Purification of LC/GC-MS based biomolecular expression profiles using a topic model

Liquid (or gas) chromatography coupled with mass spectrometry (LC/GC-MS) allows quantitative comparison of biomolecular abundance in clinical samples to help with the discovery of candidate biomarkers for complex diseases such as cancer. A fundamental challenge in quantitation of biomolecules for cancer biomarker discovery is owing to the heterogeneous nature of clinical samples. Various contaminations from related disease tissues or adjacent non-cancerous constituents in a sample confound the characterization of molecular expression profiles and thus hinder the discovery of reliable biomarkers. This issue has been raised and discussed in analysis of microarray and RNA-seq data in cancer genomics studies. To the best of our knowledge, the issue has not yet been rigorously addressed in analyzing LC/GC-MS data that are generated in a variety of omic studies including proteomics and metabolomics. Purification of LC/GC-MS based biomolecular expression profiles is highly desired prior to subsequent analysis, e.g., quantitative comparison of the abundance of biomolecules in clinical samples. In this study, we applied a topic model to computationally deconvolute each of LC/GC-MS based cancer expression profiles and infer the underlying sample-specific pure cancer profiles. We demonstrated the capability of the model in capturing mixture proportions of contaminants and cancer profiles on a synthetic LC-MS dataset. Improved performances were also achieved on experimental LC-MS based serum proteomic and GC-MS based tissue metabolomic datasets acquired from patients with hepatocellular carcinoma (HCC).