Predictor Of Prognosis In Breast Cancer Research Articles

Circulating HOTAIR expression predicts the clinical response to neoadjuvant chemotherapy in patients with breast cancer.

Long noncoding RNA HOTAIR has been detected in the serum of patients with various malignances and may be served as novel biomarker for diagnosis and prognosis prediction of breast cancer. However, the value of circulating HOTAIR to predict the response to neoadjuvant chemotherapy (NAC) remains unclear. In the present study, we analyzed whether pretreatment circulating HOTAIR levels predict the response to NAC and investigated prognostic impact of circulating HOTAIR on disease-free survival (DFS) in breast cancer patients treated with NAC. Circulating HOTAIR levels in the serum of 112 breast cancer patients before NAC were measured using quantitative real-time PCR. The correlation of circulating HOTAIR with the clinicopathologic status and the response to NAC were analyzed. Kaplan-Meier survival analysis and log-rank test were used to estimate the DFS. In 112 serum samples obtained before NAC, high circulating HOTAIR was associated with larger tumor size, more positive lymph nodes as well as more distant metastasis. However, there was no significant correlation between the circulating HOTAIR levels and age, Ki67 status or hormone receptor. Furthermore, patients with high circulating HOTAIR achieved less clinical response as well as pathologic complete response than those with low circulating HOTAIR (p< 0.05). The Kaplan-Meier survival curve with a median follow-up of 48 months demonstrated that patients with high circulating HOTAIR expression had a worse disease-free survival than those with low circulating HOTAIR (log-rank p= 0.012). High circulating HOTAIR level correlates with less response to neoadjuvant chemotherapy as well as a worse prognosis in breast cancer patients. Therefore, the present study provides a favorable basis to use circulating HOTAIR as a predictor of neoadjuvant chemotherapy response.

Tumor stiffness measured by quantitative and qualitative shear wave elastography of breast cancer.

To correlate clinicoradiologic and pathological features of breast cancer with quantitative and qualitative shear wave elastographic parameters. 82 breast cancers in 75 patients examined by B-mode ultrasound and shear wave elastography (SWE) were included. SWE parameters including quantitative factors [maximum elasticity (Emax), mean elasticity (Emean), elasticity ratio (Eratio) and standard deviation (SD)] and qualitative factor (color pattern) were correlated with clinicoradiologic and pathological features using univariate and multivariate linear regression analyses. Presence of symptoms and larger tumor size on ultrasound were significantly associated with higher Emax, Emean, Eratio, and SD (all p < 0.05) on univariate analysis. Older age was significantly correlated with higher Emax and Emean (p = 0.026, 0.018). Lymphovascular invasion and larger pathologic size were significantly associated with higher Emax (p = 0.036, 0.043) and SD (p < 0.001, 0.019). No immunohistochemical biomarkers were significantly correlated with SWE parameters. There was no significant correlation between color pattern and any variable. Multivariate logistic regression analysis showed that the symptom, tumor size on ultrasound and lymphovascular invasion were independent factors that influenced the SWE values. Tumor stiffness as measured by SWE and B-mode ultrasound could help predict cancer prognosis. Advances in knowledge: Clinicoradiologic factors had correlation with quantitative and qualitative SWE parameters. Using SWE parameters and B-mode ultrasound, we can predict breast cancer prognosis.

Expression of NAMPT is associated with breast invasive ductal carcinoma development and prognosis.

Nicotinamide phosphoribosyltransferase (NAMPT) possesses various functions in human cells, and altered NAMPT expression is associated with human carcinogenesis. The present study detected the expression of NAMPT in normal and cancerous breast tissues from 83 patients using immunohistochemistry, and analyzed its association with the clinicopathological and survival data of the patients. NAMPT was significantly overexpressed in the breast invasive ductal carcinoma tissues compared with adjacent normal mammary gland tissues. Upregulated NAMPT expression was associated with a larger tumor size, lymph node metastasis, advanced clinical tumor-node-metastasis stages, and estrogen receptor and progesterone receptor expression. Furthermore, NAMPT expression was associated with poor overall and disease-free survival in patients with breast cancer. In conclusion, NAMPT increased protein expression in tumor cells may contribute to the development and progression of breast invasive ductal carcinoma. Thus, detection of NAMPT expression might be useful as a biomarker for the early detection and prognosis prediction of breast cancer.

A multimodal deep neural network for human breast cancer prognosis prediction by integrating multi-dimensional data.

Breast cancer is a highly aggressive type of cancer with very low median survival. Accurate prognosis prediction of breast cancer can spare a significant number of patients from receiving unnecessary adjuvant systemic treatment and its related expensive medical costs. Previous work relies mostly on selected gene expression data to create a predictive model. The emergence of deep learning methods and multi-dimensional data offers opportunities for more comprehensive analysis of the molecular characteristics of breast cancer and therefore can improve diagnosis, treatment and prevention. In this study, we propose a Multimodal Deep Neural Network by integrating Multi-dimensional Data (MDNNMD) for the prognosis prediction of breast cancer. The novelty of the method lies in the design of our method's architecture and the fusion of multi-dimensional data. The comprehensive performance evaluation results show that the proposed method achieves a better performance than the prediction methods with single-dimensional data and other existing approaches. The source code implemented by TensorFlow 1.0 deep learning library can be downloaded from the Github: https://github.com/USTC-HIlab/MDNNMD.

DCE-MRI texture analysis with tumor subregion partitioning for predicting Ki-67 status of estrogen receptor-positive breast cancers.

Breast tumor heterogeneity is related to risk factors that lead to worse prognosis, yet such heterogeneity has not been well studied. To predict the Ki-67 status of estrogen receptor (ER)-positive breast cancer patients via analysis of tumor heterogeneity with subgroup identification based on patterns of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Retrospective study. Seventy-seven breast cancer patients with ER-positive breast cancer were investigated, of whom 51 had low Ki-67 expression. T1 -weighted 3.0T DCE-MR images. Each tumor was partitioned into multiple subregions using three methods based on patterns of dynamic enhancement: 1) time to peak (TTP), 2) peak enhancement rate (PER), and 3) kinetic pattern clustering (KPC). In each tumor subregion, 18 texture features were computed. Univariate and multivariate logistic regression analyses were performed using a leave-one-out-based cross-validation (LOOCV) method. The partitioning results were compared with the same feature extraction methods across the whole tumor. In the univariate analysis, the best-performing feature was the texture statistic of sum variance in the tumor subregion with early TTP for differentiating between patients with high and low Ki-67 expression (area under the receiver operating characteristic curves, AUC = 0.748). Multivariate analysis showed that features from the tumor subregion associated with early TTP yielded the highest performance (AUC = 0.807) among the subregions for predicting the Ki-67 status. Among all regions, the tumor area with high PER at a precontrast MR image achieved the highest performance (AUC = 0.722), while the subregion that exhibited the highest overall enhancement rate based on KPC had an AUC of 0.731. These three models based on intratumoral texture analysis significantly (P < 0.01) outperformed the model using features from the whole tumor (AUC = 0.59). Texture analysis of intratumoral heterogeneity has the potential to serve as a valuable clinical marker to enhance the prediction of breast cancer prognosis. 4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2017.

BCScreen: A gene panel to test for breast carcinogenesis in chemical safety screening

Targeted gene lists have been used in clinical settings to specify breast tumor type, and to predict breast cancer prognosis and response to treatment. Separately, panels have been curated to predict systemic toxicity and xenoestrogen activity as a part of chemical screening strategies. However, currently available panels do not specifically target biological processes relevant to breast development and carcinogenesis. We have developed a gene panel called the Breast Carcinogen Screen (BCScreen) as a tool to identify potential breast carcinogens and characterize mechanisms of toxicity. First, we used four seminal reviews to identify 14 key characteristics of breast carcinogenesis, such as apoptosis, immunomodulation, and genotoxicity. Then, using a hybrid data and knowledge-driven framework, we systematically combined information from whole transcriptome data from genomic databases, biomedical literature, the CTD chemical-gene interaction database, and primary literature review to generate a panel of 500 genes relevant to breast carcinogenesis. We used normalized pointwise mutual information (NPMI) to rank genes that frequently co-occurred with key characteristics in biomedical literature. We found that many genes identified for BCScreen were not included in prognostic breast cancer or systemic toxicity panels. For example, more than half of BCScreen genes were not included in the Tox21 S1500+ general toxicity gene list. Of the 230 that did overlap between the two panels, representation varied across characteristics of carcinogenesis ranging from 21% for genes associated with epigenetics to 82% for genes associated with xenobiotic metabolism. Enrichment analysis of BCScreen identified pathways and processes including response to steroid hormones, cancer, cell cycle, apoptosis, DNA damage and breast cancer. The biologically-based systematic approach to gene prioritization demonstrated here provides a flexible framework for creating disease-focused gene panels to support discovery related to etiology. With validation, BCScreen may also be useful for toxicological screening relevant to breast carcinogenesis.

Abstract P1-08-09: Aurora kinase-A protein stability is negatively regulated by eEF1α2 and PTEN in breast cancer: Prognostic and therapeutic implications

Abstract Background: The AURKA gene encoding Aurora kinase-A (Aurora-A) protein is localized on chromosome 20q13 that is frequently amplified and overexpressed across multiple cancer types correlating with patient prognosis. Aurora-A plays a pivotal role in faithful segregation of chromosomes and normal progression of mitosis, peaking at G2/M followed by degradation at the end of mitosis by APC/C (Cdh1). However, regulation of Aurora-A protein stability in human cancer cells is not well elucidated. Here, we show that Aurora-A is targeted for ubiquitination and degradation by SCF complex involving eEF1α2 and PTEN in human breast cancer cells. Methods: Using a panel of breast cancer cell lines, as in vitro models, the eEF1α2 was knocked down or ectopically expressed to test the stability of Aurora-A protein. For in vivo models, tissue micro arrays of human breast cancer were immunostained for Aurora-A and eEF1α2 expression and categorized values were statistically tested by Chi-squared test. In addition, the public breast cancer dataset (Transbig) was used to predict breast cancer prognosis by Kaplan-Meier survival analysis. Results: In breast cancer cell lines and patient samples, an eEF1α2 non-expressing group showed a trend of higher Aurora-A expression than eEF1α2 expressing group, whose trend was significant in patient samples (P&amp;lt;0.05). Knocking down of eEF1α2 enhanced Aurora-A protein stability. In contrast, ectopic expression of eEF1α2 dramatically decreased Aurora-A protein stability. Inhibition of proteasome activity by MG132 could restore the Aurora-A protein in eEF1α2 expressing cells. Biochemical assays showed the direct binding between eEF1α2 and Aurora-A, and eEF1α2 dependent ubiquitination of Aurora-A. Inverse correlation of the expression levels of the two proteins was also observed throug the cell cycle, with eEF1α2 levels being high from G1 through G2 phases while Aurora-A expression peaked from G2/M phase through cytokinesis. Taken together, these findings highlight eEF1α2 as a novel negative regulator destabilizing Aurora-A through ubiquitin-proteasome proteolytic pathway. Further, mechanistic studies revealed that eEF1α2 enhanced the interaction of SCF E3 ubiquitin ligase complex protein; FBXW7 and Cul1, with Aurora-A. In line with this scenario, knocking down of Cul1 increased Aurora-A level. Since PTEN loss was reported to stabilize Aurora-A through inhibiting SCF complex, we tested the significance of PTEN loss in our model. Knocking down of PTEN further stabilized Aurora-A suggesting an independent role of PTEN from eEF1α2 in destabilizing Aurora-A. When eEF1α2 expressing cells were treated with AKT inhibitor, Aurora-A was destabilized with enhanced bindings between Aurora-A and FBXW7/Cul1. Lastly, low PTEN expression correlated with poor prognosis of Aurora-A over expressing breast cancer patients (P&amp;lt;0.01). Conclusions: Aurora-A overexpression in human breast cancer cells may be associated with loss of eEF1α2 and PTEN due to reduced interaction of SCF with Aurora-A. Findings indicate significant prognostic and therapeutic implications of altered expression of eEF1α2/PTEN/Aurora-A pathways among Aurora-A subset of breast cancer patients. Citation Format: Treekitkarnmongkol W, Kai K, Katayama H, Tian W, Rodriguez-Canales J, Sahin AA, Sen S. Aurora kinase-A protein stability is negatively regulated by eEF1α2 and PTEN in breast cancer: Prognostic and therapeutic implications [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P1-08-09.

Current status of the prognostic molecular biomarkers in breast cancer: A systematic review.

Biomarkers that facilitate the prediction of breast cancer prognosis can improve the quality of life in patients during the long period of illness and treatment. Particularly in recent years, with the advent of a more exhaustive analysis of genetic information and gene products, the molecular mechanisms at play during breast cancer have gradually become clearer. In the present study, a systematic review of the literature between 2009 and 2014 was conducted by searching for the keywords 'breast cancer', 'biomarkers', 'diagnosis', 'prognosis' and 'drug response' to clarify the present state of knowledge regarding biomarkers. In the final analysis, 16 studies on biomarkers for the breast cancer prognosis were retrieved. From these, 7 biomarkers in 9 studies were found to be strongly reliable predictors of prognosis and a further 7 biomarkers in 7 studies were poorly reliable. The use of these prognostic biomarkers should increase the options available for treatment algorithms.

A Comparative Performance Analysis of Multispectral and RGB Imaging on HER2 Status Evaluation for the Prediction of Breast Cancer Prognosis

Despite the extensive application of multispectral imaging (MSI) in biomedical multidisciplinary researches, there is a paucity of data available regarding the implication of MSI in tumor prognosis prediction. We compared the behaviors of multispectral (MS) and conventional red-green-blue (RGB) images on assessment of human epidermal growth factor receptor 2 (HER2) immunohistochemistry to explore their impact on outcome in patients with invasive breast cancer (BC). Tissue microarrays containing 240 BC patients were introduced to compare the performance of MS and RGB imaging methods on the quantitative assessment of HER2 status and the prognostic value of 5-year disease-free survival (5-DFS). Both the total and average signal optical density values of HER2 MS and RGB images were analyzed, and all patients were divided into two groups based on the different 5-DFS. The quantification of HER2 MS images was negatively correlated with 5-DFS in lymph node–negative and –positive patients (P<.05), but RGB images were not in lymph node–positive patients (P=.101). Multivariate analysis indicated that the hazard ratio (HR) of HER2 MS was higher than that of HER2 RGB (HR=2.454; 95% confidence interval [CI], 1.636-3.681 vs HR=2.060; 95% CI, 1.361-3.119). Additionally, area under curve (AUC) by receiver operating characteristic analysis for HER2 MS was greater than that for HER2 RGB (AUC=0.649; 95% CI, 0.577-0.722 vs AUC=0.596; 95% CI, 0.522-0.670) in predicting the risk for recurrence. More importantly, the quantification of HER2 MS images has higher prediction accuracy than that of HER2 RGB images (69.6% vs 65.0%) on 5-DFS. Our study suggested that better information on BC prognosis could be obtained from the quantification of HER2 MS images and MS images might perform better in predicting BC prognosis than conventional RGB images.

Co-expression modules identified from published immune signatures reveal five distinct immune subtypes in breast cancer.

There is a growing body of literature demonstrating that immune-related expression signatures predict breast cancer prognosis and chemo-/targeted-therapy responsiveness. However, it is unclear whether these signatures correlate with each other or represent distinct immune-related signals. We evaluated 57 published immune-related expression signatures in four public breast cancer datasets totaling 3295 samples. For each dataset, we used consensus clustering to group signatures together based on their co-expression pattern. Signatures that were in the same consensus cluster across all four datasets were used to define immune modules. Tumors were then classified into immune subtypes based on their module scores using consensus clustering. Survival analysis was conducted using Cox proportional hazards modeling. Consensus clustering consistently yields four distinct co-expression modules across the datasets. These modules appear to represent distinct immune components and signals, where constituent signatures relate to (1) T-cells and/or B-cells (T/B-cell), (2) interferon (IFN), (3) transforming growth factor beta (TGFB), and (4) core-serum response, dendritic cells, and/or macrophages (CSR). Subtyping of tumors based on these co-expression modules consistently yields subsets that fall into five major immune subtypes: T/B-cell/IFN High, IFN/CSR High, CSR High, TGFB High, and Immune Low. Basal and/or triple-negative breast cancer patients with CSR High tumors have significantly worse outcome relative to those within the T/B-cell/IFN High subtype. Our exploratory study identified four distinct immune co-expression modules (T/B-cell, IFN, TGFB, or CSR) from published immune signatures. Using these modules, we identified five immune subtypes with significant outcome differences in basal breast cancers.

Pattern of RECK CpG methylation as a potential marker for predicting breast cancer prognosis and drug-sensitivity.

The membrane-anchored glycoprotein RECK negatively regulates multiple metalloproteinases and is frequently downregulated in tumors. Forced RECK expression in cancer cells results in suppression of tumor angiogenesis, invasion, and metastasis in xenograft models. A previous methylome study on breast cancer tissues detected inverse correlation between RECK CpG methylation (in an intron-1 region) and relapse-free survival. In this study, we focused on another region of the RECK CpG island (a promoter/exon-1 region) and found an inverse correlation between its methylation and RECK-inducibility by an HDAC inhibitor, MS275, among a panel of breast cancer cell lines (n=15). In clinical samples (n=62), RECK intron-1 methylation was prevalent among luminal breast cancers as reported previously (26 of 38 cases; 68%) and particularly enriched in tumors of the ER+PR- subclass (10 of 10 cases) and of higher histological grades (Grade 2 and 3; 28 of 43 cases; P=0.006). In about a half of these cases, promoter/exon-1 methylation was absent, and hence, RECK may be inducible by certain drugs such as MS275. Our results indicate the value of combined use of two RECK methylation markers for predicting prognosis and drug-sensitivity of breast cancers.

Abstract P3-05-04: Estrogen receptor α-regulation of nucleocytoplasmic transport pathways as modulators of breast cancer therapy effectiveness

Abstract Estrogen Receptor α (ERα), a member of the large superfamily of nuclear receptors, exerts profound effects on gene expression, cellular response programs, and phenotypic properties of estrogen target cells. Because of these broad and important actions, ERα is considered the single most important predictor of breast cancer prognosis and is the target of endocrine therapies. The importance of kinases in cancer biology is well known, as increased kinase activity through phosphorylation, mutations or increased expression is often observed in clinical samples and is associated with a poorer prognosis. It is believed that, in therapy-resistant breast cancers, control of cellular physiology switches from ERα nuclear-initiated pathways to extranuclear-activated protein kinase pathways, which enable these cells to adopt a more aggressive phenotype. However, the mechanisms underlying the interplay between ERα and protein kinase pathways in cancer, and the processes by which ERα influences these pathways are poorly understood. Our aim in this study was to elucidate how ERα modulated extranuclear- initiated kinase signaling through alteration of the subcellular localization of ERK5. We have previously shown that upon estradiol treatment ERα elicits nuclear localization of ERK5 and Cofilin in ERα-positive breast cancer cells. This event diminishes ERK5 and Cofilin localization to regions of high actin remodeling in the cytoplasm, thereby possibly accounting for the reduced invasiveness and metastatic potential that is characteristic of many ERα-positive vs. ERα-negative breast cancer cells (PMID: 24505128). In our studies involving an endocrine-resistance cell model, we have found that ERK5 and Cofilin become localized to the cytoplasm as resistance progresses. Using several publicly available tumor gene expression databases, we have identified a signature of genes regulating nucleocytoplasmic transport that are differentially upregulated in more aggressive tumors. Additionally, using RPPA data from TCGA, we find that high expression of the signature genes correlates with higher phosphorylation of key signaling molecules like TAZ, indicative of their mislocalization in invasive breast carcinomas. Low expression of signature genes would successfully predict those patients that would respond to endocrine therapy with Luminal B type tumors that are generally more aggressive and harder to manage clinically. We verified our findings in cell line models by modulating levels and activities of these proteins by overexpression, knock-down and use of small molecule inhibitors. These findings provide insight into how ERα regulates extranuclear initiated kinase signaling by modulating nuclear transport of key kinases in breast cancer. They also suggest that therapeutic targeting of nucleocytoplasmic transport machinery in breast cancer might decrease aggressiveness of breast cancers and increase the efficiency of endocrine therapies by sequestering factors in their proper subcellular localizations where they would contribute to effective anti-estrogenic actions of endocrine targeting agents. Citation Format: Madak-Erdogan Z, Chen-Zhao Y, Wrobel K. Estrogen receptor α-regulation of nucleocytoplasmic transport pathways as modulators of breast cancer therapy effectiveness. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P3-05-04.

Abstract P5-15-05: The expression of genes involved in cholesterol biosynthesis is heterogeneous in breast cancer and may predict sensitivity to statin treatment and clinical outcome

Abstract Background: Epidemiological evidence supports a protective role of cholesterol lowering drugs against breast cancer recurrence. Preclinical studies also support this role since statins can suppress breast cancer progression in various models. The response of breast cancer cells to statin treatment is however heterogeneous. We performed a comprehensive molecular characterisation of the anti-cancer mechanisms of statins in-vitro in view of identifying biomarkers of statin sensitivity in breast cancer. Methods: Breast cancer cell lines were treated with atorvastatin and the effects on cell proliferation were determined. A panel of cell lines displaying a diverse range of sensitivity to atorvastatin treatment were selected for gene expression microarray profiling following treatment. Differentially expressed genes were identified and subjected to gene ontology analysis and links to outcome following breast cancer diagnosis were explored. Results: Statin treatment elicited a more potent inhibition of proliferation in estrogen receptor (ER) negative cell lines. This was reflected on the whole genome transcriptional scale as the ER negative cells also displayed a more robust dysregulation of mRNA transcripts relative to the less-sensitive ER expressing cells. Down-regulation of DNA replication, regulation of cell cycle progression, and other proliferation-associated biological processes via mechanisms influencing the transcriptional activity of E2F was observed in the statin-sensitive cells. Importantly, cholesterol biosynthesis via the mevalonate pathway was upregulated in all cell lines following statin treatment. This effect was directly correlated with the pre-treatment expression levels of target genes involved in this biological process, suggesting a link with sensitivity to statin treatment. The expression of cholesterol biosynthesis genes was also found to be variable in primary breast tumors and a significant association was observed between decreased expression and an improved recurrence-free and overall survival, especially in ER positive tumors. Conclusions: These data suggest that the normal feedback regulation of the mevalonate pathway induced by statins may be compromised in a subset of breast cancer cells and may be exploited to predict breast cancer prognosis and sensitivity to statin treatment. Citation Format: Kimbung S, Feldt M, Bosch A, Borgquist S. The expression of genes involved in cholesterol biosynthesis is heterogeneous in breast cancer and may predict sensitivity to statin treatment and clinical outcome. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P5-15-05.

Abstract P5-08-12: Co-expression modules identified from published immune signatures reveals five distinct immune subtypes in breast cancer

Abstract Introduction Immune modulating therapies offer an attractive novel approach in the treatment of breast cancer. There is a growing body of literature demonstrating that immune-related expression signatures predict breast cancer prognosis and chemo- and/or targeted therapy responsiveness. However, it is unclear how these signatures relate to one another. Here we evaluated 58 immune signatures in breast cancer and generated co-expression modules to classify patients into immune subtypes. Methods We evaluated 58 published expression signatures related to immune function in 5 breast cancer gene expression datasets (TCGA (n=817), METABRIC (n=1992), EMC344 (n=344), pooled triple negative: GSE31519 (n=579), pooled neoadjuvant chemotherapy treated: GSE25066 (n=508)). For each dataset, consensus clustering was used to subset the signatures based on their co-expression pattern. Signatures in the same consensus cluster across all 5 datasets were used to define immune modules. Module scores were computed as the average across their constituent signatures. Patients were classified into immune subtypes based on their module scores using consensus clustering. Overall survival (OS) differences between immune subtypes were assessed using Cox proportional hazard modeling in basal breast cancers from the METABRIC dataset (n=329). Results Consensus clustering of the 58 expression signatures consistently yields four distinct co-expression modules across the five datasets. These modules appear to represent distinct immune components and signals, with constituent signatures relating to: 1) T-cells and/or B-cells (T/B-cell), 2) interferon (IFN), 3) transforming growth factor beta (TGFB), 4) core serum response, dendritic cells and/or macrophages (CSR). Of note, the T/B-cell module contains 20 of the 58 signatures evaluated; and the CSR module is highly correlated to proliferation (r=0.81). Subtyping of patients based on these co-expression modules consistently yields subsets that fall into five major immune subtypes. The expression pattern of the four modules within each immune subtype is summarized below: T/B-cellIFNTGFBCSRT/B-cell/IFN HighHighHighIntermediateIntermediateIFN/CSR HighLowHighLowHighImmune LowLowLowLowLowCSR HighLowLowLowHighTGFB HighLowLowHighLowImmune Co-expression Modules (columns); Immune Subtypes (rows) These immune subtypes are associated with differences in overall survival in the METABRIC basal breast cancer cases, where the CSR High subtype has the worst outcome (10-year OS: 23%). In comparison, the subsets corresponding to the T/B-cell/IFN High subtype have better outcomes (Hazard ratio: 0.43, p = 0.018). In contrast, no significant outcome differences were observed between the poor outcome CSR-High subtype and the remaining three immune subtypes (p&amp;gt;0.05). Conclusion Our exploratory study identified four distinct immune co-expression modules (T/B-cell, IFN, TGFB, or CSR) from a collection of published immune signatures. Using these modules, we identified 5 immune subtypes with prognostic significance in basal breast cancers. We propose to test representative signatures from the 4 modules and the combined immune subtypes as predictive biomarkers of response to immunotherapies. Citation Format: Amara D, Wolf D, van 't Veer L, Esserman LJ, Campbell MJ, Yau C. Co-expression modules identified from published immune signatures reveals five distinct immune subtypes in breast cancer. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P5-08-12.

Long-term survival analysis of different breast cancer molecular subtypes: Shanghai Breast Cancer Survival Study

To analyze the survival of breast cancer molecular subtypes and to examine the effect of therapy on the long-term prognosis of different subtypes. This study included 3 586 breast cancer patients with estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor-2 (HER2) information in Shanghai Breast Cancer Survival Study, a population-based prospective cohort study established in 2002. Molecular subtypes, based on immunohistochemistry were categorized as follows: Luminal A, Luminal B, HER2, and triple-negative subtype. Characteristics and clinical data were collected through questionnaires and medical records at baseline survey and sequential follow-up surveys. Survival rates of different molecular subtypes were analyzed and compared with Log-rank tests. Multiple Cox regression models were used to evaluate the effect of therapy on long-term prognosis of different molecular subtypes. Among the 3 586 cases, Luminal A, Luminal B, HER2 and triple-negative breast cancer subtypes accounted for 54.5%, 16.6%, 13.9%, and 14.9%, respectively. With a median follow-up of 10.3 years (ranging 0.6 to 12.8 years), the 10-year overall survival (OS) rates for the four subtypes were 82.7% (95% CI: 80.9% to 84.4%), 77.7% (95% CI: 74.1% to 80.8%), 76.3% (95% CI: 72.3% to 79.8%), and 74.8% (95% CI: 70.9% to 78.3%), respectively. The 10-year disease to free survival (DFS) rates were 79.0% (95% CI: 76.7% to 81.0%), 76.0% (95% CI: 71.9% to 79.5%), 73.6% (95% CI: 68.9% to 77.7%), and 74.5% (95% CI: 69.4% to 78.9%), respectively. Significant difference in survival among four subtypes was observed (Log-rank test, P<0.01). Multivariate Cox regression indicated that hormonal therapy can significantly reduce the long-term risk of total mortality and recurrence breast cancer specific mortality among Luminal A subtype patients. Adjuvant chemotherapy could improve the long-term prognosis of triple-negative breast cancer. No benefit from radiotherapy was observed for four subtypes of breast cancer in terms of long-term prognosis. Molecular subtypes based on ER/PR/HER2 could provide important information to predict breast cancer prognosis. The hormonal status was an important basis for individualized therapy and precision medicine.

Cyclin D3 predicts disease-free survival in breast cancer.

BackgroundCyclin D3, which induces progression through the G1 phase of the cell cycle, is a regulator of Cyclin-dependent kinases 4 and 6. Previous studies revealed that abnormal expression of Cyclin D3 was found in many different cancers. However, the role of Cyclin D3 in breast cancer (BC) remains unknown. The aim of this study is to examine the expression pattern of Cyclin D3 in BC and to evaluate its biological role and clinical significance in prognosis prediction. The mechanism involved is also evaluated.MethodsImmunohistochemical staining was used to detect the expression of Cyclin D3. qRT-PCR was used to detect the mRNA level of Cyclin D3 in BC tissues and BC cell lines. Transwell assay was used to examine the role of Cyclin D3 in the migration and invasion of BC cells. Mass Spectrometry was used to search for the interacting protein with Cyclin D3. Co-Immunoprecipitation assay and GST-Pull Down assay were used to validate the interaction of Cyclin D3 and its interaction protein.ResultsThrough detecting Cyclin D3 expression in 243 breast cancer patients’ tissue array, we found Cyclin D3 expression was correlated with ER status (p = 0.000), PR status (p = 0.001), HER2 status (p = 0.002) and tumor differentiation (p = 0.045). The Kaplan–Meier survival curves indicated that the disease free survival (DFS) was significantly poor in high Cyclin D3 expression BC patients (p = 0.004). Furthermore, expression of Cyclin D3 was significantly associated with BC prognosis and was shown to be an independent prognostic marker in breast cancer (p = 0.028). By IHC staining and qPCR detection, Cyclin D3 expression was found to be down-regulated both in BC tissues and in BC cell lines compared with the corresponding normal controls. Further investigation showed Cyclin D3 was involved in the metastasis of BC cells and physically interacted with actin in vivo and in vitro.ConclusionOur studies revealed that Cyclin D3 was upregulated in breast cancer and represented a novel predictor of BC prognosis.Electronic supplementary materialThe online version of this article (doi:10.1186/s12935-015-0245-6) contains supplementary material, which is available to authorized users.

Biological and Clinical Significance of MAD2L1 and BUB1, Genes Frequently Appearing in Expression Signatures for Breast Cancer Prognosis.

To investigate the biologic relevance and clinical implication of genes involved in multiple gene expression signatures for breast cancer prognosis, we identified 16 published gene expression signatures, and selected two genes, MAD2L1 and BUB1. These genes appeared in 5 signatures and were involved in cell-cycle regulation. We analyzed the expression of these genes in relation to tumor features and disease outcomes. In vitro experiments were also performed in two breast cancer cell lines, MDA-MB-231 and MDA-MB-468, to assess cell proliferation, migration and invasion after knocking down the expression of these genes. High expression of these genes was found to be associated with aggressive tumors and poor disease-free survival of 203 breast cancer patients in our study, and the association with survival was confirmed in an online database consisting of 914 patients. In vitro experiments demonstrated that lowering the expression of these genes by siRNAs reduced tumor cell growth and inhibited cell migration and invasion. Our investigation suggests that MAD2L1 and BUB1 may play important roles in breast cancer progression, and measuring the expression of these genes may assist the prediction of breast cancer prognosis.

Combined Androgen and Estrogen Receptor Status in Breast Cancer: Treatment Prediction and Prognosis in a Population-Based Prospective Cohort.

To evaluate whether tumor androgen receptor (AR) expression was prognostic and/or predictive for endocrine treatment alone or in combination with estrogen receptor (ER). The AR has been hypothesized to have differential prognostic roles in breast cancer depending on tumor ER status, and to influence endocrine treatment response. A population-based prospective cohort of 1,026 patients diagnosed with primary invasive breast cancer in Lund, Sweden, between 2002 and 2012 was followed until June 2014. Associations between immunohistochemical AR expression in tumor tissue microarrays, patient and tumor characteristics, and AR genotypes were analyzed. Disease-free survival (DFS) by AR status, and combined ER/AR status was assessed in various treatment groups. AR expression was assessable in 913 tumors. AR(+) tumors (85.0%) were associated with higher age (P = 0.036) and favorable tumor characteristics. The AR(+) status was a prognostic marker for DFS (LogRank P = 0.025). There was an interaction between AR and ER expression with respect to prognosis (adjusted P(interaction) ≤ 0.024). Tumors with discordant hormone receptor expressions (ER(+)AR(-) or ER(-)AR(+)) demonstrated worse prognosis compared with concordant tumor expressions (ER(+)AR(+) or ER(-)AR(-)) in multivariable models [adjusted HRs (95% confidence intervals); ≥ 1.99 (1.28-3.10), P ≤ 0.002]. ER(+)AR(-) indicated early treatment failure with aromatase inhibitors (AI) among chemonaïve patients aged 50 or older. Prediction of breast cancer prognosis and treatment response was improved by combining AR and ER status. AR negativity predicted early treatment failure with AI but not tamoxifen, a finding that warrants confirmation in a randomized setting. Patients may benefit from anti-androgens or selective AR modulators.

Circulating Biomarkers in Breast Cancer Prognosis and Survival: A Systematic Review

PurposePrevious studies of survivors of breast cancer have drawn heterogeneous conclusions regarding the association between circulating biomarkers and breast cancer prognosis and survival. We sought to determine the role of circulating biomarkers in breast cancer prognosis and survival through the conduct of a systematic review and meta‐analysis.MethodsA MEDLINE search was performed from its inception through 2014. Studies met the inclusion criteria if they: (1) included at least 10 women diagnosed with breast cancer of any age, stage, and menopausal status; (2) assessed circulating biomarkers at least one year prior to ascertaining breast cancer prognosis and survival outcomes; (3) evaluated all‐cause mortality, breast cancer death, recurrence, or disease‐free survival.ResultsTwenty‐six studies met the inclusion criteria for systematic review. Fourteen studies were subject to meta‐analysis that included a total of 11,954 breast cancer survivors. High circulating levels of C‐reactive protein (CRP) were associated with an increased risk of all‐cause mortality (HR = 1.7, 95% CI: 1.1‐2.5), breast cancer death (HR = 1.8, 95% CI: 1.4‐2.4), recurrence (HR = 1.3, 95% CI: 0.9‐1.9), and reduced disease‐free survival (HR = 1.9, 95% CI: 1.3‐2.2). High circulating levels of glucose were associated with an increased risk of all‐cause mortality (HR = 1.9, 95% CI: 1.2‐2.8) and breast cancer recurrence (HR = 1.6, 95% CI: 1.0‐2.6). Existing evidence did not support associations between other circulating biomarkers and breast cancer prognosis and survival.ConclusionsCirculating CRP and glucose can serve as valuable biomarkers to predict breast cancer prognosis and survival.

마이크로어레이 데이터와 PPI 데이터를 이용한 에스트로겐 수용체 음성 유방암 환자의 예후 특이 네트워크 식별 및 예후 예측

This study proposes an algorithm for predicting breast cancer prognosis based on genetic network. We identify prognosis-specific network using gene expression data and PPI(protein-protein interaction) data. To acquire the network, we calculate Pearson's correlation coefficient(PCC) between genes in all PPI pairs ∙제1저자 : 황유현 ∙제2저자 : 오민 ∙교신저자 : 윤영미 ∙투고일 : 2014. 11. 12, 심사일 : 2014. 12. 31, 게재확정일 : 2015. 1. 29. * 가천대학교 컴퓨터공학과(Dept. of Computer Engineering, Gachon University) **가천대학교 컴퓨터공학과 교수(교신저자)(Dept. of Computer Engineering, Gachon University) ※이 논문은 정부(교육과학기술부)의 재원으로 한국연구재단의 기초연구사업 지원을 받아 수행된 것임(NRF-2010-0008639). 138 Journal of The Korea Society of Computer and Information February 2015 using gene expression data. We develop a prediction model for breast cancer patients with estrogen-receptor-negative using the network as a classifier. We compare classification performance of our algorithm with existing algorithms on independent data and shows our algorithm is improved. In addition, we make an functionality analysis on the genes in the prognosis-specific network using GO(Gene Ontology) enrichment validation. ▸