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

Nicolas MacagnoDaniel PissalouxArnaud de la FouchardiereMarie KaranianSylvie LantuejoulFrancoise Galateau-SalleAlexandra MeurgeyCatherine Chassagne-ClementIsabelle TreilleuxCaroline RenardJuliette RousselJulie GervasoniVincent CockenpotCarole CrozesAline BaltresAurélie HoulierSandrine PaindavoineLaurent AlbertiAdeline DucFrançois Le LoarerArmelle DufresneMehdi BrahmiNadège CorradiniJean Yves BlayFranck Tirode
Published in: Genes, chromosomes & cancer (2022)
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.
Keyphrases
  • single cell
  • machine learning
  • stem cells
  • bone marrow
  • deep learning
  • randomized controlled trial
  • rna seq
  • small molecule
  • genome wide
  • high throughput
  • nucleic acid