Comprehensive transcriptomic analysis to identify biological and clinical differences in cholangiocarcinoma.

BACKGROUND: Cholangiocarcinoma (CC) is a rare and aggressive disease with limited therapeutic options and a poor prognosis. All available public records of cohorts reporting transcriptomic data on intrahepatic cholangiocarcinoma (ICC) and extrahepatic cholangiocarcinoma (ECC) were collected with the aim to provide a comprehensive gene expression-based classification with clinical relevance. METHODS: A total of 543 patients with primary tumor tissues profiled by RNAseq and microarray platforms from seven public datasets were used as a discovery set to identify distinct biological subgroups. Group predictors developed on the discovery sets were applied to a single cohort of 131 patients profiled with RNAseq for validation and assessment of clinical relevance leveraging machine learning techniques. RESULTS: By unsupervised clustering analysis of gene expression data we identified both in the ICC and ECC discovery datasets four subgroups characterized by a distinct type of immune infiltrate and signaling pathways. We next developed class predictors using short gene list signatures and identified in an independent dataset subgroups of ICC tumors at different prognosis. CONCLUSIONS: The developed class-predictor allows identification of CC subgroups with specific biological features and clinical behavior at single-sample level. Such results represent the starting point for a complete molecular characterization of CC, including integration of genomics data to develop in clinical practice.

Towards automated discrimination of lipids versus peptides from full scan mass spectra.

Although physicochemical fractionation techniques play a crucial role in the analysis of complex mixtures, they are not necessarily the best solution to separate specific molecular classes, such as lipids and peptides. Any physical fractionation step such as, for example, those based on liquid chromatography, will introduce its own variation and noise. In this paper we investigate to what extent the high sensitivity and resolution of contemporary mass spectrometers offers viable opportunities for computational separation of signals in full scan spectra. We introduce an automatic method that can discriminate peptide from lipid peaks in full scan mass spectra, based on their isotopic properties. We systematically evaluate which features maximally contribute to a peptide versus lipid classification. The selected features are subsequently used to build a random forest classifier that enables almost perfect separation between lipid and peptide signals without requiring ion fragmentation and classical tandem MS-based identification approaches. The classifier is trained on in silico data, but is also capable of discriminating signals in real world experiments. We evaluate the influence of typical data inaccuracies of common classes of mass spectrometry instruments on the optimal set of discriminant features. Finally, the method is successfully extended towards the classification of individual lipid classes from full scan mass spectral features, based on input data defined by the Lipid Maps Consortium.