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The Sub-Cellular Localization of WRAP53 Has Prognostic Impact in Breast Cancer.

ABSTRACT: WRAP53 protein controls intracellular trafficking of DNA repair proteins, the telomerase enzyme, and splicing factors. Functional loss of the protein has been linked to carcinogenesis, premature aging and neurodegeneration. The aim of this study was to investigate the prognostic significance of WRAP53 protein expression in breast cancer. A tissue microarray was constructed from primary breast tumors and immunostained by a polyclonal WRAP53 antibody to assess the protein expression pattern. Two different patient cohorts with long term follow-up were studied; a test- and a validation set of 154 and 668 breast tumor samples respectively. Breast cancer patients with tumor cells lacking the expression of WRAP53 in the nucleus had a significantly poorer outcome compared to patients with tumor cells expressing this protein in the nuclei (HR = 1.95, 95%CI = 1.09-3.51, p = 0.025). Nuclear localization of WRAP53 was further shown to be an independent marker of prognosis in multivariate analysis (HR = 2.57, 95%CI = 1.27-5.19, p = 0.008), and also significantly associated with better outcome in patients with TP53 mutation. Here we show that the sub-cellular localization of the WRAP53 protein has a significant impact on breast cancer survival, and thus has a potential as a clinical marker in diagnostics and treatment.

SUBMITTER: Silwal-Pandit L

PROVIDER: S-EPMC4603798 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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The Sub-Cellular Localization of WRAP53 Has Prognostic Impact in Breast Cancer.

Silwal-Pandit Laxmi L Russnes Hege H Borgen Elin E Skarpeteig Veronica V Moen Vollan Hans Kristian HK Schlichting Ellen E Kåresen Rolf R Naume Bjørn B Børresen-Dale Anne-Lise AL Farnebo Marianne M Langerød Anita A

PloS one 20151013 10

WRAP53 protein controls intracellular trafficking of DNA repair proteins, the telomerase enzyme, and splicing factors. Functional loss of the protein has been linked to carcinogenesis, premature aging and neurodegeneration. The aim of this study was to investigate the prognostic significance of WRAP53 protein expression in breast cancer. A tissue microarray was constructed from primary breast tumors and immunostained by a polyclonal WRAP53 antibody to assess the protein expression pattern. Two d ...[more]

PMID: 26460974

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Project description:BackgroundThe expansion of automatic imaging technologies has created a need to be able to efficiently compare and review large sets of image data. To enable comparisons of image data between samples we need to define the normal variation within distinct images of the same sample. Even with tightly controlled experimental conditions, protein expression can vary widely between cells, and because of the difficulty in viewing and comparing large image sets this might not be observed. Here we introduce a novel methodology, iCluster, for visualizing, clustering and comparing large sub-cellular localization image sets. For each member of an image set, iCluster generates statistics that have been found to be useful in distinguishing sub-cellular localization. The statistics are mapped into two or three dimensions such as to preserve distances between the statistics vectors. The complete image set is then visualized in two or three dimensions using the coordinates so determined. The result is images that are statistically similar are spatially close in the visualization allowing for easy comparison of images that are similar and distinguishment of dissimilar images into distinct clusters.ResultsThe methodology was tested on a set of 502 previously published images containing 10 known sub-cellular localizations. The clustering of images of like type was evaluated both by examining the classes of nearest neighbors to each image and by visual inspection. In three dimensions, 3-neighbor classification accuracy was 83.2%. Visually, each class clustered well with the majority of classes localizing to distinct regions of the space. In two dimensions, 3-neighbor classification accuracy was 68.9%, though visually clustering into classes could be readily discerned. Computational expense was found to be relatively low, and sets of up to 1400 images visualized and interacted with in real time.ConclusionThe feasibility of automated spatial layout to allow comparison and discrimination of high throughput sub-cellular imaging has been demonstrated. There are many potential applications such as image database curation, semi-automated interactive classification, outlier detection and reference image comparison. By allowing the observation of the full range of imaging data available using modern microscopes these methods will provide an invaluable tool for cell biologists.

Project description:PurposeIn addition to impacting incidence, risk factors for breast cancer may also influence recurrence and survival from the disease. However, it is unclear how these factors affect combinatorial biomarkers for aiding treatment decision-making in breast cancer.MethodsPatients were 8179 women with histologically confirmed invasive breast cancer, diagnosed and treated in a large cancer hospital in Beijing, China. Individual clinicopathological (tumor size, grade, lymph nodes) and immunohistochemical (IHC: ER, PR, HER2, KI67) markers were used to define clinically relevant combinatorial prognostic biomarkers, including the Nottingham Prognostic Index (NPI: combining size, grade, nodes) and IHC4 score (combining ER, PR, HER2, KI67). Odds ratios (ORs) and 95% confidence intervals (CIs) for associations between breast cancer risk factors and quartiles (Q1-Q4) of NPI and IHC4 were assessed in multivariable polytomous logistic regression models.ResultsOverall, increasing parity (ORtrend(95% CI) = 1.20(1.05-1.37);Ptrend = 0.007), overweight (OR(95% CI)vs normal = 1.60(1.29-1.98)), and obesity (OR(95% CI) vs normal = 2.12(1.43-3.14)) were associated with higher likelihood of developing tumors with high (Q4) versus low (Q1) NPI score. Conversely, increasing age (ORtrend(95% CI) = 0.75(0.66-0.84);Ptrend < 0.001) and positive family history of breast cancer (FHBC) (OR(95% CI) = 0.66(0.45-0.95)) were inversely associated with NPI. Only body mass index (BMI) was associated with IHC4, with overweight (OR(95% CI) vs normal = 0.82(0.66-1.02)) and obese (OR(95% CI) vs normal = 0.52(0.36-0.76)) women less likely to develop high IHC4 tumors. Notably, elevated BMI was associated with higher NPI irrespective of hormone receptor-expression status.ConclusionsOur findings indicate that factors affecting breast cancer incidence, particularly age, parity, FHBC, and BMI, may impact clinically relevant prognostic biomarkers with implications for surveillance, prognostication, and counseling.

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The Sub-Cellular Localization of WRAP53 Has Prognostic Impact in Breast Cancer.

Publications

The Sub-Cellular Localization of WRAP53 Has Prognostic Impact in Breast Cancer.

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