among proteins. Through comprehensive evaluations on benchmark signaling pathways, we demonstrate that pyPARAGON outperforms state-of-the-art approaches in node propagation and edge inference. Furthermore, pyPARAGON exhibits promising performance in discovering cancer driver networks. Notably, we demonstrate its utility in network-based stratification of patient tumors by integrating phosphoproteomic data from 105 breast cancer tumors with the interactome and demonstrating tumor-specific signaling pathways. Overall, pyPARAGON is a novel tool for analyzing and integrating multi-omic data in the context of signaling networks.
Neurodevelopmental disorders and cancer networks share pathways, but differ in mechanisms, signaling strength, and outcome
(Nature Portfolio, 2023) Tunçbağ, Nurcan; Yavuz, Bengi Ruken; Arici, M. Kaan; Demirel, Habibe Cansu; Tsai, Chung-Jung; Jang, Hyunbum; Nussinov, Ruth; Department of Chemical and Biological Engineering; KUYTAM (Koç University Surface Science and Technology Center); School of Medicine; Yes; College of Engineering; Research Center; SCHOOL OF MEDICINE
Epidemiological studies suggest that individuals with neurodevelopmental disorders (NDDs) are more prone to develop certain types of cancer. Notably, however, the case statistics can be impacted by late discovery of cancer in individuals afflicted with NDDs, such as intellectual disorders, autism, and schizophrenia, which may bias the numbers. As to NDD-associated mutations, in most cases, they are germline while cancer mutations are sporadic, emerging during life. However, somatic mosaicism can spur NDDs, and cancer-related mutations can be germline. NDDs and cancer share proteins, pathways, and mutations. Here we ask (i) exactly which features they share, and (ii) how, despite their commonalities, they differ in clinical outcomes. To tackle these questions, we employed a statistical framework followed by network analysis. Our thorough exploration of the mutations, reconstructed disease-specific networks, pathways, and transcriptome levels and profiles of autism spectrum disorder (ASD) and cancers, point to signaling strength as the key factor: strong signaling promotes cell proliferation in cancer, and weaker (moderate) signaling impacts differentiation in ASD. Thus, we suggest that signaling strength, not activating mutations, can decide clinical outcome.
Review: cancer and neurodevelopmental disorders: multi-scale reasoning and computational guide
(Frontiers Media Sa, 2024) Demirel, Habibe Cansu; Tunçbağ, Nurcan; Nussinov, Ruth; Yavuz, Bengi Ruken; Arıcı, M. Kaan; Jang, Hyunbum; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; KUTTAM (Koç University Research Center for Translational Medicine); Yes; College of Engineering; GRADUATE SCHOOL OF SCIENCES AND ENGINEERING; Research Center
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.
