Molecular Subtypes of High-Grade Serous Ovarian Cancer: The Holy Grail?
Menée à partir de l'analyse du profil génétique d'échantillons tumoraux prélevés sur 174 patientes atteintes d'un cancer séreux de l'ovaire de haut grade, puis répliquée à partir de données portant sur 185 autres patientes, cette étude identifie 4 sous-types moléculaires et évalue leur intérêt pour aider la prise de décision thérapeutique
With the development of robust genomic platforms and extensive genetic profiling of tumors, the identification of previously unrecognized cancer subtypes has become a reality. Molecular subtypes can reflect important biology, developmental origins, and most importantly have clinical utility. While genomic subtyping efforts have rapidly produced results for some cancers, the identification of molecular subtypes has been difficult for high-grade serous ovarian cancer (HSOC). What makes subtyping so difficult? The Cancer Genome Atlas (TCGA) (1) showed that in HSOC, hundreds of genes are affected by recurrent focal copy number and promoter methylation events, as well as by a small number of recurrent somatic short variant mutations. These extensive genetic abnormalities are likely due to a profound abnormality in DNA repair, resulting in genomic chaos and, in addition to the recurrent driver events, a large numbers of passenger events. It is predictable that HSOC would be genetically plastic with rapid evolution during the disease course, with extensive heterogeneity at the time of initial diagnosis. This would make the identification of specific tumor subtypes particularly challenging. In the face of such complexity, patients can be grouped based on combinations of genomic or epigenetic events in an almost arbitrary number of ways.
So how can high-throughput transcriptomic data help in identifying clinically relevant subtypes? First, it can identify groups of patients whose disparate genomic events have similar expression footprints. Second, it can help to prioritize alterations with strong expression phenotype over ones with only small effect on gene expression. Unsupervised clustering of transcriptome data organizes tumors into discrete groups based on these two criteria, and it is important to acknowledge that this process will almost always succeed in …
Journal of the National Cancer Institute , éditorial, 2014