Elevated APOBEC3B Correlates with Poor Outcomes for Estrogen-Receptor-Positive Breast Cancers

Recent observations connected DNA cytosine deaminase APOBEC3B to the genetic evolution of breast cancer. We addressed whether APOBEC3B is associated with breast cancer clinical outcomes. APOBEC3B messenger RNA (mRNA) levels were related in 1,491 primary breast cancers to disease-free (DFS), metastasis-free (MFS), and overall survival (OS). For independent validation, APOBEC3B mRNA expression was associated with patient outcome data in five additional cohorts (over 3,500 breast cancer cases). In univariate Cox regression analysis, increasing APOBEC3B expression as a continuous variable was associated with worse DFS, MFS, and OS (hazard ratio [HR] = 1.20, 1.21, and 1.24, respectively; all P < .001). Also, in untreated ER-positive (ER+), but not in ER−, lymph-node-negative patients, high APOBEC3B levels were associated with a poor DFS (continuous variable: HR = 1.29, P = .001; dichotomized at the median level, HR = 1.66, P = .0002). This implies that APOBEC3B is a marker of pure prognosis in ER + disease. These findings were confirmed in the analyses of five independent patient sets. In these analyses, APOBEC3B expression dichotomized at the median level was associated with adverse outcomes (METABRIC discovery and validation, 788 and 706 ER + cases, disease-specific survival (DSS), HR = 1.77 and HR = 1.77, respectively, both P < .001; Affymetrix dataset, 754 ER + cases, DFS, HR = 1.57, P = 2.46E-04; NKI295, 181 ER + cases, DFS, HR = 1.72, P = .054; and BIG 1-98, 1,219 ER + cases, breast-cancer-free interval (BCFI), HR = 1.42, P = 0.0079). APOBEC3B is a marker of pure prognosis and poor outcomes for ER + breast cancer, which strongly suggests that genetic aberrations induced by APOBEC3B contribute to breast cancer progression. Electronic supplementary material The online version of this article (doi: 10.1007/s12672-014-0196-8) contains supplementary material, which is available to authorized users

A critical evaluation of network and pathway based classifiers for outcome prediction in breast cancer

Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single gene classifiers, the gain in performance is difficult to assess. This stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance. Here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches. Contrary to previous claims, we find that composite feature classifiers do not outperform simple single gene classifiers. We investigate the effect of (1) the number of selected features; (2) the specific gene set from which features are selected; (3) the size of the training set and (4) the heterogeneity of the data set on the performance of composite feature and single gene classifiers. Strikingly, we find that randomization of secondary data sources, which destroys all biological information in these sources, does not result in a deterioration in performance of composite feature classifiers. Finally, we show that when a proper correction for gene set size is performed, the stability of single gene sets is similar to the stability of composite feature sets. Based on these results there is currently no reason to prefer prognostic classifiers based on composite features over single gene classifiers for predicting outcome in breast cancer

Role of variant allele fraction and rare SNP filtering to improve cellular DNA repair endpoint association

BackgroundLarge cancer genome studies continue to reveal new players in treatment response and tumorigenesis. The discrimination of functional alterations from the abundance of passenger genetic alterations still poses challenges and determines DNA sequence variant selection procedures. Here we evaluate variant selection strategies that select homozygous variants and rare SNPs and assess its value in detecting tumor cells with DNA repair defects.MethodsTo this end we employed a panel of 29 patient-derived head and neck squamous cell carcinoma (HNSCC) cell lines, of which a subset harbors DNA repair defects. Mitomycin C (MMC) sensitivity was used as functional endpoint of DNA crosslink repair deficiency. 556 genes including the Fanconi anemia (FA) and homologous recombination (HR) genes, whose products strongly determine MMC response, were capture-sequenced.ResultsWe show a strong association between MMC sensitivity, thus loss of DNA repair function, and the presence of homozygous and rare SNPs in the relevant FA/HR genes. Excluding such selection criteria impedes the discrimination of crosslink repair status by mutation analysis. Applied to all KEGG pathways, we find that the association with MMC sensitivity is strongest in the KEGG FA pathway, therefore also demonstrating the value of such selection strategies for exploratory analyses. Variant analyses in 56 clinical samples demonstrate that homozygous variants occur more frequently in tumor suppressor genes than oncogenes further supporting the role of a homozygosity criterion to improve gene function association or tumor suppressor gene identification studies.ConclusionTogether our data show that the detection of relevant genes or of repair pathway defected tumor cells can be improved by the consideration of allele zygosity and SNP allele frequencies

The Cascadia Initiative : a sea change In seismological studies of subduction zones

Author Posting. © The Oceanography Society, 2014. This article is posted here by permission of The Oceanography Society for personal use, not for redistribution. The definitive version was published in Oceanography 27, no. 2 (2014): 138-150, doi:10.5670/oceanog.2014.49.Increasing public awareness that the Cascadia subduction zone in the Pacific Northwest is capable of great earthquakes (magnitude 9 and greater) motivates the Cascadia Initiative, an ambitious onshore/offshore seismic and geodetic experiment that takes advantage of an amphibious array to study questions ranging from megathrust earthquakes, to volcanic arc structure, to the formation, deformation and hydration of the Juan De Fuca and Gorda Plates. Here, we provide an overview of the Cascadia Initiative, including its primary science objectives, its experimental design and implementation, and a preview of how the resulting data are being used by a diverse and growing scientific community. The Cascadia Initiative also exemplifies how new technology and community-based experiments are opening up frontiers for marine science. The new technology—shielded ocean bottom seismometers—is allowing more routine investigation of the source zone of megathrust earthquakes, which almost exclusively lies offshore and in shallow water. The Cascadia Initiative offers opportunities and accompanying challenges to a rapidly expanding community of those who use ocean bottom seismic data.The Cascadia Initiative is supported by the National Science Foundation; the CIET is supported under grants OCE- 1139701, OCE-1238023, OCE‐1342503, OCE-1407821, and OCE-1427663 to the University of Oregon

Comparison of normalisation methods for surface-enhanced laser desorption and ionisation (SELDI) time-of-flight (TOF) mass spectrometry data

<p>Abstract</p> <p>Background</p> <p>Mass spectrometry for biological data analysis is an active field of research, providing an efficient way of high-throughput proteome screening. A popular variant of mass spectrometry is SELDI, which is often used to measure sample populations with the goal of developing (clinical) classifiers. Unfortunately, not only is the data resulting from such measurements quite noisy, variance between replicate measurements of the same sample can be high as well. Normalisation of spectra can greatly reduce the effect of this technical variance and further improve the quality and interpretability of the data. However, it is unclear which normalisation method yields the most informative result.</p> <p>Results</p> <p>In this paper, we describe the first systematic comparison of a wide range of normalisation methods, using two objectives that should be met by a good method. These objectives are minimisation of inter-spectra variance and maximisation of signal with respect to class separation. The former is assessed using an estimation of the coefficient of variation, the latter using the classification performance of three types of classifiers on real-world datasets representing two-class diagnostic problems. To obtain a maximally robust evaluation of a normalisation method, both objectives are evaluated over multiple datasets and multiple configurations of baseline correction and peak detection methods. Results are assessed for statistical significance and visualised to reveal the performance of each normalisation method, in particular with respect to using no normalisation. The normalisation methods described have been implemented in the freely available MASDA R-package.</p> <p>Conclusion</p> <p>In the general case, normalisation of mass spectra is beneficial to the quality of data. The majority of methods we compared performed significantly better than the case in which no normalisation was used. We have shown that normalisation methods that scale spectra by a factor based on the dispersion (e.g., standard deviation) of the data clearly outperform those where a factor based on the central location (e.g., mean) is used. Additional improvements in performance are obtained when these factors are estimated locally, using a sliding window within spectra, instead of globally, over full spectra. The underperforming category of methods using a globally estimated factor based on the central location of the data includes the method used by the majority of SELDI users.</p

Vemurafenib plus cobimetinib in unresectable stage IIIc or stage IV melanoma

Background: In patients with BRAFV600 mutated unresectable stage IIIc or metastatic melanoma, molecular targeted therapy with combined BRAF/MEK-inhibitor vemurafenib plus cobimetinib has shown a significantly improved progression-free survival and overall survival compared to treatment with vemurafenib alone. Nevertheless, the majority of BRAFV600 mutation-positive melanoma patients will eventually develop resistance to treatment. Molecular imaging with 18F-Fluorodeoxyglucose (18F-FDG) PET has been used to monitor response to vemurafenib in some BRAFV600 mutated metastatic melanoma patients, showing a rapid decline of 18F-FDG uptake within 2 weeks following treatment. Furthermore, preliminary results suggest that metabolic alterations might predict the development of resistance to treatment. 18F-Fluoro-3'-deoxy-3'L-fluorothymidine (18F-FLT), a PET-tracer visualizing proliferation, might be more suitable to predict response or resistance to therapy than 18F-FDG. Methods: This phase II, open-label, multicenter study evaluates whether metabolic response to treatment with vemurafenib plus cobimetinib in the first 7 weeks as assessed by 18F-FDG/18F-FLT PET can predict progression-free survival and whether early changes in 18F-FDG/18F-FLT can be used for early detection of treatment response compared to standard response assessment with RECISTv1.1 ceCT at 7 weeks. Ninety patients with BRAFV600E/K mutated unresectable stage IIIc/IV melanoma will be included. Prior to and during treatment all patients will undergo 18F-FDG PET/CT and in 25 patients additional 18F-FLT PET/CT is performed. Histopathological tumor characterization is assessed in a subset of 40 patients to unravel mechanisms of resistance. Furthermore, in all patients, blood samples are taken for pharmacokinetic analysis of vemurafenib/cobimetinib. Outcomes are correlated with PET/CT-imaging and therapy response.

Gene Expression Profiles from Formalin Fixed Paraffin Embedded Breast Cancer Tissue Are Largely Comparable to Fresh Frozen Matched Tissue

BACKGROUND AND METHODS: Formalin Fixed Paraffin Embedded (FFPE) samples represent a valuable resource for cancer research. However, the discovery and development of new cancer biomarkers often requires fresh frozen (FF) samples. Recently, the Whole Genome (WG) DASL (cDNA-mediated Annealing, Selection, extension and Ligation) assay was specifically developed to profile FFPE tissue. However, a thorough comparison of data generated from FFPE RNA and Fresh Frozen (FF) RNA using this platform is lacking. To this end we profiled, in duplicate, 20 FFPE tissues and 20 matched FF tissues and evaluated the concordance of the DASL results from FFPE and matched FF material. METHODOLOGY AND PRINCIPAL FINDINGS: We show that after proper normalization, all FFPE and FF pairs exhibit a high level of similarity (Pearson correlation >0.7), significantly larger than the similarity between non-paired samples. Interestingly, the probes showing the highest correlation had a higher percentage G/C content and were enriched for cell cycle genes. Predictions of gene expression signatures developed on frozen material (Intrinsic subtype, Genomic Grade Index, 70 gene signature) showed a high level of concordance between FFPE and FF matched pairs. Interestingly, predictions based on a 60 gene DASL list (best match with the 70 gene signature) showed very high concordance with the MammaPrint® results. CONCLUSIONS AND SIGNIFICANCE: We demonstrate that data generated from FFPE material with the DASL assay, if properly processed, are comparable to data extracted from the FF counterpart. Specifically, gene expression profiles for a known set of prognostic genes for a specific disease are highly comparable between two conditions. This opens up the possibility of using both FFPE and FF material in gene expressions analyses, leading to a vast increase in the potential resources available for cancer research

Identification of Networks of Co-Occurring, Tumor-Related DNA Copy Number Changes Using a Genome-Wide Scoring Approach

Tumorigenesis is a multi-step process in which normal cells transform into malignant tumors following the accumulation of genetic mutations that enable them to evade the growth control checkpoints that would normally suppress their growth or result in apoptosis. It is therefore important to identify those combinations of mutations that collaborate in cancer development and progression. DNA copy number alterations (CNAs) are one of the ways in which cancer genes are deregulated in tumor cells. We hypothesized that synergistic interactions between cancer genes might be identified by looking for regions of co-occurring gain and/or loss. To this end we developed a scoring framework to separate truly co-occurring aberrations from passenger mutations and dominant single signals present in the data. The resulting regions of high co-occurrence can be investigated for between-region functional interactions. Analysis of high-resolution DNA copy number data from a panel of 95 hematological tumor cell lines correctly identified co-occurring recombinations at the T-cell receptor and immunoglobulin loci in T- and B-cell malignancies, respectively, showing that we can recover truly co-occurring genomic alterations. In addition, our analysis revealed networks of co-occurring genomic losses and gains that are enriched for cancer genes. These networks are also highly enriched for functional relationships between genes. We further examine sub-networks of these networks, core networks, which contain many known cancer genes. The core network for co-occurring DNA losses we find seems to be independent of the canonical cancer genes within the network. Our findings suggest that large-scale, low-intensity copy number alterations may be an important feature of cancer development or maintenance by affecting gene dosage of a large interconnected network of functionally related genes

Integrated Ugi-Based Assembly of Functionally, Skeletally, and Stereochemically Diverse 1,4-Benzodiazepin-2-ones

A practical, integrated and versatile U-4CR-based assembly of 1,4-benzodiazepin-2-ones exhibiting functionally, skeletally, and stereochemically diverse substitution patterns is described. By virtue of its convergence, atom economy, and bond-forming efficiency, the methodology documented herein exemplifies the reconciliation of structural complexity and experimental simplicity in the context of medicinal chemistry projects.This work was financially supported by the Galician Government (Spain), Projects: 09CSA016234PR and GPC-2014-PG037. J.A. thanks FUNDAYACUCHO (Venezuela) for a predoctoral grant and Deputación da Coruña (Spain) for a postdoctoral research grant. A.N.-V. thanks the Spanish government for a Ramón y Cajal research contract

The consensus molecular subtypes of colorectal cancer

Colorectal cancer (CRC) is a frequently lethal disease with heterogeneous outcomes and drug responses. To resolve inconsistencies among the reported gene expression-based CRC classifications and facilitate clinical translation, we formed an international consortium dedicated to large-scale data sharing and analytics across expert groups. We show marked interconnectivity between six independent classification systems coalescing into four consensus molecular subtypes (CMSs) with distinguishing features: CMS1 (microsatellite instability immune, 14%), hypermutated, microsatellite unstable and strong immune activation; CMS2 (canonical, 37%), epithelial, marked WNT and MYC signaling activation; CMS3 (metabolic, 13%), epithelial and evident metabolic dysregulation; and CMS4 (mesenchymal, 23%), prominent transforming growth factor-beta activation, stromal invasion and angiogenesis. Samples with mixed features (13%) possibly represent a transition phenotype or intratumoral heterogeneity. We consider the CMS groups the most robust classification system currently available for CRC-with clear biological interpretability-and the basis for future clinical stratification and subtype-based targeted interventions