Biomedical subjects

Lodewyk F A Wessels

Publications and source records attributed to Lodewyk F A Wessels.

12 recordsLinked to original sources

PubMed2026

C-Terminal Truncation and Fusion Partner Determine Oncogenicity of FGFR3.

UNLABELLED: Genomic alterations affecting components of the fibroblast growth factor (FGF) signaling axis can trigger aberrant pathway activation and tumor development. Genomic truncation of the FGF receptor 2 (FGFR2) exon 18 (E18) disrupts the FGFR2 carboxy (C)-terminal tail, acting as a potent driver alteration across multiple tumor types. In this study, we analyzed human oncogenomic datasets to reveal that E18 truncations are similarly prevalent in FGFR3, an FGFR2 paralog. FGFR3 E18 truncations primarily occur due to rearrangements (RE) that involve transforming acidic coiled-coil-containing protein 3 (TACC3), resulting in FGFR3ΔE18-TACC3 gene fusions. In contrast to E18-truncated FGFR2, functional in vitro and in vivo examination of Fgfr3 variants demonstrated that the truncation of Fgfr3 E18 is insufficient to promote oncogenic activity in cell lines or in the lungs and mammary glands of mice. Only the combination of an Fgfr3 E18 truncation with a RE partner gene that encodes a receptor-dimerizing domain resulted in the development of tumors, which were sensitive to FGFR inhibition. Overall, these findings suggest that patients with cancers that are positive for rearranged FGFR3, resulting in E18 truncation and a fusion to dimerizing partners, should be considered for FGFR-targeted therapies. SIGNIFICANCE: FGFR3, unlike its paralog FGFR2, requires both a C-terminal truncation and fusion to a partner gene that retains the expression of a dimerizing domain to effectively drive oncogenic signaling and tumorigenesis.

Receptor, Fibroblast Growth Factor, Type 3↗

PubMed2006

Converting a breast cancer microarray signature into a high-throughput diagnostic test.

BACKGROUND: A 70-gene tumor expression profile was established as a powerful predictor of disease outcome in young breast cancer patients. This profile, however, was generated on microarrays containing 25,000 60-mer oligonucleotides that are not designed for processing of many samples on a routine basis. RESULTS: To facilitate its use in a diagnostic setting, the 70-gene prognosis profile was translated into a customized microarray (MammaPrint) containing a reduced set of 1,900 probes suitable for high throughput processing. RNA of 162 patient samples from two previous studies was subjected to hybridization to this custom array to validate the prognostic value. Classification results obtained from the original analysis were then compared to those generated using the algorithms based on the custom microarray and showed an extremely high correlation of prognosis prediction between the original data and those generated using the custom mini-array (p < 0.0001). CONCLUSION: In this report we demonstrate for the first time that microarray technology can be used as a reliable diagnostic tool. The data clearly demonstrate the reproducibility and robustness of the small custom-made microarray. The array is therefore an excellent tool to predict outcome of disease in breast cancer patients.

Biomarkers, Tumor↗

PubMed2006

Cross-validated Cox regression on microarray gene expression data.

This paper describes how penalized Cox regression, in combination with cross-validated partial likelihood can be employed to obtain reliable survival prediction models for high dimensional microarray data. The suggested procedure is demonstrated on a breast cancer survival data set consisting of 295 tumours as collected in the National Cancer Institute in Amsterdam and previously reported in more general papers. The main aim of this paper it to show how generally accepted biostatistical procedures can be employed to analyse high-dimensional data.

Breast Neoplasms↗

PubMed2006

A comparison of univariate and multivariate gene selection techniques for classification of cancer datasets.

BACKGROUND: Gene selection is an important step when building predictors of disease state based on gene expression data. Gene selection generally improves performance and identifies a relevant subset of genes. Many univariate and multivariate gene selection approaches have been proposed. Frequently the claim is made that genes are co-regulated (due to pathway dependencies) and that multivariate approaches are therefore per definition more desirable than univariate selection approaches. Based on the published performances of all these approaches a fair comparison of the available results can not be made. This mainly stems from two factors. First, the results are often biased, since the validation set is in one way or another involved in training the predictor, resulting in optimistically biased performance estimates. Second, the published results are often based on a small number of relatively simple datasets. Consequently no generally applicable conclusions can be drawn. RESULTS: In this study we adopted an unbiased protocol to perform a fair comparison of frequently used multivariate and univariate gene selection techniques, in combination with a ränge of classifiers. Our conclusions are based on seven gene expression datasets, across several cancer types. CONCLUSION: Our experiments illustrate that, contrary to several previous studies, in five of the seven datasets univariate selection approaches yield consistently better results than multivariate approaches. The simplest multivariate selection approach, the Top Scoring method, achieves the best results on the remaining two datasets. We conclude that the correlation structures, if present, are difficult to extract due to the small number of samples, and that consequently, overly-complex gene selection algorithms that attempt to extract these structures are prone to overtraining.

Algorithms↗

PubMed2006

Predicting a local recurrence after breast-conserving therapy by gene expression profiling.

INTRODUCTION: To tailor local treatment in breast cancer patients there is a need for predicting ipsilateral recurrences after breast-conserving therapy. After adequate treatment (excision with free margins and radiotherapy), young age and incompletely excised extensive intraductal component are predictors for local recurrence, but many local recurrences can still not be predicted. Here we have used gene expression profiling by microarray analysis to identify gene expression profiles that can help to predict local recurrence in individual patients. METHODS: By using previously established gene expression profiles with proven value in predicting metastasis-free and overall survival (wound-response signature, 70-gene prognosis profile and hypoxia-induced profile) and training towards an optimal prediction of local recurrences in a training series, we establish a classifier for local recurrence after breast-conserving therapy. RESULTS: Validation of the different gene lists shows that the wound-response signature is able to separate patients with a high (29%) or low (5%) risk of a local recurrence at 10 years (sensitivity 87.5%, specificity 75%). In multivariable analysis the classifier is an independent predictor for local recurrence. CONCLUSION: Our findings indicate that gene expression profiling can identify subgroups of patients at increased risk of developing a local recurrence after breast-conserving therapy.

Adult↗

PubMed2005

Changes in gene expression associated with response to neoadjuvant chemotherapy in breast cancer.

PURPOSE: At present, clinically useful markers predicting response of primary breast carcinomas to either doxorubicin-cyclophosphamide (AC) or doxorubicin-docetaxel (AD) are lacking. We investigated whether gene expression profiles of the primary tumor could be used to predict treatment response to either of those chemotherapy regimens. PATIENTS AND METHODS: Within a single-institution, randomized, phase II trial, patients with locally advanced breast cancer received six courses of either AC (n = 24) or AD (n = 24) neoadjuvant chemotherapy. Gene expression profiles were generated from core-needle biopsies obtained before treatment and correlated with the response of the primary tumor to the chemotherapy administered. Additionally, pretreatment gene expression profiles were compared with those in tumors remaining after chemotherapy. RESULTS: Ten (20%) of 48 patients showed a (near) pathologic complete remission of the primary tumor after treatment. No gene expression pattern correlating with response could be identified for all patients or for the AC or AD groups separately. The comparison of the pretreatment biopsy and the tumor excised after chemotherapy revealed differences in gene expression in tumors that showed a partial remission but not in tumors that did not respond to chemotherapy. CONCLUSION: No gene expression profile predicting the response of primary breast carcinomas to AC- or AD-based neoadjuvant chemotherapy could be detected in this interim analysis. More subtle differences in gene expression are likely to be present but can only be reliably identified by studying a larger group of patients. Response of a breast tumor to neoadjuvant chemotherapy results in alterations in gene expression.

Adult↗

PubMed2005

A protocol for building and evaluating predictors of disease state based on microarray data.

MOTIVATION: Microarray gene expression data are increasingly employed to identify sets of marker genes that accurately predict disease development and outcome in cancer. Many computational approaches have been proposed to construct such predictors. However, there is, as yet, no objective way to evaluate whether a new approach truly improves on the current state of the art. In addition no 'standard' computational approach has emerged which enables robust outcome prediction. RESULTS: An important contribution of this work is the description of a principled training and validation protocol, which allows objective evaluation of the complete methodology for constructing a predictor. We review the possible choices of computational approaches, with specific emphasis on predictor choice and reporter selection strategies. Employing this training-validation protocol, we evaluated different reporter selection strategies and predictors on six gene expression datasets of varying degrees of difficulty. We demonstrate that simple reporter selection strategies (forward filtering and shrunken centroids) work surprisingly well and outperform partial least squares in four of the six datasets. Similarly, simple predictors, such as the nearest mean classifier, outperform more complex classifiers. Our training-validation protocol provides a robust methodology to evaluate the performance of new computational approaches and to objectively compare outcome predictions on different datasets.

Algorithms↗

PubMed2005

Comparative genomic hybridization profiles in human BRCA1 and BRCA2 breast tumors highlight differential sets of genomic aberrations.

BRCA1 or BRCA2 germline mutations cause approximately 30% of breast cancers within high-risk families. This represents 5% of total breast cancer incidence. Although BRCA1 and BRCA2 are both implicated in DNA repair and genome stability, it is unknown whether BRCA1 and BRCA2 are associated with similar or distinct diseases. In a previous study we reported that BRCA1-related breast carcinomas show a distinct genomic profile as determined by comparative genomic hybridization (CGH). We now hypothesize that, if functionally equivalent, mutations in BRCA1 and BRCA2 would result in similar genomic profiles in tumors. Here we report the chromosomal gains and losses as measured by CGH in 25 BRCA2-associated breast tumors and compared them with our existing 36 BRCA1 and 30 control profiles. We compared all chromosomal regions and determined the regions of differential gain or loss between tumor classes and controls. BRCA2 and control tumors have very similar genomic profiles. As a consequence, and in contrast to BRCA1-associated tumors, CGH profiles from BRCA2-associated tumors could not be distinguished from control tumors using the classification methodology as we have developed before. The largest number of significant differences existed between BRCA1 and controls, followed by BRCA1 compared with BRCA2, suggesting different tumor development pathways for BRCA1 and BRCA2.

Breast Neoplasms↗

PubMed2005

An expression profile for diagnosis of lymph node metastases from primary head and neck squamous cell carcinomas.

Metastasis is the process by which cancers spread to distinct sites in the body. It is the principal cause of death in individuals suffering from cancer. For some types of cancer, early detection of metastasis at lymph nodes close to the site of the primary tumor is pivotal for appropriate treatment. Because it can be difficult to detect lymph node metastases reliably, many individuals currently receive inappropriate treatment. We show here that DNA microarray gene-expression profiling can detect lymph node metastases for primary head and neck squamous cell carcinomas that arise in the oral cavity and oropharynx. The predictor, established with an 82-tumor training set, outperforms current clinical diagnosis when independently validated. The 102 predictor genes offer unique insights into the processes underlying metastasis. The results show that the metastatic state can be deciphered from the primary tumor gene-expression pattern and that treatment can be substantially improved.

Carcinoma, Squamous Cell↗

PubMed2004

Gene expression profiling in follicular lymphoma to assess clinical aggressiveness and to guide the choice of treatment.

Follicular lymphoma (FL) is a disease characterized by a long clinical course marked by frequent relapses that vary in clinical aggressiveness over time. Therefore, the main dilemma at each relapse is the choice for the most effective treatment for optimal disease control and failure-free survival while at the same time avoiding overtreatment and harmful side effects. The selection for more aggressive treatment is currently based on histologic grading and clinical criteria; however, in up to 30% of all cases these methods prove to be insufficient. Using supervised classification on a training set of paired samples from patients who experienced either an indolent or aggressive disease course, a gene expression profile of 81 genes was established that could, with an accuracy of 100%, distinguish low-grade from high-grade disease. This profile accurately classified 93% of the FL samples in an independent validation set. Most important, in a third series of FL cases where histologic grading was ambiguous, precluding meaningful morphologic guidance, the 81-gene profile shows a classification accuracy of 94%. The FL stratification profile is a more reliable marker of clinical behavior than the currently used histologic grading and clinical criteria and may provide an important alternative to guide the choice of therapy in patients with FL both at presentation and at relapse.

Adult↗

PubMed2003

Gene expression profiles of primary breast tumors maintained in distant metastases.

It has been debated for decades how cancer cells acquire metastatic capability. It is unclear whether metastases are derived from distinct subpopulations of tumor cells within the primary site with higher metastatic potential, or whether they originate from a random fraction of tumor cells. Here we show, by gene expression profiling, that human primary breast tumors are strikingly similar to the distant metastases of the same patient. Unsupervised hierarchical clustering, multidimensional scaling, and permutation testing, as well as the comparison of significantly expressed genes within a pair, reveal their genetic similarity. Our findings suggest that metastatic capability in breast cancer is an inherent feature and is not based on clonal selection.

Breast Neoplasms↗

PubMed2002

Molecular classification of breast carcinomas by comparative genomic hybridization: a specific somatic genetic profile for BRCA1 tumors.

In approximately 70% of the families with a high frequency of early-onset breast and/or ovarian cancer, BRCA1 or BRCA2 germline mutations cannot be identified with the current screening regime. Therefore, we used data mining to identify a somatic genetic signature to differentiate BRCA1 mutation carriers from non-BRCA1 carriers based on the genetic characteristics of their breast carcinomas. For this purpose, we developed a molecular classifier, which assigns a given tumor to either the BRCA1 or control group based on somatic genetic profiles as revealed by comparative genomic hybridization. This was performed on breast tumors selected from two groups of patients: 28 proven BRCA1 germline mutation carriers; and a control group consisting of 42 breast tumors from patients with unknown BRCA1 or BRCA2 status. We show that BRCA1 breast carcinomas exhibit specific somatic genetic aberrations and can be distinguished from control tumors with an accuracy of 84% (sensitivity of 96% and specificity of 76%). Chromosomal bands used by this classifier include regions on chromosomes 3p, 3q, and 5q. The classifier miss-assigned one patient with a BRCA1 mutation to the non-BRCA1 class. The germline mutation in this patient is a 62bp deletion in the last exon of BRCA1 (5622del62). Possibly, this mutation may give a different phenotypic effect than do mutations in other regions of the gene. Validation on an independent set of BRCA1 and sporadic tumors showed that the BRCA1 classifier correctly identified all 6 BRCA1 tumors and assigned 4 of the 19 control patients to the BRCA1 class. The resulting accuracy on the validation set is 84%.

Breast Neoplasms↗