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Metasignatures identify two major subtypes of breast cancer.

ABSTRACT: Genome-wide expression data from tumors and cell lines in breast cancer, together with drug response of cell lines, open prospects for integrative analyses that can lead to better personalized therapy. Drug responses and expression data collected from cell lines and tumors were used to generate tripartite networks connecting clusters of patients to cell lines and cell lines to drugs, to connect drugs to patients. Various approaches were applied to connect cell lines to tumor clusters: a standard method that uses a biomarker gene set, and new methods that compute metasignatures for transcription factors and histone modifications given upregulated genes in cell lines or tumors. The results from the metasignature analysis identify two major clusters of patients: one enriched for active histone marks and one for repressive marks. The tumors enriched for activation marks are correlated with poor prognosis. Overall, the analyses suggest new patient clustering, discover dysregulated pathways, and recommend individualized use of drugs to treat subsets of patients.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e35; doi:10.1038/psp.2013.11; advance online publication 27 March 2013.

SUBMITTER: Duan Q

PROVIDER: S-EPMC3615534 | biostudies-literature | 2013 Mar

REPOSITORIES: biostudies-literature

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Metasignatures identify two major subtypes of breast cancer.

Duan Q Q Kou Y Y Clark N R NR Gordonov S S Ma'ayan A A

CPT: pharmacometrics & systems pharmacology 20130327

Genome-wide expression data from tumors and cell lines in breast cancer, together with drug response of cell lines, open prospects for integrative analyses that can lead to better personalized therapy. Drug responses and expression data collected from cell lines and tumors were used to generate tripartite networks connecting clusters of patients to cell lines and cell lines to drugs, to connect drugs to patients. Various approaches were applied to connect cell lines to tumor clusters: a standard ...[more]

PMID: 23836026

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Project description:BackgroundSingle sample predictors (SSPs) and Subtype classification models (SCMs) are gene expression-based classifiers used to identify the four primary molecular subtypes of breast cancer (basal-like, HER2-enriched, luminal A, and luminal B). SSPs use hierarchical clustering, followed by nearest centroid classification, based on large sets of tumor-intrinsic genes. SCMs use a mixture of Gaussian distributions based on sets of genes with expression specifically correlated with three key breast cancer genes (estrogen receptor [ER], HER2, and aurora kinase A [AURKA]). The aim of this study was to compare the robustness, classification concordance, and prognostic value of these classifiers with those of a simplified three-gene SCM in a large compendium of microarray datasets.MethodsThirty-six publicly available breast cancer datasets (n = 5715) were subjected to molecular subtyping using five published classifiers (three SSPs and two SCMs) and SCMGENE, the new three-gene (ER, HER2, and AURKA) SCM. We used the prediction strength statistic to estimate robustness of the classification models, defined as the capacity of a classifier to assign the same tumors to the same subtypes independently of the dataset used to fit it. We used Cohen κ and Cramer V coefficients to assess concordance between the subtype classifiers and association with clinical variables, respectively. We used Kaplan-Meier survival curves and cross-validated partial likelihood to compare prognostic value of the resulting classifications. All statistical tests were two-sided.ResultsSCMs were statistically significantly more robust than SSPs, with SCMGENE being the most robust because of its simplicity. SCMGENE was statistically significantly concordant with published SCMs (κ = 0.65-0.70) and SSPs (κ = 0.34-0.59), statistically significantly associated with ER (V = 0.64), HER2 (V = 0.52) status, and histological grade (V = 0.55), and yielded similar strong prognostic value.ConclusionOur results suggest that adequate classification of the major and clinically relevant molecular subtypes of breast cancer can be robustly achieved with quantitative measurements of three key genes.

Project description:The aim of this study was to describe breast tumor subtypes by common breast cancer risk factors and to determine correlates of subtypes using baseline data from two pooled prospective breast cancer studies within a large health maintenance organization.Tumor data on 2544 invasive breast cancer cases subtyped by estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 (Her2) status were obtained (1868 luminal A tumors, 294 luminal B tumors, 288 triple-negative tumors and 94 Her2-overexpressing tumors). Demographic, reproductive and lifestyle information was collected either in person or by mailed questionnaires. Case-only odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using logistic regression, adjusting for age at diagnosis, race/ethnicity, and study origin.Compared with luminal A cases, luminal B cases were more likely to be younger at diagnosis (P = 0.0001) and were less likely to consume alcohol (OR = 0.74, 95% CI = 0.56 to 0.98), use hormone replacement therapy (HRT) (OR = 0.66, 95% CI = 0.46 to 0.94), and oral contraceptives (OR = 0.73, 95% CI = 0.55 to 0.96). Compared with luminal A cases, triple-negative cases tended to be younger at diagnosis (P < or = 0.0001) and African American (OR = 3.14, 95% CI = 2.12 to 4.16), were more likely to have not breastfed if they had parity greater than or equal to three (OR = 1.68, 95% CI = 1.00 to 2.81), and were more likely to be overweight (OR = 1.82, 95% CI = 1.03 to 3.24) or obese (OR = 1.97, 95% CI = 1.03 to 3.77) if premenopausal. Her2-overexpressing cases were more likely to be younger at diagnosis (P = 0.03) and Hispanic (OR = 2.19, 95% CI = 1.16 to 4.13) or Asian (OR = 2.02, 95% CI = 1.05 to 3.88), and less likely to use HRT (OR = 0.45, 95% CI = 0.26 to 0.79).These observations suggest that investigators should consider tumor heterogeneity in associations with traditional breast cancer risk factors. Important modifiable lifestyle factors that may be related to the development of a specific tumor subtype, but not all subtypes, include obesity, breastfeeding, and alcohol consumption. Future work that will further categorize triple-negative cases into basal and non-basal tumors may help to elucidate these associations further.

Project description:Purpose: The hypoxic tumor microenvironment was reported to be involved in different tumorigenesis mechanisms of breast cancer (BC). We aimed to establish a hypoxia-related gene signature to identify a new BC subtype through the clustering analysis and explore potential compounds targeting the BC subtypes. Methods: Gene expression data and clinical features of BC and adjacent non-tumor tissues were downloaded from the Cancer Genome Atlas-Breast cancer (TCGA-BRCA) database. We comprehensively revealed the activity changes of Gene Ontology (GO) biological processes (BP) gene sets in BC by gene set variation analysis (GSVA) and identified three hypoxia-related BC subtypes. We then matched the differentially expressed gene profile of each subtype with the gene profile in CMap database to identify the potential agents targeting the BC subtypes. Results: 562 of Gene Ontology biological processes gene sets significantly correlated with hypoxia score in breast cancer. 969 BC patients were clustered into three subtypes based on the enrichment score of hypoxia-associated gene sets. Subtype 1 patients displayed better survival than subtype 2 and 3. KEGG pathway enrichment analysis of each subtype was performed based on the unique differential expression genes profile. In subtype 1, the upregulated genes were associated with lipid and amino acid metabolism regulation; in subtype 2, the upregulated genes were associated with metabolic energy regulation, while in subtype 3, the upregulated genes were associated with apoptosis and protein process. Using the CMap database, 55, 111 and 63 compounds were identified, targeting subtype 1, 2, and 3, respectively. Conclusion: In this study, novel hypoxia-related subtypes were developed for patients with BC. In addition, biological processes associated with differential expression genes profile and potential therapeutic target compounds were identified in each subtype. The new classification might provide a better understanding of the role of hypoxia in breast cancer and more individualized treatment for patients.

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Metasignatures identify two major subtypes of breast cancer.

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Metasignatures identify two major subtypes of breast cancer.

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