Expression and methylation patterns partition luminal-A breast tumors into distinct prognostic subgroups

Abstract

BackgroundBreast cancer is a heterogeneous disease comprising several biologically different types, exhibiting diverse responses to treatment. In the past years, gene expression profiling has led to definition of several “intrinsic subtypes” of breast cancer (basal-like, HER2-enriched, luminal-A, luminal-B and normal-like), and microarray based predictors such as PAM50 have been developed. Despite their advantage over traditional histopathological classification, precise identification of breast cancer subtypes, especially within the largest and highly variable luminal-A class, remains a challenge. In this study, we revisited the molecular classification of breast tumors using both expression and methylation data obtained from The Cancer Genome Atlas (TCGA).MethodsUnsupervised clustering was applied on 1148 and 679 breast cancer samples using RNA-Seq and DNA methylation data, respectively. Clusters were evaluated using clinical information and by comparison to PAM50 subtypes. Differentially expressed genes and differentially methylated CpGs were tested for enrichment using various annotation sets. Survival analysis was conducted on the identified clusters using the log-rank test and Cox proportional hazards model.ResultsThe clusters in both expression and methylation datasets had only moderate agreement with PAM50 calls, while our partitioning of the luminal samples had better five-year prognostic value than the luminal-A/luminal-B assignment as called by PAM50. Our analysis partitioned the expression profiles of the luminal-A samples into two biologically distinct subgroups exhibiting differential expression of immune-related genes, with one subgroup carrying significantly higher risk for five-year recurrence. Analysis of the luminal-A samples using methylation data identified a cluster of patients with poorer survival, characterized by distinct hyper-methylation of developmental genes. Cox multivariate survival analysis confirmed the prognostic significance of the two partitions after adjustment for commonly used factors such as age and pathological stage.ConclusionsModern genomic datasets reveal large heterogeneity among luminal breast tumors. Our analysis of these data provides two prognostic gene sets that dissect and explain tumor variability within the luminal-A subgroup, thus, contributing to the advancement of subtype-specific diagnosis and treatment.Electronic supplementary materialThe online version of this article (doi:10.1186/s13058-016-0724-2) contains supplementary material, which is available to authorized users.

DeepTRIAGE: Interpretable and Individualised Biomarker Scores using Attention Mechanism for the Classification of Breast Cancer Sub-types

“…This suggests that the luminal A samples which are closest to luminal B samples in the PCA tend to be worse tumours. This is consistent with the literature which describes luminal B cancer as a more severe disease (Dai et al (2015)), as well as Netanely et al's observation that luminal cancers exist along a phenotypic continuum of severity (Netanely et al (2016)). Thus, our method provides a biological explanation for some of the variance associated with the diagnostically-relevant differences in luminal sub-types.…”

“…After filtering any genes with zero counts across all samples, we performed an effective library size normalisation of the count data using DESeq2 (Anders and Huber (2010)). To retrieve luminal A (LumA) and luminal B (LumB) sub-type status for the TCGA BRCA samples, we downloaded the "PAM50" labels from the supplementary data of Netanely et al (2016). With 1148 PAM50 labels retrieved, we excluded patients that had more than one tumour sample sequenced.…”

“…This is especially true when considering the luminal sub-types: luminal A cancers have a much better prognosis than luminal B cancers and can be treated with endocrine therapy alone (Dai et al (2015)). Yet, luminal A remains one of the most diverse cancer sub-types in terms of its molecular signature and severity (Netanely et al (2016)). As a case study, we focus our analysis on the difficult problem of classifying breast cancer sub-types based on gene expression signatures, and approach it using an end-to-end supervised deep learning model.…”

Erratum to: Expression and methylation patterns partition luminal-A breast tumors into distinct prognostic subgroups

“…After publication of this article [1], the authors requested to add an official acknowledgement for using the TCGA Breast Cancer dataset in their article. The following information has therefore been added to the Acknowledgements section: “The results published here are based upon data generated by The Cancer Genome Atlas managed by the NCI and NHGRI.…”

PROMO: An interactive tool for analyzing clinically-labeled multi-omic cancer datasets

“…An early version of PROMO was developed as part of a study where we identified distinct prognostic subgroups in Luminal-A breast tumors based on expression and methylation data [17]. The analysis workflow in that project provides an example of the key steps in a typical application of PROMO ( Figure 1): Data are imported, filtered and preprocessed.…”