A Prognosis Classifier for Breast Cancer Based on Conserved Gene Regulation between Mammary Gland Development and Tumorigenesis: A Multiscale Statistical Model

<div><p>Identification of novel cancer genes for molecular therapy and diagnosis is a current focus of breast cancer research. Although a few small gene sets were identified as prognosis classifiers, more powerful models are still needed for the definition of effective gene sets for the diagnosis and treatment guidance in breast cancer. In the present study, we have developed a novel statistical approach for systematic analysis of intrinsic correlations of gene expression between development and tumorigenesis in mammary gland. Based on this analysis, we constructed a predictive model for prognosis in breast cancer that may be useful for therapy decisions. We first defined developmentally associated genes from a mouse mammary gland epithelial gene expression database. Then, we found that the cancer modulated genes were enriched in this developmentally associated genes list. Furthermore, the developmentally associated genes had a specific expression profile, which associated with the molecular characteristics and histological grade of the tumor. These result suggested that the processes of mammary gland development and tumorigenesis share gene regulatory mechanisms. Then, the list of regulatory genes both on the developmental and tumorigenesis process was defined an 835-member prognosis classifier, which showed an exciting ability to predict clinical outcome of three groups of breast cancer patients (the predictive accuracy 64∼72%) with a robust prognosis prediction (hazard ratio 3.3∼3.8, higher than that of other clinical risk factors (around 2.0–2.8)). In conclusion, our results identified the conserved molecular mechanisms between mammary gland development and neoplasia, and provided a unique potential model for mining unknown cancer genes and predicting the clinical status of breast tumors. These findings also suggested that developmental roles of genes may be important criteria for selecting genes for prognosis prediction in breast cancer.</p> </div

The 835 prognosis classifier could predict clinical outcome in a large set of breast cancer patients.

<p>The intrinsic dataset was applied to 144 node positive and 151 node negative primary breast tumors. The accuracy of prediction (<b>A</b>) or the prognosis value (<b>B</b>) of 835 prognosis classifier and tumor risk factors was assessed by the same approach as described in the legend of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0060131#pone-0060131-g005" target="_blank">Fig. 5B</a>.</p

The 835 prognosis classifier acts as a powerful predictor of clinical outcome in 78 breast cancer patients.

<p><b>A.</b> 78 breast cancer samples were first classified by the expression profiles of the 835 prognosis classifier, using unsupervised classification. The survival curve of the two groups was then compared with Kaplan Meier analysis to define clinical outcome (lower panel). Accuracy of classification was assessed with Fisher exact test (upper table). <b>B.</b> The distribution of tumors risk factors in the four groups classified by clinical metastasis and by the 835 prognosis classifier. <b>C.</b> The prognostic value of the 835 prognosis classifier and tumor risk factors.</p

The enrichment of ontology in 835 intrinsic genes.

<p>EASE score<0.05.</p>#<p>genes with red word are cancer mutant gene identified in reference (Nat Rev Cancer,4(3):177).</p

Defining the 835 prognosis classifier from the developmentally associated genes based on their expression in tumors.

<p>For each developmentally associated gene, we first counted the number of the breast cancer datasets in which it was “altered” in expression. Based on this database number, all developmental genes were then grouped into six subsets (Sub0, Sub1, Sub2, Sub3, Sub4, and Sub5). The percentage of a literature-based cancer modulated genes in each subset is shown in table (<b>A</b>) and histogram (<b>B</b>). The results of non-developmental genes with same assay method are shown as a control. The details are described in the text.</p

Definition of mammary gland developmentally associated genes.

<p>Overview of data processing for defining the developmentally associated gene subset was discribled in <b>A.</b> The probes were filtered systematically with different cutoffs:p value of gene expression among different time points and the optimal fold of maximum/minimum expression of a gene at different developmental time points, which should have a maximum Odds ratio of literature-based mammary gland-cycle associated genes in developmentally associated and non-developmentally associated genes subset. A higher Odds ratio means that a greater number of developmental genes were correctly classified. The figure <b>B</b> shows the curve of Odd ratios in the developmentally associated and non-developmentally associated genes subset defined by a cutoff with different ratio of maximum to minimum expression of each gene at different time points in the developmental progress. <b>C.</b> Fisher exact test to assess the frequency of validated cancer gene expression in the group of mammary gland developmentally associated genes. The validated cancer genes were obtained from previously published papers (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0060131#pone.0060131.s005" target="_blank">File S5</a>).</p

Identification of genes associated with the developmental phases of growth, lactation, and involution among the mammary gland developmentally associated gene subset.

<p><b>A.</b> The developmentally associated genes were clustered into three groups by Principal Component Analysis. Expression profiles of genes in mammary pregnancy cycle are represented as dots in PC1 (1^st principal component axis) and PC2 (2^nd principal component axis). All probe sets were grouped into three groups: growth (PC1>0), involution (PC1<0&PC2>0) and lactation (PC1<0&PC2<0) based on the number of genes that have peak expression at a particular developmental time (showed in B). <b>B.</b> The time of peak expression for each developmentally associated gene was plotted on a histogram and classified according to the developmental phase (growth, yellow; lactation, blue; involution, purple). The column represents the number of genes that have peak expression at a particular developmental time. <b>C.</b> The frequency of a literature-based cancer modulated genes in the gene subsets associated with the three different stages of mammary gland development. The “growth” group contained more literature-based cancer modulated genes (20%) than the “lactation” (14.7%) and the involution (17%) groups (<i>p</i><0.05).</p

Tyrosine Phosphatase Shp2 Mediates the Estrogen Biological Action in Breast Cancer via Interaction with the Estrogen Extranuclear Receptor

<div><p>The extranuclear estrogen receptor pathway opens up novel perspectives in many physiological and pathological processes, especially in breast carcinogenesis. However, its function and mechanisms are not fully understood. Herein we present data identifying Shp2, a SH2-containing tyrosine phosphatase, as a critical component of extranuclear ER pathway in breast cancer. The research checked that the effect of Shp2 on the tumor formation and growth in animal model and investigated the regulation of Shp2 on the bio-effect and signaling transduction of estrogen in breast cancer cell lines. The results showed that Shp2 was highly expressed in more than 60% of total 151 breast cancer cases. The inhibition of Shp2 activity by PHPS1 (a Shp2 inhibitor) delayed the development of dimethylbenz(a)anthracene (DMBA)-induced tumors in the rat mammary gland and also blocked tumor formation in MMTV-pyvt transgenic mice. Estradiol (E2) stimulated protein expression and phosphorylation of Shp2, and induced Shp2 binding to ERα and IGF-1R around the membrane to facilitate the phosphorylation of Erk and Akt in breast cancer cells MCF7. Shp2 was also involved in several biological effects of the extranuclear ER-initiated pathway in breast cancer cells. Specific inhibitors (phps1, phps4 and NSC87877) or small interference RNAs (siRNA) of Shp2 remarkably suppressed E2-induced gene transcription (Cyclin D1 and trefoil factor 1 (TFF1)), rapid DNA synthesis and late effects on cell growth. These results introduced a new mechanism for Shp2 oncogenic action and shed new light on extranuclear ER-initiated action in breast tumorigenesis by identifying a novel associated protein, Shp2, for extranuclear ER pathway, which might benefit the therapy of breast cancer.</p></div

E2 induced the protein expression and phosphorylation of Shp2 and Gab2 in breast cancer cells.

<p>A: the protein and mRNA levels of Shp2 were shown in MCF7 cells treated with 10 nM E2 for different times. Actin served as the total protein control. B: Bcap 37 cells were treated with 10 nM E2, and Shp2 protein expression was checked by Western blotting. C: MCF7 cells were stimulated with different doses of E2 for 15 min, and then the phosphorylated protein of Shp2 or Gab2 was checked using the special antibody for active Shp2 or Gab2 (anti-phos-Shp2 or anti-phos-Gab2). Erk protein was also assessed as a total protein control, and the phospho-Erk level was recorded as an E2 activity control.</p

Suppressions of Shp2 activity blocked the tumor development in rodent mammary gland.

<p>A: The number and size of most of tumor in rats injected with phps1 (a specific inhibitor of Shp2 activity) were smaller than those in rats with saline. Each red circle represents a tumor. B: Rats were treated with DMBA to induce tumor formation in mammary glands, and then injected with the Shp2 activity inhibitor phps1. The number of tumors was counted at each observation day. Spots indicate the total number of tumors by injecting with saline (blue triangle) or phps1 (red square) at different days after DMBA treatment. C: phps1 treatment also decreased the number and size of tumors in MMTV-pyvt transgenic mice compared with control mice. Tumors were labeled with a red circle. D: Most tumors in mice injected with phps1 were smaller than those in control mice. Spots show the longer tumor diameter. The black bar is an average value.</p