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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).

Introduction Melanoma is the most lethal malignancy of the skin. The Cancer Genome Atlas (TCGA) Network proposed a molecular classification of melanoma, consisting in three subtypes: keratin-high, immune-high and membrane-low. However, this classification has not been translated into therapeutic advances yet. The aim of this study is to characterise molecular differences of melanoma at biological and molecular levels and propose a novel molecular classification with clinical impact. Material and methods A novel approach based on the existence of different molecular informative layers was used in this study to establish independent sets of information. A probabilistic graphical model, followed by successive sparse k-means and consensus cluster analyses were used to classify melanoma tumour samples from the TCGA cohort. Then, molecular classification was validated in another public cohort: GSE65904. Results and discussions We established that there were at least two different kinds of molecular information: an immune layer and a histological layer. Immune high and immune low groups were established based on immune layer information. Keratin low-proliferation low, melanogenesis high-membrane low, melanogenesis low-membrane high and keratin high groups were established based on molecular layer information. This suggests that the information related to the immune system is independent from other molecular features and is distributed among the groups established by the TCGA classification. Besides, the immune and histological assignments showed different clinical outcomes in another dataset, identifying two immune groups with prognostic value. Conclusion In this work we proposed a novel analytical approach based on informative molecular layers. In this way, two independent classifications, an immune-based and a histological-based classification, were established. Immune classification showed prognostic value in an independent cohort. Besides, the histological and the immune layers may deserve additional research for define new potential targeted therapies.