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Identification of a novel luminal molecular subtype of breast cancer.

ABSTRACT: 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.

SUBMITTER: Dvorkin-Gheva A

PROVIDER: S-EPMC4116208 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Identification of a novel luminal molecular subtype of breast cancer.

Dvorkin-Gheva Anna A Hassell John A JA

PloS one 20140730 7

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 informatio ...[more]

PMID: 25076125

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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.

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.

Project description:PurposePatients with triple-negative breast cancer (TNBC) have an increased risk of distant metastasis compared to those with other subtypes. In this study, we aimed to identify the genes associated with distant metastasis in TNBC and their underlying mechanisms.MethodsWe established patient-derived xenograft (PDX) models using surgically resected breast cancer tissues from 31 patients with TNBC. Among these, 15 patients subsequently developed distant metastases. Candidate metastasis-associated genes were identified using RNA sequencing. In vitro wound healing, proliferation, migration, and invasion assays and in vivo tumor xenograft and metastasis assays were performed to determine the functional importance of aldo-keto reductase family 1 member C2 (AKR1C2). Additionally, we used the METABRIC dataset to investigate the potential role of AKR1C2 in regulating TNBC subtypes and their downstream signaling activities.ResultsRNA sequencing of primary and PDX tumors showed that genes involved in steroid hormone biosynthesis, including AKR1C2, were significantly upregulated in patients who subsequently developed metastasis. In vitro and in vivo assays showed that silencing of AKR1C2 resulted in reduced cell proliferation, migration, invasion, tumor growth, and incidence of lung metastasis. AKR1C2 was upregulated in the luminal androgen receptor (LAR) subtype of TNBC in the METABRIC dataset, and AKR1C2 silencing resulted in the downregulation of LAR classifier genes in TNBC cell lines. The androgen receptor (AR) gene was a downstream mediator of AKR1C2-associated phenotypes in TNBC cells. AKR1C2 expression was associated with gene expression pathways that regulate AR expression, including JAK-STAT signaling or interleukin 6 (IL-6). The levels of phospho-signal transducer and activator of transcription and IL-6, along with secreted IL-6, were significantly downregulated in AKR1C2-silenced TNBC cells.ConclusionOur data indicate that AKR1C2 is an important regulator of cancer growth and metastasis in TNBC and may be a critical determinant of LAR subtype features.

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Identification of a novel luminal molecular subtype of breast cancer.

Publications

Identification of a novel luminal molecular subtype of breast cancer.

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