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Updated: Oct 5, 2025
Optimization of a Multiplex RNA-based Expression Assay Using Breast Cancer Archival Material
Published on: August 1, 2018
Wholistic approach: Transcriptomic analysis and beyond using archival material for molecular diagnosis
Nicolas Macagno1,2,3,4, Daniel Pissaloux1,5, Arnaud de la Fouchardière1,5
1Department of Biopathology, Centre Léon Bérard, Lyon, France.
Abstract:
Many neoplasms remain unclassified after histopathological examination, which requires further molecular analysis. To this regard, mesenchymal neoplasms are particularly challenging due to the combination of their rarity and the large number of subtypes, and many entities still lack robust diagnostic hallmarks. RNA transcriptomic profiles have proven to be a reliable basis for the classification of previously unclassified tumors and notably for mesenchymal neoplasms. Using exome-based RNA capture sequencing on more than 5000 samples of archival material (formalin-fixed, paraffin-embedded), the combination of expression profiles analyzes (including several clustering methods), fusion genes, and small nucleotide variations has been developed at the Centre Léon Bérard (CLB) in Lyon for the molecular diagnosis of challenging neoplasms and the discovery of new entities. The molecular basis of the technique, the protocol, and the bioinformatics algorithms used are described herein, as well as its advantages and limitations.
Insights
Molecular analysis using RNA transcriptomic profiles aids in classifying challenging neoplasms, particularly rare mesenchymal tumors. This approach combines expression analysis, fusion genes, and small nucleotide variations for accurate molecular diagnosis and discovery of new tumor entities.
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.
