Lodewyk F A Wessels

Netherlands Cancer Institute, Amsterdamo, North Holland, Netherlands

Are you Lodewyk F A Wessels?

Claim your profile

Publications (151)1210.57 Total impact

  • Source
  • [Show abstract] [Hide abstract]
    ABSTRACT: RIP140 is a transcriptional coregulator involved in energy homeostasis, ovulation and mammary gland development. Although conclusive evidence is lacking, reports have implicated a role for RIP140 in breast cancer. Here, we explored the mechanistic role of RIP140 in breast cancer and its involvement in Estrogen Receptor alpha (ERα) transcriptional regulation of gene expression. Using ChIP-seq analysis, we demonstrate that RIP140 shares over 80% of its binding sites with ERα, colocalizing with its interaction partners FOXA1, GATA3, p300, CBP and p160 family members at H3K4me1-demarcated enhancer regions. RIP140 is required for ERα-complex formation, ERα-mediated gene expression and ERα-dependent breast cancer cell proliferation. Genes affected following RIP140 silencing could be used stratify tamoxifen-treated breast cancer cohorts, based on clinical outcome. Importantly, this gene signature was only effective in endocrine treated conditions. Cumulatively, our data suggests that RIP140 plays an important role in ERα-mediated transcriptional regulation in breast cancer and response to tamoxifen treatment.
    Cancer research. 08/2014;
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: 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.
    Hormones & cancer. 08/2014;
  • [Show abstract] [Hide abstract]
    ABSTRACT: Tamoxifen is one of the most widely used endocrine agents for the treatment of estrogen receptor α (ERα)-positive breast cancer. Although effective in most patients, resistance to tamoxifen is a clinically significant problem and the mechanisms responsible remain elusive. To address this problem, we performed a large scale loss-of-function genetic screen in ZR-75-1 luminal breast cancer cells to identify candidate resistance genes. In this manner, we found that loss of function in the deubiquitinase USP9X prevented proliferation arrest by tamoxifen, but not by the ER downregulator fulvestrant. RNAi-mediated attenuation of USP9X was sufficient to stabilize ERα on chromatin in the presence of tamoxifen, causing a global tamoxifen-driven activation of ERα-responsive genes. Using a gene signature defined by their differential expression after USP9X attenuation in the presence of tamoxifen, we were able to define patients with ERα-positive breast cancer experiencing a poor outcome after adjuvant treatment with tamoxifen. The signature was specific in its lack of correlation with survival in patients with breast cancer who did not receive endocrine therapy. Overall, our findings identify a gene signature as a candidate biomarker of response to tamoxifen in breast cancer. Cancer Res; 74(14); 3810-20. ©2014 AACR.
    Cancer research. 07/2014; 74(14):3810-20.
  • [Show abstract] [Hide abstract]
    ABSTRACT: The influence of local chromatin context on gene expression can be explored by integrating a transcription reporter at different locations in the genome as a sensor. Here we provide a detailed protocol for analyzing thousands of reporters integrated in parallel (TRIP) at a genome-wide level. TRIP is based on tagging each reporter with a unique barcode, which is used for independent reporter expression analysis and integration site mapping. Compared with previous methods for studying position effects, TRIP offers a 100-1,000-fold higher throughput in a faster and less-labor-intensive manner. The entire experimental protocol takes ∼42 d to complete, with high-throughput sequencing and data analysis requiring an additional ∼11 d. TRIP was developed by using transcription reporters in mouse embryonic stem (mES) cells, but because of its flexibility the method can be used to probe the influence of chromatin context on a variety of molecular processes in any transfectable cell line.
    Nature Protocol 06/2014; 9(6):1255-81. · 8.36 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Genomic information is encoded on a wide range of distance scales, ranging from tens of bases to megabases. We developed a multiscale framework to analyze and visualize the information content of genomic signals. Different types of signals, such as G+C content or DNA methylation, are characterized by distinct patterns of signal enrichment or depletion across scales spanning several orders of magnitude. These patterns are associated with a variety of genomic annotations. By integrating the information across all scales, we demonstrated improved prediction of gene expression from polymerase II chromatin immunoprecipitation sequencing (ChIP-seq) measurements, and we observed that gene expression differences in colorectal cancer are related to methylation patterns that extend beyond the single-gene scale. Our software is available at https://github.com/tknijnen/msr/.
    Nature Methods 04/2014; · 23.57 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: There are no effective therapies for the ∼30% of human malignancies with mutant RAS oncogenes. Using a kinome-centered synthetic lethality screen, we find that suppression of the ERBB3 receptor tyrosine kinase sensitizes KRAS mutant lung and colon cancer cells to MEK inhibitors. We show that MEK inhibition results in MYC-dependent transcriptional upregulation of ERBB3, which is responsible for intrinsic drug resistance. Drugs targeting both EGFR and ERBB2, each capable of forming heterodimers with ERBB3, can reverse unresponsiveness to MEK inhibition by decreasing inhibitory phosphorylation of the proapoptotic proteins BAD and BIM. Moreover, ERBB3 protein level is a biomarker of response to combinatorial treatment. These data suggest a combination strategy for treating KRAS mutant colon and lung cancers and a way to identify the tumors that are most likely to benefit from such combinatorial treatment.
    Cell Reports. 04/2014; 7(1):86-93.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Treatment of BRAF(V600E) mutant melanoma by small molecule drugs that target the BRAF or MEK kinases can be effective, but resistance develops invariably1, 2. In contrast, colon cancers that harbour the same BRAF(V600E) mutation are intrinsically resistant to BRAF inhibitors, due to feedback activation of the epidermal growth factor receptor (EGFR)3, 4. Here we show that 6 out of 16 melanoma tumours analysed acquired EGFR expression after the development of resistance to BRAF or MEK inhibitors. Using a chromatin-regulator-focused short hairpin RNA (shRNA) library, we find that suppression of sex determining region Y-box 10 (SOX10) in melanoma causes activation of TGF-β signalling, thus leading to upregulation of EGFR and platelet-derived growth factor receptor-β (PDGFRB), which confer resistance to BRAF and MEK inhibitors. Expression of EGFR in melanoma or treatment with TGF-β results in a slow-growth phenotype with cells displaying hallmarks of oncogene-induced senescence. However, EGFR expression or exposure to TGF-β becomes beneficial for proliferation in the presence of BRAF or MEK inhibitors. In a heterogeneous population of melanoma cells having varying levels of SOX10 suppression, cells with low SOX10 and consequently high EGFR expression are rapidly enriched in the presence of drug, but this is reversed when the drug treatment is discontinued. We find evidence for SOX10 loss and/or activation of TGF-β signalling in 4 of the 6 EGFR-positive drug-resistant melanoma patient samples. Our findings provide a rationale for why some BRAF or MEK inhibitor-resistant melanoma patients may regain sensitivity to these drugs after a ‘drug holiday’ and identify patients with EGFR-positive melanoma as a group that may benefit from re-treatment after a drug holiday.
    Nature 04/2014; 508(7494):118-22. · 38.60 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Retroviral insertional mutagenesis is a powerful tool for identifying putative cancer genes in mice. To uncover the regulatory mechanisms by which common insertion loci affect downstream processes, we supplemented genotyping data with genome-wide mRNA expression profiling data for 97 tumors induced by retroviral insertional mutagenesis. We developed locus expression signature analysis, an algorithm to construct and interpret the differential gene expression signature associated with each common insertion locus. Comparing locus expression signatures to promoter affinity profiles allowed us to build a detailed map of transcription factors whose protein-level regulatory activity is modulated by a particular locus. We also predicted a large set of drugs that might mitigate the effect of the insertion on tumorigenesis. Taken together, our results demonstrate the potential of a locus-specific signature approach for identifying mammalian regulatory mechanisms in a cancer context.
    Proceedings of the National Academy of Sciences 04/2014; · 9.81 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The ability of retroviruses and transposons to insert their genetic material into host DNA makes them widely used tools in molecular biology, cancer research and gene therapy. However, these systems have biases that may strongly affect research outcomes. To address this issue, we generated very large datasets consisting of [Formula: see text] to [Formula: see text] unselected integrations in the mouse genome for the Sleeping Beauty (SB) and piggyBac (PB) transposons, and the Mouse Mammary Tumor Virus (MMTV). We analyzed [Formula: see text] (epi)genomic features to generate bias maps at both local and genome-wide scales. MMTV showed a remarkably uniform distribution of integrations across the genome. More distinct preferences were observed for the two transposons, with PB showing remarkable resemblance to bias profiles of the Murine Leukemia Virus. Furthermore, we present a model where target site selection is directed at multiple scales. At a large scale, target site selection is similar across systems, and defined by domain-oriented features, namely expression of proximal genes, proximity to CpG islands and to genic features, chromatin compaction and replication timing. Notable differences between the systems are mainly observed at smaller scales, and are directed by a diverse range of features. To study the effect of these biases on integration sites occupied under selective pressure, we turned to insertional mutagenesis (IM) screens. In IM screens, putative cancer genes are identified by finding frequently targeted genomic regions, or Common Integration Sites (CISs). Within three recently completed IM screens, we identified 7%-33% putative false positive CISs, which are likely not the result of the oncogenic selection process. Moreover, results indicate that PB, compared to SB, is more suited to tag oncogenes.
    PLoS Genetics 04/2014; 10(4):e1004250. · 8.52 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Lymph-node metastasis (LNM) predict high recurrence rates in breast cancer patients. Systemic treatment aims to eliminate (micro)metastatic cells. However decisions regarding systemic treatment depend largely on clinical and molecular characteristics of primary tumours. It remains, however, unclear to what extent metastases resemble the cognate primary breast tumours, especially on a genomic level, and as such will be eradicated by the systemic therapy chosen. In this study we used high-resolution aCGH to investigate DNA copy number differences between primary breast cancers and their paired LNMs. To date, no recurrent LNM-specific genomic aberrations have been identified using array comparative genomic hybridization (aCGH) analysis. In our study we employ a high-resolution platform and we stratify on different breast cancer subtypes, both aspects that might have underpowered previously performed studies.To test the possibility that genomic instability in triple-negative breast cancers (TNBCs) might cause increased random and potentially also recurrent copy number aberrations (CNAs) in their LNMs, we studied 10 primary TNBC-LNM pairs and 10 ER-positive (ER+) pairs and verified our findings adding additionally 5 TNBC-LNM and 22 ER+-LNM pairs. We found that all LNMs clustered nearest to their matched tumour except for two cases, of which one was due to the presence of two distinct histological components in one tumour. We found no significantly altered CNAs between tumour and their LNMs in the entire group or in the subgroups. Within the TNBC subgroup, no absolute increase in CNAs was found in the LNMs compared to their primary tumours, suggesting that increased genomic instability does not lead to more CNAs in LNMs. Our findings suggest a high clonal relationship between primary breast tumours and its LNMs, at least prior to treatment, and support the use of primary tumour characteristics to guide adjuvant systemic chemotherapy in breast cancer patients.
    PLoS ONE 01/2014; 9(8):e103177. · 3.53 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Despite continuous efforts, not a single predictor of breast cancer chemotherapy resistance has made it into the clinic yet. However, it has become clear in recent years that breast cancer is a collection of molecularly distinct diseases. With ever increasing amounts of breast cancer data becoming available, we set out to study if gene expression based predictors of chemotherapy resistance that are specific for breast cancer subtypes can improve upon the performance of generic predictors. We trained predictors of resistance that were specific for a subtype and generic predictors that were not specific for a particular subtype, i.e. trained on all subtypes simultaneously. Through a rigorous double-loop cross-validation we compared the performance of these two types of predictors on the different subtypes on a large set of tumors all profiled on the same expression platform (n = 394). We evaluated predictors based on either mRNA gene expression or clinical features. For HER2+, ER- breast cancer, subtype specific predictor based on clinical features outperformed the generic, non-specific predictor. This can be explained by the fact that the generic predictor included HER2 and ER status, features that are predictive over the whole set, but not within this subtype. In all other scenarios the generic predictors outperformed the subtype specific predictors or showed equal performance. Since it depends on the specific context which type of predictor - subtype specific or generic- performed better, it is highly recommended to evaluate both specific and generic predictors when attempting to predict treatment response in breast cancer.
    PLoS ONE 01/2014; 9(2):e88551. · 3.53 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The most common risk factor for developing hepatocellular carcinoma (HCC) is chronic infection with hepatitis B virus (HBV). To better understand the evolutionary forces driving HCC, we performed a near-saturating transposon mutagenesis screen in a mouse HBV model of HCC. This screen identified 21 candidate early stage drivers and a very large number (2,860) of candidate later stage drivers that were enriched for genes that are mutated, deregulated or functioning in signaling pathways important for human HCC, with a striking 1,199 genes being linked to cellular metabolic processes. Our study provides a comprehensive overview of the genetic landscape of HCC.
    Nature Genetics 12/2013; · 35.21 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Most of the macromolecular structures in the Protein Data Bank (PDB), which are used daily by thousands of educators and scientists alike, are determined by X-ray crystallography. It was examined whether the crystallographic models and data were deposited to the PDB at the same time as the publications that describe them were submitted for peer review. This condition is necessary to ensure pre-publication validation and the quality of the PDB public archive. It was found that a significant proportion of PDB entries were submitted to the PDB after peer review of the corresponding publication started, and many were only submitted after peer review had ended. It is argued that clear description of journal policies and effective policing is important for pre-publication validation, which is key in ensuring the quality of the PDB and of peer-reviewed literature.
    Acta Crystallographica Section D Biological Crystallography 12/2013; 69(Pt 12):2293-5. · 12.67 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Aromatase inhibitors are the major first-line treatment of estrogen receptor-positive breast cancer, but resistance to treatment is common. To date, no biomarkers have been validated clinically to guide subsequent therapy in these patients. In this study, we mapped the genome-wide chromatin-binding profiles of estrogen receptor α (ERα), along with the epigenetic modifications H3K4me3 and H3K27me3, that are responsible for determining gene transcription (n = 12). Differential binding patterns of ERα, H3K4me3, and H3K27me3 were enriched between patients with good or poor outcomes after aromatase inhibition. ERα and H3K27me3 patterns were validated in an additional independent set of breast cancer cases (n = 10). We coupled these patterns to array-based proximal gene expression and progression-free survival data derived from a further independent cohort of 72 aromatase inhibitor-treated patients. Through this approach, we determined that the ERα and H3K27me3 profiles predicted the treatment outcomes for first-line aromatase inhibitors. In contrast, the H3K4me3 pattern identified was not similarly informative. The classification potential of these genes was only partially preserved in a cohort of 101 patients who received first-line tamoxifen treatment, suggesting some treatment selectivity in patient classification. Cancer Res; 73(22); 6632-41. ©2013 AACR.
    Cancer Research 11/2013; 73(22):6632-41. · 9.28 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Traditional methods that aim to identify biomarkers that distinguish between two groups, like Significance Analysis of Microarrays or the t-test, perform optimally when such biomarkers show homogeneous behavior within each group and differential behavior between the groups. However, in many applications, this is not the case. Instead, a subgroup of samples in one group shows differential behavior with respect to all other samples. To successfully detect markers showing such imbalanced patterns of differential signal, a different approach is required. We propose a novel method, specifically designed for the Detection of Imbalanced Differential Signal (DIDS). We use an artificial dataset and a human breast cancer dataset to measure its performance and compare it with three traditional methods and four approaches that take imbalanced signal into account. Supported by extensive experimental results, we show that DIDS outperforms all other approaches in terms of power and positive predictive value. In a mouse breast cancer dataset, DIDS is the only approach that detects a functionally validated marker of chemotherapy resistance. DIDS can be applied to any continuous value data, including gene expression data, and in any context where imbalanced differential signal is manifested.
    Nucleic Acids Research 09/2013; · 8.81 Impact Factor
  • Source
    Dataset: 2403373a
  • [Show abstract] [Hide abstract]
    ABSTRACT: Reporter genes integrated into the genome are a powerful tool to reveal effects of regulatory elements and local chromatin context on gene expression. However, so far such reporter assays have been of low throughput. Here, we describe a multiplexing approach for the parallel monitoring of transcriptional activity of thousands of randomly integrated reporters. More than 27,000 distinct reporter integrations in mouse embryonic stem cells, obtained with two different promoters, show ∼1,000-fold variation in expression levels. Data analysis indicates that lamina-associated domains act as attenuators of transcription, likely by reducing access of transcription factors to binding sites. Furthermore, chromatin compaction is predictive of reporter activity. We also found evidence for crosstalk between neighboring genes and estimate that enhancers can influence gene expression on average over ∼20 kb. The multiplexed reporter assay is highly flexible in design and can be modified to query a wide range of aspects of gene regulation.
    Cell 08/2013; 154(4):914-27. · 31.96 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In most colorectal cancer (CRC) patients, outcome cannot be predicted because tumors with similar clinicopathological features can have differences in disease progression and treatment response. Therefore a better understanding of the CRC biology is required to identify those patients who will benefit from chemotherapy and to find a more tailored therapy plan for other patients. Based on unsupervised classification of whole genome data from 188 stage I-IV CRC patients, a molecular classification was developed that consist of at least three major intrinsic subtypes (A-, B-, C-type). The subtypes were validated in 543 stage II-III patients and were associated with prognosis and benefit from chemotherapy. The heterogeneity of the intrinsic subtypes is largely based on 3 biological hallmarks of the tumor: epithelial-to-mesenchymal transition, deficiency in mismatch repair genes that result in high mutation frequency associated with MSI, and cellular proliferation. A-type tumors, observed in 22% of the patients, have the best prognosis, have frequent BRAF mutations and a deficient DNA mismatch repair system. C-type patients (16%) have the worst outcome, a mesenchymal gene expression phenotype, and show no benefit from adjuvant chemotherapy treatment. Both A-type and B-type tumors have a more proliferative and epithelial phenotype and B-types benefit from adjuvant chemotherapy. B-type tumors (62%) show a low overall mutation frequency consistent with the absence of DNA mismatch repair deficiency. Classification based on molecular subtypes made it possible to expand and improve CRC classification beyond standard molecular and immunohistochemical assessment and might help in the future to guide treatment in CRC patients. © 2013 Wiley Periodicals, Inc.
    International Journal of Cancer 07/2013; · 6.20 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Intrinsic subtypes are widely accepted for the classification of breast cancer. Lacking gene expression data, surrogate classifications based on immunohistochemistry (IHC) have been proposed. A recent St. Gallen consensus meeting recommends to use this "surrogate intrinsic subtypes" for predicting adjuvant chemotherapy resistance, implying that "Surrogate Luminal A" breast cancers should only receive endocrine therapy. In this study we assessed both gene expression based intrinsic subtypes as well as surrogate intrinsic subtypes regarding their power to predict neoadjuvant chemotherapy benefit. Single institution data of 560 breast cancer patients were reviewed. Gene expression data was available for 247 patients. Subtypes were determined on the basis of IHC, Ki67, histological grade, endocrine responsiveness, and gene expression, and were correlated with chemotherapy response and recurrence-free survival. In ER+/HER2- tumors, a high histological grade was the best predictor for chemotherapy benefit, both in terms of pCR (p = 0.004) and recurrence-free survival (p = 0.002). The gene expression based and surrogate intrinsic subtype based on Ki67 had no predictive or prognostic value in ER+/HER2- tumors. Histological grade, ER, PR, and HER2 were the best predictive factors for chemotherapy response in breast cancer. We propose to continue the conventional use of these markers.
    Breast Cancer Research and Treatment 07/2013; · 4.47 Impact Factor

Publication Stats

6k Citations
1,210.57 Total Impact Points

Institutions

  • 2004–2014
    • Netherlands Cancer Institute
      • • Division of Molecular Carcinogenesis
      • • Division of Experimental Therapy
      • • Division of Molecular Biology
      • • Division of Molecular Genetics
      Amsterdamo, North Holland, Netherlands
  • 1999–2014
    • Delft University of Technology
      • • Faculty of Electrical Engineering, Mathematics and Computer Sciences (EEMCS)
      • • Department of Biotechnology
      Delft, South Holland, Netherlands
  • 2012
    • Centrum Wiskunde & Informatica
      Amsterdamo, North Holland, Netherlands
  • 2006
    • Leiden University Medical Centre
      • Department of Medical Statistics and Bioinformatics
      Leiden, South Holland, Netherlands
    • Slotervaartziekenhuis
      Amsterdamo, North Holland, Netherlands