Modeling molecular development of breast cancer in canine mammary tumors.

Understanding the changes in diverse molecular pathways underlying the development of breast tumors is critical for improving diagnosis, treatment, and drug development. Here, we used RNA-profiling of canine mammary tumors (CMTs) coupled with a robust analysis framework to model molecular changes in human breast cancer. Our study leveraged a key advantage of the canine model, the frequent presence of multiple naturally occurring tumors at diagnosis, thus providing samples spanning normal tissue and benign and malignant tumors from each patient. We showed human breast cancer signals, at both expression and mutation level, are evident in CMTs. Profiling multiple tumors per patient enabled by the CMT model allowed us to resolve statistically robust transcription patterns and biological pathways specific to malignant tumors versus those arising in benign tumors or shared with normal tissues. We showed that multiple histological samples per patient is necessary to effectively capture these progression-related signatures, and that carcinoma-specific signatures are predictive of survival for human breast cancer patients. To catalyze and support similar analyses and use of the CMT model by other biomedical researchers, we provide FREYA, a robust data processing pipeline and statistical analyses framework.

The NEOLETRIB trial: neoadjuvant treatment with Letrozole and Ribociclib in ER-positive, HER2-negative breast cancer.

Chemotherapy is used as neoadjuvant therapy for all subgroups of breast cancer, including ER-positive, and HER2-negative cases. However, studies have suggested that using aromatase inhibitors combined with CDK4/6-inhibitors might be an appropriate alternative in selected patients. Thus, the NEOLETRIB trial evaluates the response of ER-positive, HER2-negative luminal A/B breast cancer to the combination of letrozole and ribociclib in the neoadjuvant setting. Comprehensive molecular biology procedures, including sequential single-cell RNA-sequencing of tumor biopsies, are performed during 6 months of treatment with extensive biobanking of blood samples, tumor biopsies and gut microbiome specimens. Our findings will hopefully contribute to an improved selection of patients who may benefit from this drug combination and give new insights into the intra-tumoral changes during this treatment.Trial registration number: NCT05163106 (ClinicalTrials.gov).

[Box: see text].

Modeling of Mouse Experiments Suggests that Optimal Anti-Hormonal Treatment for Breast Cancer is Diet-Dependent.

Estrogen receptor positive breast cancer is frequently treated with anti-hormonal treatment such as aromatase inhibitors (AI). Interestingly, a high body mass index has been shown to have a negative impact on AI efficacy, most likely due to disturbances in steroid metabolism and adipokine production. Here, we propose a mathematical model based on a system of ordinary differential equations to investigate the effect of high-fat diet on tumor growth. We inform the model with data from mouse experiments, where the animals are fed with high-fat or control (normal) diet. By incorporating AI treatment with drug resistance into the model and by solving optimal control problems we found differential responses for control and high-fat diet. To the best of our knowledge, this is the first attempt to model optimal anti-hormonal treatment for breast cancer in the presence of drug resistance. Our results underline the importance of considering high-fat diet and obesity as factors influencing clinical outcomes during anti-hormonal therapies in breast cancer patients.

Cis-regulatory mutations associate with transcriptional and post-transcriptional deregulation of gene regulatory programs in cancers.

Most cancer alterations occur in the noncoding portion of the human genome, where regulatory regions control gene expression. The discovery of noncoding mutations altering the cells' regulatory programs has been limited to few examples with high recurrence or high functional impact. Here, we show that transcription factor binding sites (TFBSs) have similar mutation loads to those in protein-coding exons. By combining cancer somatic mutations in TFBSs and expression data for protein-coding and miRNA genes, we evaluate the combined effects of transcriptional and post-transcriptional alterations on the regulatory programs in cancers. The analysis of seven TCGA cohorts culminates with the identification of protein-coding and miRNA genes linked to mutations at TFBSs that are associated with a cascading trans-effect deregulation on the cells' regulatory programs. Our analyses of cis-regulatory mutations associated with miRNAs recurrently predict 12 mature miRNAs (derived from 7 precursors) associated with the deregulation of their target gene networks. The predictions are enriched for cancer-associated protein-coding and miRNA genes and highlight cis-regulatory mutations associated with the dysregulation of key pathways associated with carcinogenesis. By combining transcriptional and post-transcriptional regulation of gene expression, our method predicts cis-regulatory mutations related to the dysregulation of key gene regulatory networks in cancer patients.

A regulatory network comprising let-7 miRNA and SMUG1 is associated with good prognosis in ER+ breast tumours.

Single-strand selective uracil-DNA glycosylase 1 (SMUG1) initiates base excision repair (BER) of uracil and oxidized pyrimidines. SMUG1 status has been associated with cancer risk and therapeutic response in breast carcinomas and other cancer types. However, SMUG1 is a multifunctional protein involved, not only, in BER but also in RNA quality control, and its function in cancer cells is unclear. Here we identify several novel SMUG1 interaction partners that functions in many biological processes relevant for cancer development and treatment response. Based on this, we hypothesized that the dominating function of SMUG1 in cancer might be ascribed to functions other than BER. We define a bad prognosis signature for SMUG1 by mapping out the SMUG1 interaction network and found that high expression of genes in the bad prognosis network correlated with lower survival probability in ER+ breast cancer. Interestingly, we identified hsa-let-7b-5p microRNA as an upstream regulator of the SMUG1 interactome. Expression of SMUG1 and hsa-let-7b-5p were negatively correlated in breast cancer and we found an inhibitory auto-regulatory loop between SMUG1 and hsa-let-7b-5p in the MCF7 breast cancer cells. We conclude that SMUG1 functions in a gene regulatory network that influence the survival and treatment response in several cancers.

DNA methylation changes in response to neoadjuvant chemotherapy are associated with breast cancer survival.

BACKGROUND: Locally advanced breast cancer is a heterogeneous disease with respect to response to neoadjuvant chemotherapy (NACT) and survival. It is currently not possible to accurately predict who will benefit from the specific types of NACT. DNA methylation is an epigenetic mechanism known to play an important role in regulating gene expression and may serve as a biomarker for treatment response and survival. We investigated the potential role of DNA methylation as a prognostic marker for long-term survival (> 5 years) after NACT in breast cancer. METHODS: DNA methylation profiles of pre-treatment (n = 55) and post-treatment (n = 75) biopsies from 83 women with locally advanced breast cancer were investigated using the Illumina HumanMethylation450 BeadChip. The patients received neoadjuvant treatment with epirubicin and/or paclitaxel. Linear mixed models were used to associate DNA methylation to treatment response and survival based on clinical response to NACT (partial response or stable disease) and 5-year survival, respectively. LASSO regression was performed to identify a risk score based on the statistically significant methylation sites and Kaplan-Meier curve analysis was used to estimate survival probabilities using ten years of survival follow-up data. The risk score developed in our discovery cohort was validated in an independent validation cohort consisting of paired pre-treatment and post-treatment biopsies from 85 women with locally advanced breast cancer. Patients included in the validation cohort were treated with either doxorubicin or 5-FU and mitomycin NACT. RESULTS: DNA methylation patterns changed from before to after NACT in 5-year survivors, while no significant changes were observed in non-survivors or related to treatment response. DNA methylation changes included an overall loss of methylation at CpG islands and gain of methylation in non-CpG islands, and these changes affected genes linked to transcription factor activity, cell adhesion and immune functions. A risk score was developed based on four methylation sites which successfully predicted long-term survival in our cohort (p = 0.0034) and in an independent validation cohort (p = 0.049). CONCLUSION: Our results demonstrate that DNA methylation patterns in breast tumors change in response to NACT. These changes in DNA methylation show potential as prognostic biomarkers for breast cancer survival.

Genome-wide and transcriptome-wide association studies of mammographic density phenotypes reveal novel loci.

BACKGROUND: Mammographic density (MD) phenotypes, including percent density (PMD), area of dense tissue (DA), and area of non-dense tissue (NDA), are associated with breast cancer risk. Twin studies suggest that MD phenotypes are highly heritable. However, only a small proportion of their variance is explained by identified genetic variants. METHODS: We conducted a genome-wide association study, as well as a transcriptome-wide association study (TWAS), of age- and BMI-adjusted DA, NDA, and PMD in up to 27,900 European-ancestry women from the MODE/BCAC consortia. RESULTS: We identified 28 genome-wide significant loci for MD phenotypes, including nine novel signals (5q11.2, 5q14.1, 5q31.1, 5q33.3, 5q35.1, 7p11.2, 8q24.13, 12p11.2, 16q12.2). Further, 45% of all known breast cancer SNPs were associated with at least one MD phenotype at p < 0.05. TWAS further identified two novel genes (SHOX2 and CRISPLD2) whose genetically predicted expression was significantly associated with MD phenotypes. CONCLUSIONS: Our findings provided novel insight into the genetic background of MD phenotypes, and further demonstrated their shared genetic basis with breast cancer.

miRNA normalization enables joint analysis of several datasets to increase sensitivity and to reveal novel miRNAs differentially expressed in breast cancer.

Different miRNA profiling protocols and technologies introduce differences in the resulting quantitative expression profiles. These include differences in the presence (and measurability) of certain miRNAs. We present and examine a method based on quantile normalization, Adjusted Quantile Normalization (AQuN), to combine miRNA expression data from multiple studies in breast cancer into a single joint dataset for integrative analysis. By pooling multiple datasets, we obtain increased statistical power, surfacing patterns that do not emerge as statistically significant when separately analyzing these datasets. To merge several datasets, as we do here, one needs to overcome both technical and batch differences between these datasets. We compare several approaches for merging and jointly analyzing miRNA datasets. We investigate the statistical confidence for known results and highlight potential new findings that resulted from the joint analysis using AQuN. In particular, we detect several miRNAs to be differentially expressed in estrogen receptor (ER) positive versus ER negative samples. In addition, we identify new potential biomarkers and therapeutic targets for both clinical groups. As a specific example, using the AQuN-derived dataset we detect hsa-miR-193b-5p to have a statistically significant over-expression in the ER positive group, a phenomenon that was not previously reported. Furthermore, as demonstrated by functional assays in breast cancer cell lines, overexpression of hsa-miR-193b-5p in breast cancer cell lines resulted in decreased cell viability in addition to inducing apoptosis. Together, these observations suggest a novel functional role for this miRNA in breast cancer. Packages implementing AQuN are provided for Python and Matlab: https://github.com/YakhiniGroup/PyAQN.

Tumour hypoxia causes DNA hypermethylation by reducing TET activity.

Hypermethylation of the promoters of tumour suppressor genes represses transcription of these genes, conferring growth advantages to cancer cells. How these changes arise is poorly understood. Here we show that the activity of oxygen-dependent ten-eleven translocation (TET) enzymes is reduced by tumour hypoxia in human and mouse cells. TET enzymes catalyse DNA demethylation through 5-methylcytosine oxidation. This reduction in activity occurs independently of hypoxia-associated alterations in TET expression, proliferation, metabolism, hypoxia-inducible factor activity or reactive oxygen species, and depends directly on oxygen shortage. Hypoxia-induced loss of TET activity increases hypermethylation at gene promoters in vitro. In patients, tumour suppressor gene promoters are markedly more methylated in hypoxic tumour tissue, independent of proliferation, stromal cell infiltration and tumour characteristics. Our data suggest that up to half of hypermethylation events are due to hypoxia, with these events conferring a selective advantage. Accordingly, increased hypoxia in mouse breast tumours increases hypermethylation, while restoration of tumour oxygenation abrogates this effect. Tumour hypoxia therefore acts as a novel regulator of DNA methylation.

Lack of cross-resistance between non-steroidal and steroidal aromatase inhibitors in breast cancer patients: the potential role of the adipokine leptin.

PURPOSE: The aromatase inactivator exemestane may cause clinical disease stabilization following progression on non-steroidal aromatase inhibitors like letrozole in patients with metastatic breast cancer, indicating that additional therapeutic effects, not necessarily related to estrogen-suppression, may be involved in this well-known "lack of cross-resistance". METHODS: Postmenopausal women with ER positive, HER-2 negative, locally advanced breast cancer were enrolled in the NEOLETEXE-trial and randomized to sequential treatment starting with either letrozole (2.5 mg o.d.) or exemestane (25 mg o.d.) followed by the alternative aromatase inhibitor. Serum levels of 54 cytokines, including 12 adipokines were assessed using Luminex xMAP technology (multiple ELISA). RESULTS: Serum levels of leptin were significantly decreased during treatment with exemestane (p < 0.001), regardless whether exemestane was given as first or second neoadjuvant therapy. In contrast, letrozole caused a non-significant increase in serum leptin levels in vivo. CONCLUSIONS: Our findings suggest an additional and direct effect of exemestane on CYP-19 (aromatase) synthesis presumably due to effects on the CYP19 promoter use that is not present during therapy with the non-steroidal aromatase inhibitor letrozole. Our findings provide new insights into the influence of clinically important aromatase inhibitors on cytokine levels in vivo that contribute to the understanding of the clinically observed lack of cross-resistance between non-steroidal and steroidal aromatase inhibitors in breast cancer patients. TRIAL REGISTRATION: Registered on March 23rd 2015 in the National trial database of Norway (Registration number: REK-SØ-84-2015).

Subtype-specific transcriptional regulators in breast tumors subjected to genetic and epigenetic alterations.

MOTIVATION: Breast cancer consists of multiple distinct tumor subtypes, and results from epigenetic and genetic aberrations that give rise to distinct transcriptional profiles. Despite previous efforts to understand transcriptional deregulation through transcription factor networks, the transcriptional mechanisms leading to subtypes of the disease remain poorly understood. RESULTS: We used a sophisticated computational search of thousands of expression datasets to define extended signatures of distinct breast cancer subtypes. Using ENCODE ChIP-seq data of surrogate cell lines and motif analysis we observed that these subtypes are determined by a distinct repertoire of lineage-specific transcription factors. Furthermore, specific pattern and abundance of copy number and DNA methylation changes at these TFs and targets, compared to other genes and to normal cells were observed. Overall, distinct transcriptional profiles are linked to genetic and epigenetic alterations at lineage-specific transcriptional regulators in breast cancer subtypes. AVAILABILITY AND IMPLEMENTATION: The analysis code and data are deposited at https://bitbucket.org/qzhu/breast.cancer.tf/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Immune phenotype of tumor microenvironment predicts response to bevacizumab in neoadjuvant treatment of ER-positive breast cancer.

Antiangiogenic drugs are potentially a useful supplement to neoadjuvant chemotherapy for a subgroup of patients with human epidermal growth factor receptor 2 (HER2) negative breast cancer, but reliable biomarkers for improved response are lacking. Here, we report on a randomized phase II clinical trial to study the added effect of bevacizumab in neoadjuvant chemotherapy with FEC100 (5-fluorouracil, epirubicin and cyclophosphamide) and taxanes (n = 132 patients). Gene expression from the tumors was obtained before neoadjuvant treatment, and treatment response was evaluated by residual cancer burden (RCB) at time of surgery. Bevacizumab increased the proportion of complete responders (RCB class 0) from 5% to 20% among patients with estrogen receptor (ER) positive tumors (P = .02). Treatment with bevacizumab was associated with improved 8-year disease-free survival (P = .03) among the good responders (RCB class 0 or I). Patients treated with paclitaxel (n = 45) responded better than those treated with docetaxel (n = 21; P = .03). Improved treatment response was associated with higher proliferation rate and an immune phenotype characterized by high presence of classically activated M1 macrophages, activated NK cells and memory activated CD4 T cells. Treatment with bevacizumab increased the number of adverse events, including hemorrhage, hypertension, infection and febrile neutropenia, but despite this, the ECOG status was not affected.

Serum levels of inflammation-related markers and metabolites predict response to neoadjuvant chemotherapy with and without bevacizumab in breast cancers.

Angiogenesis is necessary for tumor growth and has been targeted in breast cancer; however, it is unclear which patients will respond and benefit from antiangiogenic therapy. We report noninvasive monitoring of patient response to neoadjuvant chemotherapy given alone or in combination with anti-vascular endothelial growth factor (bevacizumab) in a randomized clinical trial. At four time points during neoadjuvant chemotherapy ± bevacizumab of receptor tyrosine-protein kinase erbB-2-negative breast cancers, we measured metabolites and inflammation-related markers in patient's serum. We report significant changes in the levels of several molecules induced by bevacizumab, the most prominent being an increase in pentraxin 3 (PTX3) and von Willebrand factor (VWF). Serum levels of AXL, VWF and pulmonary and activation-regulated cytokine (PARC/CCL18) reflected response to chemotherapy alone or in combination with bevacizumab. We further analyzed serum cytokines in relation to tumor characteristics such as gene expression, tumor metabolites and tumor infiltrating leukocytes. We found that VWF and growth-differentiation factor 15 tumor mRNA levels correlated with their respective serum protein levels suggesting that these cytokines may be produced by tumors and outflow to the bloodstream while influencing the tumor microenvironment locally. Finally, we used binomial logistic regression which allowed to predict patient's response using only 10 noninvasive biomarkers. Our study highlights the potential of monitoring circulating levels of cytokines and metabolites during breast cancer therapy.

Loss of progesterone receptor is associated with distinct tyrosine kinase profiles in breast cancer.

PURPOSE: The aim of this study was to assess protein tyrosine kinase profiles in primary breast cancer samples in correlation with the distinct hormone and growth receptor profiles ER, PR, and HER2. EXPERIMENTAL DESIGN: Pamchip® microarrays were used to measure the phosphorylation of 144 tyrosine kinase substrates in 29 ER+ breast cancer samples and cell lines MCF7, BT474 and ZR75-1. mRNA expression data from the METABRIC cohort and publicly available PR chip-sequencing data were used for validation purposes, together with RT-PCR. RESULTS: In ER+ breast tumors and cell lines, we observed that the loss of PR expression correlated to higher kinase activity in samples and cell lines that were HER2-. A number of kinases, representing mostly proteins within the PI3K/AKT pathway, were identified as responsible for the differential phosphorylation between PR- and PR+ in ER+/HER2- tumors. We used the METABRIC cohort to analyze mRNA expression from 977 ER+/HER2- breast cancers. Twenty four kinase-encoding genes were identified as differentially expressed between PR+ and PR-, dividing ER+/HER2- samples in two distinct clusters with significant differences in survival (p < 0.05). Four kinase genes, LCK, FRK, FGFR4, and MST1R, were identified as potential direct targets of PR. CONCLUSIONS: Our results suggest that the PR status has a profound effect on tyrosine kinases, especially for FGFR4 and LCK genes, in ER+/HER2- breast cancer patients. The influence of these genes on the PI3K/AKT signaling pathway may potentially lead to novel drug targets for ER+/PR- breast cancer patients.

MiR-18a and miR-18b are expressed in the stroma of oestrogen receptor alpha negative breast cancers.

BACKGROUND: Previously, we have shown that miR-18a and miR-18b gene expression strongly correlates with high proliferation, oestrogen receptor -negativity (ER-), cytokeratin 5/6 positivity and basal-like features of breast cancer. METHODS: We investigated the expression and localization of miR-18a and -18b in formalin fixed paraffin embedded (FFPE) tissue from lymph node negative breast cancers (n = 40), by chromogenic in situ hybridization (CISH). The expression level and in situ localization of miR-18a and -18b was assessed with respect to the presence of tumour infiltrating lymphocytes (TILs) and immunohistochemical markers for ER, CD4, CD8, CD20, CD68, CD138, PAX5 and actin. Furthermore, in two independent breast cancer cohorts (94 and 377 patients) the correlation between miR-18a and -18b expression and the relative quantification of 22 immune cell types obtained from the CIBERSORT tool was assessed. RESULTS: CISH demonstrated distinct and specific cytoplasmic staining for both miR-18a and miR-18b, particularly in the intratumoural stroma and the stroma surrounding the tumour margin. Staining by immunohistochemistry revealed some degree of overlap of miR-18a and -18b with CD68 (monocytes/macrophages), CD138 (plasma cells) and the presence of high percentages of TILs. CIBERSORT analysis showed a strong correlation between M1-macrophages and CD4+ memory activated T-cells with mir-18a and -18b. CONCLUSIONS: Our study demonstrates that miR-18a and miR-18b expression is associated with ER- breast tumours that display a high degree of inflammation. This expression is potentially associated specifically with macrophages. These results suggest that miR-18a and miR-18b may play a role in the systemic immunological response in ER- tumours.

Molecularly matched therapy in the context of sensitivity, resistance, and safety; patient outcomes in end-stage cancer - the MetAction study.

Background: In precision cancer medicine, the challenge is to prioritize DNA driver events, account for resistance markers, and procure sufficient information for treatment that maintains patient safety. The MetAction project, exploring how tumor molecular vulnerabilities predict therapy response, first established the required workflow for DNA sequencing and data interpretation (2014-2015). Here, we employed it to identify molecularly matched therapy and recorded outcome in end-stage cancer (2016-2019).Material and methods: Metastatic tissue from 26 patients (16 colorectal cancer cases) was sequenced by the Oncomine assay. The study tumor boards interpreted called variants with respect to sensitivity or resistance to matched therapy and recommended single-agent or combination treatment if considered tolerable. The primary endpoint was the rate of progression-free survival 1.3-fold longer than for the most recent systemic therapy. The objective response rate and overall survival were secondary endpoints.Results: Both common and rare actionable alterations were identified. Thirteen patients were found eligible for therapy following review of tumor sensitivity and resistance variants and patient tolerability. The interventions were inhibitors of ALK/ROS1-, BRAF-, EGFR-, FGFR-, mTOR-, PARP-, or PD-1-mediated signaling for 2-3 cases each. Among 10 patients who received treatment until radiologic evaluation, 6 (46% of the eligible cases) met the primary endpoint. Four colorectal cancer patients (15% of the total study cohort) had objective response. The only serious adverse event was a transient colitis, which appeared in 1 of the 2 patients given PD-1 inhibitor with complete response. Apart from those two, overall survival was similar for patients who did and did not receive study treatment.Conclusions: The systematic MetAction approach may point forward to a refined framework for how to interpret the complexity of sensitivity versus resistance and patient safety that resides in tumor sequence data, for the possibly improved outcome of precision cancer medicine in future studies. ClinicalTrials.gov, identifier: NCT02142036.

Associations between clinical symptoms, plasma norepinephrine and deregulated immune gene networks in subgroups of adolescent with Chronic Fatigue Syndrome.

BACKGROUND: Chronic Fatigue Syndrome (CFS) is one of the most important causes of disability among adolescents while limited knowledge exists on genetic determinants underlying disease pathophysiology. METHODS: We analyzed deregulated immune-gene modules using Pathifier software on whole blood gene expression data (29 CFS patients, 18 controls). Deconvolution of immune cell subtypes based on gene expression profile was performed using CIBERSORT. Supervised consensus clustering on pathway deregulation score (PDS) was used to define CFS subgroups. Associations between PDS and immune, neuroendocrine/autonomic and clinical markers were examined. The impact of plasma norepinephrine level on clinical markers over time was assessed in a larger cohort (91 patients). RESULTS: A group of 29 immune-gene sets was shown to differ patients from controls and detect subgroups within CFS. Group 1P (high PDS, low norepinephrine, low naïve CD4+ composition) had strong association with levels of serum C-reactive protein and Transforming Growth Factor-beta. Group 2P (low PDS, high norepinephrine, high naïve CD4+ composition) had strong associations with neuroendocrine/autonomic markers. The corresponding plasma norepinephrine level delineated 91 patients into two subgroups with significant differences in fatigue score. CONCLUSION: We identified 29 immune-gene sets linked to plasma norepinephrine level that could delineate CFS subgroups. Plasma norepinephrine stratification revealed that lower levels of norepinephrine were associated with higher fatigue. Our data suggests potential involvement of neuro-immune dysregulation and genetic stratification in CFS.

The NEOLETEXE trial: a neoadjuvant cross-over study exploring the lack of cross resistance between aromatase inhibitors.

The aromatase inhibitor letrozole (Femar®/Femara®) and the aromatase inactivator exemestane (Aromasin®) differ in their biochemical effect on the aromatase enzyme. Letrozole is a competitive aromatase inhibitor while exemestane binds irreversibly to the aromatase enzyme. This pharmacological difference is of clinical interest since a lack of cross-resistance has been documented. It has been demonstrated in several clinical trials that exemestane may cause a disease regression following resistance to nonsteroidal aromatase inhibitors. The exact mechanism(s) behind this phenomenon is yet unknown. Here, we present the NEOLETEXE trial with the aim of exploring the individual mechanisms involved behind the observed lack of cross resistance. Clinical trial registration: The trial has been approved by the Regional Ethics Committee of South-East Norway (project number 2015/84).

Somatic EP300-G211S mutations are associated with overall somatic mutational patterns and breast cancer specific survival in triple-negative breast cancer.

PURPOSE: We have compared the mutational profiles of human breast cancer tumor samples belonging to all major subgroups with special emphasis on triple-negative breast cancer (TNBC). Our major goal was to identify specific mutations that could be potentially used for clinical decision making in TNBC patients. PATIENTS AND METHODS: Primary tumor specimens from 149 Norwegian breast cancer patients were available. We analyzed the tissue samples for somatic mutations in 44 relevant breast cancer genes by targeted next-generation sequencing. As a second confirmatory technique, we performed pyrosequencing on selected samples. RESULTS: We observed a distinct subgroup of TNBC patients, characterized by an almost completely lack of pathogenic somatic mutations. A point mutation in the adenoviral E1A binding protein p300 (EP300-G211S) was significantly correlated to this TNBC subgroup. The EP300-G211S mutation was exclusively found in the TNBC patients and its presence reduced the chance for other pathological somatic mutations in typical breast cancer genes investigated in our gene panel by 94.9% (P < 0.005). Interestingly, the EP300-G211S mutation also predicted a lower risk for relapses and decreased breast cancer-specific mortality during long-term follow-up of the patients. CONCLUSION: Next-generation sequencing revealed specific mutations in EP300 to be associated with the mutational patterns in typical breast cancer genes and long-term outcome of triple-negative breast cancer patients.

Targeted exploration and analysis of large cross-platform human transcriptomic compendia.

We present SEEK (search-based exploration of expression compendia; http://seek.princeton.edu/), a query-based search engine for very large transcriptomic data collections, including thousands of human data sets from many different microarray and high-throughput sequencing platforms. SEEK uses a query-level cross-validation-based algorithm to automatically prioritize data sets relevant to the query and a robust search approach to identify genes, pathways and processes co-regulated with the query. SEEK provides multigene query searching with iterative metadata-based search refinement and extensive visualization-based analysis options.