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Integrative clustering reveals a novel split in the luminal A subtype of breast cancer with impact on outcome.

ABSTRACT: 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 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. RESULTS:Based 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. CONCLUSIONS:The 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.

SUBMITTER: Aure MR

PROVIDER: S-EPMC5372339 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Integrative clustering reveals a novel split in the luminal A subtype of breast cancer with impact on outcome.

Aure Miriam Ragle MR Vitelli Valeria V Jernström Sandra S Kumar Surendra S Krohn Marit M Due Eldri U EU Haukaas Tonje Husby TH Leivonen Suvi-Katri SK Vollan Hans Kristian Moen HK Lüders Torben T Rødland Einar E Vaske Charles J CJ Zhao Wei W Møller Elen K EK Nord Silje S Giskeødegård Guro F GF Bathen Tone Frost TF Caldas Carlos C Tramm Trine T Alsner Jan J Overgaard Jens J Geisler Jürgen J Bukholm Ida R K IR Naume Bjørn B Schlichting Ellen E Sauer Torill T Mills Gordon B GB Kåresen Rolf R Mælandsmo Gunhild M GM Lingjærde Ole Christian OC Frigessi Arnoldo A Kristensen Vessela N VN Børresen-Dale Anne-Lise AL Sahlberg Kristine K KK

Breast cancer research : BCR 20170329 1

<h4>Background</h4>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.<h4>Methods</h4>Tumor 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. Pa ...[more]

PMID: 28356166

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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.

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.

Dataset Information

Integrative clustering reveals a novel split in the luminal A subtype of breast cancer with impact on outcome.

Publications

Integrative clustering reveals a novel split in the luminal A subtype of breast cancer with impact on outcome.

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