Dataset Information

Identification of a Notch transcriptomic signature for breast cancer [dataset1]

ABSTRACT: Dysregulated Notch signalling contributes to breast cancer development and progression, but validated tools to measure the level of Notch signalling in breast cancer subtypes and in response to systemic therapy are largely lacking. A transcriptomic signature of Notch signalling would thus be warranted, and in this report, we have established such a classifier. To generate the signature, we first identified Notch-regulated genes from six basal-like breast cancer cell lines subjected to elevated and reduced Notch signalling by culturing on immobilized Notch ligand Jagged1 or blockade of Notch by -secretase inhibitors, respectively. From this cadre of Notch-regulated genes, we developed candidate transcriptomic signatures that were trained on a breast cancer patient dataset (the TCGA-BRCA cohort) and an optimal 20-gene signature was selected from analysis of the transcriptomes of a broader breast cancer cell line cohort. We validated the signature on two independent patient datasets (METABRIC and Oslo2) and it showed an improved coherence score and tumour specificity compared with previously published signatures. Furthermore, the signature score was particularly high for basal-like breast cancer, indicating an enhanced level of Notch signalling in this subtype. The signature score was increased after neoadjuvant treatment in the PROMIX and BEAUTY patient cohorts, and a lower signature score generally correlated with better clinical outcome. Collectively, the 20-gene transcriptional signature has the potential to better stratify patients and to evaluate the response of future Notch-based therapies for breast cancer.

ORGANISM(S): Homo sapiens

PROVIDER: GSE235052 | GEO | 2024/01/05

REPOSITORIES: GEO

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Project description:Breast cancer is a heterogeneous disease comprised of at least five major subtypes. Luminal subtype tumors confer a more favourable patient prognosis, which is in part, attributed to the Estrogen Receptor-alpha (ER) positivity and anti-hormone responsiveness of these tumors. Expression of the forkhead box transcription factor, FOXA1, also correlates with the luminal subtype and patient survival, but is present in a subset of ER-negative tumors. Similarly, FOXA1 is consistently expressed in luminal breast cancer cell lines even in the absence of ER. In contrast, basal breast cancer cell lines do not express FOXA1, and loss of FOXA1 in luminal cells increases migration and invasion, characteristics of the basal subtype. To delineate an ER-independent role for FOXA1 in maintaining the luminal phenotype, and hence a more favourable prognosis, we performed cDNA microarray analyses on luminal FOXA1-positive, ER-positive (MCF7, T47D) and FOXA1-positive, ER-negative (MDA-MB-453, SKBR3) cell lines in the presence or absence of transient FOXA1 silencing. This resulted in three FOXA1 transcriptomes: (1) a luminal-signature (consistent across cell lines), (2) an ER-positive signature (restricted to MCF7 and T47D) and (3) an ER-negative signature (restricted to MDA-MB-453 and SKBR3). Use of Gene Set Enrichment Analyses (GSEA) as a phenotyping tool revealed that FOXA1 silencing resulted in a transcriptome shift from luminal to basal gene expression signatures. FOXA1 binds to both luminal and basal genes within luminal breast cancer cells, suggesting that it not only transactivates luminal genes, but also represses basal-associated genes. From these results we conclude that FOXA1 controls plasticity between basal and luminal cells, playing a dominant role in repressing the basal phenotype, and thus tumor aggressiveness, in luminal breast cancer cells. Although it has been proposed that FOXA1-targeting agents may be useful for treating luminal tumors, these data suggest that this approach may promote transitions toward a more aggressive cancer. FOXA1 siRNA treated breast cell lines compared directly to nonspecific siRNA treated cell lines using Agilent 4X44 microarrays.

Project description:Background: Ductal carcinoma in situ (DCIS) is the most common type of in situ premalignant breast cancers. What drives DCIS to invasive breast cancer is unclear. Basal-like invasive breast cancers are aggressive. We have previously shown that N-Ras is highly expressed selectively in basal-like subtypes of invasive breast cancers and can promote their growth and progression. In this study, we investigated whether NRAS expression at the DCIS stage can control transition from luminal DCIS to basal-like invasive breast cancers. Methods: Wilcoxon rank-sum test was performed to assess expression of NRAS in DCIS compared to invasive breast tumors in patients. NRAS levels were also determined by fluorescence in situ hybridization in patient tumor microarrays (TMAs) with concurrent normal, DCIS, and invasive breast cancer, and association of NRAS mRNA levels with DCIS and invasive breast cancer was assessed by paired Wilcoxon signed-rank test. Pearson’s correlation was calculated between NRAS mRNA levels and basal biomarkers in the TMAs, as well as in patient datasets. RNA-seq data was generated in cell lines and unsupervised hierarchical clustering was performed after combining with RNA-seq data from a previously published patient cohort. Results: Invasive breast cancers showed higher NRAS mRNA levels compared to DCIS samples. These NRAShigh lesions were also enriched with basal-like features, such as basal gene expression signatures, lower ER, and higher p53 protein and Ki67 levels. We have shown previously that N-Ras drives aggressive features in DCIS-like and basal-like SUM102PT cells. Here, we found that NRAS-silencing induced a shift to a luminal gene expression pattern. Conversely, NRAS overexpression in the luminal DCIS SUM225 cells induced a basal-like gene expression pattern, as well as an epithelial-to-mesenchymal transition signature. Furthermore, these cells formed disorganized mammospheres containing cell masses with an apparent reduction in adhesion. Conclusions: These data suggest that elevated NRAS levels in DCIS is not only a marker but can also control the emergence of basal-like features leading to more aggressive tumor activity, thus supporting the therapeutic hypothesis that targeting N-Ras and/or downstream pathways may block disease progression for a subset of DCIS patients with high NRAS.

Project description:A gene expression signature characterizes expression data from breast cancer samples of patients with pathological complete response (pCR) or residual disease (RD) following the neoadjuvant trial. Several gene expression profiles have been reported to predict breast cancer response to neoadjuvant chemotherapy. These studies often consider breast cancer as a homogeneous entity, although higher rates of pathologic complete response (pCR) are known to occur within the basal-like subclass. We postulated that profiles with higher predictive accuracy could be derived from a subset analysis of basal-like tumors in isolation. Using a previously described ‘‘intrinsic’’ signature to differentiate breast tumor subclasses, we identified 50 basal-like tumors from two independent clinical trials associated with gene expression profile data. 24 tumor data sets (included in this GEO submission) were derived from a 119-patient neoadjuvant trial at our institution and an additional 26 tumor data sets were identified from a published data set (Hess et al. J Clin Oncol 24:4236–4244, 2006). The combined 50 basal-like tumors were partitioned to form a 37 sample training set with 13 sequestered for validation. Clinical surveillance occurred for a mean of 26 months. We identified a 23-gene profile which predicted pCR in basal-like breast cancers with 92% predictive accuracy in the sequestered validation data set. Furthermore, distinct cluster of patients with high rates of cancer recurrence was observed based on cluster analysis with the 23-gene signature. Disease-free survival analysis of these three clusters revealed significantly reduced survival in the patients of this high recurrence cluster. We identified a 23- gene signature which predicts response of basal-like breast cancer to neoadjuvant chemotherapy as well as disease-free survival. This signature is independent of tissue collection method and chemotherapeutic regimen. Keywords: Disease state analysis

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Identification of a Notch transcriptomic signature for breast cancer [dataset1]

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