Bio

Academic Appointments


Honors & Awards


  • Honorary Fellow, Belgian American Educational Foundation (BAEF) (06-01-2009)
  • Henri Benedictus Fellow, King Baudouin Foundation (06-01-2009)
  • Honorary Fulbright Scholar, Commission for Educational Exchange between the United States of America, Belgium and Luxembourg (01-01-2010)

Boards, Advisory Committees, Professional Organizations


  • Member, International Society for Computational Biology (ISCB) (2006 - Present)
  • Member, American Association for Cancer Research (AACR) (2010 - Present)

Professional Education


  • Certificate, Stanford Business School, Stanford Ignite (2012)
  • Ph.D, University of Leuven, Belgium, BIoinformatics (2008)
  • M.S., University of Leuven, Belgium, Artificial Intelligence (2004)
  • M.S., University College, Ghent, Belgium, Electrical Engineering/Computer Science (2003)

Research & Scholarship

Current Research and Scholarly Interests


My research focuses on using advanced machine learning methods to integrate molecular data of cancer patients. These data sources are often called omics such genomics, transcriptomics or proteomics. In addition, currently I'm also investigating strategies to couple these omics data sources with MRI imaging data.

Teaching

2013-14 Courses


Postdoctoral Advisees


Publications

Journal Articles


  • Identification of ovarian cancer driver genes by using module network integration of multi-omics data INTERFACE FOCUS Gevaert, O., Villalobos, V., Sikic, B. I., Plevritis, S. K. 2013; 3 (4)
  • Identifying master regulators of cancer and their downstream targets by integrating genomic and epigenomic features. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Gevaert, O., Plevritis, S. 2013: 123-134

    Abstract

    Vast amounts of molecular data characterizing the genome, epigenome and transcriptome are becoming available for a variety of cancers. The current challenge is to integrate these diverse layers of molecular biology information to create a more comprehensive view of key biological processes underlying cancer. We developed a biocomputational algorithm that integrates copy number, DNA methylation, and gene expression data to study master regulators of cancer and identify their targets. Our algorithm starts by generating a list of candidate driver genes based on the rationale that genes that are driven by multiple genomic events in a subset of samples are unlikely to be randomly deregulated. We then select the master regulators from the candidate driver and identify their targets by inferring the underlying regulatory network of gene expression. We applied our biocomputational algorithm to identify master regulators and their targets in glioblastoma multiforme (GBM) and serous ovarian cancer. Our results suggest that the expression of candidate drivers is more likely to be influenced by copy number variations than DNA methylation. Next, we selected the master regulators and identified their downstream targets using module networks analysis. As a proof-of-concept, we show that the GBM and ovarian cancer module networks recapitulate known processes in these cancers. In addition, we identify master regulators that have not been previously reported and suggest their likely role. In summary, focusing on genes whose expression can be explained by their genomic and epigenomic aberrations is a promising strategy to identify master regulators of cancer.

    View details for PubMedID 23424118

  • Prognostic PET F-18-FDG Uptake Imaging Features Are Associated with Major Oncogenomic Alterations in Patients with Resected Non-Small Cell Lung Cancer CANCER RESEARCH Nair, V. S., Gevaert, O., Davidzon, G., Napel, S., Graves, E. E., Hoang, C. D., Shrager, J. B., Quon, A., Rubin, D. L., Plevritis, S. K. 2012; 72 (15): 3725-3734

    Abstract

    Although 2[18F]fluoro-2-deoxy-d-glucose (FDG) uptake during positron emission tomography (PET) predicts post-surgical outcome in patients with non-small cell lung cancer (NSCLC), the biologic basis for this observation is not fully understood. Here, we analyzed 25 tumors from patients with NSCLCs to identify tumor PET-FDG uptake features associated with gene expression signatures and survival. Fourteen quantitative PET imaging features describing FDG uptake were correlated with gene expression for single genes and coexpressed gene clusters (metagenes). For each FDG uptake feature, an associated metagene signature was derived, and a prognostic model was identified in an external cohort and then tested in a validation cohort of patients with NSCLC. Four of eight single genes associated with FDG uptake (LY6E, RNF149, MCM6, and FAP) were also associated with survival. The most prognostic metagene signature was associated with a multivariate FDG uptake feature [maximum standard uptake value (SUV(max)), SUV(variance), and SUV(PCA2)], each highly associated with survival in the external [HR, 5.87; confidence interval (CI), 2.49-13.8] and validation (HR, 6.12; CI, 1.08-34.8) cohorts, respectively. Cell-cycle, proliferation, death, and self-recognition pathways were altered in this radiogenomic profile. Together, our findings suggest that leveraging tumor genomics with an expanded collection of PET-FDG imaging features may enhance our understanding of FDG uptake as an imaging biomarker beyond its association with glycolysis.

    View details for DOI 10.1158/0008-5472.CAN-11-3943

    View details for Web of Science ID 000307354100004

    View details for PubMedID 22710433

  • Non-Small Cell Lung Cancer: Identifying Prognostic Imaging Biomarkers by Leveraging Public Gene Expression Microarray Data-Methods and Preliminary Results RADIOLOGY Gevaert, O., Xu, J., Hoang, C. D., Leung, A. N., Xu, Y., Quon, A., Rubin, D. L., Napel, S., Plevritis, S. K. 2012; 264 (2): 387-396

    Abstract

    To identify prognostic imaging biomarkers in non-small cell lung cancer (NSCLC) by means of a radiogenomics strategy that integrates gene expression and medical images in patients for whom survival outcomes are not available by leveraging survival data in public gene expression data sets.A radiogenomics strategy for associating image features with clusters of coexpressed genes (metagenes) was defined. First, a radiogenomics correlation map is created for a pairwise association between image features and metagenes. Next, predictive models of metagenes are built in terms of image features by using sparse linear regression. Similarly, predictive models of image features are built in terms of metagenes. Finally, the prognostic significance of the predicted image features are evaluated in a public gene expression data set with survival outcomes. This radiogenomics strategy was applied to a cohort of 26 patients with NSCLC for whom gene expression and 180 image features from computed tomography (CT) and positron emission tomography (PET)/CT were available.There were 243 statistically significant pairwise correlations between image features and metagenes of NSCLC. Metagenes were predicted in terms of image features with an accuracy of 59%-83%. One hundred fourteen of 180 CT image features and the PET standardized uptake value were predicted in terms of metagenes with an accuracy of 65%-86%. When the predicted image features were mapped to a public gene expression data set with survival outcomes, tumor size, edge shape, and sharpness ranked highest for prognostic significance.This radiogenomics strategy for identifying imaging biomarkers may enable a more rapid evaluation of novel imaging modalities, thereby accelerating their translation to personalized medicine.

    View details for DOI 10.1148/radiol.12111607

    View details for Web of Science ID 000306660000010

    View details for PubMedID 22723499

  • A Seven-Gene Set Associated with Chronic Hypoxia of Prognostic Importance in Hepatocellular Carcinoma CLINICAL CANCER RESEARCH Van Malenstein, H., Gevaert, O., Libbrecht, L., Daemen, A., Allemeersch, J., Nevens, F., Van Cutsem, E., Cassiman, D., De Moor, B., Verslype, C., van Pelt, J. 2010; 16 (16): 4278-4288

    Abstract

    Hepatocellular carcinomas (HCC) have an unpredictable clinical course, and molecular classification could provide better insights into prognosis and patient-directed therapy. We hypothesized that in HCC, certain microenvironmental regions exist with a characteristic gene expression related to chronic hypoxia which would induce aggressive behavior.We determined the gene expression pattern for human HepG2 liver cells under chronic hypoxia by microarray analysis. Differentially expressed genes were selected and their clinical values were assessed. In our hypothesis-driven analysis, we included available independent microarray studies of patients with HCC in one single analysis. Three microarray studies encompassing 272 patients were used as training sets to determine a minimal prognostic gene set, and one recent study of 91 patients was used for validation.Using computational methods, we identified seven genes (out of 3,592 differentially expressed under chronic hypoxia) that showed correlation with poor prognostic indicators in all three training sets (65/139/73 patients) and this was validated in a fourth data set (91 patients). Retrospectively, the seven-gene set was associated with poor survival (hazard ratio, 1.39; P = 0.007) and early recurrence (hazard ratio, 2.92; P = 0.007) in 135 patients. Moreover, using a hypoxia score based on this seven-gene set, we found that patients with a score of >0.35 (n = 42) had a median survival of 307 days, whereas patients with a score of < or =0.35 (n = 93) had a median survival of 1,602 days (P = 0.005).We identified a unique, liver-specific, seven-gene signature associated with chronic hypoxia that correlates with poor prognosis in HCCs.

    View details for DOI 10.1158/1078-0432.CCR-09-3274

    View details for Web of Science ID 000280830300024

    View details for PubMedID 20592013

  • Intrinsic Gene Expression Profiles of Gliomas Are a Better Predictor of Survival than Histology CANCER RESEARCH Gravendeel, L. A., Kouwenhoven, M. C., Gevaert, O., de Rooi, J. J., Stubbs, A. P., Duijm, J. E., Daemen, A., Bleeker, F. E., Bralten, L. B., Kloosterhof, N. K., De Moor, B., Eilers, P. H., van der Spek, P. J., Kros, J. M., Smitt, P. A., van den Bent, M. J., French, P. J. 2009; 69 (23): 9065-9072

    Abstract

    Gliomas are the most common primary brain tumors with heterogeneous morphology and variable prognosis. Treatment decisions in patients rely mainly on histologic classification and clinical parameters. However, differences between histologic subclasses and grades are subtle, and classifying gliomas is subject to a large interobserver variability. To improve current classification standards, we have performed gene expression profiling on a large cohort of glioma samples of all histologic subtypes and grades. We identified seven distinct molecular subgroups that correlate with survival. These include two favorable prognostic subgroups (median survival, >4.7 years), two with intermediate prognosis (median survival, 1-4 years), two with poor prognosis (median survival, <1 year), and one control group. The intrinsic molecular subtypes of glioma are different from histologic subgroups and correlate better to patient survival. The prognostic value of molecular subgroups was validated on five independent sample cohorts (The Cancer Genome Atlas, Repository for Molecular Brain Neoplasia Data, GSE12907, GSE4271, and Li and colleagues). The power of intrinsic subtyping is shown by its ability to identify a subset of prognostically favorable tumors within an external data set that contains only histologically confirmed glioblastomas (GBM). Specific genetic changes (epidermal growth factor receptor amplification, IDH1 mutation, and 1p/19q loss of heterozygosity) segregate in distinct molecular subgroups. We identified a subgroup with molecular features associated with secondary GBM, suggesting that different genetic changes drive gene expression profiles. Finally, we assessed response to treatment in molecular subgroups. Our data provide compelling evidence that expression profiling is a more accurate and objective method to classify gliomas than histologic classification. Molecular classification therefore may aid diagnosis and can guide clinical decision making.

    View details for DOI 10.1158/0008-5472.CAN-09-2307

    View details for Web of Science ID 000272362800029

    View details for PubMedID 19920198

  • Recurrent Copy Number Alterations in BRCA1-Mutated Ovarian Tumors Alter Biological Pathways HUMAN MUTATION Leunen, K., Gevaert, O., Daemen, A., Vanspauwen, V., Michils, G., De Moor, B., Moerman, P., Vergote, I., Legius, E. 2009; 30 (12): 1693-1702

    Abstract

    Array CGH was used to identify recurrent copy number alterations (RCNA) characteristic of either BRCA1-related or sporadic ovarian cancer. After preprocessing, both groups of patients were modeled using a recurrent Hidden Markov Model to detect RCNA. RCNA with a probability higher than 80% were called. After removing RCNA present in both groups, the genes present in the remaining RCNA were investigated for enrichment of pathways from external databases. More RCNA were observed in the BRCA1 group, and they display more losses than gains compared to the sporadic group. When focusing on the type of RCNA, no significant difference in length was seen for the gains, but there was a statistically significant difference for the losses. In the sporadic group, a great proportion of the altered regions contain genes known to have a function in cell adhesion and complement activation, whereas the BRCA1 samples are characterized by alterations in the HOX genes, metalloproteinases, tumor suppressor genes, and the estrogen-signaling pathways. We conclude that BRCA1 ovarian tumors present a different type, number, and length of RCNA; a huge amount of the genome is lost, resulting in important genomic instability. Moreover, important biological pathways are altered differentially when compared to the sporadic group.

    View details for DOI 10.1002/humu.21135

    View details for Web of Science ID 000272796400011

    View details for PubMedID 19802895

  • Cross-Species Functional Analysis of Cancer-Associated Fibroblasts Identifies a Critical Role for CLCF1 and IL-6 in Non-Small Cell Lung Cancer In Vivo CANCER RESEARCH Vicent, S., Sayles, L. C., Vaka, D., Khatri, P., Gevaert, O., Chen, R., Zheng, Y., Gillespie, A. K., Clarke, N., Xu, Y., Shrager, J., Hoang, C. D., Plevritis, S., Butte, A. J., Sweet-Cordero, E. A. 2012; 72 (22): 5744-5756

    Abstract

    Cancer-associated fibroblasts (CAF) have been reported to support tumor progression by a variety of mechanisms. However, their role in the progression of non-small cell lung cancer (NSCLC) remains poorly defined. In addition, the extent to which specific proteins secreted by CAFs contribute directly to tumor growth is unclear. To study the role of CAFs in NSCLCs, a cross-species functional characterization of mouse and human lung CAFs was conducted. CAFs supported the growth of lung cancer cells in vivo by secretion of soluble factors that directly stimulate the growth of tumor cells. Gene expression analysis comparing normal mouse lung fibroblasts and mouse lung CAFs identified multiple genes that correlate with the CAF phenotype. A gene signature of secreted genes upregulated in CAFs was an independent marker of poor survival in patients with NSCLC. This secreted gene signature was upregulated in normal lung fibroblasts after long-term exposure to tumor cells, showing that lung fibroblasts are "educated" by tumor cells to acquire a CAF-like phenotype. Functional studies identified important roles for CLCF1-CNTFR and interleukin (IL)-6-IL-6R signaling in promoting growth of NSCLCs. This study identifies novel soluble factors contributing to the CAF protumorigenic phenotype in NSCLCs and suggests new avenues for the development of therapeutic strategies.

    View details for DOI 10.1158/0008-5472.CAN-12-1097

    View details for Web of Science ID 000311141300012

    View details for PubMedID 22962265

  • Evaluation of a panel of 28 biomarkers for the non-invasive diagnosis of endometriosis HUMAN REPRODUCTION Vodolazkaia, A., El-Aalamat, Y., Popovic, D., Mihalyi, A., Bossuyt, X., Kyama, C. M., Fassbender, A., Bokor, A., SCHOLS, D., Huskens, D., Meuleman, C., Peeraer, K., Tomassetti, C., Gevaert, O., Waelkens, E., Kasran, A., De Moor, B., D'Hooghe, T. M. 2012; 27 (9): 2698-2711

    Abstract

    At present, the only way to conclusively diagnose endometriosis is laparoscopic inspection, preferably with histological confirmation. This contributes to the delay in the diagnosis of endometriosis which is 6-11 years. So far non-invasive diagnostic approaches such as ultrasound (US), MRI or blood tests do not have sufficient diagnostic power. Our aim was to develop and validate a non-invasive diagnostic test with a high sensitivity (80% or more) for symptomatic endometriosis patients, without US evidence of endometriosis, since this is the group most in need of a non-invasive test.A total of 28 inflammatory and non-inflammatory plasma biomarkers were measured in 353 EDTA plasma samples collected at surgery from 121 controls without endometriosis at laparoscopy and from 232 women with endometriosis (minimal-mild n = 148; moderate-severe n = 84), including 175 women without preoperative US evidence of endometriosis. Surgery was done during menstrual (n = 83), follicular (n = 135) and luteal (n = 135) phases of the menstrual cycle. For analysis, the data were randomly divided into an independent training (n = 235) and a test (n = 118) data set. Statistical analysis was done using univariate and multivariate (logistic regression and least squares support vector machines (LS-SVM) approaches in training- and test data set separately to validate our findings.In the training set, two models of four biomarkers (Model 1: annexin V, VEGF, CA-125 and glycodelin; Model 2: annexin V, VEGF, CA-125 and sICAM-1) analysed in plasma, obtained during the menstrual phase, could predict US-negative endometriosis with a high sensitivity (81-90%) and an acceptable specificity (68-81%). The same two models predicted US-negative endometriosis in the independent validation test set with a high sensitivity (82%) and an acceptable specificity (63-75%).In plasma samples obtained during menstruation, multivariate analysis of four biomarkers (annexin V, VEGF, CA-125 and sICAM-1/or glycodelin) enabled the diagnosis of endometriosis undetectable by US with a sensitivity of 81-90% and a specificity of 63-81% in independent training- and test data set. The next step is to apply these models for preoperative prediction of endometriosis in an independent set of patients with infertility and/or pain without US evidence of endometriosis, scheduled for laparoscopy.

    View details for DOI 10.1093/humrep/des234

    View details for Web of Science ID 000307502000016

    View details for PubMedID 22736326

  • Combined mRNA microarray and proteomic analysis of eutopic endometrium of women with and without endometriosis HUMAN REPRODUCTION Fassbender, A., Verbeeck, N., Boernigen, D., Kyama, C. M., Bokor, A., Vodolazkaia, A., Peeraer, K., Tomassetti, C., Meuleman, C., Gevaert, O., Van de Plas, R., Ojeda, F., De Moor, B., Moreau, Y., Waelkens, E., D'Hooghe, T. M. 2012; 27 (7): 2020-2029

    Abstract

    An early semi-invasive diagnosis of endometriosis has the potential to allow early treatment and minimize disease progression but no such test is available at present. Our aim was to perform a combined mRNA microarray and proteomic analysis on the same eutopic endometrium sample obtained from patients with and without endometriosis.mRNA and protein fractions were extracted from 49 endometrial biopsies obtained from women with laparoscopically proven presence (n= 31) or absence (n= 18) of endometriosis during the early luteal (n= 27) or menstrual phase (n= 22) and analyzed using microarray and proteomic surface enhanced laser desorption ionization-time of flight mass spectrometry, respectively. Proteomic data were analyzed using a least squares-support vector machines (LS-SVM) model built on 70% (training set) and 30% of the samples (test set).mRNA analysis of eutopic endometrium did not show any differentially expressed genes in women with endometriosis when compared with controls, regardless of endometriosis stage or cycle phase. mRNA was differentially expressed (P< 0.05) in women with (925 genes) and without endometriosis (1087 genes) during the menstrual phase when compared with the early luteal phase. Proteomic analysis based on five peptide peaks [2072 mass/charge (m/z); 2973 m/z; 3623 m/z; 3680 m/z and 21133 m/z] using an LS-SVM model applied on the luteal phase endometrium training set allowed the diagnosis of endometriosis (sensitivity, 91; 95% confidence interval (CI): 74-98; specificity, 80; 95% CI: 66-97 and positive predictive value, 87.9%; negative predictive value, 84.8%) in the test set.mRNA expression of eutopic endometrium was comparable in women with and without endometriosis but different in menstrual endometrium when compared with luteal endometrium in women with endometriosis. Proteomic analysis of luteal phase endometrium allowed the diagnosis of endometriosis with high sensitivity and specificity in training and test sets. A potential limitation of our study is the fact that our control group included women with a normal pelvis as well as women with concurrent pelvic disease (e.g. fibroids, benign ovarian cysts, hydrosalpinges), which may have contributed to the comparable mRNA expression profile in the eutopic endometrium of women with endometriosis and controls.

    View details for DOI 10.1093/humrep/des127

    View details for Web of Science ID 000305458800016

  • Proteomics Analysis of Plasma for Early Diagnosis of Endometriosis OBSTETRICS AND GYNECOLOGY Fassbender, A., Waelkens, E., Verbeeck, N., Kyama, C. M., Bokor, A., Vodolazkaia, A., Van De Plas, R., Meuleman, C., Peeraer, K., Tomassetti, C., Gevaert, O., Ojeda, F., De Moor, B., D'Hooghe, T. 2012; 119 (2): 276-285

    Abstract

    To test the hypothesis that differential surface-enhanced laser desorption/ionization time-of-flight mass spectrometry protein or peptide expression in plasma can be used in infertile women with or without pelvic pain to predict the presence of laparoscopically and histologically confirmed endometriosis, especially in the subpopulation with a normal preoperative gynecologic ultrasound examination.Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry analysis was performed on 254 plasma samples obtained from 89 women without endometriosis and 165 women with endometriosis (histologically confirmed) undergoing laparoscopies for infertility with or without pelvic pain. Data were analyzed using least squares support vector machines and were divided randomly (100 times) into a training data set (70%) and a test data set (30%).Minimal-to-mild endometriosis was best predicted (sensitivity 75%, 95% confidence interval [CI] 63-89; specificity 86%, 95% CI 71-94; positive predictive value 83.6%, negative predictive value 78.3%) using a model based on five peptide and protein peaks (range 4.898-14.698 m/z) in menstrual phase samples. Moderate-to-severe endometriosis was best predicted (sensitivity 98%, 95% CI 84-100; specificity 81%, 95% CI 67-92; positive predictive value 74.4%, negative predictive value 98.6%) using a model based on five other peptide and protein peaks (range 2.189-7.457 m/z) in luteal phase samples. The peak with the highest intensity (2.189 m/z) was identified as a fibrinogen ?-chain peptide. Ultrasonography-negative endometriosis was best predicted (sensitivity 88%, 95% CI 73-100; specificity 84%, 95% CI 71-96) using a model based on five peptide peaks (range 2.058-42.065 m/z) in menstrual phase samples.A noninvasive test using proteomic analysis of plasma samples obtained during the menstrual phase enabled the diagnosis of endometriosis undetectable by ultrasonography with high sensitivity and specificity.II.

    View details for DOI 10.1097/AOG.0b013e31823fda8d

    View details for Web of Science ID 000299604300012

    View details for PubMedID 22270279

  • Atypical Neurofibromas in Neurofibromatosis Type 1 are Premalignant Tumors GENES CHROMOSOMES & CANCER Beert, E., Brems, H., Daniels, B., De Wever, I., Van Calenbergh, F., Schoenaers, J., Debiec-Rychter, M., Gevaert, O., De Raedt, T., Van den Bruel, A., de Ravel, T., Cichowski, K., Kluwe, L., Mautner, V., Sciot, R., Legius, E. 2011; 50 (12): 1021-1032

    Abstract

    Benign peripheral nerve sheath tumors (PNSTs) are a characteristic feature of neurofibromatosis type I (NF1) patients. NF1 individuals have an 8-13% lifetime risk of developing a malignant PNST (MPNST). Atypical neurofibromas are symptomatic, hypercellular PNSTs, composed of cells with hyperchromatic nuclei in the absence of mitoses. Little is known about the origin and nature of atypical neurofibromas in NF1 patients. In this study, we classified the atypical neurofibromas in the spectrum of NF1-associated PNSTs by analyzing 65 tumor samples from 48 NF1 patients. We compared tumor-specific chromosomal copy number alterations between benign neurofibromas, atypical neurofibromas, and MPNSTs (low-, intermediate-, and high-grade) by karyotyping and microarray-based comparative genome hybridization (aCGH). In 15 benign neurofibromas (4 subcutaneous and 11 plexiform), no copy number alterations were found, except a single event in a plexiform neurofibroma. One highly significant recurrent aberration (15/16) was identified in the atypical neurofibromas, namely a deletion with a minimal overlapping region (MOR) in chromosome band 9p21.3, including CDKN2A and CDKN2B. Copy number loss of the CDKN2A/B gene locus was one of the most common events in the group of MPNSTs, with deletions in low-, intermediate-, and high-grade MPNSTs. In one tumor, we observed a clear transition from a benign-atypical neurofibroma toward an intermediate-grade MPNST, confirmed by both histopathology and aCGH analysis. These data support the hypothesis that atypical neurofibromas are premalignant tumors, with the CDKN2A/B deletion as the first step in the progression toward MPNST.

    View details for DOI 10.1002/gcc.20921

    View details for Web of Science ID 000296443600005

    View details for PubMedID 21987445

  • Prediction of lymph node involvement in breast cancer from primary tumor tissue using gene expression profiling and miRNAs BREAST CANCER RESEARCH AND TREATMENT Smeets, A., Daemen, A., Vanden Bempt, I., Gevaert, O., Claes, B., Wildiers, H., Drijkoningen, R., Van Hummelen, P., Lambrechts, D., De Moor, B., Neven, P., Sotiriou, C., Vandorpe, T., Paridaens, R., Christiaens, M. R. 2011; 129 (3): 767-776

    Abstract

    The aim of this study was to investigate whether lymph node involvement in breast cancer is influenced by gene or miRNA expression of the primary tumor. For this purpose, we selected a very homogeneous patient population to minimize heterogeneity in other tumor and patient characteristics. First, we compared gene expression profiles of primary tumor tissue from a group of 96 breast cancer patients balanced for lymph node involvement using Affymetrix Human U133 Plus 2.0 microarray chip. A model was built by weighted Least-Squares Support Vector Machines and validated on an internal and external dataset. Next, miRNA profiling was performed on a subset of 82 tumors using Human MiRNA-microarray chips (Illumina). Finally, for each miRNA the number of significant inverse correlated targets was determined and compared with 1000 sets of randomly chosen targets. A model based on 241 genes was built (AUC 0.66). The AUC for the internal dataset was 0.646 and 0. 651 for the external datasets. The model includes multiple kinases, apoptosis-related, and zinc ion-binding genes. Integration of the microarray and miRNA data reveals ten miRNAs suppressing lymph node invasion and one miRNA promoting lymph node invasion. Our results provide evidence that measurable differences in gene and miRNA expression exist between node negative and node positive patients and thus that lymph node involvement is not a genetically random process. Moreover, our data suggest a general deregulation of the miRNA machinery that is potentially responsible for lymph node invasion.

    View details for DOI 10.1007/s10549-010-1265-5

    View details for Web of Science ID 000294680600010

    View details for PubMedID 21116709

  • Ectopic pregnancy: using the hCG ratio to select women for expectant or medical management ACTA OBSTETRICIA ET GYNECOLOGICA SCANDINAVICA Kirk, E., Van Calster, B., Condous, G., Papageorghiou, A. T., Gevaert, O., Van Huffel, S., De Moor, B., Timmerman, D., Bourne, T. 2011; 90 (3): 264-272

    Abstract

    To identify variables that can be used to select women with an ectopic pregnancy for expectant or medical management with systemic methotrexate.Cohort study.Early Pregnancy Unit of a London teaching hospital.Women with a tubal ectopic pregnancy managed non-surgically.The diagnosis of tubal ectopic pregnancy was made using transvaginal sonography. Human chorionic gonadotrophin (hCG) levels had to be taken at 0 hour and 48 hours pre-treatment. Other recorded variables include presenting complaints, gestational age, progesterone levels, size of the ectopic mass and appearance of the ectopic on transvaginal sonography. Women were followed up until the outcome (success or failure) of management was known.Univariable analysis was performed to identify the variables associated with successful management using area under curves and relative risks.Thirty-nine women underwent expectant management (overall success rate 71.8%) and 42 had medical management (overall success rate 76.2%). The pre-treatment hCG ratio (hCG 48 hours/hCG 0 hour) was related to the failure of both expectant (area under curve 0.86, 95% CI 0.67-0.94) and medical (area under curve 0.79, 95% CI 0.58-0.90) management. History of ectopic pregnancy was related to failure of expectant management only (relative risk 0.46, 95% CI 0.16-0.92).The most important variable for predicting the likelihood of successful non-surgical management was the pre-treatment hCG ratio. New studies are required to validate the use of this variable and of history of ectopic pregnancy to predict the likelihood of successful non-surgical management in clinical practice.

    View details for DOI 10.1111/j.1600-0412.2010.01053.x

    View details for Web of Science ID 000288825600010

    View details for PubMedID 21306315

  • Evaluation of endometrial biomarkers for semi-invasive diagnosis of endometriosis FERTILITY AND STERILITY Kyama, C. M., Mihalyi, A., Gevaert, O., Waelkens, E., Simsa, P., Van De Plas, R., Meuleman, C., De Moor, B., D'Hooghe, T. M. 2011; 95 (4): 1338-U173

    Abstract

    To test the hypothesis that specific proteins and peptides are expressed differentially in eutopic endometrium of women with and without endometriosis and at specific stages of the disease (minimal, mild, moderate, or severe) during the secretory phase.Patients with endometriosis were compared with controls.University hospital.A total of 29 patients during the secretory phase were selected for this study on the basis of cycle phase and presence or absence of endometriosis.Endometriosis was confirmed laparoscopically and histologically in 19 patients with endometriosis of revised American Society for Reproductive Medicine stages (9 minimal-mild and 10 moderate-severe), and the presence of a normal pelvis was documented by laparoscopy in 10 controls.Protein expression of endometrium was evaluated with use of surface-enhanced laser desorption/ionization time-of-flight mass spectrometry. The differential expression of protein mass peaks was analyzed with use of support vector machine algorithms and logistic regression models.Data preprocessing resulted in differential expression of 73, 30, and 131 mass peaks between controls and patients with endometriosis (all stages), with minimal-mild endometriosis, and with moderate-severe endometriosis, respectively. Endometriosis was diagnosed with high sensitivity (89.5%) and specificity (90%) with use of five down-regulated mass peaks (1.949 kDa, 5.183 kDa, 8.650 kDa, 8.659 kDa, and 13.910 kDa) obtained after support vector machine ranking and logistic regression classification. With use of a similar analysis, minimal-mild endometriosis was diagnosed with four mass peaks (two up-regulated: 35.956 kDa and 90.675 kDa and two down-regulated: 1.924 kDa and 2.504 kDa) with maximal sensitivity (100%) and specificity (100%). The 90.675-kDa and 35.956-kDa mass peaks were identified as T-plastin and annexin V, respectively.Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry analysis of secretory phase endometrium combined with bioinformatics puts forward a prospective panel of potential biomarkers with sensitivity of 100% and specificity of 100% for the diagnosis of minimal to mild endometriosis.

    View details for DOI 10.1016/j.fertnstert.2010.06.084

    View details for Web of Science ID 000288010900024

    View details for PubMedID 20800833

  • TRIzol treatment of secretory phase endometrium allows combined proteomic and mRNA microarray analysis of the same sample in women with and without endometriosis REPRODUCTIVE BIOLOGY AND ENDOCRINOLOGY Fassbender, A., Simsa, P., Kyama, C. M., Waelkens, E., Mihalyi, A., Meuleman, C., Gevaert, O., Van De Plas, R., De Moor, B., D'Hooghe, T. M. 2010; 8

    Abstract

    According to mRNA microarray, proteomics and other studies, biological abnormalities of eutopic endometrium (EM) are involved in the pathogenesis of endometriosis, but the relationship between mRNA and protein expression in EM is not clear. We tested for the first time the hypothesis that EM TRIzol extraction allows proteomic Surface Enhanced Laser Desorption/Ionisation Time-of-Flight Mass Spectrometry (SELDI-TOF MS) analysis and that these proteomic data can be related to mRNA (microarray) data obtained from the same EM sample from women with and without endometriosis.Proteomic analysis was performed using SELDI-TOF-MS of TRIzol-extracted EM obtained during secretory phase from patients without endometriosis (n = 6), patients with minimal-mild (n = 5) and with moderate-severe endometriosis (n = 5), classified according to the system of the American Society of Reproductive Medicine. Proteomic data were compared to mRNA microarray data obtained from the same EM samples.In our SELDI-TOF MS study 32 peaks were differentially expressed in endometrium of all women with endometriosis (stages I-IV) compared with all controls during the secretory phase. Comparison of proteomic results with those from microarray revealed no corresponding genes/proteins.TRIzol treatment of secretory phase EM allows combined proteomic and mRNA microarray analysis of the same sample, but comparison between proteomic and microarray data was not evident, probably due to post-translational modifications.

    View details for DOI 10.1186/1477-7827-8-123

    View details for Web of Science ID 000284485100001

    View details for PubMedID 20964823

  • Improved Microarray-Based Decision Support with Graph Encoded Interactome Data PLOS ONE Daemen, A., Signoretto, M., Gevaert, O., Suykens, J. A., De Moor, B. 2010; 5 (4)

    Abstract

    In the past, microarray studies have been criticized due to noise and the limited overlap between gene signatures. Prior biological knowledge should therefore be incorporated as side information in models based on gene expression data to improve the accuracy of diagnosis and prognosis in cancer. As prior knowledge, we investigated interaction and pathway information from the human interactome on different aspects of biological systems. By exploiting the properties of kernel methods, relations between genes with similar functions but active in alternative pathways could be incorporated in a support vector machine classifier based on spectral graph theory. Using 10 microarray data sets, we first reduced the number of data sources relevant for multiple cancer types and outcomes. Three sources on metabolic pathway information (KEGG), protein-protein interactions (OPHID) and miRNA-gene targeting (microRNA.org) outperformed the other sources with regard to the considered class of models. Both fixed and adaptive approaches were subsequently considered to combine the three corresponding classifiers. Averaging the predictions of these classifiers performed best and was significantly better than the model based on microarray data only. These results were confirmed on 6 validation microarray sets, with a significantly improved performance in 4 of them. Integrating interactome data thus improves classification of cancer outcome for the investigated microarray technologies and cancer types. Moreover, this strategy can be incorporated in any kernel method or non-linear version of a non-kernel method.

    View details for DOI 10.1371/journal.pone.0010225

    View details for Web of Science ID 000276853800015

    View details for PubMedID 20419106

  • Non-invasive diagnosis of endometriosis based on a combined analysis of six plasma biomarkers HUMAN REPRODUCTION Mihalyi, A., Gevaert, O., Kyama, C. M., Simsa, P., Pochet, N., De Smet, F., De Moor, B., Meuleman, C., Billen, J., Blanckaert, N., Vodolazkaia, A., Fulop, V., D'Hooghe, T. M. 2010; 25 (3): 654-664

    Abstract

    Lack of a non-invasive diagnostic test contributes to the long delay between onset of symptoms and diagnosis of endometriosis. The aim of this study was to evaluate the combined performance of six potential plasma biomarkers in the diagnosis of endometriosis.This case-control study was conducted in 294 infertile women, consisting of 93 women with a normal pelvis and 201 women with endometriosis. We measured plasma concentrations of interleukin (IL)-6, IL-8, tumour necrosis factor-alpha, high-sensitivity C-reactive protein (hsCRP), and cancer antigens CA-125 and CA-19-9. Analyses were done using the Kruskal-Wallis test, Mann-Whitney test, receiver operator characteristic, stepwise logistic regression and least squares support vector machines (LSSVM).Plasma levels of IL-6, IL-8 and CA-125 were increased in all women with endometriosis and in those with minimal-mild endometriosis, compared with controls. In women with moderate-severe endometriosis, plasma levels of IL-6, IL-8 and CA-125, but also of hsCRP, were significantly higher than in controls. Using stepwise logistic regression, moderate-severe endometriosis was diagnosed with a sensitivity of 100% (specificity 84%) and minimal-mild endometriosis was detected with a sensitivity of 87% (specificity 71%) during the secretory phase. Using LSSVM analysis, minimal-mild endometriosis was diagnosed with a sensitivity of 94% (specificity 61%) during the secretory phase and with a sensitivity of 92% (specificity 63%) during the menstrual phase.Advanced statistical analysis of a panel of six selected plasma biomarkers on samples obtained during the secretory phase or during menstruation allows the diagnosis of both minimal-mild and moderate-severe endometriosis with high sensitivity and clinically acceptable specificity.

    View details for DOI 10.1093/humrep/dep425

    View details for Web of Science ID 000274490700014

    View details for PubMedID 20007161

  • A taxonomy of epithelial human cancer and their metastases BMC MEDICAL GENOMICS Gevaert, O., Daemen, A., De Moor, B., Libbrecht, L. 2009; 2

    Abstract

    Microarray technology has allowed to molecularly characterize many different cancer sites. This technology has the potential to individualize therapy and to discover new drug targets. However, due to technological differences and issues in standardized sample collection no study has evaluated the molecular profile of epithelial human cancer in a large number of samples and tissues. Additionally, it has not yet been extensively investigated whether metastases resemble their tissue of origin or tissue of destination.We studied the expression profiles of a series of 1566 primary and 178 metastases by unsupervised hierarchical clustering. The clustering profile was subsequently investigated and correlated with clinico-pathological data. Statistical enrichment of clinico-pathological annotations of groups of samples was investigated using Fisher exact test. Gene set enrichment analysis (GSEA) and DAVID functional enrichment analysis were used to investigate the molecular pathways. Kaplan-Meier survival analysis and log-rank tests were used to investigate prognostic significance of gene signatures.Large clusters corresponding to breast, gastrointestinal, ovarian and kidney primary tissues emerged from the data. Chromophobe renal cell carcinoma clustered together with follicular differentiated thyroid carcinoma, which supports recent morphological descriptions of thyroid follicular carcinoma-like tumors in the kidney and suggests that they represent a subtype of chromophobe carcinoma. We also found an expression signature identifying primary tumors of squamous cell histology in multiple tissues. Next, a subset of ovarian tumors enriched with endometrioid histology clustered together with endometrium tumors, confirming that they share their etiopathogenesis, which strongly differs from serous ovarian tumors. In addition, the clustering of colon and breast tumors correlated with clinico-pathological characteristics. Moreover, a signature was developed based on our unsupervised clustering of breast tumors and this was predictive for disease-specific survival in three independent studies. Next, the metastases from ovarian, breast, lung and vulva cluster with their tissue of origin while metastases from colon showed a bimodal distribution. A significant part clusters with tissue of origin while the remaining tumors cluster with the tissue of destination.Our molecular taxonomy of epithelial human cancer indicates surprising correlations over tissues. This may have a significant impact on the classification of many cancer sites and may guide pathologists, both in research and daily practice. Moreover, these results based on unsupervised analysis yielded a signature predictive of clinical outcome in breast cancer. Additionally, we hypothesize that metastases from gastrointestinal origin either remember their tissue of origin or adapt to the tissue of destination. More specifically, colon metastases in the liver show strong evidence for such a bimodal tissue specific profile.

    View details for DOI 10.1186/1755-8794-2-69

    View details for Web of Science ID 000273595600001

    View details for PubMedID 20017941

  • Density of small diameter sensory nerve fibres in endometrium: a semi-invasive diagnostic test for minimal to mild endometriosis HUMAN REPRODUCTION Bokor, A., Kyama, C. M., Vercruysse, L., Fassbender, A., Gevaert, O., Vodolazkaia, A., De Moor, B., Fulop, V., D'Hooghe, T. 2009; 24 (12): 3025-3032

    Abstract

    The aim of our study was to test the hypothesis that multiple-sensory small-diameter nerve fibres are present in a higher density in endometrium from patients with endometriosis when compared with women with a normal pelvis, enabling the development of a semi-invasive diagnostic test for minimal-mild endometriosis.Secretory phase endometrium samples (n = 40), obtained from women with laparoscopically/histologically confirmed minimal-mild endometriosis (n = 20) and from women with a normal pelvis (n = 20) were selected from the biobank at the Leuven University Fertility Centre. Immunohistochemistry was performed to localize neural markers for sensory C, Adelta, adrenergic and cholinergic nerve fibres in the functional layer of the endometrium. Sections were immunostained with anti-human protein gene product 9.5 (PGP9.5), anti-neurofilament protein, anti-substance P (SP), anti-vasoactive intestinal peptide (VIP), anti-neuropeptide Y and anti-calcitonine gene-related polypeptide. Statistical analysis was done using the Mann-Whitney U-test, receiver operator characteristic analysis, stepwise logistic regression and least-squares support vector machines.The density of small nerve fibres was approximately 14 times higher in endometrium from patients with minimal-mild endometriosis (1.96 +/- 2.73) when compared with women with a normal pelvis (0.14 +/- 0.46, P < 0.0001).The combined analysis of neural markers PGP9.5, VIP and SP could predict the presence of minimal-mild endometriosis with 95% sensitivity, 100% specificity and 97.5% accuracy. To confirm our findings, prospective studies are required.

    View details for DOI 10.1093/humrep/dep283

    View details for Web of Science ID 000272069500009

    View details for PubMedID 19690351

  • Molecular Response to Cetuximab and Efficacy of Preoperative Cetuximab-Based Chemoradiation in Rectal Cancer JOURNAL OF CLINICAL ONCOLOGY Debucquoy, A., Haustermans, K., Daemen, A., Aydin, S., Libbrecht, L., Gevaert, O., De Moor, B., Tejpar, S., McBride, W. H., Penninckx, F., Scalliet, P., Stroh, C., Vlassak, S., Sempoux, C., Machiels, J. 2009; 27 (17): 2751-2757

    Abstract

    To characterize the molecular pathways activated or inhibited by cetuximab when combined with chemoradiotherapy (CRT) in rectal cancer and to identify molecular profiles and biomarkers that might improve patient selection for such treatments.Forty-one patients with rectal cancer (T3-4 and/or N+) received preoperative radiotherapy (1.8 Gy, 5 days/wk, 45 Gy) in combination with capecitabine and cetuximab (400 mg/m2 as initial dose 1 week before CRT followed by 250 mg/m2 /wk for 5 weeks). Biopsies and plasma samples were taken before treatment, after cetuximab but before CRT, and at the time of surgery. Proteomics and microarrays were used to monitor the molecular response to cetuximab and to identify profiles and biomarkers to predict treatment efficacy.Cetuximab on its own downregulated genes involved in proliferation and invasion and upregulated inflammatory gene expression, with 16 genes being significantly influenced in microarray analysis. The decrease in proliferation was confirmed by immunohistochemistry for Ki67 (P = .01) and was accompanied by an increase in transforming growth factor-alpha in plasma samples (P < .001). Disease-free survival (DFS) was better in patients if epidermal growth factor receptor expression was upregulated in the tumor after the initial cetuximab dose (P = .02) and when fibro-inflammatory changes were present in the surgical specimen (P = .03). Microarray and proteomic profiles were predictive of DFS.Our study showed that a single dose of cetuximab has a significant impact on the expression of genes involved in tumor proliferation and inflammation. We identified potential biomarkers that might predict response to cetuximab-based CRT.

    View details for DOI 10.1200/JCO.2008.18.5033

    View details for Web of Science ID 000266782100005

    View details for PubMedID 19332731

  • Prediction of cancer outcome using DNA microarray technology: past, present and future. Expert opinion on medical diagnostics Gevaert, O., De Moor, B. 2009; 3 (2): 157-165

    Abstract

    Background: The use of DNA microarray technology to predict cancer outcome already has a history of almost a decade. Although many breakthroughs have been made, the promise of individualized therapy is still not fulfilled. In addition, new technologies are emerging that also show promise in outcome prediction of cancer patients. Objective: The impact of DNA microarray and other 'omics' technologies on the outcome prediction of cancer patients was investigated. Whether integration of omics data results in better predictions was also examined. Methods: DNA microarray technology was focused on as a starting point because this technology is considered to be the most mature technology from all omics technologies. Next, emerging technologies that may accomplish the same goals but have been less extensively studied are described. Conclusion: Besides DNA microarray technology, other omics technologies have shown promise in predicting the cancer outcome or have potential to replace microarray technology in the near future. Moreover, it is shown that integration of multiple omics data can result in better predictions of cancer outcome; but, owing to the lack of comprehensive studies, validation studies are required to verify which omics has the most information and whether a combination of multiple omics data improves predictive performance.

    View details for DOI 10.1517/17530050802680172

    View details for PubMedID 23485162

  • A kernel-based integration of genome-wide data for clinical decision support. Genome medicine Daemen, A., Gevaert, O., Ojeda, F., Debucquoy, A., Suykens, J. A., Sempoux, C., Machiels, J., Haustermans, K., De Moor, B. 2009; 1 (4): 39-?

    Abstract

    Although microarray technology allows the investigation of the transcriptomic make-up of a tumor in one experiment, the transcriptome does not completely reflect the underlying biology due to alternative splicing, post-translational modifications, as well as the influence of pathological conditions (for example, cancer) on transcription and translation. This increases the importance of fusing more than one source of genome-wide data, such as the genome, transcriptome, proteome, and epigenome. The current increase in the amount of available omics data emphasizes the need for a methodological integration framework.We propose a kernel-based approach for clinical decision support in which many genome-wide data sources are combined. Integration occurs within the patient domain at the level of kernel matrices before building the classifier. As supervised classification algorithm, a weighted least squares support vector machine is used. We apply this framework to two cancer cases, namely, a rectal cancer data set containing microarray and proteomics data and a prostate cancer data set containing microarray and genomics data. For both cases, multiple outcomes are predicted.For the rectal cancer outcomes, the highest leave-one-out (LOO) areas under the receiver operating characteristic curves (AUC) were obtained when combining microarray and proteomics data gathered during therapy and ranged from 0.927 to 0.987. For prostate cancer, all four outcomes had a better LOO AUC when combining microarray and genomics data, ranging from 0.786 for recurrence to 0.987 for metastasis.For both cancer sites the prediction of all outcomes improved when more than one genome-wide data set was considered. This suggests that integrating multiple genome-wide data sources increases the predictive performance of clinical decision support models. This emphasizes the need for comprehensive multi-modal data. We acknowledge that, in a first phase, this will substantially increase costs; however, this is a necessary investment to ultimately obtain cost-efficient models usable in patient tailored therapy.

    View details for DOI 10.1186/gm39

    View details for PubMedID 19356222

  • A kernel-based integration of genome-wide data for clinical decision support GENOME MEDICINE Daemen, A., Gevaert, O., Ojeda, F., Debucquoy, A., Suykens, J. A., Sempoux, C., Machiels, J., Haustermans, K., De Moor, B. 2009; 1

    View details for DOI 10.1186/gm39

    View details for Web of Science ID 000208627000039

  • SUPERVISED CLASSIFICATION OF ARRAY CGH DATA WITH HMM-BASED FEATURE SELECTION PACIFIC SYMPOSIUM ON BIOCOMPUTING 2009 Daemen, A., Gevaert, O., Leunen, K., Legius, E., Vergote, I., De Moor, B. 2009: 468-479

    Abstract

    For different tumour types, extended knowledge about the molecular mechanisms involved in tumorigenesis is lacking. Looking for copy number variations (CNV) by Comparative Genomic Hybridization (CGH) can help however to determine key elements in this tumorigenesis. As genome-wide array CGH gives the opportunity to evaluate CNV at high resolution, this leads to huge amount of data, necessitating adequate mathematical methods to carefully select and interpret these data.Two groups of patients differing in cancer subtype were defined in two publicly available array CGH data sets as well as in our own data set on ovarian cancer. Chromosomal regions characterizing each group of patients were gathered using recurrent hidden Markov Models (HMM). The differential regions were reduced to a subset of features for classification by integrating different univariate feature selection methods. Weighted Least Squares Support Vector Machines (LS-SVM), a supervised classification method which takes unbalancedness of data sets into account, resulted in leave-one-out or 10-fold cross-validation accuracies ranging from 88 to 95.5%.The combination of recurrent HMMs for the detection of copy number alterations with LS-SVM classifiers offers a novel methodological approach for classification based on copy number alterations. Additionally, this approach limits the chromosomal regions that are necessary to classify patients according to cancer subtype.

    View details for Web of Science ID 000263639700045

    View details for PubMedID 19209723

  • Pain experienced during transvaginal ultrasound, saline contrast sonohysterography, hysteroscopy and office sampling: a comparative study ULTRASOUND IN OBSTETRICS & GYNECOLOGY Van den Bosch, T., Verguts, J., Daemen, A., Gevaert, O., Domali, E., Claerhout, F., Vandenbroucke, V., De Moor, B., Deprest, J., Timmerman, D. 2008; 31 (3): 346-351

    Abstract

    To evaluate and compare the pain experienced by women during transvaginal ultrasound, saline contrast sonohysterography (SCSH), diagnostic hysteroscopy and office sampling.This was a descriptive study of 402 consecutive patients presenting at a 'one-stop' Bleeding Clinic between October 2004 and November 2006. Thirty-nine percent of the patients were postmenopausal. The patients underwent the following examinations transvaginally: first ultrasound with color Doppler, second SCSH, third diagnostic hysteroscopy and fourth endometrial biopsy. After completion of the examinations the patients were asked to complete a questionnaire including a visual analog scale (VAS) about their subjective appreciation of all four examinations. Two-hundred and ninety-three (72%) patients returned the questionnaire.The median (range) VAS scores for transvaginal ultrasound, SCSH, diagnostic hysteroscopy and endometrial sampling were 1.0 (0-8.1), 2.2 (0-10), 2.7 (0-10) and 5.1 (0-10), respectively (P < 0.0001). The patients' answers to the other questions about the pain experienced, including comparison with other minor procedures such as venous blood sampling, were all concordant with the VAS scores.Transvaginal ultrasound was the procedure best accepted, followed by SCSH, hysteroscopy and endometrial sampling. These results suggest that patients would prefer SCSH over hysteroscopy as an initial diagnostic approach in the evaluation of abnormal uterine bleeding.

    View details for DOI 10.1002/uog.5263

    View details for Web of Science ID 000254541900019

    View details for PubMedID 18307203

  • Expression profiling to predict the clinical behaviour of ovarian cancer fails independent evaluation BMC CANCER Gevaert, O., De Smet, F., Van Gorp, T., Pochet, N., Engelen, K., Amant, F., De Moor, B., Timmerman, D., Vergote, I. 2008; 8

    Abstract

    In a previously published pilot study we explored the performance of microarrays in predicting clinical behaviour of ovarian tumours. For this purpose we performed microarray analysis on 20 patients and estimated that we could predict advanced stage disease with 100% accuracy and the response to platin-based chemotherapy with 76.92% accuracy using leave-one-out cross validation techniques in combination with Least Squares Support Vector Machines (LS-SVMs).In the current study we evaluate whether tumour characteristics in an independent set of 49 patients can be predicted using the pilot data set with principal component analysis or LS-SVMs.The results of the principal component analysis suggest that the gene expression data from stage I, platin-sensitive advanced stage and platin-resistant advanced stage tumours in the independent data set did not correspond to their respective classes in the pilot study. Additionally, LS-SVM models built using the data from the pilot study - although they only misclassified one of four stage I tumours and correctly classified all 45 advanced stage tumours - were not able to predict resistance to platin-based chemotherapy. Furthermore, models based on the pilot data and on previously published gene sets related to ovarian cancer outcomes, did not perform significantly better than our models.We discuss possible reasons for failure of the model for predicting response to platin-based chemotherapy and conclude that existing results based on gene expression patterns of ovarian tumours need to be thoroughly scrutinized before these results can be accepted to reflect the true performance of microarray technology.

    View details for DOI 10.1186/1471-2407-8-18

    View details for Web of Science ID 000253596800002

    View details for PubMedID 18211668

  • Integrating microarray and proteomics data to predict the response on cetuximab in patients with rectal cancer. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Daemen, A., Gevaert, O., De Bie, T., Debucquoy, A., Machiels, J., De Moor, B., Haustermans, K. 2008: 166-177

    Abstract

    To investigate the combination of cetuximab, capecitabine and radiotherapy in the preoperative treatment of patients with rectal cancer, fourty tumour samples were gathered before treatment (T0), after one dose of cetuximab but before radiotherapy with capecitabine (T1) and at moment of surgery (T2). The tumour and plasma samples were subjected at all timepoints to Affymetrix microarray and Luminex proteomics analysis, respectively. At surgery, the Rectal Cancer Regression Grade (RCRG) was registered. We used a kernel-based method with Least Squares Support Vector Machines to predict RCRG based on the integration of microarray and proteomics data on To and T1. We demonstrated that combining multiple data sources improves the predictive power. The best model was based on 5 genes and 10 proteins at T0 and T1 and could predict the RCRG with an accuracy of 91.7%, sensitivity of 96.2% and specificity of 80%.

    View details for PubMedID 18229684

  • Integration of microarray and textual data improves the prognosis prediction of breast, lung and ovarian cancer patients. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Gevaert, O., Van Vooren, S., De Moor, B. 2008: 279-290

    Abstract

    Microarray data are notoriously noisy such that models predicting clinically relevant outcomes often contain many false positive genes. Integration of other data sources can alleviate this problem and enhance gene selection and model building. Probabilistic models provide a natural solution to integrate information by using the prior over model space. We investigated if the use of text information from PUBMED abstracts in the structure prior of a Bayesian network could improve the prediction of the prognosis in cancer. Our results show that prediction of the outcome with the text prior was significantly better compared to not using a prior, both on a well known microarray data set and on three independent microarray data sets.

    View details for PubMedID 18229693

  • A framework for elucidating regulatory networks based on prior information and expression data REVERSE ENGINEERING BIOLOGICAL NETWORKS Gevaert, O., Van Vooren, S., De Moor, B. 2007; 1115: 240-248

    Abstract

    Elucidating regulatory networks is an intensively studied topic in bioinformatics. Integration of different sources of information could facilitate this task. We propose to incorporate these information sources in the structure prior of a Bayesian network. We are currently investigating two complementary sources of information: PubMed abstracts combined with publicly available taxonomies or ontologies, and known protein-DNA interactions. These priors, either separately or combined, have the potential of reducing the complexity of reverse-engineering regulatory networks while creating more robust and reliable models. Moreover this approach can easily be extended with other data sources. In such a way Bayesian networks provide a powerful framework for data integration and regulatory network modeling.

    View details for DOI 10.1196/annals.1407.002

    View details for Web of Science ID 000252037600017

    View details for PubMedID 17925352

  • Integration of clinical and microarray data with kernel methods 2007 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-16 Daemen, A., Gevaert, O., De Moor, B. 2007: 5411-5415

    Abstract

    Currently, the clinical management of cancer is based on empirical data from the literature (clinical studies) or based on the expertise of the clinician. Recently microarray technology emerged and it has the potential to revolutionize the clinical management of cancer and other diseases. A microarray allows to measure the expression levels of thousands of genes simultaneously which may reflect diagnostic or prognostic categories and sensitivity to treatment. The objective of this paper is to investigate whether clinical data, which is the basis of day-to-day clinical decision support, can be efficiently combined with microarray data, which has yet to prove its potential to deliver patient tailored therapy, using Least Squares Support Vector Machines.

    View details for Web of Science ID 000253467004088

    View details for PubMedID 18003232

  • Molecular profiling of platinum resistant ovarian cancer: Use of the model in clinical practice INTERNATIONAL JOURNAL OF CANCER Gevaert, O., Pochet, N., De Smet, F., Van Gorp, T., De Moor, B., Timmerman, D., Amant, F., Vergote, I. 2006; 119 (6): 1511-1511

    View details for DOI 10.1002/ijc.21985

    View details for Web of Science ID 000239877200043

    View details for PubMedID 16619247

  • Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks BIOINFORMATICS Gevaert, O., De Smet, F., Timmerman, D., Moreau, Y., De Moor, B. 2006; 22 (14): E184-E190

    Abstract

    Clinical data, such as patient history, laboratory analysis, ultrasound parameters--which are the basis of day-to-day clinical decision support--are often underused to guide the clinical management of cancer in the presence of microarray data. We propose a strategy based on Bayesian networks to treat clinical and microarray data on an equal footing. The main advantage of this probabilistic model is that it allows to integrate these data sources in several ways and that it allows to investigate and understand the model structure and parameters. Furthermore using the concept of a Markov Blanket we can identify all the variables that shield off the class variable from the influence of the remaining network. Therefore Bayesian networks automatically perform feature selection by identifying the (in)dependency relationships with the class variable.We evaluated three methods for integrating clinical and microarray data: decision integration, partial integration and full integration and used them to classify publicly available data on breast cancer patients into a poor and a good prognosis group. The partial integration method is most promising and has an independent test set area under the ROC curve of 0.845. After choosing an operating point the classification performance is better than frequently used indices.

    View details for DOI 10.1093/bioinformatics/btl230

    View details for Web of Science ID 000250005000023

    View details for PubMedID 16873470

  • Predicting the outcome of pregnancies of unknown location: Bayesian networks with expert prior information compared to logistic regression HUMAN REPRODUCTION Gevaert, O., De Smet, F., Kirk, E., Van Calster, B., Bourne, T., Van Huffel, S., Moreau, Y., Timmerman, D., De Moor, B., Condous, G. 2006; 21 (7): 1824-1831

    Abstract

    As women present at earlier gestations to early pregnancy units (EPUs), the number of women diagnosed with a pregnancy of unknown location (PUL) increases. Some of these women will have an ectopic pregnancy (EP), and it is this group in the PUL population that poses the greatest concern. The aim of this study was to develop Bayesian networks to predict EPs in the PUL population.Data were gathered in a single EPU from all women with a PUL. This data set was divided into a model-building (599 women with 44 EPs) and a validation (257 women with 22 EPs) data set and consisted of the following variables: vaginal bleeding, fluid in the pouch of Douglas, midline echo, lower abdominal pain, age, endometrial thickness, gestation days, the ratio of HCG at 48 and 0 h, progesterone levels (0 and 48 h) and the clinical outcome of the PUL. We developed Bayesian networks with expert information using this data set to predict EPs.The best Bayesian network used the gestational age, HCG ratio and the progesterone level at 48 h and had an area under the receiver operator characteristic curve (AUC) of 0.88 for predicting EPs when tested prospectively.Discrete-valued Bayesian networks are more complex to build than, for example, logistic regression. Nevertheless, we have demonstrated that such models can be used to predict EPs in a PUL population. Prospective interventional multicentre studies are needed to validate the use of such models in clinical practice.

    View details for DOI 10.1093/humrep/del083

    View details for Web of Science ID 000238907400027

    View details for PubMedID 16601010

  • Diagnostic accuracy of varying discriminatory zones for the prediction of ectopic pregnancy in women with a pregnancy of unknown location ULTRASOUND IN OBSTETRICS & GYNECOLOGY Condous, G., Kirk, E., Lu, C., Van Huffel, S., Gevaert, O., De Moor, B., De Smet, F., Timmerman, D., Bourne, T. 2005; 26 (7): 770-775

    Abstract

    Various serum human chorionic gonadotropin (hCG) discriminatory zones are currently used for evaluating the likelihood of an ectopic pregnancy in women classified as having a pregnancy of unknown location (PUL) following a transvaginal ultrasound examination. We evaluated the diagnostic accuracy of discriminatory zones for serum hCG levels of > 1000 IU/L, 1500 IU/L and 2000 IU/L for the detection of ectopic pregnancy in such women.This was a prospective observational study of women who were assessed in a specialized transvaginal scanning unit. All women with a PUL had serum hCG measured at presentation. Expectant management of PULs was adopted. These women were followed up with transvaginal ultrasound, monitoring of serum hormone levels and laparoscopy until a final diagnosis was established: a failing PUL, an intrauterine pregnancy (IUP), an ectopic pregnancy or a persisting PUL. The persisting PULs probably represented ectopic pregnancies which had been missed on ultrasound and these were incorporated into the ectopic pregnancy group. Three different discriminatory zones (1000 IU/L, 1500 IU/L and 2000 IU/L) were evaluated for predicting ectopic pregnancy in this PUL population.A total of 5544 consecutive women presented to the early pregnancy unit between 25 June 2001 and 14 April 2003. Of these, 569 (10.3%) women were classified as having a PUL, 42 of which were lost to follow up. Of the 527 (9.5%) cases with PUL analyzed, there were 300 (56.9%) failing PULs, 181 (34.3%) IUPs and 46 (8.7%) ectopic pregnancies. Overall, 74.6% were symptomatic and 25.4% were asymptomatic (P = 8.825E-07). The sensitivity and specificity of an hCG level of > 1000 IU/L to detect ectopic pregnancy were 21.7% (10/46) and 87.3% (420/481), respectively; for an hCG level of > 1500 IU/L these values were 15.2% (7/46) and 93.4% (449/481), respectively, and for an hCG level of > 2000 IU/L they were 10.9% (5/46) and 95.2% (458/481), respectively.Varying the discriminatory zone does not significantly improve the detection of ectopic pregnancy in a PUL population. A single measurement of serum hCG is not only potentially falsely reassuring but also unhelpful in excluding the presence of an ectopic pregnancy.

    View details for DOI 10.1002/uog.2636

    View details for Web of Science ID 000234027800015

    View details for PubMedID 16308901

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