Advancing Cancer Research Using Integrated Systems Biology Approaches

Integrated Systems Biology and Genomics have fundamentally changed how cancer research progresses. Cancer is a disease of dysregulated systems — tumors evolve through complex interactions among genetic alterations, transcriptional programs, signaling networks, metabolic rewiring, and dynamic crosstalk with the tumor microenvironment and host immunity. Moving beyond a reductionist, single-gene perspective, there is a growing need to discern candidate genes and variants within the broader context of multi-scale tumor biology. By combining high-throughput, multi-omics data (genomics, transcriptomics, proteomics, metabolomics) with computational modeling, machine learning, and artificial intelligence, researchers are developing a comprehensive picture of cancer, leading to better diagnostic tools, prognostic biomarkers, and targeted therapeutic strategies. Recent advances in single-cell and spatial profiling, high-content imaging, and network inference have further matured these capabilities, reshaping how therapeutic vulnerabilities are identified, patients are stratified, and resistance is anticipated.This Research Topic aims to advance cancer research using integrated systems biology approaches. We are particularly interested in manuscripts addressing the following concepts but are not limited to:• Understanding Tumor Complexity and Heterogeneity — including single-cell and spatial analyses of tumor states, clonal dynamics, and the tumor microenvironment• Multi-Omics Data Integration — spanning genomics, transcriptomics, proteomics, metabolomics, and epigenomics to characterize tumor trajectories• Predictive Modeling and Drug Discovery — encompassing mathematical models of tumor growth, network inference to identify therapeutic targets, and machine learning frameworks for drug response prediction and patient stratification• Clinical Translation and Precision Medicine — connecting systems-level findings to clinical decision-making, biomarker development, and precision oncology• Bench-to-Bedside Diagnostics in Cancers — integrative analyses leveraging organoids, patient-derived models, and co-culture systems for systems-level experimental validationIntegrated systems biology approaches are essential for moving beyond the “one-size-fits-all” model of cancer care. We particularly encourage studies that integrate computational predictions with experimental or in situ validation, and that contribute generalizable frameworks, datasets, or tools to the broader oncology community. By bringing together perspectives from molecular oncology, computational biology, bioengineering, and translational research, this Research Topic aims to consolidate progress and identify priorities for the next phase of integrative cancer research, paving the way for more precise and effective cancer treatment and diagnosis.Please note: Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent clinical or patient cohort, or biological validation in vitro or in vivo, which are not based on public databases) are not suitable for publication in this journal. Topic editors: Prashanth N Suravajhala, Rune Matthiesen, Nirmal Kumar Lohiya, Jyotsna Batra Source: https://www.frontiersin.org/research-topics/81413/advancing-cancer-research-using-integrated-systems-biology-approaches