Tumor proteomics by multivariate analysis on individual pathway data for characterization of vulvar cancer phenotypes.

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.

Harnessing T cell exhaustion and trogocytosis to isolate patient-derived tumor-specific TCR.

To study and then harness the tumor-specific T cell dynamics after allogeneic hematopoietic stem cell transplant, we typed the frequency, phenotype, and function of lymphocytes directed against tumor-associated antigens (TAAs) in 39 consecutive transplanted patients, for 1 year after transplant. We showed that TAA-specific T cells circulated in 90% of patients but display a limited effector function associated to an exhaustion phenotype, particularly in the subgroup of patients deemed to relapse, where exhausted stem cell memory T cells accumulated. Accordingly, cancer-specific cytolytic functions were relevant only when the TAA-specific T cell receptors (TCRs) were transferred into healthy, genome-edited T cells. We then exploited trogocytosis and ligandome-on-chip technology to unveil the specificities of tumor-specific TCRs retrieved from the exhausted T cell pool. Overall, we showed that harnessing circulating TAA-specific and exhausted T cells allow to isolate TCRs against TAAs and previously not described acute myeloid leukemia antigens, potentially relevant for T cell-based cancer immunotherapy.

Domain expertise-agnostic feature selection for the analysis of breast cancer data.

Progress in proteomics has enabled biologists to accurately measure the amount of protein in a tumor. This work is based on a breast cancer data set, result of the proteomics analysis of a cohort of tumors carried out at Karolinska Institutet. While evidence suggests that an anomaly in the protein content is related to the cancerous nature of tumors, the proteins that could be markers of cancer types and subtypes and the underlying interactions are not completely known. This work sheds light on the potential of the application of unsupervised learning in the analysis of the aforementioned data sets, namely in the detection of distinctive proteins for the identification of the cancer subtypes, in the absence of domain expertise. In the analyzed data set, the number of samples, or tumors, is significantly lower than the number of features, or proteins; consequently, the input data can be thought of as high-dimensional data. The use of high-dimensional data has already become widespread, and a great deal of effort has been put into high-dimensional data analysis by means of feature selection, but it is still largely based on prior specialist knowledge, which in this case is not complete. There is a growing need for unsupervised feature selection, which raises the issue of how to generate promising subsets of features among all the possible combinations, as well as how to evaluate the quality of these subsets in the absence of specialist knowledge. We hereby propose a new wrapper method for the generation and evaluation of subsets of features via spectral clustering and modularity, respectively. We conduct experiments to test the effectiveness of the new method in the analysis of the breast cancer data, in a domain expertise-agnostic context. Furthermore, we show that we can successfully augment our method by incorporating an external source of data on known protein complexes. Our approach reveals a large number of subsets of features that are better at clustering the samples than the state-of-the-art classification in terms of modularity and shows a potential to be useful for future proteomics research.

Alterations in the membrane-associated proteome fraction of alveolar macrophages in sarcoidosis.

BACKGROUND: Alveolar macrophages are implicated in the pathogenesis of lung sarcoidosis. Their interaction with T-cells leads to an inflammatory response that may either resolve within 2 years, or become chronic with an increased risk to develop lung fibrosis. OBJECTIVE: To perform quantitative profiling of the membrane-associated proteome of alveolar macrophages in sarcoidosis patients and healthy individuals to identify specific proteins and pathways involved in sarcoidosis pathology. METHODS: Differential proteomic analysis was performed on iTRAQ (isobaric Tag for Relative and Absolute Quantitation) labeled samples using tandem mass spectrometry. Subsequently, uni- and multivariate statistical analyses and pathway- and network analyses were performed. RESULTS: Eighty proteins were differentially expressed between healthy and sarcoidosis patients. Down-stream pathway analysis confirmed our recent reports of up-regulation of two phagocytotic pathways: Fcγ receptor-mediated phagocytosis and clathrin-mediated endocytosis signaling. An additional pathway, pyruvate metabolism, was found to be up-regulated in sarcoidosis patients. The oxidative phosphorylation pathway was differentially expressed in subgroups of sarcoidosis, with up-regulation in Löfgren's patients and down-regulation in non-Löfgren's patients. CONCLUSION: This unprecedented proteome profiling of the membrane-associated fraction of alveolar macrophages confirmed previous findings of alterations in phagocytotic pathways due to sarcoidosis, as well as indicated a differential dysregulation of the oxidative phosphorylation pathway related to disease outcome in sarcoidosis.