Integrative clustering reveals a novel split in the luminal A subtype of breast cancer with impact on outcome
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE81002 | GEO | 2017/03/01
SECONDARY ACCESSION(S): PRJNA320289
REPOSITORIES: GEO
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Project description:BackgroundBreast cancer is a heterogeneous disease at the clinical and molecular level. In this study we integrate classifications extracted from five different molecular levels in order to identify integrated subtypes.MethodsTumor tissue from 425 patients with primary breast cancer from the Oslo2 study was cut and blended, and divided into fractions for DNA, RNA and protein isolation and metabolomics, allowing the acquisition of representative and comparable molecular data. Patients were stratified into groups based on their tumor characteristics from five different molecular levels, using various clustering methods. Finally, all previously identified and newly determined subgroups were combined in a multilevel classification using a "cluster-of-clusters" approach with consensus clustering.ResultsBased on DNA copy number data, tumors were categorized into three groups according to the complex arm aberration index. mRNA expression profiles divided tumors into five molecular subgroups according to PAM50 subtyping, and clustering based on microRNA expression revealed four subgroups. Reverse-phase protein array data divided tumors into five subgroups. Hierarchical clustering of tumor metabolic profiles revealed three clusters. Combining DNA copy number and mRNA expression classified tumors into seven clusters based on pathway activity levels, and tumors were classified into ten subtypes using integrative clustering. The final consensus clustering that incorporated all aforementioned subtypes revealed six major groups. Five corresponded well with the mRNA subtypes, while a sixth group resulted from a split of the luminal A subtype; these tumors belonged to distinct microRNA clusters. Gain-of-function studies using MCF-7 cells showed that microRNAs differentially expressed between the luminal A clusters were important for cancer cell survival. These microRNAs were used to validate the split in luminal A tumors in four independent breast cancer cohorts. In two cohorts the microRNAs divided tumors into subgroups with significantly different outcomes, and in another a trend was observed.ConclusionsThe six integrated subtypes identified confirm the heterogeneity of breast cancer and show that finer subdivisions of subtypes are evident. Increasing knowledge of the heterogeneity of the luminal A subtype may add pivotal information to guide therapeutic choices, evidently bringing us closer to improved treatment for this largest subgroup of breast cancer.
| S-EPMC5372339 | biostudies-literature
Project description:Background: Breast cancer is a heterogeneous disease at the clinical and molecular level. In this study we integrate classifications extracted from five different molecular levels in order to identify integrated subtypes. Methods: Tumor tissue from 425 primary breast cancer patients of the Oslo2 study was cut and blended before being divided into fractions for DNA-, RNA- and protein isolation, and metabolomics, allowing for representative and comparable molecular data. Patients were stratified into groups based on their tumor characteristics from five different molecular levels, using various clustering methods. Finally, all previously identified and newly determined subgroups were combined in a multilevel classification using a “cluster-of-clusters” approach with consensus clustering. Results: Based on DNA copy number data, tumors were scored into three groups according to the complex arm aberration index. mRNA expression profiles divided tumors into five molecular subgroups according to PAM50 subtyping, and clustering based on microRNA expression revealed four subgroups. Reverse-phase protein array data divided tumors into five subgroups. Hierarchical clustering of tumor metabolic profiles revealed three clusters. Combining DNA copy number and mRNA expression classified tumors into seven clusters based on pathway activity levels, and using integrative clustering, tumors were classified into ten subtypes. The final consensus clustering incorporating all the aforementioned subtypes revealed six major groups. Five corresponded well with the mRNA subtypes, while a sixth group resulted from a split of the luminal A subtype where these tumors belonged to distinct microRNA clusters. Gain-of-function studies using MCF-7 cells showed that microRNAs differentially expressed between the luminal A clusters were important for cancer cell survival. These microRNAs were used to validate the split in luminal A tumors in four independent breast cancer cohorts. In two cohorts the microRNAs divided tumors into subgroups with significant different outcome, and in another a trend was observed. Conclusions: The herein identified six integrated subtypes confirm the heterogeneity of breast cancer and show that finer subdivisions of subtypes are evident. Increasing the knowledge of the luminal A subtype heterogeneity may add pivotal information guiding therapeutic choices, evidently bringing us closer to more personalized treatment for this largest subgroup of breast cancer.
2017-03-01 | GSE81000 | GEO
Project description:Background: Breast cancer is a heterogeneous disease at the clinical and molecular level. In this study we integrate classifications extracted from five different molecular levels in order to identify integrated subtypes. Methods: Tumor tissue from 425 primary breast cancer patients of the Oslo2 study was cut and blended before being divided into fractions for DNA-, RNA- and protein isolation, and metabolomics, allowing for representative and comparable molecular data. Patients were stratified into groups based on their tumor characteristics from five different molecular levels, using various clustering methods. Finally, all previously identified and newly determined subgroups were combined in a multilevel classification using a “cluster-of-clusters” approach with consensus clustering. Results: Based on DNA copy number data, tumors were scored into three groups according to the complex arm aberration index. mRNA expression profiles divided tumors into five molecular subgroups according to PAM50 subtyping, and clustering based on microRNA expression revealed four subgroups. Reverse-phase protein array data divided tumors into five subgroups. Hierarchical clustering of tumor metabolic profiles revealed three clusters. Combining DNA copy number and mRNA expression classified tumors into seven clusters based on pathway activity levels, and using integrative clustering, tumors were classified into ten subtypes. The final consensus clustering incorporating all the aforementioned subtypes revealed six major groups. Five corresponded well with the mRNA subtypes, while a sixth group resulted from a split of the luminal A subtype where these tumors belonged to distinct microRNA clusters. Gain-of-function studies using MCF-7 cells showed that microRNAs differentially expressed between the luminal A clusters were important for cancer cell survival. These microRNAs were used to validate the split in luminal A tumors in four independent breast cancer cohorts. In two cohorts the microRNAs divided tumors into subgroups with significant different outcome, and in another a trend was observed. Conclusions: The herein identified six integrated subtypes confirm the heterogeneity of breast cancer and show that finer subdivisions of subtypes are evident. Increasing the knowledge of the luminal A subtype heterogeneity may add pivotal information guiding therapeutic choices, evidently bringing us closer to more personalized treatment for this largest subgroup of breast cancer.
2017-03-01 | GSE80999 | GEO
Project description:Integrative clustering reveals a novel split in the luminal A subtype of breast cancer with impact on outcome
| PRJNA320289 | ENA
Project description:In light of the limited number of targetable oncogenic drivers in breast cancer (BRCA), it is important to identify effective and druggable gene targets for the treatment of this devastating disease. Herein, the GSE102484 dataset containing expression profiling data from 683 BRCA patients was re-analyzed using weighted gene co-expression network analysis (WGCNA). The yellow module with the highest correlation to BRCA progression was screened out, followed by functional enrichment analysis and establishment of a protein-protein interaction (PPI) network. After further validation through survival analysis and expression evaluation, CHEK1 and UBE2C were finally identified as hub genes related to the progression of BRCA, especially the luminal A breast cancer subtype. Notably, both hub genes were found to be dysregulated in multiple types of immune cells and closely correlated with tumor infiltration, as revealed by Tumor Immune Estimation Resource (TIMER) along with other bioinformatic tools. Construction of transcription factors (TF)-hub gene network further confirmed the existence of 11 TFs which could regulate both hub genes simultaneously. Our present study may facilitate the invention of targeted therapeutic drugs and provide novel insights into the understanding of the mechanism beneath the progression of BRCA.
| S-EPMC9322798 | biostudies-literature
Project description:Prostate cancer is a biologically heterogeneous disease with variable molecular alterations underlying cancer initiation and progression. Despite recent advances in understanding prostate cancer heterogeneity, better methods for classification of prostate cancer are still needed to improve prognostic accuracy and therapeutic outcomes. In this study, we computationally assembled a large virtual cohort (n = 1,321) of human prostate cancer transcriptome profiles from 38 distinct cohorts and, using pathway activation signatures of known relevance to prostate cancer, developed a novel classification system consisting of three distinct subtypes (named PCS1-3). We validated this subtyping scheme in 10 independent patient cohorts and 19 laboratory models of prostate cancer, including cell lines and genetically engineered mouse models. Analysis of subtype-specific gene expression patterns in independent datasets derived from luminal and basal cell models provides evidence that PCS1 and PCS2 tumors reflect luminal subtypes, while PCS3 represents a basal subtype. We show that PCS1 tumors progress more rapidly to metastatic disease in comparison with PCS2 or PCS3, including PSC1 tumors of low Gleason grade. To apply this finding clinically, we developed a 37-gene panel that accurately assigns individual tumors to one of the three PCS subtypes. This panel was also applied to circulating tumor cells (CTC) and provided evidence that PCS1 CTCs may reflect enzalutamide resistance. In summary, PCS subtyping may improve accuracy in predicting the likelihood of clinical progression and permit treatment stratification at early and late disease stages. Cancer Res; 76(17); 4948-58. ©2016 AACR.
| S-EPMC5047668 | biostudies-literature
Project description:Background:Soft tissue sarcomas (STSs) are heterogeneous at the clinical and molecular level and need to be further sub-clustered for treatment and prognosis. Materials And Methods:STSs were sub-clustered based on RNAseq and miRNAseq data extracted from The Cancer Genome Atlas (TCGA) through the combined process of similarity network fusion (SNF) and consensus clustering (CC). The expression and clinical characteristics of each sub-cluster were analyzed. The genes differentially expressed (lncRNAs, miRNAs, and mRNAs) between the poor prognosis and good prognosis clusters were used to construct a competing endogenous RNA (ceRNA) network. Functional enrichment analysis was conducted and a hub network was extracted from the constructed ceRNA network. Results:A total of 247 STSs were classified into three optimal sub-clusters, and patients in cluster 2 (C2) had a significantly lower rate of survival. A ceRNA network with 91 nodes and 167 edges was constructed according to the hypothesis of ceRNA. Functional enrichment analysis revealed that the network was mainly associated with organism development functions. Moreover, LncRNA (KCNQ1OT1)-miRNA (has-miR-29c-3p)-mRNA (JARID2, CDK8, DNMT3A, TET1)-competing endogenous gene pairs were identified as hub networks of the ceRNA network, in which each component showed survival significance. Conclusion:Integrative clustering analysis revealed that the STSs could be clustered into three sub-clusters. The ceRNA network, especially the subnetwork LncRNA (KCNQ1OT1)-miRNA (has-miR-29c-3p)-mRNA (JARID2, CDK8, DNMT3A, TET1) was a promising therapeutic target for the STS sub-cluster associated with a poor prognosis.
| S-EPMC7038822 | biostudies-literature
Project description:The molecular classification of human breast tumors has afforded insights into subtype specific biological processes, patient prognosis and response to therapies. However, using current methods roughly one quarter of breast tumors cannot be classified into one or another molecular subtype. To explore the possibility that the unclassifiable samples might comprise one or more novel subtypes we employed a collection of publically available breast tumor datasets with accompanying clinical information to assemble 1,593 transcript profiles: 25% of these samples could not be assigned to one of the current molecular subtypes of breast cancer. All of the unclassifiable samples could be grouped into a new molecular subtype, which we termed "luminal-like". We also identified the luminal-like subtype in an independent collection of tumor samples (NKI295). We found that patients harboring tumors of the luminal-like subtype have a better prognosis than those with basal-like breast cancer, a similar prognosis to those with ERBB2+, luminal B or claudin-low tumors, but a worse prognosis than patients with luminal A or normal-like breast tumors. Our findings suggest the occurrence of another molecular subtype of breast cancer that accounts for the vast majority of previously unclassifiable breast tumors.
| S-EPMC4116208 | biostudies-literature
Project description:Different breast cancer (BC) subtypes have unique gene expression patterns, but their regulatory mechanisms have yet to be fully elucidated. We hypothesized that the top upregulated (Yin) and downregulated (Yang) genes determine the fate of cancer cells. To reveal the regulatory determinants of these Yin and Yang genes in different BC subtypes, we developed a lasso regression model integrating DNA methylation (DM), copy number variation (CNV) and microRNA (miRNA) expression of 391 BC patients, coupled with miRNA-target interactions and transcription factor (TF) binding sites. A total of 25, 20, 15 and 24 key regulators were identified for luminal A, luminal B, Her2-enriched, and triple negative (TN) subtypes, respectively. Many of the 24 TN regulators were found to regulate the PPARA and FOXM1 pathways. The Yin Yang gene expression mean ratio (YMR) and combined risk score (CRS) signatures built with either the targets of or the TN regulators were associated with the BC patients' survival. Previously, we identified FOXM1 and PPARA as the top Yin and Yang pathways in TN, respectively. These two pathways and their regulators could be further explored experimentally, which might help to identify potential therapeutic targets for TN.
| S-EPMC6521146 | biostudies-literature
Project description:BackgroundSomatic alterations in cancer cause dysregulation of signaling pathways that control cell-cycle progression, apoptosis, and cell growth. The effect of individual alterations in these pathways differs between individual tumors and tumor types. Recognizing driver events is a complex task requiring integrating multiple molecular data, including genomics, epigenomics, and functional genomics. A common hypothesis is that these driver events share similar effects on the hallmarks of cancer. The availability of large-scale multi-omics studies allows for inferring these common effects from data. Once these effects are known, one can then deconvolve in every individual patient whether a given genomics alteration is a driver event.MethodsHere, we develop a novel data-driven approach to identify shared oncogenic expression signatures among tumors. We aim to identify gene onco-signature for classifying tumor patients in homogeneous subclasses with distinct prognoses and specific genomic alterations. We derive expression pan-cancer onco-signatures from TCGA gene expression data using a discovery set of 9107 primary pan-tumor samples together with respective matched mutational data and a list of known cancer-related genes from COSMIC database.ResultsWe use the derived ono-signatures to state their prognostic significance and apply them to the TCGA breast cancer dataset as proof of principle of our approach. We uncover a "mitochondrial" sub-group of Luminal patients characterized by its biological features and regulated by specific genetic modulators. Collectively, our results demonstrate the effectiveness of onco-signatures-based methodologies, and they also contribute to a comprehensive understanding of the metabolic heterogeneity of Luminal tumors.ConclusionsThese findings provide novel genomics evidence for developing personalized breast cancer patient treatments. The onco-signature approach, demonstrated here on breast cancer, is general and can be applied to other cancer types.
| S-EPMC9885701 | biostudies-literature