Sally Gaddis

Gene expression signature of estrogen receptor α status in breast cancer

BackgroundEstrogens are known to regulate the proliferation of breast cancer cells and to modify their phenotypic properties. Identification of estrogen-regulated genes in human breast tumors is an essential step toward understanding the molecular mechanisms of estrogen action in cancer. To this end we generated and compared the Serial Analysis of Gene Expression (SAGE) profiles of 26 human breast carcinomas based on their estrogen receptor α (ER) status. Thus, producing a breast cancer SAGE database of almost 2.5 million tags, representing over 50,000 transcripts.ResultsWe identified 520 transcripts differentially expressed between ERα-positive (+) and ERα-negative (-) primary breast tumors (Fold change ≥ 2; p < 0.05). Furthermore, we identified 220 high-affinity Estrogen Responsive Elements (EREs) distributed on the promoter regions of 163 out of the 473 up-modulated genes in ERα (+) breast tumors. In brief, we observed predominantly up-regulation of cell growth related genes, DNA binding and transcription factor activity related genes based on Gene Ontology (GO) biological functional annotation. GO terms over-representation analysis showed a statistically significant enrichment of various transcript families including: metal ion binding related transcripts (p = 0.011), calcium ion binding related transcripts (p = 0.033) and steroid hormone receptor activity related transcripts (p = 0.031). SAGE data associated with ERα status was compared with reported information from breast cancer DNA microarrays studies. A significant proportion of ERα associated gene expression changes was validated by this cross-platform comparison. However, our SAGE study also identified novel sets of genes as highly expressed in ERα (+) invasive breast tumors not previously reported. These observations were further validated in an independent set of human breast tumors by means of real time RT-PCR.ConclusionThe integration of the breast cancer comparative transcriptome analysis based on ERα status coupled to the genome-wide identification of high-affinity EREs and GO over-representation analysis, provide useful information for validation and discovery of signaling networks related to estrogen response in this malignancy.

Transcriptomic changes in human breast cancer progression as determined by serial analysis of gene expression

IntroductionGenomic and transcriptomic alterations affecting key cellular processes such us cell proliferation, differentiation and genomic stability are considered crucial for the development and progression of cancer. Most invasive breast carcinomas are known to derive from precursor in situ lesions. It is proposed that major global expression abnormalities occur in the transition from normal to premalignant stages and further progression to invasive stages. Serial analysis of gene expression (SAGE) was employed to generate a comprehensive global gene expression profile of the major changes occurring during breast cancer malignant evolution.MethodsIn the present study we combined various normal and tumor SAGE libraries available in the public domain with sets of breast cancer SAGE libraries recently generated and sequenced in our laboratory. A recently developed modified t test was used to detect the genes differentially expressed.ResultsWe accumulated a total of approximately 1.7 million breast tissue-specific SAGE tags and monitored the behavior of more than 25,157 genes during early breast carcinogenesis. We detected 52 transcripts commonly deregulated across the board when comparing normal tissue with ductal carcinoma in situ, and 149 transcripts when comparing ductal carcinoma in situ with invasive ductal carcinoma (P < 0.01).ConclusionA major novelty of our study was the use of a statistical method that correctly accounts for the intra-SAGE and inter-SAGE library sources of variation. The most useful result of applying this modified t statistics beta binomial test is the identification of genes and gene families commonly deregulated across samples within each specific stage in the transition from normal to preinvasive and invasive stages of breast cancer development. Most of the gene expression abnormalities detected at the in situ stage were related to specific genes in charge of regulating the proper homeostasis between cell death and cell proliferation. The comparison of in situ lesions with fully invasive lesions, a much more heterogeneous group, clearly identified as the most importantly deregulated group of transcripts those encoding for various families of proteins in charge of extracellular matrix remodeling, invasion and cell motility functions.

From mice to humans: Identification of commonly deregulated genes in mammary cancer via comparative SAGE studies

Genetically engineered mouse mammary cancer models have been used over the years as systems to study human breast cancer. However, much controversy exists on the utility of such models as valid equivalents to the human cancer condition. To perform an interspecies gene expression comparative study in breast cancer we used a mouse model that most closely resembles human breast carcinogenesis. This system relies on the transplant of p53 null mouse mammary epithelial cells into the cleared mammary fat pads of syngeneic hosts. Serial analysis of gene expression (SAGE) was used to obtain gene expression profiles of normal and tumor samples from this mouse mammary cancer model (>300,000 mouse mammary-specific tags). The resulting mouse data were compared with 25 of our human breast cancer SAGE libraries (>2.5 million human breast-specific tags). We observed significant similarities in the deregulation of specific genes and gene families when comparing mouse with human breast cancer SAGE data. A total of 72 transcripts were identified as commonly deregulated in both species. We observed a systematic and significant down-regulation in all of the tumors from both species of various cytokines, including CXCL1 (GRO1), LIF, interleukin 6, and CCL2. All of the mouse and most human mammary tumors also displayed decreased expression of genes known to inhibit cell proliferation, including NFKBIA (IKBα), GADD45B, and CDKN1A (p21); transcription-related genes such as CEBP, JUN, JUNB, and ELF1; and apoptosis-related transcripts such as IER3 and GADD34/PPP1R15A. Examples of overexpressed transcripts in tumors from both species include proliferation-related genes such as CCND1, CKS1B, and STMN1 (oncoprotein 18); and genes related to other functions such as SEPW1, SDFR1, DNCI2, and SP110. Importantly, abnormal expression of several of these genes has not been associated previously with breast cancer. The consistency of these observations was validated in independent mouse and human mammary cancer sets. This is the first interspecies comparison of mammary cancer gene expression profiles. The comparative analysis of mouse and human SAGE mammary cancer data validates this p53 null mouse tumor model as a useful system closely resembling human breast cancer development and progression. More importantly, these studies are allowing us to identify relevant biomarkers of potential use in human studies while leading to a better understanding of specific mechanisms of human breast carcinogenesis.

Breast Cancer Molecular Signatures as Determined by SAGE: Correlation with Lymph Node Status

Global gene expression measured by DNA microarray platforms have been extensively used to classify breast carcinomas correlating with clinical characteristics, including outcome. We generated a breast cancer Serial Analysis of Gene Expression (SAGE) high-resolution database of ∼2.7 million tags to perform unsupervised statistical analyses to obtain the molecular classification of breast-invasive ductal carcinomas in correlation with clinicopathologic features. Unsupervised statistical analysis by means of a random forest approach identified two main clusters of breast carcinomas, which differed in their lymph node status (P = 0.01); this suggested that lymph node status leads to globally distinct expression profiles. A total of 245 (55 up-modulated and 190 down-modulated) transcripts were differentially expressed between lymph node (+) and lymph node (−) primary breast tumors (fold change, ≥2; P < 0.05). Various lymph node (+) up-modulated transcripts were validated in independent sets of human breast tumors by means of real-time reverse transcription-PCR (RT-PCR). We validated significant overexpression of transcripts for HOXC10 (P = 0.001), TPD52L1 (P = 0.007), ZFP36L1 (P = 0.011), PLINP1 (P = 0.013), DCTN3 (P = 0.025), DEK (P = 0.031), and CSNK1D (P = 0.04) in lymph node (+) breast carcinomas. Moreover, the DCTN3 (P = 0.022) and RHBDD2 (P = 0.002) transcripts were confirmed to be overexpressed in tumors that recurred within 6 years of follow-up by real-time RT-PCR. In addition, meta-analysis was used to compare SAGE data associated with lymph node (+) status with publicly available breast cancer DNA microarray data sets. We have generated evidence indicating that the pattern of gene expression in primary breast cancers at the time of surgical removal could discriminate those tumors with lymph node metastatic involvement using SAGE to identify specific transcripts that behave as predictors of recurrence as well. (Mol Cancer Res 2007;5(9):881–90)

A molecular portrait of high-grade ductal carcinoma in situ

Ductal carcinoma in situ (DCIS) is a noninvasive precursor lesion to invasive breast carcinoma. We still have no understanding on why only some DCIS lesions evolve to invasive cancer whereas others appear not to do so during the life span of the patient. Here, we performed full exome (tumor vs. matching normal), transcriptome, and methylome analysis of 30 pure high-grade DCIS (HG-DCIS) and 10 normal breast epithelial samples. Sixty-two percent of HG-DCIS cases displayed mutations affecting cancer driver genes or potential drivers. Mutations were observed affecting PIK3CA (21% of cases), TP53 (17%), GATA3 (7%), MLL3 (7%) and single cases of mutations affecting CDH1, MAP2K4, TBX3, NF1, ATM, and ARID1A. Significantly, 83% of lesions displayed numerous large chromosomal copy number alterations, suggesting they might precede selection of cancer driver mutations. Integrated pathway-based modeling analysis of RNA-seq data allowed us to identify two DCIS subgroups (DCIS-C1 and DCIS-C2) based on their tumor-intrinsic subtypes, proliferative, immune scores, and in the activity of specific signaling pathways. The more aggressive DCIS-C1 (highly proliferative, basal-like, or ERBB2(+)) displayed signatures characteristic of activated Treg cells (CD4(+)/CD25(+)/FOXP3(+)) and CTLA4(+)/CD86(+) complexes indicative of a tumor-associated immunosuppressive phenotype. Strikingly, all lesions showed evidence of TP53 pathway inactivation. Similarly, ncRNA and methylation profiles reproduce changes observed postinvasion. Among the most significant findings, we observed upregulation of lncRNA HOTAIR in DCIS-C1 lesions and hypermethylation of HOXA5 and SOX genes. We conclude that most HG-DCIS lesions, in spite of representing a preinvasive stage of tumor progression, displayed molecular profiles indistinguishable from invasive breast cancer.

SAGE profiling of UV-induced mouse skin squamous cell carcinomas, comparison with acute UV irradiation effects

Ultraviolet (UV) irradiation is the primary environmental insult responsible for the development of most common skin cancers. To better understand the multiple molecular events that contribute to the development of UV‐induced skin cancer, in a first study, serial analysis of gene expression (SAGE) was used to compare the global gene expression profiles of normal SKH‐1 mice epidermis with that of UV‐induced squamous cell carcinomas (SCCs) from SKH‐1 mice. More than 200 genes were found to be differentially expressed in SCCs compared to normal skin (P < 0.0005 level of significance). As expected, genes related to epidermal proliferation and differentiation were deregulated in SCCs relative to normal skin. However, various novel genes, not previously associated with skin carcinogenesis, were also identified as deregulated in SCCs. Northern blot analyses on various selected genes validated the SAGE findings: caspase‐14 (reduced 8.5‐fold in SCCs); cathepsins D and S (reduced 3‐fold and increased 11.3‐fold, respectively, in SCCs); decorin, glutathione S‐transferase omega‐1, hypoxia‐inducible factor 1α, insulin‐like growth factor binding protein‐7, and matrix metalloproteinase‐13 (increased 18‐, 12‐, 12‐, 18.3‐, and 11‐folds, respectively, in SCCs). Chemokine (C‐C motif), ligand 27 (CCL27), which was found downregulated 12.7‐fold in SCCs by SAGE, was also observed to be strongly downregulated 6–24 h after a single and multiple UV treatments. In a second independent study we compared the expression profile of UV‐irradiated versus sham‐treated SKH‐1 epidermis. Interestingly, numerous genes determined to be deregulated 8 h after a single UV dose were also deregulated in SCCs. For instance, genes whose expression was upregulated both after acute UV‐treated skin and SCCs included keratins 6 and 16, small proline‐rich proteins, and S100 calcium binding protein A9. Studies like those described here do not only provide insights into genes and pathways involved in skin carcinogenesis but also allow us to identify early UV irradiation deregulated surrogate biomarkers of potential use in chemoprevention studies.

Serial analysis of gene expression in normal p53 null mammary epithelium.

Much evidence has accumulated implicating the p53 gene as of importance in breast carcinogenesis. However, much still remains to be uncovered on the specific downstream pathways influenced by this important activator/repressor of transcription. This study investigated the effects of a p53 null genotype on the transcriptome of ‘normal’ mouse mammary epithelium using a unique in vivo model of preneoplastic transformation. We used SAGE for the comparative analysis of p53 wild type (wt) and null mammary epithelium unexposed and exposed to hormonal stimulation. Analysis of the hormone exposed samples provided a comprehensive view of the dramatic changes in gene expression as consequence of the functional differentiation of the mammary epithelium in an in vivo system. We detected the dysregulation in p53null epithelium of <1% of the transcriptome. Changes in expression affected not only known p53 target genes, but also several unexpected genes such as Expi (Wdnm1), Cyp1b1, Gelsolin, Ramp2 and class I MHC genes. The dysregulation of specific genes and their potential use as preneoplastic markers was further validated using an independent model of premalignant mammary outgrowth lines. This is the first study to examine the transcriptome of very early stages of preneoplastic progression in an in vivo model that mimics human breast cancer.

Methods to detect replication-dependent and replication-independent DNA structure-induced genetic instability.

DNA can adopt a variety of alternative secondary (i.e., non-B DNA) conformations that play important roles in cellular metabolism, including genetic instability, disease etiology and evolution. While we still have much to learn, research in this field has expanded dramatically in the past decade. We have summarized in our previous Methods review (Wang et al., Methods, 2009) some commonly used techniques to determine non-B DNA structural conformations and non-B DNA-induced genetic instability in prokaryotes and eukaryotes. Since that time, we and others have further characterized mechanisms involved in DNA structure-induced mutagenesis and have proposed both replication-dependent and replication-independent models. Thus, in this review, we highlight some current methodologies to identify DNA replication-related and replication-independent mutations occurring at non-B DNA regions to allow for a better understanding of the mechanisms underlying DNA structure-induced genetic instability. We also describe a new web-based search engine to identify potential intramolecular triplex (H-DNA) and left-handed Z-DNA-forming motifs in entire genomes or at selected sequences of interest.

Identification of modulated genes by three classes of chemopreventive agents at preneoplastic stages in a p53-null mouse mammary tumor model.

Genetically engineered mouse cancer models are among the most useful tools for testing the in vivo effectiveness of the various chemopreventive approaches. The p53-null mouse model of mammary carcinogenesis was previously characterized by us at the cellular, molecular, and pathologic levels. In a companion article, Medina et al. analyzed the efficacy of bexarotene, gefitinib, and celecoxib as chemopreventive agents in the same model. Here we report the global gene expression effects on mammary epithelium of such compounds, analyzing the data in light of their effectiveness as chemopreventive agents. SAGE was used to profile the transcriptome of p53-null mammary epithelium obtained from mice treated with each compound versus controls. This information was also compared with SAGE data from p53-null mouse mammary tumors. Gene expression changes induced by the chemopreventive treatments revealed a common core of 87 affected genes across treatments (P < 0.05). The effective compounds, bexarotene and gefitinib, may exert their chemopreventive activity, at least in part, by affecting a set of 34 genes related to specific cellular pathways. The gene expression signature revealed various genes previously described to be associated with breast cancer, such as the activator protein-1 complex member Fos-like antigen 2 (Fosl2), early growth response 1 (Egr1), gelsolin (Gsn), and tumor protein translationally controlled 1 (Tpt1), among others. The concerted modulation of many of these transcripts before malignant transformation seems to be conducive to predominantly decrease cell proliferation. This study has revealed candidate key pathways that can be experimentally tested in the same model system and may constitute novel targets for future translational research.

Transcriptomic signature of Bexarotene (Rexinoid LGD1069) on mammary gland from three transgenic mouse mammary cancer models

BackgroundThe rexinoid bexarotene (LGD1069, Targretin) is a highly selective retinoid × receptor (RXR) agonist that inhibits the growth of pre-malignant and malignant breast cells. Bexarotene was shown to suppress the development of breast cancer in transgenic mice models without side effects. The chemopreventive effects of bexarotene are due to transcriptional modulation of cell proliferation, differentiation and apoptosis. Our goal in the present study was to obtain a profile of the genes modulated by bexarotene on mammary gland from three transgenic mouse mammary cancer models in an effort to elucidate its molecular mechanism of action and for the identification of biomarkers of effectiveness.MethodsSerial analysis of gene expression (SAGE) was employed to profile the transcriptome of p53-null, MMTV-ErbB2, and C3(1)-SV40 mammary cells obtained from mice treated with bexarotene and their corresponding controls.ResultsThis resulted in a dataset of approximately 360,000 transcript tags representing over 20,000 mRNAs from a total of 6 different SAGE libraries. Analysis of gene expression changes induced by bexarotene in mammary gland revealed that 89 genes were dysregulated among the three transgenic mouse mammary models. From these, 9 genes were common to the three models studied.ConclusionAnalysis of the indicated core of transcripts and protein-protein interactions of this commonly modulated genes indicate two functional modules significantly affected by rexinoid bexarotene related to protein biosynthesis and bioenergetics signatures, in addition to the targeting of cancer-causing genes related with cell proliferation, differentiation and apoptosis.