Protein profiling of genomic instability in endometrial cancer.

DNA aneuploidy has been identified as a prognostic factor in the majority of epithelial malignancies. We aimed at identifying ploidy-associated protein expression in endometrial cancer of different prognostic subgroups. Comparison of gel electrophoresis-based protein expression patterns between normal endometrium (n = 5), diploid (n = 7), and aneuploid (n = 7) endometrial carcinoma detected 121 ploidy-associated protein forms, 42 differentially expressed between normal endometrium and diploid endometrioid carcinomas, 37 between diploid and aneuploid endometrioid carcinomas, and 41 between diploid endometrioid and aneuploid uterine papillary serous cancer. Proteins were identified by mass spectrometry and evaluated by Ingenuity Pathway Analysis. Targets were confirmed by liquid chromatography/mass spectrometry. Mass spectrometry identified 41 distinct polypeptides and pathway analysis resulted in high-ranked networks with vimentin and Nf-κB as central nodes. These results identify ploidy-associated protein expression differences that overrule histopathology-associated expression differences and emphasize particular protein networks in genomic stability of endometrial cancer.

Genomic instability influences the transcriptome and proteome in endometrial cancer subtypes.

BACKGROUND: In addition to clinical characteristics, DNA aneuploidy has been identified as a prognostic factor in epithelial malignancies in general and in endometrial cancers in particular. We mapped ploidy-associated chromosomal aberrations and identified corresponding gene and protein expression changes in endometrial cancers of different prognostic subgroups. METHODS: DNA image cytometry classified 25 endometrioid cancers to be either diploid (n = 16) or aneuploid (n = 9), and all uterine papillary serous cancers (UPSC) to be aneuploid (n = 8). All samples were subjected to comparative genomic hybridization and gene expression profiling. Identified genes were subjected to Ingenuity pathway analysis (IPA) and were correlated to protein expression changes. RESULTS: Comparative genomic hybridization revealed ploidy-associated specific, recurrent genomic imbalances. Gene expression analysis identified 54 genes between diploid and aneuploid endometrioid carcinomas, 39 genes between aneuploid endometrioid cancer and UPSC, and 76 genes between diploid endometrioid and aneuploid UPSC to be differentially expressed. Protein profiling identified AKR7A2 and ANXA2 to show translational alterations consistent with the transcriptional changes. The majority of differentially expressed genes and proteins belonged to identical molecular functions, foremost Cancer, Cell Death, and Cellular Assembly and Organization. CONCLUSIONS: We conclude that the grade of genomic instability rather than the histopathological subtype correlates with specific gene and protein expression changes. The identified genes and proteins might be useful as molecular targets for improved diagnostic and therapeutic intervention and merit prospective validation.

The gene expression signature of genomic instability in breast cancer is an independent predictor of clinical outcome.

Recently, expression profiling of breast carcinomas has revealed gene signatures that predict clinical outcome, and discerned prognostically relevant breast cancer subtypes. Measurement of the degree of genomic instability provides a very similar stratification of prognostic groups. We therefore hypothesized that these features are linked. We used gene expression profiling of 48 breast cancer specimens that profoundly differed in their degree of genomic instability and identified a set of 12 genes that defines the 2 groups. The biological and prognostic significance of this gene set was established through survival prediction in published datasets from patients with breast cancer. Of note, the gene expression signatures that define specific prognostic subtypes in other breast cancer datasets, such as luminal A and B, basal, normal-like, and ERBB2+, and prognostic signatures including MammaPrint and Oncotype DX, predicted genomic instability in our samples. This remarkable congruence suggests a biological interdependence of poor-prognosis gene signatures, breast cancer subtypes, genomic instability, and clinical outcome.