Gene expression in histologically normal epithelium from breast cancer patients and from cancer-free prophylactic mastectomy patients shares a similar profile

Differential translation of mRNA isoforms underlies oncogenic activation of cell cycle kinase Aurora A

Aurora Kinase A (AURKA) is an oncogenic kinase with major roles in mitosis, but also exerts cell cycle- and kinase-independent functions linked to cancer. Therefore, control of its expression, as well as its activity, is crucial. A short and a long 3'UTR isoform exist for AURKA mRNA, resulting from alternative polyadenylation (APA). We initially observed that in triple-negative breast cancer, where AURKA is typically overexpressed, the short isoform is predominant and this correlates with faster relapse times of patients. The short isoform is characterized by higher translational efficiency since translation and decay rate of the long isoform are targeted by hsa-let-7a tumor-suppressor miRNA. Additionally, hsa-let-7a regulates the cell cycle periodicity of translation of the long isoform, whereas the short isoform is translated highly and constantly throughout interphase. Finally, disrupted production of the long isoform led to an increase in proliferation and migration rates of cells. In summary, we uncovered a new mechanism dependent on the cooperation between APA and miRNA targeting likely to be a route of oncogenic activation of human AURKA.

Increased angiotensin II coupled with decreased Adra1a expression enhances cardiac hypertrophy in pregnancy-associated hypertensive mice

Cardiac hypertrophy is a crucial risk factor for hypertensive disorders during pregnancy, but its progression during pregnancy remains unclear. We previously showed cardiac hypertrophy in a pregnancy-associated hypertensive (PAH) mouse model, in which an increase in angiotensin II (Ang II) levels was induced by human renin and human angiotensinogen, depending on pregnancy conditions. Here, to elucidate the factors involved in the progression of cardiac hypertrophy, we performed a comprehensive analysis of changes in gene expression in the hearts of PAH mice and compared them with those in control mice. We found that alpha-1A adrenergic receptor (Adra1a) mRNA levels in the heart were significantly reduced under PAH conditions, whereas the renin-angiotensin system was upregulated. Furthermore, we found that Adra1a-deficient PAH mice exhibited more severe cardiac hypertrophy than PAH mice. Our study suggests that Adra1a levels are regulated by renin-angiotensin system and that changes in Adra1a expression are involved in progressive cardiac hypertrophy in PAH mice.

Associations of breast cancer related exposures and gene expression profiles in normal breast tissue-The Norwegian Women and Cancer normal breast tissue study

BACKGROUND

Normal breast tissue is utilized in tissue-based studies of breast carcinogenesis. While gene expression in breast tumor tissue is well explored, our knowledge of transcriptomic signatures in normal breast tissue is still incomplete. The aim of this study was to investigate variability of gene expression in a large sample of normal breast tissue biopsies, according to breast cancer related exposures (obesity, smoking, alcohol, hormone therapy, and parity).

METHODS

We analyzed gene expression profiles from 311 normal breast tissue biopsies from cancer-free, post-menopausal women, using Illumina bead chip arrays. Principal component analysis and K-means clustering was used for initial analysis of the dataset. The association of exposures and covariates with gene expression was determined using linear models for microarrays.

RESULTS

Heterogeneity of the breast tissue and cell composition had the strongest influence on gene expression profiles. After adjusting for cell composition, obesity, smoking, and alcohol showed the highest numbers of associated genes and pathways, whereas hormone therapy and parity were associated with negligible gene expression differences.

CONCLUSION

Our results provide insight into associations between major exposures and gene expression profiles and provide an informative baseline for improved understanding of exposure-related molecular events in normal breast tissue of cancer-free, post-menopausal women.

Single-cell transcriptomic analysis of the tumor ecosystem of adenoid cystic carcinoma

Adenoid cystic carcinoma (ACC) is a malignant tumor that originates from exocrine gland epithelial cells. We profiled the transcriptomes of 49,948 cells from paracarcinoma and carcinoma tissues of three patients using single-cell RNA sequencing. Three main types of the epithelial cells were identified into myoepithelial-like cells, intercalated duct-like cells, and duct-like cells by marker genes. And part of intercalated duct-like cells with special copy number variations which altered with MYB family gene and EN1 transcriptomes were identified as premalignant cells. Developmental pseudo-time analysis showed that the premalignant cells eventually transformed into malignant cells. Furthermore, MYB and MYBL1 were found to belong to two different gene modules and were expressed in a mutually exclusive manner. The two gene modules drove ACC progression into different directions. Our findings provide novel evidence to explain the high recurrence rate of ACC and its characteristic biological behavior.

Histamine H4 Receptor Expression in Triple-negative Breast Cancer: An Exploratory Study

Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype. There are neither universally accepted prognostic markers nor molecular targets related to TNBC. The histamine H4 receptor (H4R) has been characterized in TNBC experimental models, demonstrating its critical role in tumor development and progression. In this study, H4R expression was compared in breast cancer subtypes and correlated with clinical features using The Cancer Genome Atlas data (Pan-Cancer Atlas). The H4R status was further evaluated by immunohistochemistry in 30 TNBC human samples in relation to clinicopathological parameters. Results indicate that H4R was downregulated in basal-like/TNBC compared with luminal A and normal breast-like tumors. The higher expression of H4R was associated with improved progression-free and overall survival outcomes in basal-like/TNBC. H4R immunoreactivity was detected in about 70% of tumors, and its expression was positively correlated with the levels in the histologically normal peritumoral tissue. High H4R expression in peritumoral tissue correlated with reduced number of lymph node involvement and unifocal TNBC, while it was associated with increased patient survival. In conclusion, the H4R might represent a potential prognostic biomarker in TNBC. Further studies in large cohorts are needed to better understand the significance of H4R in breast cancer biology.

Field cancerization in breast cancer

Breast cancer affects one in seven women worldwide during their lifetime. Widespread mammographic screening programs and education campaigns allow for early detection of the disease, often during its asymptomatic phase. Current practice in treatment and recurrence monitoring is based primarily on pathological evaluations but can also encompass genomic evaluations, both of which focus on the primary tumor. Although breast cancer is one of the most studied cancers, patients still recur at a rate of up to 15% within the first 10 years post‐surgery. Local recurrence was originally attributed to tumor cells contaminating histologically normal (HN) tissues beyond the surgical margin, but advances in technology have allowed for the identification of distinct aberrations that exist in the peri‐tumoral tissues themselves. One leading theory to explain this phenomenon is the field cancerization theory. Under this hypothesis, tumors arise from a field of molecularly altered cells that create a permissive environment for malignant evolution, which can occur with or without morphological changes. The traditional histopathology paradigm dictates that molecular alterations are reflected in the tissue phenotype. However, the spectrum of inter‐patient variability of normal breast tissue may obfuscate recognition of a cancerized field during routine diagnostics. In this review, we explore the concept of field cancerization focusing on HN peri‐tumoral tissues: we present the pathological and molecular features of field cancerization within these tissues and discuss how the use of peri‐tumoral tissues can affect research. Our observations suggest that pathological and molecular evaluations could be used synergistically to assess risk and guide the therapeutic management of patients. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

BTG2 as a tumor target for the treatment of luminal A breast cancer

As one of the most common breast cancer subtypes, luminal A breast cancer is sensitive to endocrine-based therapy and insensitive to chemotherapy. Patients with luminal A subtype of breast cancer have a relatively good prognosis compared with that of patients with other subtypes of breast cancer. However, with the increased incidence in endocrine resistance and severe side effects, simple endocrine therapy has become unsuitable for the treatment of luminal A breast cancer. Therefore, identifying novel therapeutic targets for luminal A breast cancer may accelerate the development of an effective therapeutic strategy. The bioinformatical analysis of the current study, which included KEGG and GO analyses of the GSE20437 dataset containing 24 healthy and 18 breast cancer tissue samples, identified key target genes associated with breast cancer. Moreover, survival analysis results revealed that a low expression of BTG2 was significantly associated with the low survival rate of patients with breast cancer, indicated that B-cell translocation gene 2 (BTG2) may be a potential target in breast cancer. However, BTG2 may be cancer type-dependent, as overexpression of BTG2 has been demonstrated to suppress the proliferation of pancreatic and lung cancer cells, but promote the proliferation of bladder cancer cells. Since the association between BTG2 and luminal A-subtype breast cancer remains unclear, it is important to understand the biological function of BTG2 in luminal A breast cancer. Based on the expression levels of estrogen receptor, progesterone receptor and human epidermal growth factor receptor, MCF-7 cells were selected in the present study as a luminal A breast cancer cell type. MTT, Transwell invasion and wound healing assays revealed that overexpression of BTG2 suppressed the levels of MCF-7 cell proliferation, migration and invasion. In addition, the downregulation of BTG2 at the mRNA and protein level was also confirmed in luminal A breast tumor tissue, which was consistent with the results in vitro. These results indicated that BTG2 may act as an effective target for the treatment of luminal A breast cancer.

A Proposed TUSC7/miR-211/Nurr1 ceRNET Might Potentially be Disturbed by a cer-SNP rs2615499 in Breast Cancer

Evidence and in silico analyses showed that TUSC7, miR-211, and Nurr1 may be involved in BC pathogenesis by ceRNET signaling axis. This study aimed to investigate the potential role of TUSC7/miR-211/Nurr1 ceRNET and rs2615499 variant as a novel cer-SNP in BC subjects. The expression assays were conducted by qPCR on tumor tissues (n = 50), tumor-adjacent normal tissues (TANTs) (n = 50), and clinically healthy control tissues (n = 50). The expression of TUSC7 and Nurr1 significantly decreased, but the level of miR-211 significantly increased in tumor tissues compared to TANTs and healthy normal tissues. Altered expression of TUSC7 and miR-211 was associated with poor prognosis of patients. The Nurr1 exhibited a double-edged sword-like activity in BC. In addition, TUSC7, Nurr1, and miR-211 expressions were significantly related to a novel BC-associated rs2615499 (A > C) located in the miR-211 binding site on Nurr1 3'-UTR. In the second part of the study, a case-control study was performed on BC patients (n = 100) and matched healthy controls (n = 100). The genomic DNA was isolated and genotyping was performed using Tetra-Primer ARMS PCR. The CC and AC genotypes were associated with higher expression levels of Nurr1 and worse outcomes of the disease. Our findings revealed that TUSC7 functions as a tumor suppressor in BC potentially via miR-211/Nurr1, which might be disturbed by the cer-SNP rs2615499. However, functional studies are needed to validate these results.

Single-cell transcriptomic analysis of the tumor ecosystems underlying initiation and progression of papillary thyroid carcinoma

The tumor ecosystem of papillary thyroid carcinoma (PTC) is poorly characterized. Using single-cell RNA sequencing, we profile transcriptomes of 158,577 cells from 11 patients’ paratumors, localized/advanced tumors, initially-treated/recurrent lymph nodes and radioactive iodine (RAI)-refractory distant metastases, covering comprehensive clinical courses of PTC. Our data identifies a “cancer-primed” premalignant thyrocyte population with normal morphology but altered transcriptomes. Along the developmental trajectory, we also discover three phenotypes of malignant thyrocytes (follicular-like, partial-epithelial-mesenchymal-transition-like, dedifferentiation-like), whose composition shapes bulk molecular subtypes, tumor characteristics and RAI responses. Furthermore, we uncover a distinct BRAF-like-B subtype with predominant dedifferentiation-like thyrocytes, enriched cancer-associated fibroblasts, worse prognosis and promising prospect of immunotherapy. Moreover, potential vascular-immune crosstalk in PTC provides theoretical basis for combined anti-angiogenic and immunotherapy. Together, our findings provide insight into the PTC ecosystem that suggests potential prognostic and therapeutic implications.

The characterisation of the papillary thyroid carcinoma (PTC) tumour microenvironment remains crucial. Here, the authors perform single-cell RNA sequencing in 11 patients and identify potential opportunities for the use of immunotherapy and its combination with anti-angiogenic therapy in PTC.

Transcriptomic landscape of male and female reproductive cancers: Similar pathways and molecular signatures predicting response to endocrine therapy

Reproductive cancers in both genders represent serious health problems, whose incidence has significantly risen over the past decades. Although considerable differences among reproductive cancers exist, we aimed to identify similar signaling pathways and key molecular oncomarkers shared among six human reproductive cancers that can advance the current knowledge of cancer biology to propose new strategies for more effective therapies. Using a computational analysis approach, here we uncover aberrant miRNAs-mRNAs networks shared in six reproductive tumor types, and identify common molecular mechanisms strictly associated with cancer promotion and aggressiveness. Based on the fact that estrogenic and androgenic signaling pathways were most active in prostate and breast cancers, we further demonstrated that both androgen and estrogen deprivation therapy are capable of regulating the expression of the same key molecular sensors associated with endoplasmic reticulum dysfunction and cell cycle in these cancers. Overall, our data reveal a potential mechanistic framework of cellular processes that are shared among reproductive cancers, and particularly, highlight the importance of hormonal deprivation in breast and prostate cancers and potentially new biomarkers of response to these therapeutic approaches.

Meta-Analysis of Microdissected Breast Tumors Reveals Genes Regulated in the Stroma but Hidden in Bulk Analysis

Transcriptome data provide a valuable resource for the study of cancer molecular mechanisms, but technical biases, sample heterogeneity, and small sample sizes result in poorly reproducible lists of regulated genes. Additionally, the presence of multiple cellular components contributing to cancer development complicates the interpretation of bulk transcriptomic profiles. To address these issues, we collected 48 microarray datasets derived from laser capture microdissected stroma or epithelium in breast tumors and performed a meta-analysis identifying robust lists of differentially expressed genes. This was used to create a database with carefully harmonized metadata that we make freely available to the research community. As predicted, combining the results of multiple datasets improved statistical power. Moreover, the separate analysis of stroma and epithelium allowed the identification of genes with different contributions in each compartment, which would not be detected by bulk analysis due to their distinct regulation in the two compartments. Our method can be profitably used to help in the discovery of biomarkers and the identification of functionally relevant genes in both the stroma and the epithelium. This database was made to be readily accessible through a user-friendly web interface.

Transcriptome Analyses of Myometrium from Fibroid Patients Reveals Phenotypic Differences Compared to Non-Diseased Myometrium

Uterine fibroid tissues are often compared to their matched myometrium in an effort to understand their pathophysiology, but it is not clear whether the myometria of uterine fibroid patients represent truly non-disease control tissues. We analyzed the transcriptomes of myometrial samples from non-fibroid patients (M) and compared them with fibroid (F) and matched myometrial (MF) samples to determine whether there is a phenotypic difference between fibroid and non-fibroid myometria. Multidimensional scaling plots revealed that M samples clustered separately from both MF and F samples. A total of 1169 differentially expressed genes (DEGs) (false discovery rate < 0.05) were observed in the MF comparison with M. Overrepresented Gene Ontology terms showed a high concordance of upregulated gene sets in MF compared to M, particularly extracellular matrix and structure organization. Gene set enrichment analyses showed that the leading-edge genes from the TGFβ signaling and inflammatory response gene sets were significantly enriched in MF. Overall comparison of the three tissues by three-dimensional principal component analyses showed that M, MF, and F samples clustered separately from each other and that a total of 732 DEGs from F vs. M were not found in the F vs. MF, which are likely understudied in the pathogenesis of uterine fibroids and could be key genes for future investigation. These results suggest that the transcriptome of fibroid-associated myometrium is different from that of non-diseased myometrium and that fibroid studies should consider using both matched myometrium and non-diseased myometrium as controls.

Characteristics of Breast Ducts in Normal-Risk and High-risk Women and Their Relationship to Ductal Cytologic Atypia

Breast ductal cytologic atypia is an important risk factor for sporadic breast cancer. Characterization of the associated normal breast tissue is needed to develop additional methods of risk assessment and new targets for breast cancer prevention. We conducted a prospective clinical trial evaluating women at normal-risk or at high-risk for sporadic breast cancer. Breast ductal cells were collected and studied cytologically and by gene expression profiling, and breast ductal architectural changes were studied by breast ductal endoscopy (BDE) and breast MRI. One hundred and forty subjects were studied, 70 at high risk (RR, 2.0-4.6) and 70 at normal risk. Cytologic atypia was present in 22.9% of high-risk and 25.7% of normal-risk subjects. Ductal endoscopy was performed in 89 subjects and revealed benign intraductal abnormalities, primarily intraductal fibrous webbing suggesting chronic inflammation, in 40.4% of high-risk and 5.4% of normal-risk subjects, respectively (P ₂ = 0.0002). Two high-risk subjects with atypia and no normal-risk subjects with atypia developed invasive breast cancer. Gene expression profiling of ductal cells showed comparable gene expression profiles without enriched expression of previously defined oncogenic signatures in subjects with cellular atypia compared with those without atypia, and in high-risk subjects compared with normal-risk subjects (FDR > 0.5). Cytologic ductal atypia in normal-risk subjects does not appear to be of clinical significance. Atypia in women at high risk may be associated with benign and malignant breast ductal abnormalities; these characteristics of high-risk ductal cells may not be reflected in gene expression profiles.

TRPM4 is overexpressed in breast cancer associated with estrogen response and epithelial-mesenchymal transition gene sets

Ion channels form an important class of drug targets in malignancies. Transient receptor potential cation channel subfamily M member 4 (TRPM4) plays oncological roles in various solid tumors. Herein, we examined TRPM4 protein expression profile by immunohistochemistry (IHC) in breast cancer cases compared with normal breast ducts, its association with clinico-demographical parameters, and its potential function in breast cancers by Gene Set Enrichment Analysis (GSEA). Data-mining demonstrated that TRPM4 transcript levels were significantly higher in The Cancer Genome Atlas series of breast cancer cases (n = 1,085) compared with normal breast tissues (n = 112) (p = 1.03 x 10⁻¹¹). Our IHC findings in tissue microarrays showed that TRPM4 protein was overexpressed in breast cancers (n = 83/99 TRPM4⁺; 83.8%) compared with normal breast ducts (n = 5/10 TRPM4⁺; 50%) (p = 0.022). Higher TRPM4 expression (median frequency cut-off) was significantly associated with higher lymph node status (N1-N2 vs N0; p = 0.024) and higher stage (IIb-IIIb vs I-IIa; p = 0.005). GSEA evaluation in three independent gene expression profiling (GEP) datasets of breast cancer cases (GSE54002, n = 417; GSE20685, n = 327; GSE23720, n = 197) demonstrated significant association of TRPM4 transcript expression with estrogen response and epithelial-mesenchymal transition (EMT) gene sets (p<0.01 and false discovery rate<0.05). These gene sets were not enriched in GEP datasets of normal breast epithelium cases (GSE10797, n = 5; GSE9574, n = 15; GSE20437, n = 18). In conclusion, TRPM4 protein expression is upregulated in breast cancers associated with worse clinico-demographical parameters, and TRPM4 potentially regulates estrogen receptor signaling and EMT progression in breast cancer.

The role of activated leukocyte cell adhesion molecule (ALCAM) in cancer progression, invasion, metastasis and recurrence: A novel cancer stem cell marker and tumor-specific prognostic marker

Activated leukocyte cell adhesion molecule (ALCAM) or CD166 is a 100 to 105 KDa transmembrane immunoglobulin which is involved in activation of T-cells, hematopoiesis, neutrophils trans-endothelial migration, angiogenesis, inflammation and tumor propagation and invasiveness through formation of homophilic and heterophilic interactions. Recently, many studies have proposed that the expression pattern of ALCAM is highly associated with the grade, stage and invasiveness of tumors. Although ALCAM is a valuable prognostic marker in different carcinomas, similar expression patterns in different tumor types may be associated with completely different prognostic states, making it to be a tumor-type-dependent prognostic marker. In addition, ALCAM isoforms provide ways for primary detection of tumor cells with metastatic potential. More importantly, this prognostic marker has shown to be considerably dependent on the cytoplasmic and membranous expression, indirect and direct regulation of post-transcriptional molecules, pro-apoptotic proteins functionalities and several other oncogenic proteins or signalling pathways. This review mainly focuses on the pathways involved in expression of ALCAM and its prognostic value of in different types of cancers and the way in which it is regulated.

Prioritization of metabolic genes as novel therapeutic targets in estrogen-receptor negative breast tumors using multi-omics data and text mining

Estrogen-receptor negative (ERneg) breast cancer is an aggressive breast cancer subtype in the need for new therapeutic options. We have analyzed metabolomics, proteomics and transcriptomics data for a cohort of 276 breast tumors (MetaCancer study) and nine public transcriptomics datasets using univariate statistics, meta-analysis, Reactome pathway analysis, biochemical network mapping and text mining of metabolic genes. In the MetaCancer cohort, a total of 29% metabolites, 21% proteins and 33% transcripts were significantly different (raw p <0.05) between ERneg and ERpos breast tumors. In the nine public transcriptomics datasets, on average 23% of all genes were significantly different (raw p <0.05). Specifically, up to 60% of the metabolic genes were significantly different (meta-analysis raw p <0.05) across the transcriptomics datasets. Reactome pathway analysis of all omics showed that energy metabolism, and biosynthesis of nucleotides, amino acids, and lipids were associated with ERneg status. Text mining revealed that several significant metabolic genes and enzymes have been rarely reported to date, including PFKP, GART, PLOD1, ASS1, NUDT12, FAR1, PDE7A, FAHD1, ITPK1, SORD, HACD3, CDS2 and PDSS1. Metabolic processes associated with ERneg tumors were identified by multi-omics integration analysis of metabolomics, proteomics and transcriptomics data. Overall results suggested that TCA anaplerosis, proline biosynthesis, synthesis of complex lipids and mechanisms for recycling substrates were activated in ERneg tumors. Under-reported genes were revealed by text mining which may serve as novel candidates for drug targets in cancer therapies. The workflow presented here can also be used for other tumor types.

Differential Connectivity in Colorectal Cancer Gene Expression Network

BACKGROUND

Colorectal cancer (CRC) is one of the challenging types of cancers; thus, exploring effective biomarkers related to colorectal could lead to significant progresses toward the treatment of this disease.

METHODS

In the present study, CRC gene expression datasets have been reanalyzed. Mutual differentially expressed genes across 294 normal mucosa and adjacent tumoral samples were then utilized in order to build two independent transcriptional regulatory networks. By analyzing the networks topologically, genes with differential global connectivity related to cancer state were determined for which the potential transcriptional regulators including transcription factors were identified.

RESULTS

The majority of differentially connected genes (DCGs) were up-regulated in colorectal transcriptome experiments. Moreover, a number of these genes have been experimentally validated as cancer or CRC-associated genes. The DCGs, including GART, TGFB1, ITGA2, SLC16A5, SOX9, and MMP7, were investigated across 12 cancer types. Functional enrichment analysis followed by detailed data mining exhibited that these candidate genes could be related to CRC by mediating in metastatic cascade in addition to shared pathways with 12 cancer types by triggering the inflammatory events.

DISCUSSION

Our study uncovered correlated alterations in gene expression related to CRC susceptibility and progression that the potent candidate biomarkers could provide a link to disease.

Pathway Network Analysis of Complex Diseases Based on Multiple Biological Networks

Biological pathways play important roles in the development of complex diseases, such as cancers, which are multifactorial complex diseases that are usually caused by multiple disorders gene mutations or pathway. It has become one of the most important issues to analyze pathways combining multiple types of high-throughput data, such as genomics and proteomics, to understand the mechanisms of complex diseases. In this paper, we propose a method for constructing the pathway network of gene phenotype and find out disease pathogenesis pathways through the analysis of the constructed network. The specific process of constructing the network includes, firstly, similarity calculation between genes expressing data combined with phenotypic mutual information and GO ontology information, secondly, calculating the correlation between pathways based on the similarity between differential genes and constructing the pathway network, and, finally, mining critical pathways to identify diseases. Experimental results on Breast Cancer Dataset using this method show that our method is better. In addition, testing on an alternative dataset proved that the key pathways we found were more accurate and reliable as biological markers of disease. These results show that our proposed method is effective.

Intra-individual Gene Expression Variability of Histologically Normal Breast Tissue

Several studies have sought to identify novel transcriptional biomarkers in normal breast or breast microenvironment to predict tumor risk and prognosis. However, systematic efforts to evaluate intra-individual variability of gene expression within normal breast have not been reported. This study analyzed the microarray gene expression data of 288 samples from 170 women in the Normal Breast Study (NBS), wherein multiple histologically normal breast samples were collected from different block regions and different sections at a given region. Intra-individual differences in global gene expression and selected gene expression signatures were quantified and evaluated in association with other patient-level factors. We found that intra-individual reliability was relatively high in global gene expression, but differed by signatures, with composition-related signatures (i.e., stroma) having higher intra-individual variability and tumorigenesis-related signatures (i.e., proliferation) having lower intra-individual variability. Histological stroma composition was the only factor significantly associated with heterogeneous breast tissue (defined as > median intra-individual variation; high nuclear density, odds ratio [OR] = 3.42, 95% confidence interval [CI] = 1.15–10.15; low area, OR = 0.29, 95% CI = 0.10–0.86). Other factors suggestively influencing the variability included age, BMI, and adipose nuclear density. Our results underscore the importance of considering intra-individual variability in tissue-based biomarker development, and have important implications for normal breast research.

Comprehensive analysis of normal adjacent to tumor transcriptomes

Histologically normal tissue adjacent to the tumor (NAT) is commonly used as a control in cancer studies. However, little is known about the transcriptomic profile of NAT, how it is influenced by the tumor, and how the profile compares with non-tumor-bearing tissues. Here, we integrate data from the Genotype-Tissue Expression project and The Cancer Genome Atlas to comprehensively analyze the transcriptomes of healthy, NAT, and tumor tissues in 6506 samples across eight tissues and corresponding tumor types. Our analysis shows that NAT presents a unique intermediate state between healthy and tumor. Differential gene expression and protein–protein interaction analyses reveal altered pathways shared among NATs across tissue types. We characterize a set of 18 genes that are specifically activated in NATs. By applying pathway and tissue composition analyses, we suggest a pan-cancer mechanism of pro-inflammatory signals from the tumor stimulates an inflammatory response in the adjacent endothelium.

Normal tissue adjacent to the tumour (NAT) is often used as a control in cancer studies. Here, the authors analyse across cancer types the transcriptomes of healthy, NAT, and tumour tissues, and find that NAT presents a unique state, potentially due to inflammatory response of the NAT to the tumour tissue.

Data-driven analysis of immune infiltrate in a large cohort of breast cancer and its association with disease progression, ER activity, and genomic complexity

The tumor microenvironment is now widely recognized for its role in tumor progression, treatment response, and clinical outcome. The intratumoral immunological landscape, in particular, has been shown to exert both pro-tumorigenic and anti-tumorigenic effects. Identifying immunologically active or silent tumors may be an important indication for administration of therapy, and detecting early infiltration patterns may uncover factors that contribute to early risk. Thus far, direct detailed studies of the cell composition of tumor infiltration have been limited; with some studies giving approximate quantifications using immunohistochemistry and other small studies obtaining detailed measurements by isolating cells from excised tumors and sorting them using flow cytometry. Herein we utilize a machine learning based approach to identify lymphocyte markers with which we can quantify the presence of B cells, cytotoxic T-lymphocytes, T-helper 1, and T-helper 2 cells in any gene expression data set and apply it to studies of breast tissue. By leveraging over 2,100 samples from existing large scale studies, we are able to find an inherent cell heterogeneity in clinically characterized immune infiltrates, a strong link between estrogen receptor activity and infiltration in normal and tumor tissues, changes with genomic complexity, and identify characteristic differences in lymphocyte expression among molecular groupings. With our extendable methodology for capturing cell type specific signal we systematically studied immune infiltration in breast cancer, finding an inverse correlation between beneficial lymphocyte infiltration and estrogen receptor activity in normal breast tissue and reduced infiltration in estrogen receptor negative tumors with high genomic complexity.

Overexpression of lipid metabolism genes and PBX1 in the contralateral breasts of women with estrogen receptor-negative breast cancer

Risk biomarkers for estrogen receptor (ER)-negative breast cancer have clear value for breast cancer prevention. We previously reported a set of lipid metabolism (LiMe) genes with high expression in the contralateral unaffected breasts (CUBs) of ER-negative cancer cases. We now further examine LiMe gene expression in both tumor and CUB, and investigate the role of Pre-B-cell leukemia homeobox-1 (PBX1) as a candidate common transcription factor for LiMe gene expression. mRNA was extracted from laser-capture microdissected epithelium from tumor and CUB of 84 subjects (28 ER-positive cases, 28 ER-negative cases, 28 healthy controls). Gene expression was quantitated by qRT-PCR. Logistic regression models were generated to predict ER status of the contralateral cancer. Protein expression of HMGCS2 and PBX1 was measured using immunohistochemistry. The effect of PBX1 on LiMe gene expression was examined by overexpressing PBX1 in MCF10A cells with or without ER, and by suppressing PBX1 in MDA-MB-453 cells. The expression of DHRS2, HMGCS2, UGT2B7, UGT2B11, ALOX15B, HPGD, UGT2B28 and GLYATL1 was significantly higher in ER-negative versus ER-positive CUBs, and predicted ER status of the tumor in test and validation sets. In contrast, LiMe gene expression was significantly lower in ER-negative than ER-positive tumors. PBX1 overexpression in MCF10A cells up-regulated most LiMe genes, but not in MCF10A cells overexpressing ER. Suppressing PBX1 in MDA-MB-453 cells resulted in decrease of LiMe gene expression. Four binding sites of PBX1 and cofactor were identified in three lipid metabolism genes using ChIP-qPCR. These data suggest a novel role for PBX1 in the regulation of lipid metabolism genes in benign breast, which may contribute to ER-negative tumorigenesis.

A Systematic Framework for Drug Repositioning from Integrated Omics and Drug Phenotype Profiles Using Pathway-Drug Network

Drug repositioning offers new clinical indications for old drugs. Recently, many computational approaches have been developed to repurpose marketed drugs in human diseases by mining various of biological data including disease expression profiles, pathways, drug phenotype expression profiles, and chemical structure data. However, despite encouraging results, a comprehensive and efficient computational drug repositioning approach is needed that includes the high-level integration of available resources. In this study, we propose a systematic framework employing experimental genomic knowledge and pharmaceutical knowledge to reposition drugs for a specific disease. Specifically, we first obtain experimental genomic knowledge from disease gene expression profiles and pharmaceutical knowledge from drug phenotype expression profiles and construct a pathway-drug network representing a priori known associations between drugs and pathways. To discover promising candidates for drug repositioning, we initialize node labels for the pathway-drug network using identified disease pathways and known drugs associated with the phenotype of interest and perform network propagation in a semisupervised manner. To evaluate our method, we conducted some experiments to reposition 1309 drugs based on four different breast cancer datasets and verified the results of promising candidate drugs for breast cancer by a two-step validation procedure. Consequently, our experimental results showed that the proposed framework is quite useful approach to discover promising candidates for breast cancer treatment.

Tamoxifen therapy benefit predictive signature coupled with prognostic signature of post-operative recurrent risk for early stage ER+ breast cancer

Two types of prognostic signatures for predicting recurrent risk of ER+ breast cancer patients have been developed: one type for patients accepting surgery only and another type for patients receiving post-operative tamoxifen therapy. However, the first type of signature cannot distinguish high-risk patients who cannot benefit from tamoxifen therapy, while the second type of signature cannot identify patients who will be at low risk of recurrence even if they accept surgery only. In this study, we proposed to develop two coupled signatures to solve these problems based on within-sample relative expression orderings (REOs) of gene pairs. Firstly, we identified a prognostic signature of post-operative recurrent risk using 544 samples of ER+ breast cancer patients accepting surgery only. Then, applying this drug-free signature to 840 samples of patients receiving post-operative tamoxifen therapy, we recognized 553 samples of patients who would have been at high risk of recurrence if they had accepted surgery only and used these samples to develop a tamoxifen therapy benefit predictive signature. The two coupled signatures were validated in independent data. The signatures developed in this study are robust against experimental batch effects and applicable at the individual levels, which can facilitate the clinical decision of tamoxifen therapy.

Sense-antisense gene-pairs in breast cancer and associated pathological pathways

More than 30% of human protein-coding genes form hereditary complex genome architectures composed of sense-antisense (SA) gene pairs (SAGPs) transcribing their RNAs from both strands of a given locus. Such architectures represent important novel components of genome complexity contributing to gene expression deregulation in cancer cells. Therefore, the architectures might be involved in cancer pathways and, in turn, be used for novel drug targets discovery. However, the global roles of SAGPs in cancer pathways has not been studied. Here we investigated SAGPs associated with breast cancer (BC)-related pathways using systems biology, prognostic survival and experimental methods. Gene expression analysis identified 73 BC-relevant SAGPs that are highly correlated in BC. Survival modelling and metadata analysis of the 1161 BC patients allowed us to develop a novel patient prognostic grouping method selecting the 12 survival-significant SAGPs. The qRT-PCR-validated 12-SAGP prognostic signature reproducibly stratified BC patients into low- and high-risk prognostic subgroups. The 1381 SAGP-defined differentially expressed genes common across three studied cohorts were identified. The functional enrichment analysis of these genes revealed the GABPA gene network, including BC-relevant SAGPs, specific gene sets involved in cell cycle, spliceosomal and proteasomal pathways. The co-regulatory function of GABPA in BC cells was supported using siRNA knockdown studies. Thus, we demonstrated SAGPs as the synergistically functional genome architectures interconnected with cancer-related pathways and associated with BC patient clinical outcomes. Taken together, SAGPs represent an important component of genome complexity which can be used to identify novel aspects of coordinated pathological gene networks in cancers.

Molecular Portrait of the Normal Human Breast Tissue and Its Influence on Breast Carcinogenesis

Normal human breast tissue consists of epithelial and nonepithelial cells with different molecular profiles and differentiation grades. This molecular heterogeneity is known to yield abnormal clones that may contribute to the development of breast carcinomas. Stem cells that are found in developing and mature breast tissue are either positive or negative for cytokeratin 19 depending on their subtype. These cells are able to generate carcinogenesis along with mature cells. However, scientific data remains controversial regarding the monoclonal or polyclonal origin of breast carcinomas. The majority of breast carcinomas originate from epithelial cells that normally express BRCA1. The consecutive loss of the BRCA1 gene leads to various abnormalities in epithelial cells. Normal breast epithelial cells also express hypoxia inducible factor (HIF) 1α and HIF-2α that are associated with a high metastatic rate and a poor prognosis for malignant lesions. The nuclear expression of estrogen receptor (ER) and progesterone receptor (PR) in normal human breast tissue is maintained in malignant tissue as well. Several controversies regarding the ability of ER and PR status to predict breast cancer outcome remain. Both ER and PR act as modulators of cell activity in normal human breast tissue. Ki-67 positivity is strongly correlated with tumor grade although its specific role in applied therapy requires further studies. Human epidermal growth factor receptor 2 (HER2) oncoprotein is less expressed in normal human breast specimens but is highly expressed in certain malignant lesions of the breast. Unlike HER2, epidermal growth factor receptor expression is similar in both normal and malignant tissues. Molecular heterogeneity is not only found in breast carcinomas but also in normal breast tissue. Therefore, the molecular mapping of normal human breast tissue might represent a key research area to fully elucidate the mechanisms of breast carcinogenesis.

Mislocalization-related disease gene discovery using gene expression based computational protein localization prediction

Protein sorting is an important mechanism for transporting proteins to their target subcellular locations after their synthesis. Mutations on genes may disrupt the well regulated protein sorting process, leading to a variety of mislocation related diseases. This paper proposes a methodology to discover such disease genes based on gene expression data and computational protein localization prediction. A kernel logistic regression based algorithm is used to successfully identify several candidate cancer genes which may cause cancers due to their mislocation within the cell. Our results also showed that compared to the gene co-expression network defined on Pearson correlation coefficients, the nonlinear Maximum Correlation Coefficients (MIC) based co-expression network give better results for subcellular localization prediction.

3'UTR shortening and EGF signaling: implications for breast cancer

Alternative polyadenylation (APA) plays a role in gene expression regulation generally by shortening of 3'UTRs (untranslated regions) upon proliferative signals and relieving microRNA-mediated repression. Owing to high proliferative indices of triple negative breast cancers (TNBCs), we hypothesized APA to cause 3'UTR length changes in this aggressive subgroup of breast cancers. Our probe-based meta-analysis approach identified 3'UTR length alterations where the significant majority was shortening events (∼70%, 113 of 165) of mostly proliferation-related transcripts in 520 TNBC patients compared with controls. Representative shortening events were further investigated for their microRNA binding potentials by computational predictions and dual-luciferase assay. In silico-predicted 3'UTR shortening events were experimentally confirmed in patient and cell line samples. To begin addressing the underlying mechanisms, we found CSTF2 (cleavage stimulation factor 2), a major regulator of 3'UTR shortening to be up-regulated in response to epidermal growth factor (EGF). EGF treatment also resulted with further shortening of the 3'UTRs. To investigate the contribution of CSTF2 and 3'UTR length alterations to the proliferative phenotype, we showed pharmacological inhibition of the EGF pathway to lead to a reduction in CSTF2 levels. Accordingly, RNAi-induced silencing of CSTF2 decreased the proliferative rate of cancer cells. Therefore, our computational and experimental approach revealed a pattern of 3'UTR length changes in TNBC patients and a potential link between APA and EGF signaling. Overall, detection of 3'UTR length alterations of various genes may help the discovery of new cancer-related genes, which may have been overlooked in conventional microarray gene expression analyses.

Gene expression abnormalities in histologically normal breast epithelium from patients with luminal type of breast cancer

The gene expression profile of breast cancer has been described as a great breakthrough on the way to comprehend differences in cancer origin, behavior and therapy. However, gene expression profile in histologically normal epithelium (HNEpi) which could harbor genetic abnormalities predisposing breast tissue to develop malignancy was minor scope for scientists in the past. Thus, we aimed to analyze gene expressions in HNEpi and breast cancer tissue (BCTis) in order to establish its value as potential diagnostic marker for cancer development. We evaluated a panel of disease-specific genes in luminal type (A/B) of breast cancer and tumor surrounding HNEpi by qRT-PCR Array in 32 microdissected samples. There was 20.2 and 2.4% deregulation rate in genes with at least 2-fold or 5-fold over-expression between luminal (A/B) type breast carcinomas and tumor surrounding HNEpi, respectively. The high-grade luminal carcinomas showed higher number of deregulated genes compared to low-grade cases (50.6 vs. 23.8% with at least 2-fold deregulation rate). The main overexpressed genes in HNEpi were KLK5, SCGB1D2, GSN, EGFR and NGFR. The significant differences in gene expression between BCTis and HNEpi samples were revealed for BAG1, C3, CCNA2, CD44, FGF1, FOSL1, ID2, IL6R, NGFB, NGFR, PAPPA, PLAU, SERPINB5, THBS1 and TP53 gene (p < 0.05) and BCL2L2, CTSB, ITGB4, JUN, KIT, KLF5, SCGB1D2, SCGB2A1, SERPINE1 (p < 0.01), and EGFR, GABRP, GSN, MAP2K7 and THBS2 (p < 0.001), and GSN, KLK5 (p < 0.0001). The ontological gene analyses revealed high deregulations in gene group directly associated with breast cancer prognosis and origin.

Anticipatory estrogen activation of the unfolded protein response is linked to cell proliferation and poor survival in estrogen receptor α-positive breast cancer

In response to cell stress, cancer cells often activate the endoplasmic reticulum (EnR) stress sensor, the unfolded protein response (UPR). Little was known about the potential role in cancer of a different mode of UPR activation; anticipatory activation of the UPR prior to accumulation of unfolded protein or cell stress. We show that estrogen, acting via estrogen receptor α (ERα), induces rapid anticipatory activation of the UPR, resulting in increased production of the antiapoptotic chaperone BiP/GRP78, preparing cancer cells for the increased protein production required for subsequent estrogen-ERα induced cell proliferation. In ERα containing cancer cells, the estrogen, 17β-estradiol (E₂) activates the UPR through a phospholipase C γ (PLCγ)-mediated opening of EnR IP₃R calcium channels, enabling passage of calcium from the lumen of the EnR into the cytosol. siRNA knockdown of ERα blocked the estrogen-mediated increase in cytosol calcium and UPR activation. Knockdown or inhibition of PLCγ, or of IP₃R, strongly inhibited the estrogen-mediated increases in cytosol calcium, UPR activation and cell proliferation. E₂-ERα activates all three arms of the UPR in breast and ovarian cancer cells in culture and in a mouse xenograft. Knockdown of ATF6α, which regulates UPR chaperones, blocked estrogen induction of BiP and strongly inhibited E₂-ERα stimulated cell proliferation. Mild and transient UPR activation by estrogen promotes an adaptive UPR response that protects cells against subsequent UPR-mediated apoptosis. Analysis of data from ERα positive breast cancers demonstrates elevated expression of a UPR gene signature that is a powerful new prognostic marker tightly correlated with subsequent resistance to tamoxifen therapy, reduced time to recurrence and poor survival. Thus, as an early component of the E₂-ERα proliferation program, the mitogen estrogen, drives rapid anticipatory activation of the UPR. Anticipatory activation of the UPR is a new role for estrogens in cancer cell proliferation and resistance to therapy.

A bayesian framework that integrates heterogeneous data for inferring gene regulatory networks

Reconstruction of gene regulatory networks (GRNs) from experimental data is a fundamental challenge in systems biology. A number of computational approaches have been developed to infer GRNs from mRNA expression profiles. However, expression profiles alone are proving to be insufficient for inferring GRN topologies with reasonable accuracy. Recently, it has been shown that integration of external data sources (such as gene and protein sequence information, gene ontology data, protein-protein interactions) with mRNA expression profiles may increase the reliability of the inference process. Here, I propose a new approach that incorporates transcription factor binding sites (TFBS) and physical protein interactions (PPI) among transcription factors (TFs) in a Bayesian variable selection (BVS) algorithm which can infer GRNs from mRNA expression profiles subjected to genetic perturbations. Using real experimental data, I show that the integration of TFBS and PPI data with mRNA expression profiles leads to significantly more accurate networks than those inferred from expression profiles alone. Additionally, the performance of the proposed algorithm is compared with a series of least absolute shrinkage and selection operator (LASSO) regression-based network inference methods that can also incorporate prior knowledge in the inference framework. The results of this comparison suggest that BVS can outperform LASSO regression-based method in some circumstances.

How to get the most from microarray data: advice from reverse genomics

BACKGROUND

Whole-genome profiling of gene expression is a powerful tool for identifying cancer-associated genes. Genes differentially expressed between normal and tumorous tissues are usually considered to be cancer associated. We recently demonstrated that the analysis of interindividual variation in gene expression can be useful for identifying cancer associated genes. The goal of this study was to identify the best microarray data-derived predictor of known cancer associated genes.

RESULTS

We found that the traditional approach of identifying cancer genes--identifying differentially expressed genes--is not very efficient. The analysis of interindividual variation of gene expression in tumor samples identifies cancer-associated genes more effectively. The results were consistent across 4 major types of cancer: breast, colorectal, lung, and prostate. We used recently reported cancer-associated genes (2011-2012) for validation and found that novel cancer-associated genes can be best identified by elevated variance of the gene expression in tumor samples.

CONCLUSIONS

The observation that the high interindividual variation of gene expression in tumor tissues is the best predictor of cancer-associated genes is likely a result of tumor heterogeneity on gene level. Computer simulation demonstrates that in the case of heterogeneity, an assessment of variance in tumors provides a better identification of cancer genes than does the comparison of the expression in normal and tumor tissues. Our results thus challenge the current paradigm that comparing the mean expression between normal and tumorous tissues is the best approach to identifying cancer-associated genes; we found that the high interindividual variation in expression is a better approach, and that using variation would improve our chances of identifying cancer-associated genes.

Genes selection comparative study in microarray data analysis

In response to the rapid development of DNA Microarray Technologies, many differentially expressed genes selection algorithms have been developed, and different comparison studies of these algorithms have been done. However, it is not clear how these methods compare with each other, especially when we used different developments tools. Here, we considered three commonly used differentially expressed genes selection approaches, namely: Fold Change, T-test and SAM, using Bioinformatics Matlab Toolbox and R/BioConductor. We used two datasets, issued from the affymetrix technology, to present results of used methods and software's in gene selection process. The results, in terms of sensitivity and specificity, indicate that the behavior of SAM is better compared to Fold Change and T-test using R/BioConductor. While, no practical differences were observed between the three gene selection methods when using Bioinformatics Matlab Toolbox. In face of our result, the ROC curve shows that: on the one hand R/BioConductor using SAM is favored for microarray selection compared to the other methods. And, on the other hand, results of the three studied gene selection methods using Bioinformatics Matlab Toolbox are still comparable for the two datasets used.

High-throughput identification of reference genes for research and clinical RT-qPCR analysis of breast cancer samples

BACKGROUND

Quantification and normalization of RT-qPCR data critically depends on the expression of so called reference genes. Our goal was to develop a strategy for the selection of reference genes that utilizes microarray data analysis and combines known approaches for gene stability evaluation and to select a set of appropriate reference genes for research and clinical analysis of breast samples with different receptor and cancer status using this strategy.

METHODS

A preliminary search of reference genes was based on high-throughput analysis of microarray datasets. The final selection and validation of the candidate genes were based on the RT-qPCR data analysis using several known methods for expression stability evaluation: comparative ∆Ct method, geNorm, NormFinder and Haller equivalence test.

RESULTS

A set of five reference genes was identified: ACTB, RPS23, HUWE1, EEF1A1 and SF3A1. The initial selection was based on the analysis of publically available well-annotated microarray datasets containing different breast cancers and normal breast epithelium from breast cancer patients and epithelium from cancer-free patients. The final selection and validation were performed using RT-qPCR data from 39 breast cancer biopsy samples. Three genes from the final set were identified by the means of microarray analysis and were novel in the context of breast cancer assay. We showed that the selected set of reference genes is more stable in comparison not only with individual genes, but also with a system of reference genes used in commercial OncotypeDX test.

CONCLUSION

A selection of reference genes for RT-qPCR can be efficiently performed by combining a preliminary search based on the high-throughput analysis of microarray datasets and final selection and validation based on the analysis of RT-qPCR data with a simultaneous examination of different expression stability measures. The identified set of reference genes proved to be less variable and thus potentially more efficient for research and clinical analysis of breast samples comparing to individual genes and the set of reference genes used in OncotypeDX assay.

Expression of VEGF and semaphorin genes define subgroups of triple negative breast cancer

Triple negative breast cancers (TNBC) are difficult to treat due to a lack of targets and heterogeneity. Inhibition of angiogenesis is a promising therapeutic strategy, but has had limited effectiveness so far in breast cancer. To quantify heterogeneity in angiogenesis-related gene expression in breast cancer, we focused on two families – VEGFs and semaphorins – that compete for neuropilin co-receptors on endothelial cells. We compiled microarray data for over 2,600 patient tumor samples and analyzed the expression of VEGF- and semaphorin-related ligands and receptors. We used principal component analysis to identify patterns of gene expression, and clustering to group samples according to these patterns. We used available survival data to determine whether these clusters had prognostic as well as therapeutic relevance. TNBC was highly associated with dysregulation of VEGF- and semaphorin-related genes; in particular, it appeared that expression of both VEGF and semaphorin genes were altered in a pro-angiogenesis direction. A pattern of high VEGFA expression with low expression of secreted semaphorins was associated with 60% of triple-negative breast tumors. While all TNBC groups demonstrated poor prognosis, this signature also correlated with lower 5-year survival rates in non-TNBC samples. A second TNBC pattern, including high VEGFC expression, was also identified. These pro-angiogenesis signatures may identify cancers that are more susceptible to VEGF inhibition.

Wnt3a-dependent and -independent protein interaction networks of chromatin-bound β-catenin in mouse embryonic stem cells

Canonical Wnt signaling is repeatedly used during development to control cell fate, and it is often implicated in human cancer. β-catenin, the effector of Wnt signaling, has a dual function in the cell and is involved in both cell adhesion and transcription. Nuclear β-catenin controls transcription through association with transcription factors of the TCF family and the recruitment of epigenetic modifiers. In this study, we used a strategy combining the genetic manipulation of mouse embryonic stem cells with affinity purification and quantitative mass spectroscopy utilizing stable isotope labeling with amino acids in cell culture to study the interactome of chromatin-bound β-catenin with and without Wnt3a stimulation. We uncovered previously unknown interactions of β-catenin with transcription factors and chromatin-modifying complexes. Our proof-of-principle experiments show that β-catenin can recruit the H3K4me2/1 demethylase LSD1 to regulate the expression of the tumor suppressor Lefty1 in mouse embryonic stem cells. The mRNA levels of LSD1 and β-catenin are inversely correlated with the levels of Lefty1 in pancreas and breast tumors, implying that this mechanism is common to mouse embryonic stem cells and cancer cells.

Identifying early events of gene expression in breast cancer with systems biology phylogenetics

Advanced omics technologies such as deep sequencing and spectral karyotyping are revealing more of cancer heterogeneity at the genetic, genomic, gene expression, epigenetic, proteomic, and metabolomic levels. With this increasing body of emerging data, the task of data analysis becomes critical for mining and modeling to better understand the relevant underlying biological processes. However, the multiple levels of heterogeneity evident within and among populations, healthy and diseased, complicate the mining and interpretation of biological data, especially when dealing with hundreds to tens of thousands of variables. Heterogeneity occurs in many diseases, such as cancers, autism, macular degeneration, and others. In cancer, heterogeneity has hampered the search for validated biomarkers for early detection, and it has complicated the task of finding clonal (driver) and nonclonal (nonexpanded or passenger) aberrations. We show that subtyping of cancer (classification of specimens) should be an a priori step to the identification of early events of cancers. Studying early events in oncogenesis can be done on histologically normal tissues from diseased individuals (HNTDI), since they most likely have been exposed to the same mutagenic insults that caused the cancer in their neighboring tissues. Polarity assessment of HNTDI data variables by using healthy specimens as outgroup(s), followed by the application of parsimony phylogenetic analysis, produces a hierarchical classification of specimens that reveals the early events of the disease ontogeny within its subtypes as shared derived changes (abnormal changes) or synapomorphies in phylogenetic terminology.

Genome-wide association studies identify four ER negative-specific breast cancer risk loci

Estrogen receptor (ER)-negative tumors represent 20-30% of all breast cancers, with a higher proportion occurring in younger women and women of African ancestry. The etiology and clinical behavior of ER-negative tumors are different from those of tumors expressing ER (ER positive), including differences in genetic predisposition. To identify susceptibility loci specific to ER-negative disease, we combined in a meta-analysis 3 genome-wide association studies of 4,193 ER-negative breast cancer cases and 35,194 controls with a series of 40 follow-up studies (6,514 cases and 41,455 controls), genotyped using a custom Illumina array, iCOGS, developed by the Collaborative Oncological Gene-environment Study (COGS). SNPs at four loci, 1q32.1 (MDM4, P = 2.1 × 10(-12) and LGR6, P = 1.4 × 10(-8)), 2p24.1 (P = 4.6 × 10(-8)) and 16q12.2 (FTO, P = 4.0 × 10(-8)), were associated with ER-negative but not ER-positive breast cancer (P > 0.05). These findings provide further evidence for distinct etiological pathways associated with invasive ER-positive and ER-negative breast cancers.

Dichotomous roles for the orphan nuclear receptor NURR1 in breast cancer

BACKGROUND

NR4A orphan nuclear receptors are involved in multiple biological processes which are important in tumorigenesis such as cell proliferation, apoptosis, differentiation, and glucose utilization. The significance of NR4A family member NURR1 (NR4A2) in breast cancer etiology has not been elucidated. The purpose of this study was to ascertain the impact of NURR1 expression on breast transformation, tumor growth, and breast cancer patient survival.

METHODS

We determined the expression of NURR1 in normal breast versus breast carcinoma in tissue microarrays (immunohistochemistry), tissue lysates (immunoblot), and at the mRNA level (publically available breast microarrays). In addition NURR1 expression was compared among breast cancer patients in cohorts based on p53 expression, estrogen receptor α expression, tumor grade, and lymph node metastases. Kaplan-Meier survival plots were used to determine the correlation between NURR1 expression and relapse free survival (RFS). Using shRNA-mediated silencing, we determined the effect of NURR1 expression on tumor growth in mouse xenografts.

RESULTS

Results from breast cancer tissue arrays demonstrate a higher NURR1 expression in the normal breast epithelium compared to breast carcinoma cells (p ≤ 0.05). Among cases of breast cancer, NURR1 expression in the primary tumors was inversely correlated with lymph node metastases (p ≤ 0.05) and p53 expression (p ≤ 0.05). Clinical stage and histological grade were not associated with variation in NURR1 expression. In gene microarrays, 4 of 5 datasets showed stronger mean expression of NURR1 in normal breast as compared to transformed breast. Additionally, NURR1 expression was strongly correlated with increase relapse free survival (HR = 0.7) in a cohort of all breast cancer patients, but showed no significant difference in survival when compared among patients whom have not been treated systemically (HR = 0.91). Paradoxically, NURR1 silenced breast xenografts showed significantly decreased growth in comparison to control, underscoring a biphasic role for NURR1 in breast cancer progression.

CONCLUSIONS

NURR1 function presents a dichotomy in breast cancer etiology, in which NURR1 expression is associated with normal breast epithelial differentiation and efficacy of systemic cancer therapy, but silencing of which attenuates tumor growth. This provides a strong rationale for the potential implementation of NURR1 as a pharmacologic target and biomarker for therapeutic efficacy in breast cancer.

Lipid metabolism genes in contralateral unaffected breast and estrogen receptor status of breast cancer

Risk biomarkers that are specific to estrogen receptor (ER) subtypes of breast cancer would aid the development and implementation of distinct prevention strategies. The contralateral unaffected breast of women with unilateral breast cancer (cases) is a good model for defining subtype-specific risk because women with ER-negative (ER-) index primaries are at high risk for subsequent ER-negative primary cancers. We conducted random fine needle aspiration of the unaffected breasts of cases. Samples from 30 subjects [15 ER-positive (ER+) and 15 ER- cases matched for age, race and menopausal status] were used for Illumina expression array analysis. Findings were confirmed using quantitative real-time PCR (qRT-PCR) in the same samples. A validation set consisting of 36 subjects (12 ER+, 12 ER- and 12 standard-risk healthy controls) was used to compare gene expression across groups. ER- case samples displayed significantly higher expression of 18 genes/transcripts, 8 of which were associated with lipid metabolism on gene ontology analysis (GO: 0006629). This pattern was confirmed by qRT-PCR in the same samples, and in the 24 cases of the validation set. When compared to the healthy controls in the validation set, significant overexpression of 4 genes (DHRS2, HMGCS2, HPGD and ACSL3) was observed in ER- cases, with significantly lower expression of UGT2B11 and APOD in ER+ cases, and decreased expression of UGT2B7 in both subtypes. These data suggest that differential expression of lipid metabolism genes may be involved in the risk for subtypes of breast cancer, and are potential biomarkers of ER-specific breast cancer risk.

Identification of human HK genes and gene expression regulation study in cancer from transcriptomics data analysis

The regulation of gene expression is essential for eukaryotes, as it drives the processes of cellular differentiation and morphogenesis, leading to the creation of different cell types in multicellular organisms. RNA-Sequencing (RNA-Seq) provides researchers with a powerful toolbox for characterization and quantification of transcriptome. Many different human tissue/cell transcriptome datasets coming from RNA-Seq technology are available on public data resource. The fundamental issue here is how to develop an effective analysis method to estimate expression pattern similarities between different tumor tissues and their corresponding normal tissues. We define the gene expression pattern from three directions: 1) expression breadth, which reflects gene expression on/off status, and mainly concerns ubiquitously expressed genes; 2) low/high or constant/variable expression genes, based on gene expression level and variation; and 3) the regulation of gene expression at the gene structure level. The cluster analysis indicates that gene expression pattern is higher related to physiological condition rather than tissue spatial distance. Two sets of human housekeeping (HK) genes are defined according to cell/tissue types, respectively. To characterize the gene expression pattern in gene expression level and variation, we firstly apply improved K-means algorithm and a gene expression variance model. We find that cancer-associated HK genes (a HK gene is specific in cancer group, while not in normal group) are expressed higher and more variable in cancer condition than in normal condition. Cancer-associated HK genes prefer to AT-rich genes, and they are enriched in cell cycle regulation related functions and constitute some cancer signatures. The expression of large genes is also avoided in cancer group. These studies will help us understand which cell type-specific patterns of gene expression differ among different cell types, and particularly for cancer.