Human Breast Cancer Datasets Research Articles

Semi-supervised learning improves gene expression-based prediction of cancer recurrence

Gene expression profiling has shown great potential in outcome prediction for different types of cancers. Nevertheless, small sample size remains a bottleneck in obtaining robust and accurate classifiers. Traditional supervised learning techniques can only work with labeled data. Consequently, a large number of microarray data that do not have sufficient follow-up information are disregarded. To fully leverage all of the precious data in public databases, we turned to a semi-supervised learning technique, low density separation (LDS). Using a clinically important question of predicting recurrence risk in colorectal cancer patients, we demonstrated that (i) semi-supervised classification improved prediction accuracy as compared with the state of the art supervised method SVM, (ii) performance gain increased with the number of unlabeled samples, (iii) unlabeled data from different institutes could be employed after appropriate processing and (iv) the LDS method is robust with regard to the number of input features. To test the general applicability of this semi-supervised method, we further applied LDS on human breast cancer datasets and also observed superior performance. Our results demonstrated great potential of semi-supervised learning in gene expression-based outcome prediction for cancer patients. bing.zhang@vanderbilt.edu. Supplementary data are available at Bioinformatics online.

Abstract 5109: Proteomics of murine BRCA1 deficient breast tumors identifies DNA repair proteins with prognostic value in human breast cancer

Abstract Background: Breast cancer is the most common malignancy in women in the western world. The outcome of breast cancer would be strongly improved if patients could be diagnosed and treated early. This especially holds for patients with hereditary breast cancer. Aim: Our aim is to identify proteins that are associated with BRCA1 deficient hereditary breast cancer. These proteins could have potential use as screening, prognostic or predictive biomarkers. Approach: In-depth proteome profiling of tumor tissues of mouse breast cancer models was employed to identify BRCA1-associated proteins. To this end, tumor tissue lysates of five BRCA1 deficient and five proficient mouse models (Liu et al., 2007) were fractionated using SDS-PAGE followed by tryptic in-gel digestion, nanoLC-MS/MS and database searching. Normalized spectral counting was used for protein quantification and beta binomial statistics to discover significantly regulated proteins. Ingenuity Pathway Analysis and COFECO protein complex analysis was used to support data interpretation. The prognostic power was assessed by the use of publicly available gene expression human breast cancer data sets. Results: We identified a total of 3614 proteins, of which 804 were differential between the genomic instable BRCA1-deficient and the genomic stable BRCA1-proficient breast tumors. Pathway analysis and protein complex identified DNA-repair and associated function like chromatin modeling and RNA processing as the major functions associated with the upregulated proteins of the BRCA1 deficient tumors. We created a core DNA-repair protein signature using pathway and protein Complex analysis. When the signature proteins were mapped to human breast cancer gene-expression data sets, they were shown to have prognostic power. Conclusion: Proteomics of genetic mouse models for familial breast cancer is a powerful strategy to discover novel candidate BRCA1 deficiency proteins with human relevance. The major upregulation of DNA-repair complexes may indicate a compensatory mechanism for loss of BRCA1 DNA-repair. Further validation studies are required to investigate prognostic candidates in tissue microarrays. Integration with data on tumor and cancer cell-secreted proteins will pinpoint which of the BRCA1 deficiency associated proteins may have potential for screening of BRCA1 tumors and whether they have predictive value for sensitivity to PARP1 inhibition and Cisplatin treatment in BRCA1 as well as non-BRCA1-deficient tumors. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 5109. doi:10.1158/1538-7445.AM2011-5109

Deletion of the Proline-Rich Region of the Murine Metastasis Susceptibility Gene Brd4 Promotes Epithelial-to-Mesenchymal Transition- and Stem Cell-Like Conversion

The bromodomain-containing chromatin-modifying factor BRD4 is an inherited susceptibility gene for breast cancer progression and metastasis, but its functionality in these settings has yet to be explored. Here we show that deletion of either of the BRD4 bromodomains had modest effects on the metastatic suppression ability of BRD4. In contrast, expression of the natural short isoform of BRD4 that truncates the protein after the SEED domain restored progression and metastatic capacity. Unexpectedly, deletion of the proline-rich region induced mesenchymal-like conversion and acquisition of cancer stem cell-like properties, which are mediated by the carboxy-terminal P-TEFb binding domain. Deletion of this proline-rich region also induced a gene expression signature that predicted poor outcome in human breast cancer data sets and that overlapped G3 grade human breast tumors. Thus our findings suggest that BRD4 may be altering the predisposition of tumors to undergo conversion to a more de-differentiated or primitive state during metastatic progression.

Fibroblast Growth Factor Receptor Signaling Dramatically Accelerates Tumorigenesis and Enhances Oncoprotein Translation in the Mouse Mammary Tumor Virus–Wnt-1 Mouse Model of Breast Cancer

Fibroblast growth factor (FGF) cooperates with the Wnt/beta-catenin pathway to promote mammary tumorigenesis. To investigate the mechanisms involved in FGF/Wnt cooperation, we genetically engineered a model of inducible FGF receptor (iFGFR) signaling in the context of the well-established mouse mammary tumor virus-Wnt-1 transgenic mouse. In the bigenic mice, iFGFR1 activation dramatically enhanced mammary tumorigenesis. Expression microarray analysis did not show transcriptional enhancement of Wnt/beta-catenin target genes but instead showed a translational gene signature that also correlated with elevated FGFR1 and FGFR2 in human breast cancer data sets. Additionally, iFGFR1 activation enhanced recruitment of RNA to polysomes, resulting in a marked increase in protein expression of several different Wnt/beta-catenin target genes. FGF pathway activation stimulated extracellular signal-regulated kinase and the phosphorylation of key translation regulators both in vivo in the mouse model and in vitro in a human breast cancer cell line. Our results suggest that cooperation of the FGF and Wnt pathways in mammary tumorigenesis is based on the activation of protein translational pathways that result in, but are not limited to, increased expression of Wnt/beta-catenin target genes (at the level of protein translation). Further, they reveal protein translation initiation factors as potential therapeutic targets for human breast cancers with alterations in FGF signaling.

Abstract 282: FGF receptor signaling accelerates Wnt-1 mammary tumorigenesis and enhances translation of Wnt/β-catenin target oncogenes

Abstract The fibroblast growth factor pathway is known to cooperate with the Wnt/B-catenin pathway in mouse mammary tumorigenesis, and both of these pathways have been implicated in the etiology of human breast cancer. Despite this evidence, little is known about the molecular mechanism contributing to FGF/Wnt pathway cooperation. To investigate the mechanisms involved in this cooperativity, we used genetically engineered mice expressing a drug-inducible model for FGF Receptor signaling (iFGFR) crossed with the well-characterized MMTV-Wnt-1 mouse model. In these bigenic mice, iFGFR1 activation resulted in a dramatic enhancement of mammary tumorigenesis. Tumor microarray analysis did not reveal transcriptional enhancement of Wnt/B-catenin targets, but instead identified a protein translational gene signature that also correlated with elevated FGFR1 and FGFR2 expression in several human breast cancer data sets. Additionally, iFGFR1 activation resulted in enhanced polysome recruitment and a marked increase in protein expression of several different Wnt/B-catenin target oncogenes. Rapid FGFR1-induced ERK activation and phosphorylation of key translation regulators was observed both in vivo in the transgenic mouse model, and in a human breast cancer cell line treated with exogenous FGF. These studies suggest that translational regulation is a key rate-limiting step required for oncogenic cooperativity between the Wnt and FGF pathways. These results not only demonstrate the oncogenic potential of FGFR1 signaling, but also highlight a possible mode of cooperation between FGF and Wnt signaling, involving synergism between two fundamental levels of gene regulation, transcription and translation. FGFRs, provide drugable targets for adjuvant therapy and several known FGFR inhibitors are currently in clinical trials. Additionally, key downstream regulators of translation initiation may also serve as potential therapeutic targets for breast cancers with enhanced FGF signaling. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 282.

Abstract 5761: An alternative isoform of the metastasis susceptibility gene Brd4 promotes EMT and metastatic progression

Abstract Recent work from our laboratory has identified the bromodomain 4 (Brd4) as a suppressor of tumor progression and metastasis. Brd4 however has an alternatively spliced isoform that truncates the protein and substitutes a different 3′-UTR. Unlike the longer isoform, expression of the short isoform (ΔC) does not impair primary tumor growth of a highly metastatic mammary tumor cell line orthotopically implanted into the mammary fat pad, although pulmonary metastasis is significantly reduced compared to control tumors. Unexpectedly, a dramatic enhancement of metastatic colonization is observed following injection of the cells directly into the circulation of mice. In vivo invasion of the cell line was not impaired by expression of the short isoform suggesting that the inability of the orthotopic tumors to metastasize was not due to the inability of the tumor cells to escape from the primary tumor. Consistent with the tail vein experiments, in vitro analyses demonstrate that ΔC-expressing cells exhibit a more malignant phenotype with an increased ability to grow in a three dimensional culture compared to control cells. Furthermore, ΔC-expressing cells are able to form tumorspheres in serum-free and non adherent conditions, suggesting a reversion to a more stem-like state. Microarray gene expression analysis demonstrates that ectopic expression of ΔC modulates the expression of a subset of EMT markers and stem cell markers. Microarray analysis further shows that ectopic expression of ΔC induces a gene expression signature that predicts poor survival in multiple human breast cancer datasets. This expression signature nearly perfectly matches a molecular classifier of high-grade tumors. Taken together, these data suggest that the short isoform may be a metastasis-promoting molecule and that metastasis susceptibility mediated by Brd4 may be the result of a complex equilibrium between the metastasis-suppressing long isoform and the metastasis-promoting shorter isoform. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 5761.

Expression Signature Developed from a Complex Series of Mouse Models Accurately Predicts Human Breast Cancer Survival

The capability of microarray platform to interrogate thousands of genes has led to the development of molecular diagnostic tools for cancer patients. Although large-scale comparative studies on clinical samples are often limited by the access of human tissues, expression profiling databases of various human cancer types are publicly available for researchers. Given that mouse models have been instrumental to our current understanding of cancer progression, we aimed to test the hypothesis that novel gene signatures possessing predictability in clinical outcome can be derived by coupling genomic analyses in mouse models of cancer with publicly available human cancer data sets. We established a complex series of syngeneic metastatic animal models using a murine breast cancer cell line. Tumor RNA was hybridized on Affymetrix MouseGenome-430A2.0 GeneChips. With the use of Venn logic, gene signatures that represent metastatic competency were derived and tested against publicly available human breast and lung cancer data sets. Survival analyses showed that the spontaneous metastasis gene signature was significantly associated with metastasis-free and overall survival (P < 0.0005). Consequently, the six-gene model was determined and showed statistical predictability in predicting survival in breast cancer patients. In addition, the model was able to stratify poor from good prognosis for lung cancer patients in most data sets analyzed. Together, our data support that novel gene signature derived from mouse models of cancer can be used for predicting human cancer outcome. Our approaches set precedence that similar strategies may be used to decipher novel gene signatures for clinical utility.

LSOSS: Detection of Cancer Outlier Differential Gene Expression

Detection of differential gene expression using microarray technology has received considerable interest in cancer research studies. Recently, many researchers discovered that oncogenes may be activated in some but not all samples in a given disease group. The existing statistical tools for detecting differentially expressed genes in a subset of the disease group mainly include cancer outlier profile analysis (COPA), outlier sum (OS), outlier robust t-statistic (ORT) and maximum ordered subset t-statistics (MOST). In this study, another approach named Least Sum of Ordered Subset Square t-statistic (LSOSS) is proposed. The results of our simulation studies indicated that LSOSS often has more power than previous statistical methods. When applied to real human breast and prostate cancer data sets, LSOSS was competitive in terms of the biological relevance of top ranked genes. Furthermore, a modified hierarchical clustering method was developed to classify the heterogeneous gene activation patterns of human breast cancer samples based on the significant genes detected by LSOSS. Three classes of gene activation patterns, which correspond to estrogen receptor (ER)+, ER- and a mixture of ER+ and ER-, were detected and each class was assigned a different gene signature.

Abrogation of TGF-β signaling enhances chemokine production and correlates with prognosis in human breast cancer

In human breast cancer, loss of carcinoma cell-specific response to TGF-beta signaling has been linked to poor patient prognosis. However, the mechanisms through which TGF-beta regulates these processes remain largely unknown. In an effort to address this issue, we have now identified gene expression signatures associated with the TGF-beta signaling pathway in human mammary carcinoma cells. The results strongly suggest that TGF-beta signaling mediates intrinsic, stromal-epithelial, and host-tumor interactions during breast cancer progression, at least in part, by regulating basal and oncostatin M-induced CXCL1, CXCL5, and CCL20 chemokine expression. To determine the clinical relevance of our results, we queried our TGF-beta-associated gene expression signatures in 4 human breast cancer data sets containing a total of 1,319 gene expression profiles and associated clinical outcome data. The signature representing complete abrogation of TGF-beta signaling correlated with reduced relapse-free survival in all patients; however, the strongest association was observed in patients with estrogen receptor-positive (ER-positive) tumors, specifically within the luminal A subtype. Together, the results suggest that assessment of TGF-beta signaling pathway status may further stratify the prognosis of ER-positive patients and provide novel therapeutic approaches in the management of breast cancer.

A signature-based method for indexing cell cycle phase distribution from microarray profiles

BackgroundThe cell cycle machinery interprets oncogenic signals and reflects the biology of cancers. To date, various methods for cell cycle phase estimation such as mitotic index, S phase fraction, and immunohistochemistry have provided valuable information on cancers (e.g. proliferation rate). However, those methods rely on one or few measurements and the scope of the information is limited. There is a need for more systematic cell cycle analysis methods.ResultsWe developed a signature-based method for indexing cell cycle phase distribution from microarray profiles under consideration of cycling and non-cycling cells. A cell cycle signature masterset, composed of genes which express preferentially in cycling cells and in a cell cycle-regulated manner, was created to index the proportion of cycling cells in the sample. Cell cycle signature subsets, composed of genes whose expressions peak at specific stages of the cell cycle, were also created to index the proportion of cells in the corresponding stages. The method was validated using cell cycle datasets and quiescence-induced cell datasets. Analyses of a mouse tumor model dataset and human breast cancer datasets revealed variations in the proportion of cycling cells. When the influence of non-cycling cells was taken into account, "buried" cell cycle phase distributions were depicted that were oncogenic-event specific in the mouse tumor model dataset and were associated with patients' prognosis in the human breast cancer datasets.ConclusionThe signature-based cell cycle analysis method presented in this report, would potentially be of value for cancer characterization and diagnostics.

Bromodomain 4 activation predicts breast cancer survival

Previous work identified the Rap1 GTPase-activating protein Sipa1 as a germ-line-encoded metastasis modifier. The bromodomain protein Brd4 physically interacts with and modulates the enzymatic activity of Sipa1. In vitro analysis of a highly metastatic mouse mammary tumor cell line ectopically expressing Brd4 demonstrates significant reduction of invasiveness without altering intrinsic growth rate. However, a dramatic reduction of tumor growth and pulmonary metastasis was observed after s.c. implantation into mice, implying that activation of Brd4 may somehow be manipulating response to tumor microenvironment in the in vivo setting. Further in vitro analysis shows that Brd4 modulates extracellular matrix gene expression, a class of genes frequently present in metastasis-predictive gene signatures. Microarray analysis of the mammary tumor cell lines identified a Brd4 activation signature that robustly predicted progression and/or survival in multiple human breast cancer datasets analyzed on different microarray platforms. Intriguingly, the Brd4 signature also almost perfectly matches a molecular classifier of low-grade tumors. Taken together, these data suggest that dysregulation of Brd4-associated pathways may play an important role in breast cancer progression and underlies multiple common prognostic signatures.

Inflammation and breast cancer. Inflammatory component of mammary carcinogenesis in ErbB2 transgenic mice

This review addresses genes differentially expressed in the mammary gland transcriptome during the progression of mammary carcinogenesis in BALB/c mice that are transgenic for the rat neu (ERBB2, or HER-2/neu) oncogene (BALB-neuT664V-E mice). The Ingenuity knowledge database was used to characterize four functional association networks whose hub genes are directly linked to inflammation (specifically, the genes encoding IL-1β, tumour necrosis factor, interferon-γ, and monocyte chemoattractant protein-1/CC chemokine ligand-2) and are increasingly expressed during such progression. In silico meta-analysis in a human breast cancer dataset suggests that proinflammatory activation in the mammary glands of these mice reflects a general pattern of human breast cancer.

ERα–CITED1 co-regulated genes expressed during pubertal mammary gland development: implications for breast cancer prognosis

Expression microarray analysis identified over 930 genes regulated during puberty in the mouse mammary gland. Most prominent were genes whose expression increased in parallel with pubertal development and remained high thereafter. Members of the Wnt, transforming growth factor-beta and oestrogen-signalling pathways were significantly overrepresented. Comparison to expression data from CITED1 knockout mice identified a subset of oestrogen-responsive genes displaying altered expression in the absence of CITED1. Included in this subset are stanniocalcin2 (Stc2) and amphiregulin (Areg). Chromatin immunoprecipitation revealed that ERalpha binds to oestrogen response elements in both the Stc2 and Areg genes in the mammary gland during puberty. Additionally, CITED1 and ERalpha localize to the same epithelial cells of the pubertal mammary gland, supporting a role for interaction of these two proteins during normal development. In a human breast cancer data set, expression of Stc2, Areg and CITED1 parallel that of ERalpha. Similar to ERalpha, CITED1 expression correlates with good outcome in breast cancer, implying that potential maintenance of the ERalpha-CITED1 co-regulated signalling pathway in breast tumours can indicate good prognosis.