Emerging trends and converging evidence in tumor evolution: A comprehensive review

Area of Science:

• Oncology
• Cancer Biology
• Evolutionary Medicine

Background:

• Tumor evolution is a spatiotemporal process involving genetic mutations, epigenetic changes, and microenvironmental interactions, driving cancer progression, metastasis, and resistance.
• Foundational models (linear, branched, neutral, parallel) explain tumor diversification via gradual mutations or genomic events, with applicability dependent on selective pressures.
• Somatic mutations act as lineage barcodes, while epigenetic dysregulation (DNA methylation, RNA modifications) provides phenotypic plasticity for rapid adaptation without DNA alteration.

Purpose of the Study:

• To elucidate the multifaceted drivers of tumor evolution, including genetic, epigenetic, and microenvironmental factors.
• To highlight the role of the tumor microenvironment as a dynamic orchestrator of cancer cell adaptation and heterogeneity.
• To emphasize the need for advanced technologies and integrated models to understand and combat tumor evolution and therapeutic resistance.

Main Methods:

• Analysis of foundational evolutionary models (linear, branched, neutral, parallel) in the context of tumor diversification.
• Investigation of somatic mutations and epigenetic dysregulation (DNA methylation, RNA modifications) as drivers of non-genetic heterogeneity.
• Exploration of tumor microenvironment influences (hypoxia, lactate, neural crosstalk) on cancer cell adaptation and spatial selection pressures.
• Leveraging emerging technologies like single-cell sequencing, spatial multi-omics, and liquid biopsies to decode intra-tumoral heterogeneity and clonal dynamics.

Main Results:

• Tumor evolution is shaped by a complex interplay of genetic, epigenetic, and microenvironmental factors, leading to phenotypic diversity.
• The tumor microenvironment actively orchestrates cancer evolution through distinct ecological niches and spatially heterogeneous selection pressures.
• Treatment pressures impose evolutionary bottlenecks, selecting for resistant clones and promoting cross-resistance.
• Emerging technologies enable real-time, multi-dimensional decoding of intra-tumoral heterogeneity and clonal evolution.

Conclusions:

• Integrating evolutionary models with multi-omics data is crucial for understanding tumor adaptation and developing temporally adaptive therapies.
• Current preclinical models require advancement to accurately recapitulate human tumor-microenvironment interactions for translational research.
• Future personalized oncology relies on artificial intelligence and longitudinal biomarker profiling to move beyond static genomic matching and control cancer evolution.