Wholistic approach: Transcriptomic analysis and beyond using archival material for molecular diagnosis

Area of Science:

• Oncology
• Genomics
• Pathology

Background:

• Many neoplasms, especially rare mesenchymal tumors, lack clear classification after standard histopathological examination.
• Existing diagnostic methods struggle with the rarity and numerous subtypes of mesenchymal neoplasms, often lacking definitive markers.
• Molecular analysis, particularly RNA transcriptomics, offers a promising avenue for classifying challenging and previously unclassified tumors.

Purpose of the Study:

• To develop and describe a comprehensive molecular diagnostic approach for challenging neoplasms.
• To leverage RNA transcriptomic profiles for the classification and discovery of new mesenchymal neoplasm entities.
• To detail the methodology, including bioinformatics, for molecular diagnosis at the Centre Léon Bérard (CLB).

Main Methods:

• Utilized exome-based RNA capture sequencing on over 5000 archival formalin-fixed, paraffin-embedded (FFPE) samples.
• Integrated expression profile analysis (including clustering methods), fusion gene detection, and small nucleotide variation analysis.
• Developed specific bioinformatics algorithms for the combined analysis of transcriptomic data.

Main Results:

• Established a robust molecular diagnostic platform combining multiple data types for complex neoplasms.
• Demonstrated the utility of RNA transcriptomic profiling in classifying difficult-to-diagnose tumors.
• The developed technique facilitates the discovery of novel tumor entities and provides diagnostic hallmarks.

Conclusions:

• RNA transcriptomic profiling, integrated with other molecular analyses, is a powerful tool for the molecular diagnosis of challenging neoplasms.
• This approach enhances the classification of rare mesenchymal tumors and aids in identifying new entities.
• The described methodology offers advantages for molecular diagnostics while acknowledging its inherent limitations.