Pathological bases for a robust application of cancer molecular classification

Area of Science:

• Oncology
• Genomics
• Molecular Biology

Background:

• Current cancer classification relies on histology, but lacks predictive power for metastatic potential, staging, and grading.
• Molecular approaches like transcriptomics and genomics aim to enhance cancer classification.
• Gene expression (RNA) analysis is popular but limited by molecule lability and epigenetic modifications.

Purpose of the Study:

• To evaluate the reliability of molecular classification systems for human cancers.
• To identify more stable molecular markers for improved cancer prognostication.
• To propose a DNA-based analytical genomic classification approach.

Main Methods:

• Review of existing molecular classification strategies in cancer research.
• Discussion of the limitations of RNA-based gene expression analysis.
• Proposal of DNA-based analysis using next-generation sequencing (NGS) for multiple targets.

Main Results:

• RNA is a labile molecule affected by preservation and epigenetic changes, impacting classification reliability.
• Intratumoral heterogeneity is a key factor in tumor progression and must be addressed in classification.
• Simultaneous analysis of multiple DNA targets via NGS provides a robust approach.

Conclusions:

• DNA-based genomic classification offers superior reliability compared to RNA-based methods.
• Addressing intratumoral heterogeneity is crucial for accurate cancer classification.
• Next-generation sequencing of multiple DNA targets is the optimal method for analytical genomic tumor classification.