Tumor Proteomics by Multivariate Analysis on Individual Pathway Data for Characterization of Vulvar Cancer Phenotypes

Abstract

Vulvar squamous cell carcinoma (VSCC) is the fourth most common gynecological cancer. Based on etiology VSCC is divided into two subtypes; one related to high-risk human papilloma virus (HPV) and one HPV negative. The two subtypes are proposed to develop via separate intracellular signaling pathways. We investigated a suggested link between HPV infection and relapse risk in VSCC through in-depth protein profiling of 14 VSCC tumor specimens. The tumor proteomes were analyzed by liquid-chromatography tandem mass spectrometry. Relative protein quantification was performed by 8-plex isobaric tags for relative and absolute quantification. Labeled peptides were fractionated by high-resolution isoelectric focusing prior to liquid-chromatography tandem mass spectrometry to reduce sample complexity. In total, 1579 proteins were regarded as accurately quantified and analyzed further. For classification of clinical groups, data analysis was performed by comparing protein level differences between tumors defined by HPV and/or relapse status. Further, we performed a biological analysis on individual tumor proteomes by matching data to known biological pathways. We here present a novel analysis approach that combines pathway alteration data on individual tumor level with multivariate statistics for HPV and relapse status comparisons. Four proteins (signal transducer and activator of transcription-1, myxovirus resistance protein 1, proteasome subunit alpha type-5 and legumain) identified as main classifiers of relapse status were validated by immunohistochemistry (IHC). Two of the proteins are interferon-regulated and on mRNA level known to be repressed by HPV. By both liquid-chromatography tandem mass spectrometry and immunohistochemistry data we could single out a subgroup of HPV negative/relapse-associated tumors. The pathway level data analysis confirmed three of the proteins, and further identified the ubiquitin-proteasome pathway as altered in the high risk subgroup. We show that pathway fingerprinting with resolution on individual tumor level adds biological information that strengthens a generalized protein analysis.