Linking the ovarian cancer transcriptome and immunome

Abstract

Background: Autoantigens have been reported in a variety of tumors, providing insight into the interplay between malignancies and the immune response, and also giving rise to novel diagnostic and therapeutic concepts. Why certain tumor-associated proteins induce an immune response remains largely elusive.

Results: This paper analyzes the proposed link between increased abundance of a protein in cancerous tissue and the increased potential of the protein for induction of a humoral immune response, using ovarian cancer as an example. Public domain data sources on differential gene expression and on autoantigens associated with this malignancy were extracted and compared, using bioinformatics analysis, on the levels of individual genes and proteins, transcriptional coregulation, joint functional pathways, and shared protein-protein interaction networks. Finally, a selected list of ovarian cancer-associated, differentially regulated proteins was tested experimentally for reactivity with antibodies prevalent in sera of ovarian cancer patients.Genes reported as showing differential expression in ovarian cancer exhibited only minor overlap with the public domain list of ovarian cancer autoantigens. However, experimental screening for antibodies directed against antigenic determinants from ovarian cancer-associated proteins yielded clear reactions with sera.

Conclusion: A link between tumor protein abundance and the likelihood of induction of a humoral immune response in ovarian cancer appears evident.

Figure 1

Protein networks based on protein-protein interaction data in OPHID. A: Individual interaction networks of Meta-UP, Meta-DOWN, Meta-ALL and SEREX-ovarian datasets as visualized using ProteoLens . B: The indices of aggregation (IA) for the given datasets with respect to the IA of ensembles of randomly generated datasets used as references are shown (means and standard deviations).

Figure 2

Box-plots giving means, errors of means, and standard deviations of ELISA signal intensities from the tumor sera pool (tumor) and the reference sera pool (reference), using equal numbers of antigenic peptides from Meta-UP (UP) and Meta-DOWN (DOWN) proteins. The OD values are ELISA signal readouts. A double asterisk indicates a highly significant difference based on Student's t-test (p = 0.0011).

Figure 3

Box-plots giving means, errors of means, and standard deviations of triplicate measurements of ELISA signals (OD, optical density) for the 12 proteins exhibiting the highest signal differences when a tumor sera pool (20 sera) and a reference sera pool (10 sera) were compared (A), and the corresponding data for the remaining 19 proteins (B). Where more than one epitope was tested for a given protein the signal based on the epitope showing strongest reactivity is provided. Black box-plots indicate tumor sera reactivity and white box-plots give reference sera reactivity. Each protein is named from its gene symbol.

Figure 4

Heat-map representation of ELISA signal intensities for the 12 most reactive epitopes of 12 individual proteins screened with 20 individual ovarian cancer patient sera. Signals are color coded for the interval [-1,1] and represent the log₂-transformed differences between the ELISA signals using tumor serum and signals derived using a control peptide as a background reference. Red coloring indicates increased reactivity of an individual tumor serum.

Figure 5

Data integration scheme: Protein-protein interaction networks unravel the link between gene expression and SEREX-ovarian data, via identified transcription factors. One of the newly identified autoantigens, DDX21, is included. The protein network was generated using genes identified by Meta-UP and SEREX-ovarian dataset searches. Genes shown in the upper right present a sample of the network, linking the various data sources to the newly identified autoantigen DDX21. In the lower left of the Figure, ELISA signal intensities of the 20 individual ovarian cancer sera tested on DDX21 are given as bar plots. Genes involved can be further grouped using gene ontology terms, showing typical gene categories involved in cancer.