Prognostic Value of Myeloid Differentiation Primary Response 88 and Toll-Like Receptor 4 in Breast Cancer Patients

Purpose

Breast cancer remains a major cause of death in women worldwide, and tumor metastasis is the leading cause of death in breast cancer patients after conventional treatment. Chronic inflammation is often related to the occurrence and growth of various malignancies. This study evaluated the prognosis of breast cancer patients based on contributors to the innate immune response: myeloid differentiation primary response 88 (MyD88) and Toll-like receptor 4 (TLR4).

Methods

We analyzed data from 205 breast invasive ductal carcinoma (IDC) patients who were treated at the Department of Breast Surgery, Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, from 2002 to 2006. Overall survival (OS) and disease-free survival (DFS) were compared.

Results

In total, 152 patients (74.15%) were disease-free without relapse or metastasis, whereas 53 (25.85%) patients developed recurrence or metastasis. A significant positive correlation was observed between MyD88 and TLR4 expression (p<0.001). Patients with high expression were more likely to experience death and recurrence/metastasis events (p<0.05). Patients with low MyD88 or TLR4 expression levels had better DFS and OS than patients with high expression levels (log-rank test: p<0.001). Patients with low MyD88 and TLR4 expression levels had better DFS and OS than patients with high expression levels of either (log-rank test: p<0.001). In a multivariate analysis, high MyD88 expression was an independent predictive factor for decreased DFS (adjusted HR, 3.324; 95% CI, 1.663–6.641; p = 0.001) and OS (adjusted HR, 4.500; 95% CI, 1.546–13.098; p = 0.006).

Conclusions

TLR4-MyD88 signaling pathway activation or MyD88 activation alone may be a risk factor for poor prognosis in breast cancer. Therefore, TLR4-MyD88 signaling pathway activation in tumor biology provides a novel potential target for breast cancer therapy.     

Prognostic breast cancer signature identified from 3D culture model accurately predicts clinical outcome across independent datasets

Background

One of the major tenets in breast cancer research is that early detection is vital for patient survival by increasing treatment options. To that end, we have previously used a novel unsupervised approach to identify a set of genes whose expression predicts prognosis of breast cancer patients. The predictive genes were selected in a well-defined three dimensional (3D) cell culture model of non-malignant human mammary epithelial cell morphogenesis as down-regulated during breast epithelial cell acinar formation and cell cycle arrest. Here we examine the ability of this gene signature (3D-signature) to predict prognosis in three independent breast cancer microarray datasets having 295, 286, and 118 samples, respectively.

Methods and Findings

Our results show that the 3D-signature accurately predicts prognosis in three unrelated patient datasets. At 10 years, the probability of positive outcome was 52, 51, and 47 percent in the group with a poor-prognosis signature and 91, 75, and 71 percent in the group with a good-prognosis signature for the three datasets, respectively (Kaplan-Meier survival analysis, p<0.05). Hazard ratios for poor outcome were 5.5 (95% CI 3.0 to 12.2, p<0.0001), 2.4 (95% CI 1.6 to 3.6, p<0.0001) and 1.9 (95% CI 1.1 to 3.2, p = 0.016) and remained significant for the two larger datasets when corrected for estrogen receptor (ER) status. Hence the 3D-signature accurately predicts breast cancer outcome in both ER-positive and ER-negative tumors, though individual genes differed in their prognostic ability in the two subtypes. Genes that were prognostic in ER+ patients are AURKA, CEP55, RRM2, EPHA2, FGFBP1, and VRK1, while genes prognostic in ER− patients include ACTB, FOXM1 and SERPINE2 (Kaplan-Meier p<0.05). Multivariable Cox regression analysis in the largest dataset showed that the 3D-signature was a strong independent factor in predicting breast cancer outcome.

Conclusions

The 3D-signature accurately predicts breast cancer outcome across multiple datasets and holds prognostic value for both ER-positive and ER-negative breast cancer. The signature was selected using a novel biological approach and hence holds promise to represent the key biological processes of breast cancer.     

PathAct: a novel method for pathway analysis using gene expression profiles

We developed PathAct, a novel method for pathway analysis to investigate the biological and clinical implications of the gene expression profiles. The advantage of PathAct in comparison with the conventional pathway analysis methods is that it can estimate pathway activity levels for individual patient quantitatively in the form of a pathway-by-sample matrix. This matrix can be used for further analysis such as hierarchical clustering and other analysis methods. To evaluate the feasibility of PathAct, comparison with frequently used gene-enrichment analysis methods was conducted using two public microarray datasets. The dataset #1 was that of breast cancer patients, and we investigated pathways associated with triple-negative breast cancer by PathAct, compared with those obtained by gene set enrichment analysis (GSEA). The dataset #2 was another breast cancer dataset with disease-free survival (DFS) of each patient. Contribution by each pathway to prognosis was investigated by our method as well as the Database for Annotation, Visualization and Integrated Discovery (DAVID) analysis. In the dataset #1, four out of the six pathways that satisfied p < 0.05 and FDR < 0.30 by GSEA were also included in those obtained by the PathAct method. For the dataset #2, two pathways (“Cell Cycle” and “DNA replication”) out of four pathways by PathAct were commonly identified by DAVID analysis. Thus, we confirmed a good degree of agreement among PathAct and conventional methods. Moreover, several applications of further statistical analyses such as hierarchical cluster analysis by pathway activity, correlation analysis and survival analysis between pathways were conducted.     

Genes Influenced by the Non-Muscle Isoform of Myosin Light Chain Kinase Impact Human Cancer Prognosis

The multifunctional non-muscle isoform of myosin light chain kinase (nmMLCK) is critical to the rapid dynamic coordination of the cytoskeleton involved in cancer cell proliferation and migration. We identified 45 nmMLCK-influenced genes by bioinformatic filtering of genome–wide expression in wild type and nmMLCK knockout (KO) mice exposed to preclinical models of murine acute inflammatory lung injury, pathologies that are well established to include nmMLCK as an essential participant. To determine whether these nmMLCK-influenced genes were relevant to human cancers, the 45 mouse genes were matched to 38 distinct human orthologs (M38 signature) (GeneCards definition) and underwent Kaplan-Meier survival analysis in training and validation cohorts. These studies revealed that in training cohorts, the M38 signature successfully identified cancer patients with poor overall survival in breast cancer (P<0.001), colon cancer (P<0.001), glioma (P<0.001), and lung cancer (P<0.001). In validation cohorts, the M38 signature demonstrated significantly reduced overall survival for high-score patients of breast cancer (P = 0.002), colon cancer (P = 0.035), glioma (P = 0.023), and lung cancer (P = 0.023). The association between M38 risk score and overall survival was confirmed by univariate Cox proportional hazard analysis of overall survival in the both training and validation cohorts. This study, providing a novel prognostic cancer gene signature derived from a murine model of nmMLCK-associated lung inflammation, strongly supports nmMLCK-involved pathways in tumor growth and progression in human cancers and nmMLCK as an attractive candidate molecular target in both inflammatory and neoplastic processes.     

Development and Validation of a Novel Platform-Independent Metastasis Signature in Human Breast Cancer

PurposeThe molecular drivers of metastasis in breast cancer are not well understood. Therefore, we sought to identify the biological processes underlying distant progression and define a prognostic signature for metastatic potential in breast cancer.ResultsWe identified a broad range of metastatic potential that was independent of intrinsic breast cancer subtypes. 146 genes were significantly associated with metastasis progression and were linked to cancer-related biological functions, including cell migration/adhesion, Jak-STAT, TGF-beta, and Wnt signaling. These genes were used to develop a platform-independent gene expression signature (M-Sig), which was trained and subsequently validated on 5 independent cohorts totaling nearly 1800 breast cancer patients with all p-values < 0.005 and hazard ratios ranging from approximately 2.5 to 3. On multivariate analysis accounting for standard clinicopathologic prognostic variables, M-Sig remained the strongest prognostic factor for metastatic progression, with p-values < 0.001 and hazard ratios > 2 in three different cohorts.ConclusionM-Sig is strongly prognostic for metastatic progression, and may provide clinical utility in combination with treatment prediction tools to better guide patient care. In addition, the platform-independent nature of the signature makes it an excellent research tool as it can be directly applied onto existing, and future, datasets.     

Yin Yang Gene Expression Ratio Signature for Lung Cancer Prognosis

Many studies have established gene expression-based prognostic signatures for lung cancer. All of these signatures were built from training data sets by learning the correlation of gene expression with the patients'' survival time. They require all new sample data to be normalized to the training data, ultimately resulting in common problems of low reproducibility and impracticality. To overcome these problems, we propose a new signature model which does not involve data training. We hypothesize that the imbalance of two opposing effects in lung cancer cells, represented by Yin and Yang genes, determines a patient’s prognosis. We selected the Yin and Yang genes by comparing expression data from normal lung and lung cancer tissue samples using both unsupervised clustering and pathways analyses. We calculated the Yin and Yang gene expression mean ratio (YMR) as patient risk scores. Thirty-one Yin and thirty-two Yang genes were identified and selected for the signature development. In normal lung tissues, the YMR is less than 1.0; in lung cancer cases, the YMR is greater than 1.0. The YMR was tested for lung cancer prognosis prediction in four independent data sets and it significantly stratified patients into high- and low-risk survival groups (p = 0.02, HR = 2.72; p = 0.01, HR = 2.70; p = 0.007, HR = 2.73; p = 0.005, HR = 2.63). It also showed prediction of the chemotherapy outcomes for stage II & III. In multivariate analysis, the YMR risk factor was more successful at predicting clinical outcomes than other commonly used clinical factors, with the exception of tumor stage. The YMR can be measured in an individual patient in the clinic independent of gene expression platform. This study provided a novel insight into the biology of lung cancer and shed light on the clinical applicability.     

A Novel Model to Combine Clinical and Pathway-Based Transcriptomic Information for the Prognosis Prediction of Breast Cancer

Breast cancer is the most common malignancy in women worldwide. With the increasing awareness of heterogeneity in breast cancers, better prediction of breast cancer prognosis is much needed for more personalized treatment and disease management. Towards this goal, we have developed a novel computational model for breast cancer prognosis by combining the Pathway Deregulation Score (PDS) based pathifier algorithm, Cox regression and L1-LASSO penalization method. We trained the model on a set of 236 patients with gene expression data and clinical information, and validated the performance on three diversified testing data sets of 606 patients. To evaluate the performance of the model, we conducted survival analysis of the dichotomized groups, and compared the areas under the curve based on the binary classification. The resulting prognosis genomic model is composed of fifteen pathways (e.g. P53 pathway) that had previously reported cancer relevance, and it successfully differentiated relapse in the training set (log rank p-value = 6.25e-12) and three testing data sets (log rank p-value<0.0005). Moreover, the pathway-based genomic models consistently performed better than gene-based models on all four data sets. We also find strong evidence that combining genomic information with clinical information improved the p-values of prognosis prediction by at least three orders of magnitude in comparison to using either genomic or clinical information alone. In summary, we propose a novel prognosis model that harnesses the pathway-based dysregulation as well as valuable clinical information. The selected pathways in our prognosis model are promising targets for therapeutic intervention.     

Identification and Comparison of Aberrant Key Regulatory Networks in Breast,Colon, Liver,Lung, and Stomach Cancers through Methylome Database Analysis

Aberrant methylation of specific CpG sites at the promoter is widely responsible for genesis and development of various cancer types. Even though the microarray-based methylome analyzing techniques have contributed to the elucidation of the methylation change at the genome-wide level, the identification of key methylation markers or top regulatory networks appearing common in highly incident cancers through comparison analysis is still limited. In this study, we in silico performed the genome-wide methylation analysis on each 10 sets of normal and cancer pairs of five tissues: breast, colon, liver, lung, and stomach. The methylation array covers 27,578 CpG sites, corresponding to 14,495 genes, and significantly hypermethylated or hypomethylated genes in the cancer were collected (FDR adjusted p-value <0.05; methylation difference >0.3). Analysis of the dataset confirmed the methylation of previously known methylation markers and further identified novel methylation markers, such as GPX2, CLDN15, and KL. Cluster analysis using the methylome dataset resulted in a diagram with a bipartite mode distinguishing cancer cells from normal cells regardless of tissue types. The analysis further revealed that breast cancer was closest with lung cancer, whereas it was farthest from colon cancer. Pathway analysis identified that either the “cancer” related network or the “cancer” related bio-function appeared as the highest confidence in all the five cancers, whereas each cancer type represents its tissue-specific gene sets. Our results contribute toward understanding the essential abnormal epigenetic pathways involved in carcinogenesis. Further, the novel methylation markers could be applied to establish markers for cancer prognosis.     

Prognostic and Predictive Significance of MYC and KRAS Alterations in Breast Cancer from Women Treated with Neoadjuvant Chemotherapy

Breast cancer is a complex disease, with heterogeneous clinical evolution. Several analyses have been performed to identify the risk factors for breast cancer progression and the patients who respond best to a specific treatment. We aimed to evaluate whether the hormone receptor expression, HER2 and MYC genes and their protein status, and KRAS codon 12 mutations may be prognostic or predictive biomarkers of breast cancer. Protein, gene and mutation status were concomitantly evaluated in 116 breast tumors from women who underwent neoadjuvant chemotherapy with doxorubicin plus cyclophosphamide. We observed that MYC expression was associated with luminal B and HER2 overexpression phenotypes compared to luminal A (p<0.05). The presence of MYC duplication or polysomy 8, as well as KRAS mutation, were also associated with the HER2 overexpression subtype (p<0.05). MYC expression and MYC gain were more frequently observed in early-onset compared to late-onset tumors (p<0.05). KRAS mutation was a risk factor of grade 3 tumors (p<0.05). A multivariate logistic regression demonstrated that MYC amplification defined as MYC/nucleus ratio of ≥2.5 was a protective factor for chemotherapy resistance. On the other hand, age and grade 2 tumors were a risk factor. Additionally, luminal B, HER2 overexpression, and triple-negative tumors presented increased odds of being resistant to chemotherapy relative to luminal A tumors. Thus, breast tumors with KRAS codon 12 mutations seem to present a worse prognosis. Additionally, MYC amplification may help in the identification of tumors that are sensitive to doxorubicin plus cyclophosphamide treatment. If confirmed in a large set of samples, these markers may be useful for clinical stratification and prognosis.     

A Fourteen Gene GBM Prognostic Signature Identifies Association of Immune Response Pathway and Mesenchymal Subtype with High Risk Group

Background

Recent research on glioblastoma (GBM) has focused on deducing gene signatures predicting prognosis. The present study evaluated the mRNA expression of selected genes and correlated with outcome to arrive at a prognostic gene signature.

Methods

Patients with GBM (n = 123) were prospectively recruited, treated with a uniform protocol and followed up. Expression of 175 genes in GBM tissue was determined using qRT-PCR. A supervised principal component analysis followed by derivation of gene signature was performed. Independent validation of the signature was done using TCGA data. Gene Ontology and KEGG pathway analysis was carried out among patients from TCGA cohort.

Results

A 14 gene signature was identified that predicted outcome in GBM. A weighted gene (WG) score was found to be an independent predictor of survival in multivariate analysis in the present cohort (HR = 2^.507; B = 0^.919; p<0^.001) and in TCGA cohort. Risk stratification by standardized WG score classified patients into low and high risk predicting survival both in our cohort (p = <0^.001) and TCGA cohort (p = 0^.001). Pathway analysis using the most differentially regulated genes (n = 76) between the low and high risk groups revealed association of activated inflammatory/immune response pathways and mesenchymal subtype in the high risk group.

Conclusion

We have identified a 14 gene expression signature that can predict survival in GBM patients. A network analysis revealed activation of inflammatory response pathway specifically in high risk group. These findings may have implications in understanding of gliomagenesis, development of targeted therapies and selection of high risk cancer patients for alternate adjuvant therapies.     

Effect of Obesity on Plasma Alkaline Phosphatase Activity in Breast Cancer

Background:Breast cancer is most common cancer in women. Obesity is one of related-risk factor in breast cancer. In obese normal subjects, alkaline phosphatase (ALP) has been studied. However, there is no previous study investigate the association between ALP and obesity in breast cancer and its correlation with other clinical characteristics. Therefore, the objective of present study is to investigate the association between ALP and clinical characteristics in generally and obesity in particularly.Methods:A cross-study 111 new diagnosed breast cancer patients was included. Plasma ALP was measured in different subgroups: patients age <40 vs >40, premenopausal vs postmenopausal, estrogen receptor-positive (ER+) vs estrogen receptor negative (ER-), metastasis vs non-metastasis and obese vs non-obese patients. Results:Significant increasing on plasma ALP were shown between groups of each age, menopausal status, metastasis, and obesity (p< 0.05, p< 0.05, p< 0.01 and p< 0.05) respectively. Positive correlation was observed between plasma ALP and age, menopausal status, metastasis, and obesity (r: 0.616, p< 0.05; r: 0.667, p< 0.01; r: 0.691, p< 0.005; and r: 0.627, p< 0.01). Multiple regression analysis was indicated that ALP can be determined by menopausal status, metastasis, and obesity (β-Coefficient = 0.428, p< 0.01; β-Coefficient = 0.534; p< 0.001; β-coefficient= 0.545; p= 0.005), respectively. Conclusion:Together, the relation between ALP and obesity indicates that ALP could have a role in maturation of preadipocytes of breast cancer patients. Further investigations are needed to confirm that there could be a potential hormonal link between ALP and obesity in breast cancer patients.Key Words: Alkaline phosphatase, Breast cancer, Metastasis, Obesity, Menopausal status     

A Thirteen-Gene Expression Signature Predicts Survival of Patients with Pancreatic Cancer and Identifies New Genes of Interest

Background

Currently, prognostication for pancreatic ductal adenocarcinoma (PDAC) is based upon a coarse clinical staging system. Thus, more accurate prognostic tests are needed for PDAC patients to aid treatment decisions.

Methods and Findings

Affymetrix gene expression profiling was carried out on 15 human PDAC tumors and from the data we identified a 13-gene expression signature (risk score) that correlated with patient survival. The gene expression risk score was then independently validated using published gene expression data and survival data for an additional 101 patients with pancreatic cancer. Patients with high-risk scores had significantly higher risk of death compared to patients with low-risk scores (HR 2.27, p = 0.002). When the 13-gene score was combined with lymph node status the risk-score further discriminated the length of patient survival time (p<0.001). Patients with a high-risk score had poor survival independent of nodal status; however, nodal status increased predictability for survival in patients with a low-risk gene signature score (low-risk N1 vs. low-risk N0: HR = 2.0, p = 0.002). While AJCC stage correlated with patient survival (p = 0.03), the 13-gene score was superior at predicting survival. Of the 13 genes comprising the predictive model, four have been shown to be important in PDAC, six are unreported in PDAC but important in other cancers, and three are unreported in any cancer.

Conclusions

We identified a 13-gene expression signature that predicts survival of PDAC patients and could prove useful for making treatment decisions. This risk score should be evaluated prospectively in clinical trials for prognostication and for predicting response to chemotherapy. Investigation of new genes identified in our model may lead to novel therapeutic targets.     

The Expression of the Nectin Complex in Human Breast Cancer and the Role of Nectin-3 in the Control of Tight Junctions during Metastasis

Introduction

Nectins are a family of integral protein molecules involved in the formation of functioning Adherens and Tight Junctions (TJ). Aberrant expression is associated with cancer progression but little is known how this effects changes in cell behaviour. This study aimed to ascertain the distribution of Nectins-1 to -4 in human breast cancer and the effect on junctional integrity of Nectin-3 modulation in human endothelial and breast cancer cells.

Methods

A human breast tissue cohort was processed for Q-PCR and immunohistochemistry for analysis of Nectin-1/-2/-3/-4. Nectin-3 over-expression was induced in the human breast cancer cell line MDA-MB-231 and the human endothelial cell line HECV. Functional testing was carried out to ascertain changes in cell behaviour.

Results

Q-PCR revealed a distinct reduction in node positive tumours and in patients with poor outcome. There was increased expression of Nectin-1/-2 in patients with metastatic disease, Nectin-3/-4 was reduced. IHC revealed that Nectin-3 expression showed clear changes in distribution between normal and cancerous cells. Nectin-3 over-expression in MDA-MB-231 cells showed reduced invasion and migration even when treated with HGF. Changes in barrier function resulted in MDAN3 cells showing less change in resistance after 2h treatment with HGF (p<0.001). Nectin-3 transformed endothelial cells were significantly more adhesive, irrespective of treatment with HGF (p<0.05) and had reduced growth. Barrier function revealed that transformed HECV cells had significantly tighter junctions that wildtype cells when treated with HGF (p<0.0001). HGF-induced changes in permeability were also reduced. Overexpression of Nectin-3 produced endothelial cells with significantly reduced ability to form tubules (p<0.0001). Immunoprecipitation studies discovered hitherto novel associations for Nectin-3. Moreover, HGF appeared to exert an effect on Nectin-3 via tyrosine and threonine phosphorylation.

Conclusions

Nectin-3 may be a key component in the formation of cell junctions and be a putative suppressor molecule to the invasion of breast cancer cells.     

An estrogen receptor (ER)‐related signature in predicting prognosis of ER‐positive breast cancer following endocrine treatment

Quite a few estrogen receptor (ER)‐positive breast cancer patients receiving endocrine therapy are at risk of disease recurrence and death. ER‐related genes are involved in the progression and chemoresistance of breast cancer. In this study, we identified an ER‐related gene signature that can predict the prognosis of ER‐positive breast cancer patient receiving endocrine therapy. We collected RNA expression profiling from Gene Expression Omnibus database. An ER‐related signature was developed to separate patients into high‐risk and low‐risk groups. Patients in the low‐risk group had significantly better survival than those in the high‐risk group. ROC analysis indicated that this signature exhibited good diagnostic efficiency for the 1‐, 3‐ and 5‐year disease‐relapse events. Moreover, multivariate Cox regression analysis demonstrated that the ER‐related signature was an independent risk factor when adjusting for several clinical signatures. The prognostic value of this signature was validated in the validation sets. In addition, a nomogram was built and the calibration plots analysis indicated the good performance of this nomogram. In conclusion, combining with ER status, our results demonstrated that the ER‐related prognostic signature is a promising method for predicting the prognosis of ER‐positive breast cancer patients receiving endocrine therapy.     

Expressions of ZNF436, β-catenin,EGFR, and CMTM5 in breast cancer and their clinical significances

As the leading malignancy among women, breast cancer is a serious threat to the life and health of women. In this context, it is of particular importance that a proper therapeutic target be identified for breast cancer treatment. We collected the pathological tissues of 80 patients, with the view to discovering appropriate molecular targets for the treatment of breast cancer, this paper analyzes the expressions of ZNF436, β-catenin, EGFR and CMTM5 in breast cancer tissues, as well as their correlations with breast cancer in combination with the clinicopathologic characteristics of studied patients. Immunohistochemistry was utilized to detect the expression levels of ZNF436, β-catenin, EGFR and CMTM5 in cancerous and paracancerous tissues of breast cancer patients. The expression levels of ZNF436, β-Catenin and EGFR in breast cancer tissues were significantly greater than those in paracancerous tissues in this study (p<0.05), while CMTM5 was highly expressed in paracancerous tissues (p<0.05). Additionally, the correlation of the expressions of such indicators with the staging, differentiation and lymphatic metastasis of breast cancer, were also found to be statistically significant at the level p<0.05. The different expression levels of ZNF436, β-catenin, EGFR and CMTM5 in breast cancer and paracancerous tissues open up the possibility of utilizing them as molecular markers for breast cancer. These findings provide a theoretical basis for targeted molecular therapies for breast cancer, and hence carry a significant practical significance.Key words: Breast cancer, ZNF436, β-catenin, EGFR, CMTM5, immunohistochemistry     

MRI Background Parenchymal Enhancement Is Not Associated with Breast Cancer

BackgroundPreviously, a strong positive association between background parenchymal enhancement (BPE) at magnetic resonance imaging (MRI) and breast cancer was reported in high-risk populations. We sought to determine, whether this was also true for non-high-risk patients.Methods540 consecutive patients underwent breast MRI for assessment of breast findings (BI-RADS 0–5, non-high-risk screening (no familial history of breast cancer, no known genetic mutation, no prior chest irradiation, or previous breast cancer diagnosis)) and subsequent histological work-up. For this IRB-approved study, BPE and fibroglandular tissue FGT were retrospectively assessed by two experienced radiologists according to the BI-RADS lexicon. Pearson correlation coefficients were calculated to explore associations between BPE, FGT, age and final diagnosis of breast cancer. Subsequently, multivariate logistic regression analysis, considering covariate colinearities, was performed, using final diagnosis as the target variable and BPE, FGT and age as covariates.ResultsAge showed a moderate negative correlation with FGT (r = -0.43, p<0.001) and a weak negative correlation with BPE (r = -0.28, p<0.001). FGT and BPE correlated moderately (r = 0.35, p<0.001). Final diagnosis of breast cancer displayed very weak negative correlations with FGT (r = -0.09, p = 0.046) and BPE (r = -0.156, p<0.001) and weak positive correlation with age (r = 0.353, p<0.001). On multivariate logistic regression analysis, the only independent covariate for prediction of breast cancer was age (OR 1.032, p<0.001).ConclusionsBased on our data, neither BPE nor FGT independently correlate with breast cancer risk in non-high-risk patients at MRI. Our model retained only age as an independent risk factor for breast cancer in this setting.     

Correlation of Immunoglobulin G Expression and Histological Subtype and Stage in Breast Cancer

Introduction

Recently, growing evidence indicates that immunoglobulins (Igs) are not only produced by mature B lymphocytes or plasma cells, but also by various normal cells types at immune privileged sites and neoplasm, including breast cancer. However, the association of breast cancer derived IgG with genesis and development of the disease has not yet been established.

Methods

In this study we examined the expression of IgG in 186 breast cancers, 20 benign breast lesions and 30 normal breast tissues. Both immunohistochemistry with antibodies to Igκ (immunoglobulin G κ light chain) and Igγ (immunoglobulin G heavy chain) and in situ hybridization with an antisense probe to IgG1 heavy chain constant region gene were performed. Various clinicopathological features were also analyzed.

Results

We found that IgG is specifically expressed in human breast cancer cells. Both infiltrating ductal carcinoma and infiltrating lobular carcinoma had significantly greater numbers of Igκ and Igγ positive cancer cells as compared with medullary carcinoma, carcinoma in situ, and benign lesions (all p<0.05). In addition, IgG expression was correlated with breast cancer histological subtypes (p<0.01) and AJCC stages (p<0.05), with more abundance of IgG expression in more malignant histological subtypes or in more advanced stage of the disease.

Conclusions

IgG expression in breast cancer cells is correlated with malignancy and AJCC stages of the cancers. This suggests that breast cancer derived IgG may be associated with genesis, development and prognosis of the cancer.