Coupled Mass-Spectrometry-Based Lipidomics Machine Learning Approach for Early Detection of Clear Cell Renal Cell Carcinoma.

A discovery-based lipid profiling study of serum samples from a cohort that included patients with clear cell renal cell carcinoma (ccRCC) stages I, II, III, and IV (n = 112) and controls (n = 52) was performed using ultraperformance liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry and machine learning techniques. Multivariate models based on support vector machines and the LASSO variable selection method yielded two discriminant lipid panels for ccRCC detection and early diagnosis. A 16-lipid panel allowed discriminating ccRCC patients from controls with 95.7% accuracy in a training set under cross-validation and 77.1% accuracy in an independent test set. A second model trained to discriminate early (I and II) from late (III and IV) stage ccRCC yielded a panel of 26 compounds that classified stage I patients from an independent test set with 82.1% accuracy. Thirteen species, including cholic acid, undecylenic acid, lauric acid, LPC(16:0/0:0), and PC(18:2/18:2), identified with level 1 exhibited significantly lower levels in samples from ccRCC patients compared to controls. Moreover, 3α-hydroxy-5α-androstan-17-one 3-sulfate, cis-5-dodecenoic acid, arachidonic acid, cis-13-docosenoic acid, PI(16:0/18:1), PC(16:0/18:2), and PC(O-16:0/20:4) contributed to discriminate early from late ccRCC stage patients. The results are auspicious for early ccRCC diagnosis after validation of the panels in larger and different cohorts.

An improved catalogue of putative synaptic genes defined exclusively by temporal transcription profiles through an ensemble machine learning approach.

BACKGROUND: Assembly and function of neuronal synapses require the coordinated expression of a yet undetermined set of genes. Previously, we had trained an ensemble machine learning model to assign a probability of having synaptic function to every protein-coding gene in Drosophila melanogaster. This approach resulted in the publication of a catalogue of 893 genes which we postulated to be very enriched in genes with a still undocumented synaptic function. Since then, the scientific community has experimentally identified 79 new synaptic genes. Here we use these new empirical data to evaluate our original prediction. We also implement a series of changes to the training scheme of our model and using the new data we demonstrate that this improves its predictive power. Finally, we added the new synaptic genes to the training set and trained a new model, obtaining a new, enhanced catalogue of putative synaptic genes. RESULTS: The retrospective analysis demonstrate that our original catalogue was significantly enriched in new synaptic genes. When the changes to the training scheme were implemented using the original training set we obtained even higher enrichment. Finally, applying the new training scheme with a training set including the 79 new synaptic genes, resulted in an enhanced catalogue of putative synaptic genes. Here we present this new catalogue and announce that a regularly updated version will be available online at: http://synapticgenes.bnd.edu.uy CONCLUSIONS: We show that training an ensemble of machine learning classifiers solely with the whole-body temporal transcription profiles of known synaptic genes resulted in a catalogue with a significant enrichment in undiscovered synaptic genes. Using new empirical data provided by the scientific community, we validated our original approach, improved our model an obtained an arguably more precise prediction. This approach reduces the number of genes to be tested through hypothesis-driven experimentation and will facilitate our understanding of neuronal function. AVAILABILITY: http://synapticgenes.bnd.edu.uy.

A pan-cancer somatic mutation embedding using autoencoders.

BACKGROUND: Next generation sequencing instruments are providing new opportunities for comprehensive analyses of cancer genomes. The increasing availability of tumor data allows to research the complexity of cancer disease with machine learning methods. The large available repositories of high dimensional tumor samples characterised with germline and somatic mutation data requires advance computational modelling for data interpretation. In this work, we propose to analyze this complex data with neural network learning, a methodology that made impressive advances in image and natural language processing. RESULTS: Here we present a tumor mutation profile analysis pipeline based on an autoencoder model, which is used to discover better representations of lower dimensionality from large somatic mutation data of 40 different tumor types and subtypes. Kernel learning with hierarchical cluster analysis are used to assess the quality of the learned somatic mutation embedding, on which support vector machine models are used to accurately classify tumor subtypes. CONCLUSIONS: The learned latent space maps the original samples in a much lower dimension while keeping the biological signals from the original tumor samples. This pipeline and the resulting embedding allows an easier exploration of the heterogeneity within and across tumor types and to perform an accurate classification of tumor samples in the pan-cancer somatic mutation landscape.

von Hippel-Lindau mutants in renal cell carcinoma are regulated by increased expression of RSUME.

Renal cell carcinoma (RCC) is the major cause of death among patients with von Hippel-Lindau (VHL) disease. Resistance to therapies targeting tumor angiogenesis opens the question about the underlying mechanisms. Previously we have described that RWDD3 or RSUME (RWD domain-containing protein SUMO Enhancer) sumoylates and binds VHL protein and negatively regulates HIF degradation, leading to xenograft RCC tumor growth in mice. In this study, we performed a bioinformatics analysis in a ccRCC dataset showing an association of RSUME levels with VHL mutations and tumor progression, and we demonstrate the molecular mechanism by which RSUME regulates the pathologic angiogenic phenotype of VHL missense mutations. We report that VHL mutants fail to downregulate RSUME protein levels accounting for the increased RSUME expression found in RCC tumors. Furthermore, we prove that targeting RSUME in RCC cell line clones carrying missense VHL mutants results in decreased early tumor angiogenesis. The mechanism we describe is that RSUME sumoylates VHL mutants and beyond its sumoylation capacity, interacts with Type 2 VHL mutants, reduces HIF-2α-VHL mutants binding, and negatively regulates the assembly of the Type 2 VHL, Elongins and Cullins (ECV) complex. Altogether these results show RSUME involvement in VHL mutants deregulation that leads to the angiogenic phenotype of RCC tumors.