Multi-Omics Integration for Cancer Therapy

Cancer is a highly heterogeneous disease driven by complex interactions between genetic, epigenetic, transcriptomic, proteomic, and metabolic alterations. Traditional single-omics studies, while informative, often capture only a narrow layer of this complexity and can fail to explain variability in treatment response or resistance. Multi-omics integration offers a comprehensive framework to characterize tumors at multiple molecular levels simultaneously, enabling a deeper understanding of oncogenic mechanisms and more precise therapeutic targeting.

This research topic focuses on the development and application of integrative multi-omics strategies to improve cancer therapy. It aims to combine data from genomics, epigenomics, transcriptomics, proteomics, and metabolomics (as well as emerging modalities such as single-cell and spatial omics) to:

1. identify robust biomarkers for diagnosis, prognosis, and treatment stratification;
2. uncover molecular signatures that predict response or resistance to targeted therapies, immunotherapies, and combination regimens;
3. reveal novel therapeutic targets and pathways that are not apparent from single-omics analyses;
4. leverage single-cell and spatial omics to map intratumoral heterogeneity and identify cell-state-specific vulnerabilities; and
5. use integrated omics landscapes to systematically identify novel drug repurposing opportunities and synergistic combinations for cancer therapy.

The project will explore and compare computational and statistical methods for multi-omics integration, including machine learning and network-based approaches, to construct multi-layered models of tumor biology. Emphasis will be placed on clinically relevant datasets, such as patient-derived samples and longitudinal cohorts, to ensure translational impact. By linking integrated molecular profiles with clinical outcomes, the research seeks to advance precision oncology and support the design of more effective, personalized cancer treatments.

Ultimately, this topic aims to bridge the gap between high-dimensional omics data and actionable clinical decisions, contributing to a more holistic and mechanistic understanding of cancer and its therapeutic vulnerabilities.