Multi-Omics Meets Premalignancy: Paving the Way for Early Prevention of Cancer

Area of Science:

• Oncology
• Genomics
• Bioinformatics

Background:

• Premalignant lesions (PMLs) are critical targets for cancer early detection and interception.
• Advances in multi-omics and artificial intelligence (AI) offer new insights into tumorigenesis.
• Understanding PMLs across diverse cancer types is essential for developing effective prevention strategies.

Purpose of the Study:

• To catalog clinically recognized PMLs across 15 cancer types, detailing their epidemiology and malignant transformation potential.
• To summarize recent multi-omics discoveries and challenges in understanding PML evolution.
• To discuss AI-driven computational strategies for multi-omics integration and tumorigenesis trajectory inference.

Main Methods:

• Cataloging PMLs across 15 cancer types, including epidemiological data and transformation potentials.
• Reviewing bulk, single-cell, and spatial omics studies to understand PML molecular, cellular, and spatial evolution.
• Discussing network-based computational strategies and deep learning AI for multi-omics data analysis.

Main Results:

• Multi-omics technologies reveal the dynamic evolution of PMLs from precancerous states to invasive malignancies.
• AI and computational strategies facilitate multi-omics integration and inference of tumorigenesis trajectories.
• Identified PMLs across 15 cancer types with distinct epidemiological profiles and transformation potentials.

Conclusions:

• The convergence of multi-omics and AI redefines PML research for pan-cancer early risk stratification.
• High-precision early-diagnosis biomarkers and targeted pharmacological prevention strategies for PMLs can be developed.
• This approach enables exceedingly-early detection and pharmacological prevention of various cancers.