Disease-specific gene repositioning in breast cancer
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ABSTRACT:
SUBMITTER: Karen J. Meaburn
PROVIDER: S-JCBD-200909127 | bioimages |
REPOSITORIES: bioimages
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Project description:Breast cancer is one of the most common malignant tumors in women, which seriously endangers women's health. Great advances have been made over the last decades, however, most studies predict driver genes of breast cancer using biological experiments and/or computational methods, regardless of stage information. In this study, we propose a computational framework to predict the disease genes of breast cancer based on stage-specific gene regulatory networks. Firstly, we screen out differentially expressed genes and hypomethylated/hypermethylated genes by comparing tumor samples with corresponding normal samples. Secondly, we construct three stage-specific gene regulatory networks by integrating RNA-seq profiles and TF-target pairs, and apply WGCNA to detect modules from these networks. Subsequently, we perform network topological analysis and gene set enrichment analysis. Finally, the key genes of specific modules for each stage are screened as candidate disease genes. We obtain seven stage-specific modules, and identify 20, 12, and 22 key genes for three stages, respectively. Furthermore, 55%, 83%, and 64% of the genes are associated with breast cancer, for example E2F2, E2F8, TPX2, BUB1, and CKAP2L. So it may be of great importance for further verification by cancer experts.
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Project description:Background: New biomarker combinations have been increasingly developed to improve the precision of current diagnostic and therapeutic modalities. Recently, researchers have found that tumor cells are more vulnerable to ferroptosis. Furthermore, ferroptosis-related genes (FRG) are promising therapeutic targets in breast cancer patients. Therefore, this study aimed to identify FRG that could predict disease-specific survival (DSS) in breast cancer patients. Methods: Gene expression matrix and clinical data were downloaded from public databases. We included 960, 1,900, and 234 patients from the TCGA, METABRIC, and GSE3494 cohorts, respectively. Data for FRG were downloaded from the FerrDb website. Differential expression of FRG was analyzed by comparing the tumors with adjacent normal tissues. Univariate Cox analysis of DSS was performed to identify prognostic FRG. The TCGA-BRCA cohort was used to generate a nine-gene panel with the LASSO cox regression. The METABRIC and GSE3494 cohorts were used to validate the panel. The panel's median cut-off value was used to divide the patients into high- or low-risk subgroups. Analyses of immune microenvironment, functional pathways, and clinical correlation were conducted via GO and KEGG analyses to determine the differences between the two subgroups. Results: The DSS of the low-risk subgroup was longer than that of the high-risk subgroup. The panel's predictive ability was confirmed by ROC curves (TCGA cohort AUC values were 0.806, 0.695, and 0.669 for 2, 3, and 5 years respectively, and the METABRIC cohort AUC values were 0.706, 0.734, and 0.7, respectively for the same periods). The panel was an independent DSS prognostic indicator in the Cox regression analyses. (TCGA cohort: HR = 3.51, 95% CI = 1.792-6.875, p < 0.001; METABRIC cohort: HR = 1.76, 95% CI = 1.283-2.413, p < 0.001). Immune-related pathways were enriched in the high-risk subgroup. The two subgroups that were stratified by the nine-gene panel were also associated with histology type, tumor grade, TNM stage, and Her2-positive and TNBC subtypes. The patients in the high-risk subgroup, whose CTLA4 and PD-1 statuses were both positive or negative, demonstrated a substantial clinical benefit from combination therapy with anti-CTLA4 and anti-PD-1. Conclusion: The new gene panel consisting of nine FRG may be used to assess the prognosis and immune status of patients with breast cancer. A precise therapeutic approach can also be possible with risk stratification.
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Project description:NFκB is implicated in breast cancer bone metastasis and skeletal remodelling. However, the role of IKKβ, a key component of the canonical NFκB pathway, in the regulation of breast cancer osteolytic metastasis has not been investigated. Here, we describe the cancer-specific contribution of IKKβ to bone metastasis, skeletal tumour growth and osteolysis associated with breast cancer. IKKβ is highly expressed in invasive breast tumours and its level of expression was higher in patients with bone metastasis. IKKβ overexpression in parental MDA-MD-231 breast cancer cells, promoted mammary tumour growth but failed to convey osteolytic potential to these cells in mice. In contrast, IKKβ overexpression in osteotropic sub-clones of MDA-MB-231 cells with differing osteolytic phenotypes increased incidence of bone metastasis, exacerbated osteolysis and enhanced skeletal tumour growth, whereas its knockdown was inhibitory. Functional and mechanistic studies revealed that IKKβ enhanced the ability of osteotropic MDA-MB-231 cells to migrate, increase osteoclastogenesis, and to inhibit osteoblast differentiation via a mechanism mediated, at least in part, by cytoplasmic sequestering of FoxO3a and VEGFA production. Thus, tumour-selective manipulation of IKKβ and its interaction with FoxO3a may represent a novel strategy to reduce the development of secondary breast cancer in the skeleton.
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Project description:PurposeEarly-stage hormone-receptor positive breast cancer is treated with endocrine therapy and the recommended duration of these treatments has increased over time. While endocrine therapy is considered less of a burden to patients compared to chemotherapy, long-term adherence may be low due to potential adverse side effects as well as compliance fatigue. It is of high clinical utility to identify subgroups of breast cancer patients who may have excellent long-term survival without or with limited duration of endocrine therapy to aid in personalizing endocrine treatment.MethodsWe describe a new ultralow risk threshold for the 70-gene signature (MammaPrint) that identifies a group of breast cancer patients with excellent 20 year, long-term survival prognosis. Tumors of these patients are referred to as "indolent breast cancer." We used patient series on which we previously established and assessed the 70-gene signature high-low risk threshold.ResultsIn an independent validation cohort, we show that patients with indolent breast cancer had 100% breast cancer-specific survival at 15 years of follow-up.ConclusionsOur data indicate that patients with indolent disease may be candidates for limited treatment with adjuvant endocrine therapy based on their very low risk of distant recurrences or death of breast cancer.
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Project description:Breast cancer is a highly heterogeneous disease, and there are many forms of categorization for breast cancer based on gene expression profiles. Gene expression profiles are variables and may show differences if measured at different time points or under different conditions. In contrast, biological networks are relatively stable over time and under different conditions. In this study, we used a gene interaction network from a new point of view to explore the subtypes of breast cancer based on individual-specific edge perturbations measured by relative gene expression value. Our study reveals that there are four breast cancer subtypes based on gene interaction perturbations at the individual level. The new network-based subtypes of breast cancer show strong heterogeneity in prognosis, somatic mutations, phenotypic changes and enriched pathways. The network-based subtypes are closely related to the PAM50 subtypes and immunohistochemistry index. This work helps us to better understand the heterogeneity and mechanisms of breast cancer from a network perspective.
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Project description:Breast cancer is the most commonly occurring cancer in women. There were over two-million new cases in world in 2018. It is the second leading cause of death from cancer in western countries. At the molecular level, breast cancer is a heterogeneous disease, which is characterized by high genomic instability evidenced by somatic gene mutations, copy number alterations, and chromosome structural rearrangements. The genomic instability is caused by defects in DNA damage repair, transcription, DNA replication, telomere maintenance and mitotic chromosome segregation. According to molecular features, breast cancers are subdivided in subtypes, according to activation of hormone receptors (estrogen receptor and progesterone receptor), of human epidermal growth factors receptor 2 (HER2), and or BRCA mutations. In-depth analyses of the molecular features of primary and metastatic breast cancer have shown the great heterogeneity of genetic alterations and their clonal evolution during disease development. These studies have contributed to identify a repertoire of numerous disease-causing genes that are altered through different mutational processes. While early-stage breast cancer is a curable disease in about 70% of patients, advanced breast cancer is largely incurable. However, molecular studies have contributed to develop new therapeutic approaches targeting HER2, CDK4/6, PI3K, or involving poly(ADP-ribose) polymerase inhibitors for BRCA mutation carriers and immunotherapy.
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Project description:Though xenografts are used extensively for drug development in breast cancer, how well xenografts reflect the breadth of primary breast tumor subtypes has not been well characterized. Moreover, few studies have compared the gene expression of xenograft tumors to the primary tumors from which they were derived. Here we investigate whether the ability of human breast tumors (n = 20) to create xenografts in immune-deficient mice is associated with breast cancer immunohistochemical (IHC) and intrinsic subtype. We also characterize how precisely the gene expression of xenografts reprises that of parent breast tumors, using hierarchical clustering and other correlation-based techniques applied to Agilent 44K gene expression data from 16 samples including four matched primary tumor-xenograft pairs. Of the breast tumors studied, 25 % (5/20) generated xenografts. Receptor and intrinsic subtype were significant predictors of xenograft success, with all (4/4) triple-negative (TN) tumors and no (0/12) HR+Her2- tumors forming xenografts (P = 0.0005). Tumor cell expression of ALDH1, a stem cell marker, trended toward successful engraftment (P = 0.14), though CDK5/6, a basal marker, did not. Though hierarchical clustering across the 500 most variable genes segregated human breast tumors from xenograft tumors, when clustering was performed over the PAM50 gene set the primary tumor-xenograft pairs clustered together, with all IHC subtypes clustered in distinct groups. Greater similarity between primary tumor-xenograft pairs relative to random pairings was confirmed by calculation of the within-pair between-pair scatter ratio (WPBPSR) distribution (P = 0.0269), though there was a shift in the xenografts toward more aggressive features including higher proliferation scores relative to the primary. Triple-negative breast tumors demonstrate superior ability to create xenografts compared to HR+ tumors, which may reflect higher proliferation or relatively stroma-independent growth of this subtype. Xenograft tumors' gene expression faithfully resembles that of their parent tumors, yet also demonstrates a shift toward more aggressive molecular features.
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Project description:Genes have preferential non-random spatial positions within the cell nucleus. The nuclear position of a subset of genes differ between cell types and some genes undergo repositioning events in disease, including cancer. It is currently unclear whether the propensity of a gene to reposition reflects an intrinsic property of the locus or the tissue. Using quantitative FISH analysis of a set of genes which reposition in cancer, we test here the tissue specificity of gene repositioning in normal and malignant breast or prostate tissues. We find tissue-specific organization of the genome in normal breast and prostate with 40 % of genes occupying differential positions between the two tissue types. While we demonstrate limited overlap between gene sets that repositioned in breast and prostate cancer, we identify two genes that undergo disease-related gene repositioning in both cancer types. Our findings indicate that gene repositioning in cancer is tissue-of-origin specific.
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Project description:Breast cancer is a highly heterogeneous disease with respect to molecular alterations and cellular composition making therapeutic and clinical outcome unpredictable. This diversity creates a significant challenge in developing tumor classifications that are clinically reliable with respect to prognosis prediction.This paper describes an unsupervised context analysis to infer context-specific gene regulatory networks from 1,614 samples obtained from publicly available gene expression data, an extension of a previously published methodology. We use the context-specific gene regulatory networks to classify the tumors into clinically relevant subgroups, and provide candidates for a finer sub-grouping of the previously known intrinsic tumors with a focus on Basal-like tumors. Our analysis of pathway enrichment in the key contexts provides an insight into the biological mechanism underlying the identified subtypes of breast cancer.The use of context-specific gene regulatory networks to identify biological contexts from heterogenous breast cancer data set was able to identify genomic drivers for subgroups within the previously reported intrinsic subtypes. These subgroups (contexts) uphold the clinical relevant features for the intrinsic subtypes and were associated with increased survival differences compared to the intrinsic subtypes. We believe our computational approach led to the generation of novel rationalized hypotheses to explain mechanisms of disease progression within sub-contexts of breast cancer that could be therapeutically exploited once validated.
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Project description:BackgroundBreast cancer is a heterogeneous disease for which prognosis and treatment strategies are largely governed by the receptor status (estrogen, progesterone and Her2) of the tumor cells. Gene expression profiling of whole breast tumors further stratifies breast cancer into several molecular subtypes which also co-segregate with the receptor status of the tumor cells. We postulated that cancer associated fibroblasts (CAFs) within the tumor stroma may exhibit subtype specific gene expression profiles and thus contribute to the biology of the disease in a subtype specific manner. Several studies have reported gene expression profile differences between CAFs and normal breast fibroblasts but in none of these studies were the results stratified based on tumor subtypes.MethodsTo address whether gene expression in breast cancer associated fibroblasts varies between breast cancer subtypes, we compared the gene expression profiles of early passage primary CAFs isolated from twenty human breast cancer samples representing three main subtypes; seven ER+, seven triple negative (TNBC) and six Her2+.ResultsWe observed significant expression differences between CAFs derived from Her2+ breast cancer and CAFs from TNBC and ER + cancers, particularly in pathways associated with cytoskeleton and integrin signaling. In the case of Her2+ breast cancer, the signaling pathways found to be selectively up regulated in CAFs likely contribute to the enhanced migration of breast cancer cells in transwell assays and may contribute to the unfavorable prognosis of Her2+ breast cancer.ConclusionsThese data demonstrate that in addition to the distinct molecular profiles that characterize the neoplastic cells, CAF gene expression is also differentially regulated in distinct subtypes of breast cancer.
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