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Leon County Democrat Group

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Gennady Ustinov
Gennady Ustinov

Prognostic Free Download [VERIFIED]


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Prognostic Free Download



Gene expression of peripheral myelin protein 22 (PMP22) and the epithelial membrane proteins (EMPs) was found to be differentially expressed in invasive and non-invasive breast cell lines in a previous study. We want to evaluate the prognostic impact of the expression of these genes on breast cancer.


In univariable Cox regression analyses PMP22 and the EMPs were not associated with disease-free survival or tumor-related mortality. However, multivariable Cox regression revealed that patients with higher than median PMP22 gene expression have a 3.47 times higher risk to die of cancer compared to patients with equal values on clinical covariables but lower PMP22 expression. They also have a 1.77 times higher risk to relapse than those with lower PMP22 expression. The proportion of explained variation in overall survival due to PMP22 gene expression was 6.5% and thus PMP22 contributes equally to prognosis of overall survival as nodal status and estrogen receptor status. Cross validation demonstrates that 5-years survival rates can be refined by incorporating PMP22 into the prediction model.


PMP22 gene expression is a novel independent prognostic factor for disease-free survival and overall survival for breast cancer patients. Including it into a model with established prognostic factors will increase the accuracy of prognosis.


Breast cancer is by far the most frequent cancer of women with about one million new cases every year worldwide. Even though the prognosis for breast cancer patients is rather good, it is still the leading cause of cancer mortality in women causing about 400,000 annual deaths [1]. So far, the most important prognostic factor is lymph node status, which indicates disease-free survival and overall survival in breast cancer. The well defined predictors include the presence of hormone receptors that predict the response to endocrine therapy and the HER2 status that predicts the response to Tratuzumab. However, there is no predictive factor for chemotherapy that can be clinically used [2]. The prognosis of breast cancer is also far from being precise. Identification of new prognostic and predictive markers will not only help patients to receive the proper treatment, it can also provide new therapeutic targets.


The invasive potential of tumor cells reflects the intrinsic characteristics of tumor cells. Genes involved in the invasive process might therefore correlate with outcome of the disease and have certain prognostic and predictive values. In a previous study, we characterized the cell lines derived from breast cancer or normal breast tissues by their invasive ability to penetrate into a collagen-fibroblast matrix and compared gene expression profiles of invasive and non-invasive cell lines using Affymetrix GeneChip technology [3]. Several genes, which had not been described in the context of breast cancer, were identified and validated by RT-PCR. Two of these genes code for members of a subfamily of small hydrophobic membrane proteins, namely EMP3 and PMP22. Both are highly expressed in most of the invasive cell lines and had very low expression levels in non-invasive cell lines.


We hypothesize that these genes can have prognostic impacts on breast cancer. The objectives of the study are defined as the measurement of the expression of the EMPs and PMP22 in tumor tissues from 249 breast cancer patients using real-time RT-PCR, statistical evaluation of their prognostic impacts, and assessment of their added values to already established prognostic factors.


Tissues were homogenized using a microdismembrator and dissolved in GI lysis buffer (4 M Guanidine Isothiocyanate, 0.5% N-lauroyl-Sarcosine, 10 mM EDTA, 5 mM Sodium Citrate, and 100 μM β-mercaptoethanol). Total RNA was extracted from tumor biopsy lysates by isopycnic centrifugation as described previously [5] followed by a DNA digestion step of incubation with RNase-free DNase I (Roche Diagnostic, Mannheim, Germany) at 37C for 15 minutes. The quality of the RNA was examined with RNA 6000 Nano Chips and RNA 6000 Nano Reagent & Supplies on a 2100 Bioanalyzer (Agilent Technologies, Waldbronn, Germany). RNA concentrations were determined spectrophotometrically.


Disease-free survival is defined as time between diagnosis of disease and recurrence or distant metastasis. Overall survival is defined as time from diagnosis of disease to death of patients of breast cancer. Patients who died of causes unrelated to breast cancer were treated as censored in disease-specific survival analysis. Median follow-up time was computed by the Kaplan-Meier method with reverse status indicator as proposed by Schemper and Smith [7]. For analysis of disease-free and overall survival, tumors with differentiation grade 1 and 2 were combined for comparison with those with differentiation grade 3 and tumors with pT1 were compared with those combining pT2, pT3 and pT4. These groupings were necessary because of the low number of cases in some subgroups.


Expression data of EMP1, EMP2, EMP3 and PMP22 were obtained from all tumor samples. After log2 transformation, the data presented normal distribution (Table 1). The age of the patients at diagnosis (median: 58 yrs, range 27 - 89 yrs) and clinical data, including tumor size (pT), differentiation grade (G), nodal status (pN), and histological type of the tumors were analyzed for possible correlations with expression of EMPs and PMP22 (Table 1). Statistical analysis revealed a significant inverse correlation of EMP2 and PMP22 expression in tumor tissues with differentiation grade (p = 0.005 and p = 0.019, respectively). In addition, all gene expressions showed significant direct correlation with positive ER status. Expressions of EMP1, EMP2 and EMP3 had no prognostic values either for disease-free survival (DFS) or overall survival (OS) (Table 2 and 3). However, expression of PMP22 was a strong prognostic factor for outcome in a multivariable Cox regression model. Patients with higher than median PMP22 gene expression had a 3.47 times higher risk to die of cancer than patients with lower than median PMP22 expression (Table 2). They also had a 1.77 times higher risk to relapse than those with lower than median PMP22 expression levels (Table 3). These hazard ratio estimates were adjusted by tumor size, ER, nodal status, differentiation grade, and age of the patients (age only for overall survival). Results did not change materially if an interaction of ER status with log of survival time was added to the model in order to account for non-proportional hazards of ER status (data not shown). No significant interaction of the effect of PMP22 with any other variables in the model was detected. Analysing log PMP22 levels instead of PMP22 categories did not improve the model fit, neither of the univariate nor of the multivariable models. Furthermore, an exploratory analysis evaluating as potential cut-off values all deciles of PMP22 did not improve over the categorization at the median PMP22 value.


No association of PMP22 gene expression with OS and DFS was seen in unadjusted analyses using Kaplan-Meier curves (Figure 1). The additional prognostic value of PMP22 in multivariable models must therefore be attributed to certain correlation of PMP22 expression with other strong predictors. Indeed, PMP22 expression is positively correlated with ER status (Table 1 p PMP22 expression and ER status were compromised in unadjusted analyses. Similar but weaker associations exist between PMP22 gene expression and prognostic values of tumor size and lymph node involvement. Therefore we have computed adjusted survival curves from the multivariable Cox models, which refer to the estimated survival of patients with mean values on tumor size, lymph node involvement, differentiation grade, age and ER expression, and high or low PMP22 expression (Figure 1).


PMP22 and EMPs were selected for the evaluation of their prognostic values based on their higher expression levels in invasive breast cell lines compared to non-invasive ones. The invasiveness of these cell lines was determined by the ability of the cells to penetrate into a collagen-fibroblast matrix [12]. Cell motility and the capacity to invade into the surrounding tissues are preconditions for tumor cells to metastasize. Genes that are not expressed or expressed to less extent in non-invasive cells but are highly activated in invasive cells could be markers for prediction of tumor metastases. They could also indirectly indicate the outcome of patients. Indeed, we showed that patients with higher expression of PMP22 in their tumors have both, worse DFS and OS, suggesting that PMP22 is involved directly or indirectly in the invasion process. Our study also suggests that invasive and non-invasive cell lines provide a useful model for searching for prognostic factors.


In this study, we did not only show that PMP22 gene expression has prognostic value on DFS and OS, we also showed that PMP22 gene expression is as powerful as nodal status and ER status to predict mortality of breast cancer patients by calculating the proportion of explained variation. Traditionally, the prognostic values of gene expression were only evaluated by multivariable Cox regression model. The gain of including additional prognostic factors was not well addressed. Even though many new prognostic biomarkers have been reported, quite often they don't increase the predictive accuracy when added to the established clinical predictive factors [13]. By leaving out one of the three important prognostic factors, namely PMP22 gene expression, pN, or ER, the proportion of explained variations decreased equally, demonstrating that PMP22 expression contributes equally to prognosis as pN or ER status does. Therefore, PMP22 has potential use in clinical practice. 041b061a72


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