Li-Yun Chang

The Interactions Between GPR30 and the Major Biomarkers in Infiltrating Ductal Carcinoma of the Breast in an Asian Population

OBJECTIVE G-protein-coupled receptor 30 (GPR30) has been reported to be a novel estrogen receptor alpha (ERalpha) in vitro. Therefore, the interactions among GPR30, ERalpha, progesterone receptor (PR) and human epidermal growth factor receptor-2 (HER-2/neu), and their prognostic utilities in the infiltrating ductal carcinoma (IDC) of the breast were evaluated. MATERIALS AND METHODS Messenger RNA (mRNA) levels of GPR30, ERalpha, PR and HER-2/neu in the tumor samples of 118 Taiwanese IDC patients and 27 non-tumor mammary tissues were measured via quantitative polymerase chain reaction analyses. The correlations of GPR30 mRNA levels with clinical parameters, i.e. tumor/non-tumor, ERalpha, PR, HER-2/neu, age, lymph node metastasis, lymph-vascular invasion, grade, stage and patient survival, were assessed by using appropriate statistical analyses. RESULTS GPR30 expression was observed to be lower in IDC (p < 0.001) than in non-tumor mammary tissues. Importantly, GPR30 mRNA level was positively correlated with that of ERalpha (p = 0.001) and PR (p = 0.001) but not correlated with that of HER-2/neu when they were analyzed as continuous variables. However, lower GPR30 was noticed in tumors with HER-2/neu protein overexpression. GPR30 expression was not correlated with age, lymph node metastasis, lymph-vascular invasion, grade and stage in IDC. GPR30 expression was not an independent prognostic factor for patient survival. CONCLUSION GPR30 expression is downregulated in IDC. GPR30 is preferentially co-expressed with ER and/or PR but is lowly expressed in HER-2/neu(+) tumors. The correlation of GPR30 expression with clinical parameters, including patient survival, was not evident in this cohort.

Prediction of breast cancer and lymph node metastatic status with tumour markers using logistic regression models

AIMS Early detection of breast cancer can improve disease mortality. The aim of this study was to evaluate the effectiveness of serum biomarkers in the detection of primary breast cancer and lymph node metastatic status. METHODS Serum samples were obtained from 55 female patients with breast cancer and 39 women without breast cancer. For these subjects, clinicopathological data were collected and serum levels of carcinoembryonic antigen, breast cancer-specific cancer antigen 15.3 (CA15-3), tissue polypeptide-specific antigen (TPS), soluble interleukin-2 receptor (sIL-2R) and insulin-like growth factor binding protein-3 (IGFBP-3) were assayed. Univariate and multivariate logistic regression were performed to evaluate the association between biomarkers and breast cancer, as well as lymph node metastatic status. RESULTS For breast cancer prediction, the serum level of TPS had the best predictive value, with a sensitivity of 80% at an optimal cut-off value of 69.1 U L(-1). The combination of TPS, CA15-3 and IGFBP-3 with logistic regression model increased the sensitivity to 85%. For lymph node metastasis prediction, the serum level of sIL-2R had the best predictive value, with a sensitivity of 66% at an optimal cut-off value of 286 U mL(-1). The combination of sIL-2R and TPS with logistic regression model increased the sensitivity to 69%. CONCLUSION TPS may be useful in the detection of primary breast cancer, while sIL-2R may be useful in lymph node metastasis prediction. The combination of more than one biomarker with logistic regression model can improve the predictive sensitivity.

Identification of prognostic genes for recurrent risk prediction in triple negative breast cancer patients in Taiwan.

Discrepancies in the prognosis of triple negative breast cancer exist between Caucasian and Asian populations. Yet, the gene signature of triple negative breast cancer specifically for Asians has not become available. Therefore, the purpose of this study is to construct a prediction model for recurrence of triple negative breast cancer in Taiwanese patients. Whole genome expression profiling of breast cancers from 185 patients in Taiwan from 1995 to 2008 was performed, and the results were compared to the previously published literature to detect differences between Asian and Western patients. Pathway analysis and Cox proportional hazard models were applied to construct a prediction model for the recurrence of triple negative breast cancer. Hierarchical cluster analysis showed that triple negative breast cancers from different races were in separate sub-clusters but grouped in a bigger cluster. Two pathways, cAMP-mediated signaling and ephrin receptor signaling, were significantly associated with the recurrence of triple negative breast cancer. After using stepwise model selection from the combination of the initial filtered genes, we developed a prediction model based on the genes SLC22A23, PRKAG3, DPEP3, MORC2, GRB7, and FAM43A. The model had 91.7% accuracy, 81.8% sensitivity, and 94.6% specificity under leave-one-out support vector regression. In this study, we identified pathways related to triple negative breast cancer and developed a model to predict its recurrence. These results could be used for assisting with clinical prognosis and warrant further investigation into the possibility of targeted therapy of triple negative breast cancer in Taiwanese patients.

Vascularity Change and Tumor Response to Neoadjuvant Chemotherapy for Advanced Breast Cancer

For advanced breast cancer with severe local disease (ABC) (stage III/IV), neoadjuvant chemotherapy improves local control and surgical outcome. However, about approximately 20 to 30% of advanced cancers show either no or poor response to chemotherapy. To prevent unnecessary treatment, a capability of predicting clinical response to neoadjuvant chemotherapy of ABC is highly desirable. Vascularity index (VI) of breast cancers was derived from the quantification results in 30 ABC patients by using power Doppler sonography. Power Doppler sonography evaluation was performed every one to two weeks during chemotherapy. The overall response rate for 30 advanced patients tested was 70%, when 50% or more reduction in tumor size was the objective clinical response. Chemotherapy response was unrelated to the original tumor size (p = 0.563) or chemotherapy agents used (p = 0.657). The median VI for all 30 patients was 4.99%. The response rates for hypervascular tumors vs. hypovascular tumors, based on initial median value, were 86.7% and 53.3%, respectively (p = 0.109). The average VIs in responders and nonresponders were 7.67 +/- 4.77% and 4.01 +/- 3.82% (p = 0.052). There was a tendency for responders who have a relatively high initial vascularity. The VI change in responder group shows a pattern of initial increasing in vascularity followed by decreasing in vascularity. All patients (17/17) with a VI increment of >5% during chemotherapy had good chemotherapy response, whereas in patients with a VI increment of <5%, the response rate was 30.8% (4/13) (p < 0.001). For patients with a peak VI of >10% during chemotherapy, the response rate was 94.1% (16/17). However, in patients with a peak VI of <10%, the response rate was 38.5% (5/13) (p = 0.001). This prediction was made mostly within one month (25.47 +/- 12.96 d for VI increments >5% and 25.44 +/- 12.41 d for VI increased to >10%). In the meantime, the differences in size reduction shown in B-mode sonography were insignificant between responders and nonresponders (patient group with VI increment >5%, p = 0.308; patient group with peak VI >10%, p = 0.396). In conclusion, we propose that VI as determined by using power Doppler sonography is a good and inexpensive clinical tool for monitoring vascularity changes during neoadjuvant chemotherapy in ABC patients. Two parameters--VI increment >5% and peak VI >10%--are potential early predictors for good responses to neoadjuvant chemotherapy within one month in patients with ABC.

Major Functional Transcriptome of an Inferred Center Regulator of an ER(-) Breast Cancer Model System

We aimed to find clinically relevant gene activities ruled by the signal transducer and activator of transcription 3 (STAT3) proteins in an ER(–) breast cancer population via network approach. STAT3 is negatively associated with both lymph nodal category and stage. MYC is a component of STAT3 network. MYC and STAT3 may co-regulate gene expressions for Warburg effect, stem cell like phenotype, cell proliferation and angiogenesis. We identified a STAT3 network in silico showing its ability in predicting its target gene expressions primarily for specific tumor subtype, tumor progression, treatment options and prognostic features. The aberrant expressions of MYC and STAT3 are enriched in triple negatives (TN). They promote histological grade, vascularity, metastasis and tumor anti-apoptotic activities. VEGFA, STAT3, FOXM1 and METAP2 are druggable targets. High levels of METAP2, MMP7, IGF2 and IGF2R are unfavorable prognostic factors. STAT3 is an inferred center regulator at early cancer development predominantly in TN.

Statistical identification of gene association by CID in application of constructing ER regulatory network.

BackgroundA variety of high-throughput techniques are now available for constructing comprehensive gene regulatory networks in systems biology. In this study, we report a new statistical approach for facilitating in silico inference of regulatory network structure. The new measure of association, coefficient of intrinsic dependence (CID), is model-free and can be applied to both continuous and categorical distributions. When given two variables X and Y, CID answers whether Y is dependent on X by examining the conditional distribution of Y given X. In this paper, we apply CID to analyze the regulatory relationships between transcription factors (TFs) (X) and their downstream genes (Y) based on clinical data. More specifically, we use estrogen receptor α (ERα) as the variable X, and the analyses are based on 48 clinical breast cancer gene expression arrays (48A).ResultsThe analytical utility of CID was evaluated in comparison with four commonly used statistical methods, Galton-Pearsons correlation coefficient (GPCC), Students t-test (STT), coefficient of determination (CoD), and mutual information (MI). When being compared to GPCC, CoD, and MI, CID reveals its preferential ability to discover the regulatory association where distribution of the mRNA expression levels on X and Y does not fit linear models. On the other hand, when CID is used to measure the association of a continuous variable (Y) against a discrete variable (X), it shows similar performance as compared to STT, and appears to outperform CoD and MI. In addition, this study established a two-layer transcriptional regulatory network to exemplify the usage of CID, in combination with GPCC, in deciphering gene networks based on gene expression profiles from patient arrays.ConclusionCID is shown to provide useful information for identifying associations between genes and transcription factors of interest in patient arrays. When coupled with the relationships detected by GPCC, the association predicted by CID are applicable to the construction of transcriptional regulatory networks. This study shows how information from different data sources and learning algorithms can be integrated to investigate whether relevant regulatory mechanisms identified in cell models can also be partially re-identified in clinical samples of breast cancers.Availabilitythe implementation of CID in R codes can be freely downloaded from http://homepage.ntu.edu.tw/~lyliu/BC/.

A supervised network analysis on gene expression profiles of breast tumors predicts a 41-gene prognostic signature of the transcription factor MYB across molecular subtypes.

Background. MYB is predicted to be a favorable prognostic predictor in a breast cancer population. We proposed to find the inferred mechanism(s) relevant to the prognostic features of MYB via a supervised network analysis. Methods. Both coefficient of intrinsic dependence (CID) and Galton Piersons correlation coefficient (GPCC) were combined and designated as CIDUGPCC. It is for the univariate network analysis. Multivariate CID is for the multivariate network analysis. Other analyses using bioinformatic tools and statistical methods are included. Results. ARNT2 is predicted to be the essential gene partner of MYB. We classified four prognostic relevant gene subpools in three breast cancer cohorts as feature types I–IV. Only the probes in feature type II are the potential prognostic feature of MYB. Moreover, we further validated 41 prognosis relevant probes to be the favorable prognostic signature. Surprisingly, two additional family members of MYB are elevated to promote poor prognosis when both levels of MYB and ARNT2 decline. Both MYBL1 and MYBL2 may partially decrease the tumor suppressive activities that are predicted to be up-regulated by MYB and ARNT2. Conclusions. The major prognostic feature of MYB is predicted to be determined by the MYB subnetwork (41 probes) that is relevant across subtypes.

In Silico Prediction for Regulation of Transcription Factors onTheir Shared Target Genes Indicates Relevant Clinical Implications in a Breast Cancer Population

Aberrant transcriptional activities have been documented in breast cancers. Studies often find some transcription factors to be inappropriately regulated and enriched in certain pathological states. The promoter regions of most target genes have binding sites for their transcription factors. An ample of evidence supports their combinatorial effect on their shared target gene expressions. Here, we used a new statistic method, bivariate CID, to predict combinatorial interaction activity between ERα and a transcription factor (E2F1or GATA3 or ERRα) in regulating target gene expression via four regulatory mechanisms. We identified gene sets in three signal transduction pathways perturbed in breast tumors: cell cycle, VEGF, and PDGFRB. Bivariate network analysis revealed several target genes previously implicated in tumor angiogenesis are among the predicted shared targets, including VEGFA, PDGFRB. In summary, our analysis suggests the importance for the multivariate space of an inferred ERα transcriptional regulatory network in breast cancer diagnostic and therapeutic development.

Prognostic features of signal transducer and activator of transcription 3 in an ER(+) breast cancer model system.

The aberrantly expressed signal transducer and activator of transcription 3 (STAT3) predicts poor prognosis, primarily in estrogen receptor positive (ER(+)) breast cancers. Activated STAT3 is overexpressed in luminal A subtype cells. The mechanisms contributing to the prognosis and/or subtype relevant features of STAT3 in ER(+) breast cancers are through multiple interacting regulatory pathways, including STAT3-MYC, STAT3-ERα, and STAT3-MYC-ERα interactions, as well as the direct action of activated STAT3. These data predict malignant events, treatment responses and a novel enhancer of tamoxifen resistance. The inferred crosstalk between ERα and STAT3 in regulating their shared target gene-METAP2 is partially validated in the luminal B breast cancer cell line-MCF7. Taken together, we identify a poor prognosis relevant gene set within the STAT3 network and a robust one in a subset of patients. VEGFA, ABL1, LYN, IGF2R and STAT3 are suggested therapeutic targets for further study based upon the degree of differential expression in our model.

Correction to: Prognostic Features of Signal Transducer and Activator of Transcription 3 in an ER(+) Breast Cancer Model System.

[This corrects the article on p. 21 in vol. 13, PMID: 24526833.].