Sander Canisius

Oestrogen receptor–co‐factor–chromatin specificity in the transcriptional regulation of breast cancer

The complexity of oestrogen receptor α (ERα)‐mediated transcription is becoming apparent, but global insight into the co‐regulatory proteins that assist ERα transcription is incomplete. Here, we present the most comprehensive chromatin‐binding landscape of ERα co‐regulatory proteins to date. We map by ChIP‐seq the essential p160 co‐regulators (SRC1, SRC2 and SRC3), and the histone acetyl transferases p300 and CBP in MCF‐7 breast cancer cells. We find a complex network of co‐regulator binding, with preferential binding sites for each co‐regulator. Unlike previous suggestions, we find SRC recruitment almost exclusively following ligand treatment. Interestingly, we find specific subsets of genes regulated by ligand‐dependent and ‐independent co‐regulator recruitment. Co‐factor‐binding profiles were integrated with expression data from cell lines and primary tumour cohorts, to reveal specific transcriptional networks that influence clinical outcome. Genes that are bound by SRC3, but not other p160 proteins, have predictive value in cohorts of breast cancer patients. By generating a robust and global view of co‐factor‐binding properties, we discover new levels of co‐regulator complexity, but also reveal specific gene networks that may influence endocrine response.

Applying Spelling Error Correction Techniques for Improving Semantic Role Labelling

This paper describes our approach to the CoNLL-2005 shared task: semantic role labelling. We do many of the obvious things that can be found in the other submissions as well. We use syntactic trees for deriving instances, partly at the constituent level and partly at the word level. On both levels we edit the data down to only the predicted positive cases of verb-constituent or verb-word pairs exhibiting a verb-argument relation, and we train two next-level classifiers that assign the appropriate labels to the positively classified cases. Each classifier is trained on data in which the features have been selected to optimize generalization performance on the particular task. We apply different machine learning algorithms and combine their predictions.

Dependency Parsing by Inference over High-recall Dependency Predictions

As more and more syntactically-annotated corpora become available for a wide variety of languages, machine learning approaches to parsing gain interest as a means of developing parsers without having to repeat some of the labor-intensive and language-specific activities required for traditional parser development, such as manual grammar engineering, for each new language. The CoNLL-X shared task on multi-lingual dependency parsing (Buchholz et al., 2006) aims to evaluate and advance the state-of-the-art in machine learning-based dependency parsing by providing a standard benchmark set comprising thirteen languages. In this paper, we describe two different machine learning approaches to the CoNLL-X shared task.

A novel independence test for somatic alterations in cancer shows that biology drives mutual exclusivity but chance explains most co-occurrence

In cancer, mutually exclusive or co-occurring somatic alterations across genes can suggest functional interactions. Existing tests for such patterns make the unrealistic assumption of identical gene alteration probabilities across tumors. We present Discrete Independence Statistic Controlling for Observations with Varying Event Rates (DISCOVER), a novel test that is more sensitive than other methods and controls its false positive rate. A pan-cancer analysis using DISCOVER finds no evidence for widespread co-occurrence, and most co-occurrences previously detected do not exceed expectation by chance. Many mutual exclusivities are identified involving well-known genes related to cell cycle and growth factor signaling, as well as lesser known regulators of Hedgehog signaling.

Improved morpho-phonological sequence processing with constraint satisfaction inference

In performing morpho-phonological sequence processing tasks, such as letter-phoneme conversion or morphological analysis, it is typically not enough to base the output sequence on local decisions that map local-context input windows to single output tokens. We present a global sequence-processing method that repairs inconsistent local decisions. The approach is based on local predictions of overlapping trigrams of output tokens, which open up a space of possible sequences; a data-driven constraint satisfaction inference step then searches for the optimal output sequence. We demonstrate significant improvements in terms of word accuracy on English and Dutch letter-phoneme conversion and morphological segmentation, and we provide qualitative analyses of error types prevented by the constraint satisfaction inference method.

Association of breast cancer risk with genetic variants showing differential allelic expression: Identification of a novel breast cancer susceptibility locus at 4q21

There are significant inter-individual differences in the levels of gene expression. Through modulation of gene expression, cis-acting variants represent an important source of phenotypic variation. Consequently, cis-regulatory SNPs associated with differential allelic expression are functional candidates for further investigation as disease-causing variants. To investigate whether common variants associated with differential allelic expression were involved in breast cancer susceptibility, a list of genes was established on the basis of their involvement in cancer related pathways and/or mechanisms. Thereafter, using data from a genome-wide map of allelic expression associated SNPs, 313 genetic variants were selected and their association with breast cancer risk was then evaluated in 46,451 breast cancer cases and 42,599 controls of European ancestry ascertained from 41 studies participating in the Breast Cancer Association Consortium. The associations were evaluated with overall breast cancer risk and with estrogen receptor negative and positive disease. One novel breast cancer susceptibility locus on 4q21 (rs11099601) was identified (OR = 1.05, P = 5.6x10-6). rs11099601 lies in a 135 kb linkage disequilibrium block containing several genes, including, HELQ, encoding the protein HEL308 a DNA dependant ATPase and DNA Helicase involved in DNA repair, MRPS18C encoding the Mitochondrial Ribosomal Protein S18C and FAM175A (ABRAXAS), encoding a BRCA1 BRCT domain-interacting protein involved in DNA damage response and double-strand break (DSB) repair. Expression QTL analysis in breast cancer tissue showed rs11099601 to be associated with HELQ (P = 8.28x10-14), MRPS18C (P = 1.94x10-27) and FAM175A (P = 3.83x10-3), explaining about 20%, 14% and 1%, respectively of the variance inexpression of these genes in breast carcinomas.

proSQLite: Prolog File Based Databases via an SQLite Interface

We present a succinct yet powerful interface library to the SQLite database system. The single file, server-less approach of SQLite along with the natural integration of relational data within Prolog, render the library a useful addition to the existing database libraries in modern open-source engines. We detail the architecture and predicates of the library and provide example deployment scenarios. A simple bioinformatics example is presented throughout to illustrate proSQLitets main functions. Finally, this paper discusses the strengths of the system and highlights possible extensions.

Sponge-supported cultures of primary head and neck tumors for an optimized preclinical model

Treatment of advanced head and neck cancer is associated with low survival, high toxicity and a widely divergent individual response. The sponge-gel-supported histoculture model was previously developed to serve as a preclinical model for predicting individual treatment responses. We aimed to optimize the sponge-gel-supported histoculture model and provide more insight in cell specific behaviour by evaluating the tumor and its microenvironment using immunohistochemistry. We collected fresh tumor biopsies from 72 untreated patients and cultured them for 7 days. Biopsies from 57 patients (79%) were successfully cultured and 1451 tumor fragments (95.4%) were evaluated. Fragments were scored for percentage of tumor, tumor viability and proliferation, EGF-receptor expression and presence of T-cells and macrophages. Median tumor percentage increased from 53% at day 0 to 80% at day 7. Viability and proliferation decreased after 7 days, from 90% to 30% and from 30% to 10%, respectively. Addition of EGF, folic acid and hydrocortisone can lead to improved viability and proliferation, however this was not systematically observed. No patient subgroup could be identified with higher culture success rates. Immune cells were still present at day 7, illustrating that the tumor microenvironment is sustained. EGF supplementation did not increase viability and proliferation in patients overexpressing EGF-Receptor.

Molecular characterization of breast and lung tumors by integration of multiple data types with sparse-factor analysis

Effective cancer treatment is crucially dependent on the identification of the biological processes that drive a tumor. However, multiple processes may be active simultaneously in a tumor. Clustering is inherently unsuitable to this task as it assigns a tumor to a single cluster. In addition, the wide availability of multiple data types per tumor provides the opportunity to profile the processes driving a tumor more comprehensively. Here we introduce Functional Sparse-Factor Analysis (funcSFA) to address these challenges. FuncSFA integrates multiple data types to define a lower dimensional space capturing the relevant variation. A tailor-made module associates biological processes with these factors. FuncSFA is inspired by iCluster, which we improve in several key aspects. First, we increase the convergence efficiency significantly, allowing the analysis of multiple molecular datasets that have not been pre-matched to contain only concordant features. Second, FuncSFA does not assign tumors to discrete clusters, but identifies the dominant driver processes active in each tumor. This is achieved by a regression of the factors on the RNA expression data followed by a functional enrichment analysis and manual curation step. We apply FuncSFA to the TCGA breast and lung datasets. We identify EMT and Immune processes common to both cancer types. In the breast cancer dataset we recover the known intrinsic subtypes and identify additional processes. These include immune infiltration and EMT, and processes driven by copy number gains on the 8q chromosome arm. In lung cancer we recover the major types (adenocarcinoma and squamous cell carcinoma) and processes active in both of these types. These include EMT, two immune processes, and the activity of the NFE2L2 transcription factor. In summary, FuncSFA is a robust method to perform discovery of key driver processes in a collection of tumors through unsupervised integration of multiple molecular data types and functional annotation. Author Summary In order to select effective cancer treatment, we need to determine which biological processes are active in a tumor. To this end, tumors have been quantified by high dimensional molecular measurements such as RNA sequencing and DNA copy number profiling. In order to support decision making, these measurements need to be condensed into interpretable summaries. Such summaries can be made interpretable by connecting them to biological processes. Biological process activity is continuous and multiple biological processes are taking place in a single tumor. Therefore, the biological processes associated with a tumor are misrepresented by clustering, which tries to put every tumor in a single cluster. In the method introduced in this paper (funcSFA), molecular measurements are summarized into a small number factors. A factor is a continuous value per tumor that aims to represent the activity of a biological process. When applied to breast and lung cancer, funcSFA identifies factors covering well known biology of these tumor types. FuncSFA also finds novel factors covering biology whose importance is not yet widely recognized in these tumor types. Some of the factors suggest treatment opportunities that can be further investigated in cell lines and mice.

Abstract P4-09-05: Microarray anlyses of breast cancers identify CH25H, a cholesterol gene, as a potential marker and target for late metastatic reccurences.

Background: However hormone receptor–positive, early-stage breast cancer is a disease with a long natural history and improved survival with 5-year adjuvant endocrine treatments. Yet late recurrence remains an important issue in adjuvant therapy. Predictors of late recurrence are not yet well characterized. Method: A total of 252 breast primary tumors were selected at the Netherlands Cancer Institute from retrospective series of ER+, HER2− breast cancer patients with a follow-up of at least 10 years. Gene expression analysis was performed using Agilent 4×44K microarrays. In order to identify genes associated to late survival differences, we used the survdiff function implemented in the R package survival and we set the parameter rho to −1 to give weight to the later part of the survival curve. The survdiff function was applied to each probe individually for DMFS time considering the probe as a covariate dichotomized into 2 groups (above and below the median expression across all samples). The parameter “strata” was used to stratify the 140 patients based on additional clinico-pathological parameters (Grade, Diameter, Lymph node status and MammaPrint), in order to find genes that add prognostic value to those parameters already known. This approach uses the distant-metastasis free survival (DMFS) time as a continuous variable. Results: After univariate analysis, MammaPrint, diameter, lymph node status and grade were significantly associated to late DMFS differences (Chi-square test p-values equal to 0.016, 0.004, In order to independently validate the prognostic power of these two genes, we tested their performance in the validation set of treated patients (n = 112) and in three publicly available datasets. In all datasets, the CH25H gene confirmed to be significantly associated to metastasis-free survival time in all tested series. Conclusions: These results might indicate that CH25H is an independent marker of late metastatic relapses. CH25H catalyzes the formation of 25-hydroxycholesterol from cholesterol, leading to repress cholesterol biosynthetic enzymes. In the last years, it is emerging that lipid metabolism plays an important role in breast cancer development and progression. Taken together, these findings make the CH25H gene a potential target for late metastasis control in breast cancer. These results warrant further prospective investigation and functional characterization of CH25H in this setting. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P4-09-05.