PO-509 Novel molecular classification of muscle-invasive bladder cancer opens new treatment opportunities

Introduction Muscle-invasive bladder cancer (MIBC) is associated with a high risk of relapse and metastasis. The standard treatment consists of radical cystectomy followed by cisplatin-based chemotherapy. Therefore, the molecular characterisation of MIBC in order to find new therapeutic targets is still necessary. Material and methods In this study, using The Cancer Genome Atlas (TCGA) cohort, functional structure and layer analyses were done to characterise MIBC tumours at functional level. Functional structure was developed using probabilistic graphical models and gene ontology analyses. Layer analyses were based on sparse k-means and Consensus Cluster Algorithm (CCA). Sparse k-means assigns a weight to each variable, based on its relevance in the sample classification. Then, a CCA using variables that were selected by the sparse K-means method was applied to define the optimum number of groups for each case. This sparse k-means-CCA workflow was performed successive times to explore the existence of independent informative molecular layers. Once relevant genes for one molecular layer are identified, these genes were removed from the dataset and the sparse k-means-CCA workflow was performed again, allowing the identification of different layers of molecular information and establishing different classifications based on various molecular features. Gene ontology analyses were done for each layer. Results and discussions A probabilistic graphical model with a functional structure was obtained. Sixteen layers were functionally characterised and the first three layers were analysed in depth. The first layer defined luminal and basal groups. Luminal tumours had a better prognosis than basal ones. Strikingly, luminal tumours showed higher expression of the androgen receptor, suggesting that this group is a good candidate for receiving treatment with androgen receptor inhibitors. The second layer established two groups based on genes related to extracellular matrix function. Lastly, the third layer defined two groups on the basis of immune information. The immune high group had a better prognosis and was characterised by higher expression of CTLA4 and PDL1, so this group may be a candidate for receiving immunotherapy. Conclusion Using probabilistic graphical models and layer analyses it is possible to establish independently a molecular and an immune classification. Using these approaches, we also proposed a specific treatment for luminal tumours (androgen receptor inhibitors) and for immune high tumours (immunotherapy).