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Background: The new paradigm of precision medicine brought an increasing interest in survival prediction based on the integration of multi-omics and multi-sources data. Several models have been developed to address this task, but their performances are widely variable depending on the specific disease and are often poor on noisy datasets, such as in the case of non-small cell lung cancer (NSCLC). Objective: The aim of this work is to introduce a novel computational approach, named multi-omic two-layer SVM (mtSVM), and to exploit it to get a survival-based risk stratification of NSCLC patients from an ongoing observational prospective cohort clinical study named PROMOLE. Methods: The model implements a model-based integration by means of a two-layer feed-forward network of FastSurvivalSVMs, and it can be used to get individual survival estimates or survival-based risk stratification. Despite being designed for NSCLC, its range of applicability can potentially cover the full spectrum of survival analysis problems where integration of different data sources is needed, independently of the pathology considered. Results: The model is here applied to the case of NSCLC, and compared with other state-of-the-art methods, proving excellent performance. Notably, the model, trained on data from The Cancer Genome Atlas (TCGA), has been validated on an independent cohort (from the PROMOLE study), and the results were consistent. Gene-set enrichment analysis of the risk groups, as well as exome analysis, revealed well-defined molecular profiles, such as a prognostic mutational gene signature with potential implications in clinical practice.

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BackgroundThe method “Learning by mistakes” was developed in Italy to conduct occupational injury investigations and to collect information on the genesis of injuries. The aim is to analyze data classified with this method in order to identify patterns among the factors contributing to injury dynamics.MethodsData regarding 673 factors, corresponding to 354 occupational fatalities that occurred in the Piedmont region (north‐west of Italy) during 2005‐2014 were considered. Latent Class Analysis (LCA) was applied to find patterns among these factors.ResultsThe eight‐class model was selected. Most of the factors fell in the class “Fall from height or vehicle rollover due to incorrect practice” (40.56%) while the remaining factors where heterogeneously distributed in the other classes.ConclusionsAll the classes found allow for a logical interpretation. Systematic use of LCA could aid in uncovering new, unexpected patterns of factors not otherwise detectable by analysis of the single fatal accident.