Dataset Information

Direct Transcriptional Consequences of Somatic Mutation in Breast Cancer.

ABSTRACT: Disordered transcriptomes of cancer encompass direct effects of somatic mutation on transcription, coordinated secondary pathway alterations, and increased transcriptional noise. To catalog the rules governing how somatic mutation exerts direct transcriptional effects, we developed an exhaustive pipeline for analyzing RNA sequencing data, which we integrated with whole genomes from 23 breast cancers. Using X-inactivation analyses, we found that cancer cells are more transcriptionally active than intermixed stromal cells. This is especially true in estrogen receptor (ER)-negative tumors. Overall, 59% of substitutions were expressed. Nonsense mutations showed lower expression levels than expected, with patterns characteristic of nonsense-mediated decay. 14% of 4,234 rearrangements caused transcriptional abnormalities, including exon skips, exon reusage, fusions, and premature polyadenylation. We found productive, stable transcription from sense-to-antisense gene fusions and gene-to-intergenic rearrangements, suggesting that these mutation classes drive more transcriptional disruption than previously suspected. Systematic integration of transcriptome with genome data reveals the rules by which transcriptional machinery interprets somatic mutation.

SUBMITTER: Shlien A

PROVIDER: S-EPMC4987284 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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Direct Transcriptional Consequences of Somatic Mutation in Breast Cancer.

Shlien Adam A Raine Keiran K Fuligni Fabio F Arnold Roland R Nik-Zainal Serena S Dronov Serge S Mamanova Lira L Rosic Andrej A Ju Young Seok YS Cooke Susanna L SL Ramakrishna Manasa M Papaemmanuil Elli E Davies Helen R HR Tarpey Patrick S PS Van Loo Peter P Wedge David C DC Jones David R DR Martin Sancha S Marshall John J Anderson Elizabeth E Hardy Claire C Barbashina Violetta V Aparicio Samuel A J R SA Sauer Torill T Garred Øystein Ø Vincent-Salomon Anne A Mariani Odette O Boyault Sandrine S Fatima Aquila A Langerød Anita A Borg Åke Å Thomas Gilles G Richardson Andrea L AL Børresen-Dale Anne-Lise AL Polyak Kornelia K Stratton Michael R MR Campbell Peter J PJ

Cell reports 20160804 7

Disordered transcriptomes of cancer encompass direct effects of somatic mutation on transcription, coordinated secondary pathway alterations, and increased transcriptional noise. To catalog the rules governing how somatic mutation exerts direct transcriptional effects, we developed an exhaustive pipeline for analyzing RNA sequencing data, which we integrated with whole genomes from 23 breast cancers. Using X-inactivation analyses, we found that cancer cells are more transcriptionally active than ...[more]

PMID: 27498871

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Project description:Exploring the molecular mechanisms of breast cancer is essential for the early prediction, diagnosis, and treatment of cancer patients. The large scale of data obtained from the high-throughput sequencing technology makes it difficult to identify the driver mutations and a minimal optimal set of genes that are critical to the classification of cancer. In this study, we propose a novel method without any prior information to identify mutated genes associated with breast cancer. For the somatic mutation data, it is processed to a mutated matrix, from which the mutation frequency of each gene can be obtained. By setting a reasonable threshold for the mutation frequency, a mutated gene set is filtered from the mutated matrix. For the gene expression data, it is used to generate the gene expression matrix, while the mutated gene set is mapped onto the matrix to construct a co-expression profile. In the stage of feature selection, we propose a staged feature selection algorithm, using fold change, false discovery rate to select differentially expressed genes, mutual information to remove the irrelevant and redundant features, and the embedded method based on gradient boosting decision tree with Bayesian optimization to obtain an optimal model. In the stage of evaluation, we propose a weighted metric to modify the traditional accuracy to solve the sample imbalance problem. We apply the proposed method to The Cancer Genome Atlas breast cancer data and identify a mutated gene set, among which the implicated genes are oncogenes or tumor suppressors previously reported to be associated with carcinogenesis. As a comparison with the integrative network, we also perform the optimal model on the individual gene expression and the gold standard PMA50. The results show that the integrative network outperforms the gene expression and PMA50 in the average of most metrics, which indicate the effectiveness of our proposed method by integrating multiple data sources, and can discover the associated mutated genes in breast cancer.

Project description:Breast cancer (BC) is a genetically heterogeneous disease with high prevalence in Northern Europe. However, there has been no detailed investigation into the Scandinavian somatic landscape. Here, in a homogeneous Swedish cohort, we describe the somatic events underlying BC, leveraging a targeted next-generation sequencing approach. We designed a 20.5 Mb array targeting coding and regulatory regions of genes with a known role in BC (n = 765). The selected genes were either from human BC studies (n = 294) or from within canine mammary tumor associated regions (n = 471). A set of predominantly estrogen receptor positive tumors (ER + 85%) and their normal tissue counterparts (n = 61) were sequenced to ~ 140 × and 85 × mean target coverage, respectively. MuTect2 and VarScan2 were employed to detect single nucleotide variants (SNVs) and copy number aberrations (CNAs), while MutSigCV (SNVs) and GISTIC (CNAs) algorithms estimated the significance of recurrent somatic events. The significantly mutated genes (q ≤ 0.01) were PIK3CA (28% of patients), TP53 (21%) and CDH1 (11%). However, histone modifying genes contained the largest number of variants (KMT2C and ARID1A, together 28%). Mutations in KMT2C were mutually exclusive with PI3KCA mutations (p ≤ 0. 001) and half of these affect the formation of a functional PHD domain. The tumor suppressor CDK10 was deleted in 80% of the cohort while the oncogene MDM4 was amplified. Mutational signature analyses pointed towards APOBEC deaminase activity (COSMIC signature 2) and DNA mismatch repair (COSMIC signature 6). We noticed two significantly distinct patterns related to patient age; TP53 being more mutated in the younger group (29% vs 9% of patients) and CDH23 mutations were absent from the older group. The increased somatic mutation prevalence in the histone modifying genes KMT2C and ARID1A distinguishes the Swedish cohort from previous studies. KMT2C regulates enhancer activation and assists tumor proliferation in a hormone-rich environment, possibly pointing to a role in ER + BC, especially in older cases. Finally, age of onset appears to affect the mutational landscape suggesting that a larger age-diverse population incorporating more molecular subtypes should be studied to elucidate the underlying mechanisms.

Project description:BACKGROUND: Certain somatic alterations in breast cancer can define prognosis and response to therapy. This study investigated the frequencies, prognostic effects, and predictive effects of known cancer somatic mutations using a randomized, adjuvant, phase III clinical trial dataset. METHODS: The FinHER trial was a phase III, randomized adjuvant breast cancer trial involving 1010 women. Patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer were further randomized to 9 weeks of trastuzumab or no trastuzumab. Seven hundred five of 1010 tumors had sufficient DNA for genotyping of 70 somatic hotspot mutations in 20 genes using mass spectrometry. Distant disease-free survival (DDFS), overall survival (OS), and interactions with trastuzumab were explored with Kaplan-Meier and Cox regression analyses. All statistical tests were two-sided. RESULTS: Median follow-up was 62 months. Of 705 tumors, 687 were successfully genotyped. PIK3CA mutations (exons 1, 2, 4, 9, 13, 18, and 20) were present in 25.3% (174 of 687) and TP53 mutations in 10.2% (70 of 687). Few other mutations were found: three ERBB2 and single cases of KRAS, ALK, STK11/LKB1, and AKT2. PIK3CA mutations were associated with estrogen receptor positivity (P < .001) and the luminal-A phenotype (P = .04) but were not statistically significantly associated with prognosis (DDFS: hazard ratio [HR] = 0.88, 95% confidence [CI] = 0.58 to 1.34, P = .56; OS: HR = 0.603, 95% CI = .32 to 1.13, P = .11), although a statistically significant nonproportional prognostic effect was observed for DDFS (P = .002). PIK3CA mutations were not statistically significantly associated with trastuzumab benefit (P(interaction): DDFS P = .14; OS P = .24). CONCLUSIONS: In this dataset, targeted genotyping revealed only two alterations at a frequency greater than 10%, with other mutations observed infrequently. PIK3CA mutations were associated with a better outcome, however this effect disappeared after 3 years. There were no statistically significant associations with trastuzumab benefit.

Project description:Genome-wide association studies have reported nearly 100 common germline susceptibility loci associated with the risk for breast cancer. Tumour sequencing studies have characterised somatic mutation profiles in breast cancer patients. The relationship between breast cancer susceptibility loci and somatic mutation patterns in breast cancer remains largely unexplored.We used single-nucleotide polymorphism (SNP) genotyping array data and tumour exome sequencing data available from 638 breast cancer patients of European ancestry from The Cancer Genome Atlas (TCGA) project. We analysed both genotype data and, when necessary, imputed genotypes for 90 known breast cancer susceptibility loci. We performed linear regression models to investigate possible associations between germline risk variants with total somatic mutation count (TSMC), as well as specific mutation types. We examined individual SNP genotypes, as well as a multi-SNP polygenic risk score (PRS). Models were statistically adjusted for age at diagnosis, stage, oestrogen-receptor (ER) and progesterone-receptor (PR) status of breast cancer. We also performed stratified analyses by ER and PR status.We observed a significant inverse association (P=8.75 × 10(-6); FDR=0.001) between the risk allele in rs2588809 of the gene RAD51B and TSMC across all breast cancer patients, for both ER(+) and ER(-) tumours. This association was also evident for different types of mutations. The PRS analysis for all patients, with or without rs2588809, showed a significant inverse association (P=0.01 and 0.04, respectively) with TSMC. This inverse association was significant in ER(+) patients with the ER(+)-specific PRS (P=0.02), but not among ER(-) patients for the ER(-)-specific PRS (P=0.39).We observed an inverse association between common germline risk variants and TSMC, which, if confirmed, could provide new insights into how germline variation informs our understanding of somatic mutation patterns in breast cancer.

Dataset Information

Direct Transcriptional Consequences of Somatic Mutation in Breast Cancer.

Publications

Direct Transcriptional Consequences of Somatic Mutation in Breast Cancer.

Similar Datasets

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