Clinical relevance of Ephs and ephrins in cancer: lessons from breast, colorectal, and lung cancer profiling

Pre-clinical studies provide compelling evidence that members of the Eph family of receptor tyrosine kinases and their ephrin ligands promote tumor growth, invasion and metastasis, and neovascularization. Tumor suppressive roles have also been reported for the receptors, and ligand-dependent versus ligand-independent signaling has emerged as one key mechanism underlying tumor suppressive function as opposed to oncogenic effects. Determining how these observations relate to clinical outcome is a crucial step for translating the biological and mechanistic data into new molecularly targeted therapies. Expression profiling in human patient samples bridges this gap and provides valuable clinical relevance to laboratory observations. In addition to analyses performed using privately assembled patient tumor samples, publically available microarray datasets and tissue microarrays linked to clinical data have emerged as tractable tools for addressing the clinical relevance of specific molecules and families of related molecules. This review summarizes the clinical relevance of specific Eph and ephrin molecules in human breast, colorectal, and lung cancers.     

RB-pathway disruption in breast cancer: Differential association with disease subtypes,disease-specific prognosis and therapeutic response

In breast cancer, inactivation of the RB tumor suppressor gene is believed to occur via multiple mechanisms to facilitate tumorigenesis. However, the prognostic and predictive value of RB status in disease-specific clinical outcomes has remained uncertain. We investigated RB pathway deregulation in the context of both ER-positive and ER-negative disease using combined microarray datasets encompassing over 900 breast cancer patient samples. Disease-specific characteristics of RB pathway deregulation were investigated in this dataset by evaluating correlation among pathway genes as well as differential expression across patient tumor populations defined by ER status. Survival analysis among these breast cancer samples demonstrates that the RB-loss signature is associated with poor disease outcome within several independent cohorts. Within the ER-negative subpopulation, the RB-loss signature is associated with improved response to chemotherapy and longer relapse-free survival. Additionally, while individual genes in the RB target signature closely reproduce its prognostic value, they also serve to predict and monitor response to therapeutic compounds, such as the cytostatic agent PD-0332991. These results indicate that the RB-loss signature expression is associated with poor outcome in breast cancer, but predicts improved response to chemotherapy based on data in ER-negative populations. While the RB-loss signature, as a whole, demonstrates prognostic and predictive utility, a small subset of markers could be sufficient to stratify patients based on RB function and inform the selection of appropriate therapeutic regimens.Key words: RB, breast cancer, microarray, proliferation, cytostatics     

RB-pathway disruption in breast cancer

In breast cancer, inactivation of the RB tumor suppressor gene is believed to occur via multiple mechanisms to facilitate tumorigenesis. However, the prognostic and predictive value of RB status in disease-specific clinical outcomes has remained uncertain. We investigated RB pathway deregulation in the context of both ER-positive and ER-negative disease using combined microarray datasets encompassing over 900 breast cancer patient samples. Disease-specific characteristics of RB pathway deregulation were investigated in this dataset by evaluating correlation among pathway genes as well as differential expression across patient tumor populations defined by ER status. Survival analysis among these breast cancer samples demonstrates that the RB-loss signature is associated with poor disease outcome within several independent cohorts. Within the ER-negative subpopulation, the RB-loss signature is associated with improved response to chemotherapy and longer relapse-free survival. Additionally, while individual genes in the RB target signature closely reproduce its prognostic value, they also serve to predict and monitor response to therapeutic compounds, such as the cytostatic agent PD-0332991. These results indicate that the RB-loss signature expression is associated with poor outcome in breast cancer, but predicts improved response to chemotherapy based on data in ER-negative populations. While the RB-loss signature, as a whole, demonstrates prognostic and predictive utility, a small subset of markers could be sufficient to stratify patients based on RB function and inform the selection of appropriate therapeutic regimens.     

Invasiveness of breast carcinoma cells and transcript profile: Eph receptors and ephrin ligands as molecular markers of potential diagnostic and prognostic application

The Eph family of receptors, with 14 members in humans, makes up the largest group of receptor tyrosine kinases. These Eph receptors, along with their ligands, the 8 members of the ephrin family of ligands are involved in diverse developmental functions, including hindbrain development in vertebrates, tissue patterning, and angiogenesis. These Eph receptors and ephrin ligands have also been identified as important regulators in the development and progression of cancer. We have presented here a systematic and comprehensive investigation of the Eph/ephrin expression profiles of MCF-10A, MCF-7, and MDA-MB-231 cells representing normal breast, non-invasive breast tumor, and invasive tumor, respectively, based on their characteristic phenotypes in Matrigel matrix. The data have allowed us to correlate the gene expression profile with the cell phenotype that has potential application in tumor diagnostics. We demonstrate here that upregulation of EphA2, A7, A10, and ephrinA2 and B3 is likely involved in tumorigenesis and/or invasiveness, while downregulation of EphA1, A3, A4, A8, B3, B4, B6, and ephrinA1 and B1 may be particularly important in invasiveness. Based on these results we discuss the role of EphA2 and ephrinA1 combination in malignancy. The data have provided clues as to the importance of these molecules in the progression of breast cancer and specifically identified EphB6, a kinase-deficient receptor, which is downregulated in the most aggressive cell line, as reported for several other cancer types including neuroblastoma and melanoma suggesting its potential as a prognostic indicator in breast cancer as well.     

Meta-analysis of breast cancer microarray studies in conjunction with conserved <Emphasis Type="Italic">cis</Emphasis>-elements suggest patterns for coordinate regulation

Background  

Gene expression measurements from breast cancer (BrCa) tumors are established clinical predictive tools to identify tumor subtypes, identify patients showing poor/good prognosis, and identify patients likely to have disease recurrence. However, diverse breast cancer datasets in conjunction with diagnostic clinical arrays show little overlap in the sets of genes identified. One approach to identify a set of consistently dysregulated candidate genes in these tumors is to employ meta-analysis of multiple independent microarray datasets. This allows one to compare expression data from a diverse collection of breast tumor array datasets generated on either cDNA or oligonucleotide arrays.     

A prognostic gene expression profile that predicts circulating tumor cell presence in breast cancer patients

The detection of circulating tumor cells (CTCs) in the peripheral blood and microarray gene expression profiling of the primary tumor are two promising new technologies able to provide valuable prognostic data for patients with breast cancer. Meta-analyses of several established prognostic breast cancer gene expression profiles in large patient cohorts have demonstrated that despite sharing few genes, their delineation of patients into "good prognosis" or "poor prognosis" are frequently very highly correlated, and combining prognostic profiles does not increase prognostic power. In the current study, we aimed to develop a novel profile which provided independent prognostic data by building a signature predictive of CTC status rather than outcome. Microarray gene expression data from an initial training cohort of 72 breast cancer patients for which CTC status had been determined in a previous study using a multimarker QPCR-based assay was used to develop a CTC-predictive profile. The generated profile was validated in two independent datasets of 49 and 123 patients and confirmed to be both predictive of CTC status, and independently prognostic. Importantly, the "CTC profile" also provided prognostic information independent of the well-established and powerful '70-gene' prognostic breast cancer signature. This profile therefore has the potential to not only add prognostic information to currently-available microarray tests but in some circumstances even replace blood-based prognostic CTC tests at time of diagnosis for those patients already undergoing testing by multigene assays.     

High accordance in prognosis prediction of colorectal cancer across independent datasets by multi-gene module expression profiles

A considerable portion of patients with colorectal cancer have a high risk of disease recurrence after surgery. These patients can be identified by analyzing the expression profiles of signature genes in tumors. But there is no consensus on which genes should be used and the performance of specific set of signature genes varies greatly with different datasets, impeding their implementation in the routine clinical application. Instead of using individual genes, here we identified functional multi-gene modules with significant expression changes between recurrent and recurrence-free tumors, used them as the signatures for predicting colorectal cancer recurrence in multiple datasets that were collected independently and profiled on different microarray platforms. The multi-gene modules we identified have a significant enrichment of known genes and biological processes relevant to cancer development, including genes from the chemokine pathway. Most strikingly, they recruited a significant enrichment of somatic mutations found in colorectal cancer. These results confirmed the functional relevance of these modules for colorectal cancer development. Further, these functional modules from different datasets overlapped significantly. Finally, we demonstrated that, leveraging above information of these modules, our module based classifier avoided arbitrary fitting the classifier function and screening the signatures using the training data, and achieved more consistency in prognosis prediction across three independent datasets, which holds even using very small training sets of tumors.     

The use of tissue microarrays for semiquantitative evaluation of ATPaseC1 expression is ineffective

Abstract

We described earlier the possible role of ATPaseC1 expression as a diagnostic and prognostic marker for oral cancer; others have reported its use for tumors of the lung and breast. We assessed ATPaseC1 expression in a sample of oral squamous cell carcinoma (OSCC) using tissue microarrays (TMAs) to analyze the relation between ATPaseC1 expression and clinical, histopathological and prognostic parameters. We performed a retrospective study of 48 cases of OSCC. We constructed TMAs using two different regions of each tumor. V-ATPaseC1 immunohistochemistry was performed and assessed semiquantitatively. ATPaseC1 staining was observed in most of the neoplastic cells in all tumors. Staining was diffusely cytoplasmic and, to a lesser extent, nuclear. The degree of concordance between the measurements performed in tissue microarray 1 (TMA1) and tissue microarray 2 (TMA2), as evaluated using the intra-class correlation coefficient (ICC), was low. We found great variability in the immunohistochemical staining of the different regions of each tumor. We found 16 cases with mild expression (33.3%), 20 with moderate expression (41.7%) and 12 with intense expression (25%). Differences in the clinical-pathological variables studied were not statistically significant. The difficulty of immunohistochemical evaluation, the heterogeneity of the carcinomas and the fact that evaluation of expression requires semiquantitative analysis render the reliability of the results obtained from TMA-based techniques questionable.     

Molecular Characteristics and Metastasis Predictor Genes of Triple-Negative Breast Cancer: A Clinical Study of Triple-Negative Breast Carcinomas

Background

Triple-negative breast cancer is a subtype of breast cancer with aggressive tumor behavior and distinct disease etiology. Due to the lack of an effective targeted medicine, treatment options for triple-negative breast cancer are few and recurrence rates are high. Although various multi-gene prognostic markers have been proposed for the prediction of breast cancer outcome, most of them were proven clinically useful only for estrogen receptor-positive breast cancers. Reliable identification of triple-negative patients with a favorable prognosis is not yet possible.

Methodology/Principal Findings

Clinicopathological information and microarray data from 157 invasive breast carcinomas were collected at National Taiwan University Hospital from 1995 to 2008. Gene expression data of 51 triple-negative and 106 luminal breast cancers were generated by oligonucleotide microarrays. Hierarchical clustering analysis revealed that the majority (94%) of triple-negative breast cancers were tightly clustered together carrying strong basal-like characteristics. A 45-gene prognostic signature giving 98% predictive accuracy in distant recurrence of our triple-negative patients was determined using the receiver operating characteristic analysis and leave-one-out cross validation. External validation of the prognostic signature in an independent microarray dataset of 59 early-stage triple-negative patients also obtained statistical significance (hazard ratio 2.29, 95% confidence interval (CI) 1.04–5.06, Cox P = 0.04), outperforming five other published breast cancer prognostic signatures. The 45-gene signature identified in this study revealed that TGF-β signaling of immune/inflammatory regulation may play an important role in distant metastatic invasion of triple-negative breast cancer.

Conclusions/Significance

Gene expression data and recurrence information of triple-negative breast cancer were collected and analyzed in this study. A novel set of 45-gene signature was found to be statistically predictive in disease recurrence of triple-negative breast cancer. The 45-gene signature, if further validated, may be a clinically useful tool in risk assessment of distant recurrence for early-stage triple-negative patients.     

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.     

Expression and prognostic significance of the autoimmune regulator gene in breast cancer cells

The autoimmune regulator gene (AIRE) plays a fundamental role in tolerance by promoting the expression of tissue-specific antigens in medullary thymic epithelial cells (mTECs). Recently, AIRE expression was detected also in human keratinocytes and in tumors originating in stratified epithelia. Here, we tested whether AIRE is expressed in cancer cells. We analyzed AIRE expression in cancer cases from The Cancer Genome Atlas (TCGA) RNA-seq dataset and we found association with better outcome. AIRE protein expression was verified by immunohistochemistry in a cohort of 39 human breast cancer specimens and its prognostic relevance was confirmed in microarray-based gene expression data set NKI-295 and KM-Plotter. Both in the RNA-seq and gene expression datasets analyzed, AIRE expression was an independent strong prognostic factor for relapse-free survival (RFS), particularly in estrogen receptor-positive tumors. Enrichment of translation-related pathways was observed in AIRE-expressing tumors by Ingenuity Pathway Analysis and a significant increase of cells in G1 phase and activation of caspase cascades was induced by AIRE transfection in breast cancer luminal cell lines, suggesting that AIRE-induced over-translation of proteins lead to cycle arrest and apoptosis. These data are the first to identify AIRE expression in breast cancer and an association with prognosis.     

Identification of Common Prognostic Gene Expression Signatures with Biological Meanings from Microarray Gene Expression Datasets

Numerous prognostic gene expression signatures for breast cancer were generated previously with few overlap and limited insight into the biology of the disease. Here we introduce a novel algorithm named SCoR (Survival analysis using Cox proportional hazard regression and Random resampling) to apply random resampling and clustering methods in identifying gene features correlated with time to event data. This is shown to reduce overfitting noises involved in microarray data analysis and discover functional gene sets linked to patient survival. SCoR independently identified a common poor prognostic signature composed of cell proliferation genes from six out of eight breast cancer datasets. Furthermore, a sequential SCoR analysis on highly proliferative breast cancers repeatedly identified T/B cell markers as favorable prognosis factors. In glioblastoma, SCoR identified a common good prognostic signature of chromosome 10 genes from two gene expression datasets (TCGA and REMBRANDT), recapitulating the fact that loss of one copy of chromosome 10 (which harbors the tumor suppressor PTEN) is linked to poor survival in glioblastoma patients. SCoR also identified prognostic genes on sex chromosomes in lung adenocarcinomas, suggesting patient gender might be used to predict outcome in this disease. These results demonstrate the power of SCoR to identify common and biologically meaningful prognostic gene expression signatures.     

Contribution of xanthine oxidoreductase to mammary epithelial and breast cancer cell differentiation in part modulates inhibitor of differentiation-1

Loss of xanthine oxidoreductase (XOR) has been linked to aggressive breast cancer in vivo and to breast cancer cell aggressiveness in vitro. In the present study, we hypothesized that the contribution of XOR to the development of the normal mammary gland may underlie its capacity to modulate breast cancer. We contrasted in vitro and in vivo developmental systems by differentiation marker and microarray analyses. Human breast cancer microarray was used for clinical outcome studies. The role of XOR in differentiation and proliferation was examined in human breast cancer cells and in a mouse xenograft model. Our data show that XOR was required for functional differentiation of mammary epithelial cells both in vitro and in vivo. Poor XOR expression was observed in a mouse ErbB2 breast cancer model, and pharmacologic inhibition of XOR increased breast cancer tumor burden in mouse xenograft. mRNA microarray analysis of human breast cancer revealed that low XOR expression was significantly associated with time to tumor relapse. The opposing expression of XOR and inhibitor of differentiation-1 (Id1) during HC11 differentiation and mammary gland development suggested a potential functional relationship. While overexpression of Id1 inhibited HC11 differentiation and XOR expression, XOR itself modulated expression of Id1 in differentiating HC11 cells. Overexpression of XOR both inhibited Id1-induced proliferation and -stimulated differentiation of Heregulin-β1-treated human breast cancer cells. These results show that XOR is an important functional component of differentiation whose diminished expression contributes to breast cancer aggressiveness, and they support XOR as both a breast cancer biomarker and a target for pharmacologic activation in therapeutic management of aggressive breast cancer.     

Integration of clinical and gene expression data has a synergetic effect on predicting breast cancer outcome

Breast cancer outcome can be predicted using models derived from gene expression data or clinical data. Only a few studies have created a single prediction model using both gene expression and clinical data. These studies often remain inconclusive regarding an obtained improvement in prediction performance. We rigorously compare three different integration strategies (early, intermediate, and late integration) as well as classifiers employing no integration (only one data type) using five classifiers of varying complexity. We perform our analysis on a set of 295 breast cancer samples, for which gene expression data and an extensive set of clinical parameters are available as well as four breast cancer datasets containing 521 samples that we used as independent validation.mOn the 295 samples, a nearest mean classifier employing a logical OR operation (late integration) on clinical and expression classifiers significantly outperforms all other classifiers. Moreover, regardless of the integration strategy, the nearest mean classifier achieves the best performance. All five classifiers achieve their best performance when integrating clinical and expression data. Repeating the experiments using the 521 samples from the four independent validation datasets also indicated a significant performance improvement when integrating clinical and gene expression data. Whether integration also improves performances on other datasets (e.g. other tumor types) has not been investigated, but seems worthwhile pursuing. Our work suggests that future models for predicting breast cancer outcome should exploit both data types by employing a late OR or intermediate integration strategy based on nearest mean classifiers.     

Profiling Eph receptor expression in cells and tissues: A targeted mass spectrometry approach

The Eph receptor tyrosine kinase family includes many members, which are often expressed together in various combinations and can promiscuously interact with multiple ephrin ligands, generating intricate networks of intracellular signals that control physiological and pathological processes. Knowing the entire repertoire of Eph receptors and ephrins expressed in a biological sample is important when studying their biological roles. Moreover, given the correlation between Eph receptor/ephrin expression and cancer pathogenesis, their expression patterns could serve important diagnostic and prognostic purposes. However, profiling Eph receptor and ephrin expression has been challenging. Here we describe a novel and straightforward approach to catalog the Eph receptors present in cultured cells and tissues. By measuring the binding of ephrin Fc fusion proteins to Eph receptors in ELISA and pull-down assays, we determined that a mixture of four ephrins is suitable for isolating both EphA and EphB receptors in a single pull-down. We then used mass spectrometry to identify the Eph receptors present in the pull-downs and estimate their relative levels. This approach was validated in cultured human cancer cell lines, human tumor xenograft tissue grown in mice, and mouse brain tissue. The new mass spectrometry approach we have developed represents a useful tool for the identification of the spectrum of Eph receptors present in a biological sample and could also be extended to profiling ephrin expression.