More and more affordable high-throughput techniques for measuring molecular features of biomedical samples have led to a huge increase in availability and size of different types of multi-omic datasets, containing, for example, genetic or histone modification data. Due to the multi-view characteristic of the data, established approaches for exploratory analysis are not directly applicable. Here we present web-rMKL, a web server that provides an integrative dimensionality reduction with subsequent clustering of samples based on data from multiple inputs. The underlying machine learning method rMKL-LPP performed best for clinical enrichment in a recent benchmark of state-of-the-art multi-view clustering algorithms. The method was introduced for a multi-omic cancer subtype discovery setting, however, it is not limited to this application scenario as exemplified by a presented use case for stem cell differentiation. web-rMKL offers an intuitive interface for uploading data and setting the parameters. rMKL-LPP runs on the back end and the user may receive notifications once the results are available. We also introduce a preprocessing tool for generating kernel matrices from tables containing numerical feature values. This program can be used to generate admissible input if no precomputed kernel matrices are available. The web server is freely available at web-rMKL.org.
In many biomedical studies an important first step is checking for confounding factors. For association studies, confounding can for example be caused by ethnic differences in the case and control groups. In many other settings there might be confounding factors like batch effects or founder effects that also need to be detected and controlled for. Detecting confounding for data from one data source is well established (e.g., genomics data). Since more and more studies are now based on data from multiple data modalities (e.g., multi-omics), we evaluated whether multi-view confounder detection can benefit from state-of-the-art methods for multi-view data integration. Especially for clustering of multi-omics data, it has been shown that these methods can perform better than methods that treat the data modalities separately. Our results show that multi-view confounder analysis is possible and that building on multi-view data integration methods is better than treating the different data modalities separately.
Background: In cancerous tissue, a complex interplay of tumour cells with different cell types from the tumour microenvironment is causing modulations in signalling processes. By directly assessing expression of a multitude of proteins and protein variants, extensive information on signalling pathways, their activation status and the effect of the immunological landscape can be obtained providing viable information for treatment response. Methods: Protein extracted from archived breast cancer tissue from patients without adjuvant therapy was subjected to high-throughput Western blotting using the DigiWest technology. Expression of 150 proteins and protein variants covering cell cycle control, apoptosis, Jak/Stat, MAPK-, Pi3K/Akt-, Wnt-, and , autophagic signalling as well as general tumour markers was monitored in a cohort of 84 patient samples. The degree of immune cell infiltration was investigated and set against treatment outcome by integrating patient specific follow-up data. Results: Characterization of the tumour microenvironment by monitoring CD8α, CD11c, CD16 and CD68 expression revealed a strong correlation of event-free patient survival with immune cell infiltration. Furthermore, the presence of tumour infiltrating lymphocytes was linked to a pronounced activation of the Jak/Stat signalling pathway and apoptotic processes. Elevated phosphorylation of peroxisome proliferator-activated receptor gamma (PPARγ, pS112) in non-immune infiltrated tumour tissue suggests a novel immune evasion mechanism in breast cancer characterized by increased PPARγ activation. Conclusion: Multiplexed immune cell marker assessment and protein profiling of tumour tissue provides functional signalling data facilitating breast cancer patient stratification.
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