Project description:The VEGF targeted antiangiogenic drug bevacizumab has shown varying results in clinical trials of breast cancer. Identifying robust biomarkers for selecting patients that may benefit from bevacizumab treatment and for monitoring of response is important for the future use of this drug. Two established xenograft models representing basal-like and luminal-like breast cancer were used to study bevacizumab treatment response on the metabolic and gene expression levels. Mice given no treatment or treated with bevacizumab, doxorubicin or the combination of these two drugs were sacrificed at day 3 or 10. High resolution magic angle spinning magnetic resonance spectroscopy (HR MAS MRS) and gene expression microarray analysis was performed on all tumor samples. Combination treatment with bevacizumab had the strongest growth inhibiting effect in the basal-like tumors, and this was reflected in a significant response in the metabolomic and transcriptomic profiles. In the luminal-like xenografts, addition of bevacizumab did not improve the effect of doxorubicin. On the global transcriptomic level, the largest changes in gene expression were observed for the most efficient treatment in each of the two xenograft models. The metabolite glycerophosphocholine (GPC) showed opposite response in the treated xenografts compared with untreated controls: lower in basal-like tumors and higher in luminal-like tumors. Lower levels of creatine, taurine and glycine were observed in the basal-like xenografts given bevacizumab as monotherapy compared with untreated xenografts. Comparing combination therapy with doxorubicin monotherapy in basal-like xenografts, 14 genes showed significant differential expression, including higher expression of very low density lipoprotein receptor (VLDLR), and lower expression of hemoglobin, theta 1 (HBQ1). Using published gene expression signatures, bevacizumab treated tumors were associated with a more hypoxic phenotype, while no evidence was found for associations between bevacizumab treatment and vascular invasion or increasing tumor grade. This study underlines the importance of characterizing biological differences between subtypes of breast cancer to identify personalized biomarkers for selecting patients for bevacizumab treatment and evaluating response to therapy. 60 samples are analyzed. For each of the two xenograft models, tumors were collected from animals that were untreated or treated with bevacizumab (5 mg/kg), doxorubicin (8 mg/kg) or a combination of the two therapies (n = 6 tumors for each group). Bevacizumab treatment was repeated at day 4 and day 7. Animals were sacrificed and tissue harvested at either day 3 or 10 after treatment, in triplicates within each treatment group. This resulted in 24 tumor samples from each of the models. In addition, untreated and bevacizumab treated luminal-like xenografts not fed with estradiol were included for comparison (n = 12). Note that the untreated controls are overlapping with the samples in the GEO series with accession number: GSE25915.

Project description:The VEGF targeted antiangiogenic drug bevacizumab has shown varying results in clinical trials of breast cancer. Identifying robust biomarkers for selecting patients that may benefit from bevacizumab treatment and for monitoring of response is important for the future use of this drug. Two established xenograft models representing basal-like and luminal-like breast cancer were used to study bevacizumab treatment response on the metabolic and gene expression levels. Mice given no treatment or treated with bevacizumab, doxorubicin or the combination of these two drugs were sacrificed at day 3 or 10. High resolution magic angle spinning magnetic resonance spectroscopy (HR MAS MRS) and gene expression microarray analysis was performed on all tumor samples. Combination treatment with bevacizumab had the strongest growth inhibiting effect in the basal-like tumors, and this was reflected in a significant response in the metabolomic and transcriptomic profiles. In the luminal-like xenografts, addition of bevacizumab did not improve the effect of doxorubicin. On the global transcriptomic level, the largest changes in gene expression were observed for the most efficient treatment in each of the two xenograft models. The metabolite glycerophosphocholine (GPC) showed opposite response in the treated xenografts compared with untreated controls: lower in basal-like tumors and higher in luminal-like tumors. Lower levels of creatine, taurine and glycine were observed in the basal-like xenografts given bevacizumab as monotherapy compared with untreated xenografts. Comparing combination therapy with doxorubicin monotherapy in basal-like xenografts, 14 genes showed significant differential expression, including higher expression of very low density lipoprotein receptor (VLDLR), and lower expression of hemoglobin, theta 1 (HBQ1). Using published gene expression signatures, bevacizumab treated tumors were associated with a more hypoxic phenotype, while no evidence was found for associations between bevacizumab treatment and vascular invasion or increasing tumor grade. This study underlines the importance of characterizing biological differences between subtypes of breast cancer to identify personalized biomarkers for selecting patients for bevacizumab treatment and evaluating response to therapy.

Project description:Triple-negative breast cancer (TNBC) is a highly heterogeneous group of cancers, and molecular subtyping is necessary to better identify molecular-based therapies. While some classifiers have been established, no one has integrated the expression profiles of long noncoding RNAs (lncRNAs) into such subtyping criterions. Considering the emerging important role of lncRNAs in cellular processes, a novel classification integrating transcriptome profiles of both messenger RNA (mRNA) and lncRNA would help us better understand the heterogeneity of TNBC.Using human transcriptome microarrays, we analyzed the transcriptome profiles of 165 TNBC samples. We used k-means clustering and empirical cumulative distribution function to determine optimal number of TNBC subtypes. Gene Ontology (GO) and pathway analyses were applied to determine the main function of the subtype-specific genes and pathways. We conducted co-expression network analyses to identify interactions between mRNAs and lncRNAs.All of the 165 TNBC tumors were classified into four distinct clusters, including an immunomodulatory subtype (IM), a luminal androgen receptor subtype (LAR), a mesenchymal-like subtype (MES) and a basal-like and immune suppressed (BLIS) subtype. The IM subtype had high expressions of immune cell signaling and cytokine signaling genes. The LAR subtype was characterized by androgen receptor signaling. The MES subtype was enriched with growth factor signaling pathways. The BLIS subtype was characterized by down-regulation of immune response genes, activation of cell cycle, and DNA repair. Patients in this subtype experienced worse recurrence-free survival than others (log rank test, P = 0.045). Subtype-specific lncRNAs were identified, and their possible biological functions were predicted using co-expression network analyses.We developed a novel TNBC classification system integrating the expression profiles of both mRNAs and lncRNAs and determined subtype-specific lncRNAs that are potential biomarkers and targets. If further validated in a larger population, our novel classification system could facilitate patient counseling and individualize treatment of TNBC.

Project description:Although a standardized approach to radiotherapy has been used to treat breast cancer, regardless of subtype (e.g., luminal, basal), recent clinical data suggest that radiation response may vary significantly among subtypes. We hypothesized that this clinical variability may be due, in part, to differences in cellular radiation response. In this study, we utilized RNA samples for microarray analysis from two sources: 1. Paired pre- and postirradiation breast tumor tissue from 32 early-stage breast cancer patients treated in our unique preoperative radiation Phase I trial; and 2. Sixteen biologically diverse breast tumor cell lines exposed to 0 and 5 Gy irradiation. The transcriptome response to radiation exposure was derived by comparing gene expression in samples before and after irradiation. Genes with the highest coefficient of variation were selected for further evaluation and validated at the RNA and protein level. Gene editing and agonistic antibody treatment were performed to assess the impact of gene modulation on radiation response. Gene expression in our cohort of luminal breast cancer patients was distinctly different before and after irradiation. Further, two distinct patterns of gene expression were observed in our biologically diverse group of breast cancer cell lines pre- versus postirradiation. Cell lines that showed significant change after irradiation were largely luminal subtype, while gene expression in the basal and HER2+ cell lines was minimally impacted. The 100 genes with the most significant response to radiation in patients were identified and analyzed for differential patterns of expression in the radiation-responsive versus nonresponsive cell lines. Fourteen genes were identified as significant, including FAS, a member of the tumor necrosis factor receptor family known to play a critical role in programed cell death. Modulation of FAS in breast cancer cell lines altered radiation response phenotype and enhanced radiation sensitivity in radioresistant basal cell lines. Our findings suggest that cell-type-specific, radiation-induced FAS contributes to subtype-specific breast cancer radiation response and that activation of FAS pathways may be exploited for biologically tailored radiotherapy.

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.

Dataset Information

Subtype-specific response to bevacizumab is reflected in the metabolome and transcriptome of breast cancer xenografts.

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Subtype-specific response to bevacizumab is reflected in the metabolome and transcriptome of breast cancer xenografts.

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