The genomic landscape of breast cancer is complex, and inter- and intra-tumour heterogeneity are important challenges in treating the disease. In this study, we sequence 173 genes in 2,433 primary breast tumours that have copy number aberration (CNA), gene expression and long-term clinical follow-up data. We identify 40 mutation-driver (Mut-driver) genes, and determine associations between mutations, driver CNA profiles, clinical-pathological parameters and survival. We assess the clonal states of Mut-driver mutations, and estimate levels of intra-tumour heterogeneity using mutant-allele fractions. Associations between PIK3CA mutations and reduced survival are identified in three subgroups of ER-positive cancer (defined by amplification of 17q23, 11q13–14 or 8q24). High levels of intra-tumour heterogeneity are in general associated with a worse outcome, but highly aggressive tumours with 11q13–14 amplification have low levels of intra-tumour heterogeneity. These results emphasize the importance of genome-based stratification of breast cancer, and have important implications for designing therapeutic strategies.

This study reveals that TP53 mutations have different clinical relevance in molecular subtypes of breast cancer, and suggests diverse roles for TP53 in the biology underlying breast cancer development.

BackgroundCancer progression is associated with genomic instability and an accumulation of gains and losses of DNA. The growing variety of tools for measuring genomic copy numbers, including various types of array-CGH, SNP arrays and high-throughput sequencing, calls for a coherent framework offering unified and consistent handling of single- and multi-track segmentation problems. In addition, there is a demand for highly computationally efficient segmentation algorithms, due to the emergence of very high density scans of copy number.ResultsA comprehensive Bioconductor package for copy number analysis is presented. The package offers a unified framework for single sample, multi-sample and multi-track segmentation and is based on statistically sound penalized least squares principles. Conditional on the number of breakpoints, the estimates are optimal in the least squares sense. A novel and computationally highly efficient algorithm is proposed that utilizes vector-based operations in R. Three case studies are presented.ConclusionsThe R package is a software suite for segmentation of single- and multi-track copy number data using algorithms based on coherent least squares principles.

Angiosarcoma is an aggressive malignancy that arises spontaneously or secondarily to ionising radiation or chronic lymphoedema1. Previous work has identified aberrant angiogenesis, including occasional somatic mutations in angiogenesis signalling genes, as a key driver of angiosarcoma1. Here, we employed whole genome, exome, and targeted sequencing to study the somatic changes underpinning primary and secondary angiosarcoma. We identified recurrent mutations in two genes, PTPRB and PLCG1, which are intimately linked to angiogenesis. The endothelial phosphatase PTPRB, a negative regulator of vascular growth factor tyrosine kinases, harboured predominantly truncating mutations in 10/39 (26%) tumours. PLCG1, a signal transducer of tyrosine kinases, presented with a recurrent, likely activating R707Q missense variant in 3/34 cases (9%). Overall, 15/39 (38%) tumours harboured at least one driver mutation in angiogenesis signalling genes. Our findings inform and reinforce current therapeutic efforts to target angiogenesis signalling in angiosarcoma.

Combined analyses of molecular data, such as DNA copy-number alteration, mRNA and protein expression, point to biological functions and molecular pathways being deregulated in multiple cancers. Genomic, metabolomic and clinical data from various solid cancers and model systems are emerging and can be used to identify novel patient subgroups for tailored therapy and monitoring. The integrative genomics methodologies that are used to interpret these data require expertise in different disciplines, such as biology, medicine, mathematics, statistics and bioinformatics, and they can seem daunting. The objectives, methods and computational tools of integrative genomics that are available to date are reviewed here, as is their implementation in cancer research.

SUMMARY Recurrent mutations in histone modifying enzymes imply key roles in tumorigenesis yet their functional relevance is largely unknown. Here we show that JARID1B, encoding a histone H3 lysine 4 (H3K4) demethylase, is frequently amplified and overexpressed in luminal breast tumors and a somatic mutation in a basal-like breast cancer results in the gain of unique chromatin binding and luminal expression and splicing patterns. Downregulation of JARID1B in luminal cells induces basal genes expression and growth arrest, which is rescued by TGFβ pathway inhibitors. Integrated JARID1B chromatin binding, H3K4 methylation, and expression profiles suggest a key function for JARID1B in luminal cell-specific expression programs. High luminal JARID1B activity is associated with poor outcome in patients with hormone receptor positive breast tumors.

Distinct molecular subtypes of breast carcinomas have been identified, but translation into clinical use has been limited. We have developed two platform independent algorithms to explore genomic architectural distortion using array comparative genomic hybridization (aCGH) data to measure 1) whole arm gains and losses (WAAI) and 2) complex rearrangements (CAAI). By applying CAAI and WAAI to data from 595 breast cancer patients we were able to separate the cases into eight subgroups with different distributions of genomic distortion. Within each subgroup data from expression analyses, sequencing and ploidy indicated that progression occurs along separate paths into more complex genotypes. Histological grade had prognostic impact only in the Luminal related groups while the complexity identified by CAAI had an overall independent prognostic power. This study emphasizes the relationship between structural genomic alterations, molecular subtype and clinical behavior, and show that objective score of genomic complexity (CAAI) is an independent prognostic marker in breast cancer.

Although molecular prognostics in breast cancer are among the most successful examples of translating genomic analysis to clinical applications, optimal approaches to breast cancer clinical risk prediction remain controversial. The Sage Bionetworks–DREAM Breast Cancer Prognosis Challenge (BCC) is a crowdsourced research study for breast cancer prognostic modeling using genome-scale data. The BCC provided a community of data analysts with a common platform for data access and blinded evaluation of model accuracy in predicting breast cancer survival on the basis of gene expression data, copy number data, and clinical covariates. This approach offered the opportunity to assess whether a crowdsourced community Challenge would generate models of breast cancer prognosis commensurate with or exceeding current best-in-class approaches. The BCC comprised multiple rounds of blinded evaluations on held-out portions of data on 1981 patients, resulting in more than 1400 models submitted as open source code. Participants then retrained their models on the full data set of 1981 samples and submitted up to five models for validation in a newly generated data set of 184 breast cancer patients. Analysis of the BCC results suggests that the best-performing modeling strategy outperformed previously reported methods in blinded evaluations; model performance was consistent across several independent evaluations; and aggregating community-developed models achieved performance on par with the best-performing individual models.