Work

From multi‐omics to cancer digital twins: Novel paradigm in cancer research and treatment response

Year: 2025

Type: article

Abstract: Integrating multi-omics in cancer research has expanded our understanding of the intricate molecular mechanisms underlying malignancies.Traditional single-omics approaches, while informative, only capture one molecular layer at a time. 1 However, cancer is a multifaceted disease driven by genetic, epigenetic, metabolic and signalling interactions.In addition, the crosstalk between tumour cells and their environment, whether occurring during malignancies or modulated through therapy, is inherently multiscale.For instance, the crosstalk spans molecular-, cellular-and systems-level processes, operating across temporal and spatial scales.Such complexity renders the tumour ecosystem an archetype of a hierarchical system, requiring integrative approaches to fully comprehend and address. 2 Multi-omics bridges this gap by integrating diverse data types from the same patients to provide a multidimensional view of the tumour ecosystem.Each of these modalities contributes unique insights into the core tumour; for example, genomics focuses on the mutational landscape, transcriptomics highlights aberrant gene expression, proteomics elucidates protein interactions, epigenomics reveals regulatory mechanisms, metabolomics uncovers metabolic reprogramming and spatial omics map biomolecules directly onto the physical position of tumour niches.3 Together, these multiple modalities resume tumours' molecular and functional dynamics, improving applications such as precision medicine, biomarker discovery, drug target identification and patient stratification (Figure 1).Integrating multi-omics requires sophisticated computational methods capable of handling high-dimensional datasets.4 To tackle these challenges, artificial intelligence (AI), machine learning (ML) and deep learning (DL) have emerged as powerful tools.The AI-and ML/DL-based models facilitate the fusion of multiple modalities into a unified framework, addressing the challenges of disparate data types that vary in size, scales, formats, distributions and noise levels.Its ability to process and harmonize

Source: Clinical and Translational Discovery

DOI: 10.1002/ctd2.70035

Authors: Sara Sadat Aghamiri, Rada Amin

Institution: University of Nebraska–Lincoln

Language: English

Topic: Cancer Genomics and Diagnostics

Subfield: Cancer Research

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,420