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665 results on '"Wessels, Lodewyk F. A."'

1. Neoadjuvant nivolumab or nivolumab plus ipilimumab in early-stage triple-negative breast cancer: a phase 2 adaptive trial

Author

Nederlof, Iris, Isaeva, Olga I., de Graaf, Manon, Gielen, Robbert C. A. M., Bakker, Noor A. M., Rolfes, Adrianne L., Garner, Hannah, Boeckx, Bram, Traets, Joleen J. H., Mandjes, Ingrid A. M., de Maaker, Michiel, van Brussel, Thomas, Chelushkin, Maksim, Champanhet, Elisa, Lopez-Yurda, Marta, van de Vijver, Koen, van den Berg, José G., Hofland, Ingrid, Klioueva, Natasja, Mann, Ritse M., Loo, Claudette E., van Duijnhoven, Frederieke H., Skinner, Victoria, Luykx, Sylvia, Kerver, Emile, Kalashnikova, Ekaterina, van Dongen, Marloes G. J., Sonke, Gabe S., Linn, Sabine C., Blank, Christian U., de Visser, Karin E., Salgado, Roberto, Wessels, Lodewyk F. A., Drukker, Caroline A., Schumacher, Ton N., Horlings, Hugo M., Lambrechts, Diether, and Kok, Marleen

Published
2024
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3. ecpc: An R-package for generic co-data models for high-dimensional prediction

Author

van Nee, Mirrelijn M., Wessels, Lodewyk F. A., and van de Wiel, Mark A.

Subjects
Statistics - Methodology, Statistics - Machine Learning
Abstract

High-dimensional prediction considers data with more variables than samples. Generic research goals are to find the best predictor or to select variables. Results may be improved by exploiting prior information in the form of co-data, providing complementary data not on the samples, but on the variables. We consider adaptive ridge penalised generalised linear and Cox models, in which the variable specific ridge penalties are adapted to the co-data to give a priori more weight to more important variables. The R-package ecpc originally accommodated various and possibly multiple co-data sources, including categorical co-data, i.e. groups of variables, and continuous co-data. Continuous co-data, however, was handled by adaptive discretisation, potentially inefficiently modelling and losing information. Here, we present an extension to the method and software for generic co-data models, particularly for continuous co-data. At the basis lies a classical linear regression model, regressing prior variance weights on the co-data. Co-data variables are then estimated with empirical Bayes moment estimation. After placing the estimation procedure in the classical regression framework, extension to generalised additive and shape constrained co-data models is straightforward. Besides, we show how ridge penalties may be transformed to elastic net penalties with the R-package squeezy. In simulation studies we first compare various co-data models for continuous co-data from the extension to the original method. Secondly, we compare variable selection performance to other variable selection methods. Moreover, we demonstrate use of the package in several examples throughout the paper.

Published
2022

5. Percolate: An Exponential Family JIVE Model to Design DNA-Based Predictors of Drug Response

Author

Mourragui, Soufiane M. C., Loog, Marco, van Nee, Mirrelijn, de Wiel, Mark A van, Reinders, Marcel J. T., Wessels, Lodewyk F. A., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, and Tang, Haixu, editor

Published
2023
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7. PD-L1 blockade in combination with carboplatin as immune induction in metastatic lobular breast cancer: the GELATO trial

Author

Voorwerk, Leonie, Isaeva, Olga I., Horlings, Hugo M., Balduzzi, Sara, Chelushkin, Maksim, Bakker, Noor A. M., Champanhet, Elisa, Garner, Hannah, Sikorska, Karolina, Loo, Claudette E., Kemper, Inge, Mandjes, Ingrid A. M., de Maaker, Michiel, van Geel, Jasper J. L., Boers, Jorianne, de Boer, Maaike, Salgado, Roberto, van Dongen, Marloes G. J., Sonke, Gabe S., de Visser, Karin E., Schumacher, Ton N., Blank, Christian U., Wessels, Lodewyk F. A., Jager, Agnes, Tjan-Heijnen, Vivianne C. G., Schröder, Carolien P., Linn, Sabine C., and Kok, Marleen

Published
2023
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8. Codon-specific KRAS mutations predict survival benefit of trifluridine/tipiracil in metastatic colorectal cancer

Author

van de Haar, Joris, Ma, Xuhui, Ooft, Salo N., van der Helm, Pim W., Hoes, Louisa R., Mainardi, Sara, Pinato, David J., Sun, Kristi, Salvatore, Lisa, Tortora, Giampaolo, Zurlo, Ina Valeria, Leo, Silvana, Giampieri, Riccardo, Berardi, Rossana, Gelsomino, Fabio, Merz, Valeria, Mazzuca, Federica, Antonuzzo, Lorenzo, Rosati, Gerardo, Stavraka, Chara, Ross, Paul, Rodriquenz, Maria Grazia, Pavarana, Michele, Messina, Carlo, Iveson, Timothy, Zoratto, Federica, Thomas, Anne, Fenocchio, Elisabetta, Ratti, Margherita, Depetris, Ilaria, Cergnul, Massimiliano, Morelli, Cristina, Libertini, Michela, Parisi, Alessandro, De Tursi, Michele, Zanaletti, Nicoletta, Garrone, Ornella, Graham, Janet, Longarini, Raffaella, Gobba, Stefania Maria, Petrillo, Angelica, Tamburini, Emiliano, La Verde, Nicla, Petrelli, Fausto, Ricci, Vincenzo, Wessels, Lodewyk F. A., Ghidini, Michele, Cortellini, Alessio, Voest, Emile E., and Valeri, Nicola

Published
2023
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9. Flexible co-data learning for high-dimensional prediction

Author

van Nee, Mirrelijn M., Wessels, Lodewyk F. A., and van de Wiel, Mark A.

Subjects
Statistics - Methodology, Statistics - Machine Learning
Abstract

Clinical research often focuses on complex traits in which many variables play a role in mechanisms driving, or curing, diseases. Clinical prediction is hard when data is high-dimensional, but additional information, like domain knowledge and previously published studies, may be helpful to improve predictions. Such complementary data, or co-data, provide information on the covariates, such as genomic location or p-values from external studies. Our method enables exploiting multiple and various co-data sources to improve predictions. We use discrete or continuous co-data to define possibly overlapping or hierarchically structured groups of covariates. These are then used to estimate adaptive multi-group ridge penalties for generalised linear and Cox models. We combine empirical Bayes estimation of group penalty hyperparameters with an extra level of shrinkage. This renders a uniquely flexible framework as any type of shrinkage can be used on the group level. The hyperparameter shrinkage learns how relevant a specific co-data source is, counters overfitting of hyperparameters for many groups, and accounts for structured co-data. We describe various types of co-data and propose suitable forms of hypershrinkage. The method is very versatile, as it allows for integration and weighting of multiple co-data sets, inclusion of unpenalised covariates and posterior variable selection. We demonstrate it on two cancer genomics applications and show that it may improve the performance of other dense and parsimonious prognostic models substantially, and stabilises variable selection., Comment: Document consists of main content (20 pages, 10 figures) and supplementary material (14 pages, 13 figures)

Published
2020

10. γδ T cells are effectors of immunotherapy in cancers with HLA class I defects

Author

de Vries, Natasja L., van de Haar, Joris, Veninga, Vivien, Chalabi, Myriam, Ijsselsteijn, Marieke E., van der Ploeg, Manon, van den Bulk, Jitske, Ruano, Dina, van den Berg, Jose G., Haanen, John B., Zeverijn, Laurien J., Geurts, Birgit S., de Wit, Gijs F., Battaglia, Thomas W., Gelderblom, Hans, Verheul, Henk M. W., Schumacher, Ton N., Wessels, Lodewyk F. A., Koning, Frits, de Miranda, Noel F. C. C., and Voest, Emile E.

Published
2023
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11. Heterofusion: Fusing genomics data of different measurement scales

Author

Smilde, Age K., Song, Yipeng, Westerhuis, Johan A., Kiers, Henk A. L., Aben, Nanne, and Wessels, Lodewyk F. A.

Subjects
Statistics - Methodology
Abstract

In systems biology, it is becoming increasingly common to measure biochemical entities at different levels of the same biological system. Hence, data fusion problems are abundant in the life sciences. With the availability of a multitude of measuring techniques, one of the central problems is the heterogeneity of the data. In this paper, we discuss a specific form of heterogeneity, namely that of measurements obtained at different measurement scales, such as binary, ordinal, interval and ratio-scaled variables. Three generic fusion approaches are presented of which two are new to the systems biology community. The methods are presented, put in context and illustrated with a real-life genomics example.

Published
2019

13. Neoadjuvant Immunotherapy in Locally Advanced Mismatch Repair–Deficient Colon Cancer

Author

Chalabi, Myriam, primary, Verschoor, Yara L., additional, Tan, Pedro Batista, additional, Balduzzi, Sara, additional, Van Lent, Anja U., additional, Grootscholten, Cecile, additional, Dokter, Simone, additional, Büller, Nikè V., additional, Grotenhuis, Brechtje A., additional, Kuhlmann, Koert, additional, Burger, Jacobus W., additional, Huibregtse, Inge L., additional, Aukema, Tjeerd S., additional, Hendriks, Eduard R., additional, Oosterling, Steven J., additional, Snaebjornsson, Petur, additional, Voest, Emile E., additional, Wessels, Lodewyk F., additional, Beets-Tan, Regina G., additional, Van Leerdam, Monique E., additional, Schumacher, Ton N., additional, van den Berg, José G., additional, Beets, Geerard L., additional, and Haanen, John B., additional

Published
2024
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15. Truncated FGFR2 is a clinically actionable oncogene in multiple cancers

Author

Zingg, Daniel, Bhin, Jinhyuk, Yemelyanenko, Julia, Kas, Sjors M., Rolfs, Frank, Lutz, Catrin, Lee, Jessica K., Klarenbeek, Sjoerd, Silverman, Ian M., Annunziato, Stefano, Chan, Chang S., Piersma, Sander R., Eijkman, Timo, Badoux, Madelon, Gogola, Ewa, Siteur, Bjørn, Sprengers, Justin, de Klein, Bim, de Goeij-de Haas, Richard R., Riedlinger, Gregory M., Ke, Hua, Madison, Russell, Drenth, Anne Paulien, van der Burg, Eline, Schut, Eva, Henneman, Linda, van Miltenburg, Martine H., Proost, Natalie, Zhen, Huiling, Wientjens, Ellen, de Bruijn, Roebi, de Ruiter, Julian R., Boon, Ute, de Korte-Grimmerink, Renske, van Gerwen, Bastiaan, Féliz, Luis, Abou-Alfa, Ghassan K., Ross, Jeffrey S., van de Ven, Marieke, Rottenberg, Sven, Cuppen, Edwin, Chessex, Anne Vaslin, Ali, Siraj M., Burn, Timothy C., Jimenez, Connie R., Ganesan, Shridar, Wessels, Lodewyk F. A., and Jonkers, Jos

Published
2022
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16. Genomic analysis defines clonal relationships of ductal carcinoma in situ and recurrent invasive breast cancer

Author

Lips, Esther H., Kumar, Tapsi, Megalios, Anargyros, Visser, Lindy L., Sheinman, Michael, Fortunato, Angelo, Shah, Vandna, Hoogstraat, Marlous, Sei, Emi, Mallo, Diego, Roman-Escorza, Maria, Ahmed, Ahmed A., Xu, Mingchu, van den Belt-Dusebout, Alexandra W., Brugman, Wim, Casasent, Anna K., Clements, Karen, Davies, Helen R., Fu, Liping, Grigoriadis, Anita, Hardman, Timothy M., King, Lorraine M., Krete, Marielle, Kristel, Petra, de Maaker, Michiel, Maley, Carlo C., Marks, Jeffrey R., Menegaz, Brian A., Mulder, Lennart, Nieboer, Frank, Nowinski, Salpie, Pinder, Sarah, Quist, Jelmar, Salinas-Souza, Carolina, Schaapveld, Michael, Schmidt, Marjanka K., Shaaban, Abeer M., Shami, Rana, Sridharan, Mathini, Zhang, John, Stobart, Hilary, Collyar, Deborah, Nik-Zainal, Serena, Wessels, Lodewyk F. A., Hwang, E. Shelley, Navin, Nicholas E., Futreal, P. Andrew, Thompson, Alastair M., Wesseling, Jelle, and Sawyer, Elinor J.

Published
2022
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17. Generalized simultaneous component analysis of binary and quantitative data

Author

Song, Yipeng, Westerhuis, Johan A., Aben, Nanne, Wessels, Lodewyk F. A., Groenen, Patrick J. F., and Smilde, Age K.

Subjects
Statistics - Methodology
Abstract

In the current era of systems biological research there is a need for the integrative analysis of binary and quantitative genomics data sets measured on the same objects. One standard tool of exploring the underlying dependence structure present in multiple quantitative data sets is simultaneous component analysis (SCA) model. However, it does not have any provisions when a part of the data are binary. To this end, we propose the generalized SCA (GSCA) model, which takes into account the distinct mathematical properties of binary and quantitative measurements in the maximum likelihood framework. Like in the SCA model, a common low dimensional subspace is assumed to represent the shared information between these two distinct types of measurements. However, the GSCA model can easily be overfitted when a rank larger than one is used, leading to some of the estimated parameters to become very large. To achieve a low rank solution and combat overfitting, we propose to use a concave variant of the nuclear norm penalty. An efficient majorization algorithm is developed to fit this model with different concave penalties. Realistic simulations (low signal-to-noise ratio and highly imbalanced binary data) are used to evaluate the performance of the proposed model in recovering the underlying structure. Also, a missing value based cross validation procedure is implemented for model selection. We illustrate the usefulness of the GSCA model for exploratory data analysis of quantitative gene expression and binary copy number aberration (CNA) measurements obtained from the GDSC1000 data sets., Comment: 19 pages, 10 figures

Published
2018

18. Approximating multivariate posterior distribution functions from Monte Carlo samples for sequential Bayesian inference

Author

Thijssen, Bram and Wessels, Lodewyk F. A.

Subjects
Statistics - Computation
Abstract

An important feature of Bayesian statistics is the opportunity to do sequential inference: the posterior distribution obtained after seeing a dataset can be used as prior for a second inference. However, when Monte Carlo sampling methods are used for inference, we only have a set of samples from the posterior distribution. To do sequential inference, we then either have to evaluate the second posterior at only these locations and reweight the samples accordingly, or we can estimate a functional description of the posterior probability distribution from the samples and use that as prior for the second inference. Here, we investigated to what extent we can obtain an accurate joint posterior from two datasets if the inference is done sequentially rather than jointly, under the condition that each inference step is done using Monte Carlo sampling. To test this, we evaluated the accuracy of kernel density estimates, Gaussian mixtures, vine copulas and Gaussian processes in approximating posterior distributions, and then tested whether these approximations can be used in sequential inference. In low dimensionality, Gaussian processes are more accurate, whereas in higher dimensionality Gaussian mixtures or vine copulas perform better. In our test cases, posterior approximations are preferable over direct sample reweighting, although joint inference is still preferable over sequential inference. Since the performance is case-specific, we provide an R package mvdens with a unified interface for the density approximation methods.

Published
2017

19. Extensive androgen receptor enhancer heterogeneity in primary prostate cancers underlies transcriptional diversity and metastatic potential

Author

Kneppers, Jeroen, Severson, Tesa M., Siefert, Joseph C., Schol, Pieter, Joosten, Stacey E. P., Yu, Ivan Pak Lok, Huang, Chia-Chi Flora, Morova, Tunç, Altıntaş, Umut Berkay, Giambartolomei, Claudia, Seo, Ji-Heui, Baca, Sylvan C., Carneiro, Isa, Emberly, Eldon, Pasaniuc, Bogdan, Jerónimo, Carmen, Henrique, Rui, Freedman, Matthew L., Wessels, Lodewyk F. A., Lack, Nathan A., Bergman, Andries M., and Zwart, Wilbert

Published
2022
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22. DNA damage induces p53-independent apoptosis through ribosome stalling

Author

CMM Groep Snippert, Cancer, Onderzoek Medische Oncologie, MS Medische Oncologie, Boon, Nicolaas J, Oliveira, Rafaela A, Körner, Pierré-René, Kochavi, Adva, Mertens, Sander, Malka, Yuval, Voogd, Rhianne, van der Horst, Suzanne E M, Huismans, Maarten A, Smabers, Lidwien P, Draper, Jonne M, Wessels, Lodewyk F A, Haahr, Peter, Roodhart, Jeanine M L, Schumacher, Ton N M, Snippert, Hugo J, Agami, Reuven, Brummelkamp, Thijn R, CMM Groep Snippert, Cancer, Onderzoek Medische Oncologie, MS Medische Oncologie, Boon, Nicolaas J, Oliveira, Rafaela A, Körner, Pierré-René, Kochavi, Adva, Mertens, Sander, Malka, Yuval, Voogd, Rhianne, van der Horst, Suzanne E M, Huismans, Maarten A, Smabers, Lidwien P, Draper, Jonne M, Wessels, Lodewyk F A, Haahr, Peter, Roodhart, Jeanine M L, Schumacher, Ton N M, Snippert, Hugo J, Agami, Reuven, and Brummelkamp, Thijn R

Published
2024

23. Efficacy of pembrolizumab and biomarker analysis in patients with WGS-based intermediate to high tumor mutational load: results from the Drug Rediscovery Protocol

Author

Palliatieve Zorg, Pathologie Moleculair, Pathologie Pathologen staf, Cancer, Externen Med. Onco, Geurts, Birgit S, Zeverijn, Laurien J, Leek, Lindsay V M, van Berge Henegouwen, Jade M, Hoes, Louisa R, van der Wijngaart, Hanneke, van der Noort, Vincent, van de Haar, Joris, van Ommen-Nijhof, Annemiek, Kok, Marleen, Roepman, Paul, Jansen, Anne M L, de Leng, Wendy W J, de Jonge, Maja J A, Hoeben, Ann, van Herpen, Carla M L, Westgeest, Hans M, Wessels, Lodewyk F A, Verheul, Henk M W, Gelderblom, Hans, Voest, Emile E, Palliatieve Zorg, Pathologie Moleculair, Pathologie Pathologen staf, Cancer, Externen Med. Onco, Geurts, Birgit S, Zeverijn, Laurien J, Leek, Lindsay V M, van Berge Henegouwen, Jade M, Hoes, Louisa R, van der Wijngaart, Hanneke, van der Noort, Vincent, van de Haar, Joris, van Ommen-Nijhof, Annemiek, Kok, Marleen, Roepman, Paul, Jansen, Anne M L, de Leng, Wendy W J, de Jonge, Maja J A, Hoeben, Ann, van Herpen, Carla M L, Westgeest, Hans M, Wessels, Lodewyk F A, Verheul, Henk M W, Gelderblom, Hans, and Voest, Emile E

Published
2024

25. Preoperative ipilimumab plus nivolumab in locoregionally advanced urothelial cancer: the NABUCCO trial

Author

van Dijk, Nick, Gil-Jimenez, Alberto, Silina, Karina, Hendricksen, Kees, Smit, Laura A., de Feijter, Jeantine M., van Montfoort, Maurits L., van Rooijen, Charlotte, Peters, Dennis, Broeks, Annegien, van der Poel, Henk G., Bruining, Annemarie, Lubeck, Yoni, Sikorska, Karolina, Boellaard, Thierry N., Kvistborg, Pia, Vis, Daniel J., Hooijberg, Erik, Schumacher, Ton N., van den Broek, Maries, Wessels, Lodewyk F. A., Blank, Christian U., van Rhijn, Bas W., and van der Heijden, Michiel S.

Published
2020
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27. Publisher Correction: Truncated FGFR2 is a clinically actionable oncogene in multiple cancers

Author

Zingg, Daniel, Bhin, Jinhyuk, Yemelyanenko, Julia, Kas, Sjors M., Rolfs, Frank, Lutz, Catrin, Lee, Jessica K., Klarenbeek, Sjoerd, Silverman, Ian M., Annunziato, Stefano, Chan, Chang S., Piersma, Sander R., Eijkman, Timo, Badoux, Madelon, Gogola, Ewa, Siteur, Bjørn, Sprengers, Justin, de Klein, Bim, de Goeij-de Haas, Richard R., Riedlinger, Gregory M., Ke, Hua, Madison, Russell, Drenth, Anne Paulien, van der Burg, Eline, Schut, Eva, Henneman, Linda, van Miltenburg, Martine H., Proost, Natalie, Zhen, Huiling, Wientjens, Ellen, de Bruijn, Roebi, de Ruiter, Julian R., Boon, Ute, de Korte-Grimmerink, Renske, van Gerwen, Bastiaan, Féliz, Luis, Abou-Alfa, Ghassan K., Ross, Jeffrey S., van de Ven, Marieke, Rottenberg, Sven, Cuppen, Edwin, Chessex, Anne Vaslin, Ali, Siraj M., Burn, Timothy C., Jimenez, Connie R., Ganesan, Shridar, Wessels, Lodewyk F. A., and Jonkers, Jos

Published
2022
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28. DNA damage induces p53-independent apoptosis through ribosome stalling.

Author

Boon, Nicolaas J., Oliveira, Rafaela A., Körner, Pierré-René, Kochavi, Adva, Mertens, Sander, Malka, Yuval, Voogd, Rhianne, van der Horst, Suzanne E. M., Huismans, Maarten A., Smabers, Lidwien P., Draper, Jonne M., Wessels, Lodewyk F. A., Haahr, Peter, Roodhart, Jeanine M. L., Schumacher, Ton N. M., Snippert, Hugo J., Agami, Reuven, and Brummelkamp, Thijn R.

Published
2024

30. Loss of p53 triggers WNT-dependent systemic inflammation to drive breast cancer metastasis

Author

Wellenstein, Max D., Coffelt, Seth B., Duits, Danique E. M., van Miltenburg, Martine H., Slagter, Maarten, de Rink, Iris, Henneman, Linda, Kas, Sjors M., Prekovic, Stefan, Hau, Cheei-Sing, Vrijland, Kim, Drenth, Anne Paulien, de Korte-Grimmerink, Renske, Schut, Eva, van der Heijden, Ingrid, Zwart, Wilbert, Wessels, Lodewyk F. A., Schumacher, Ton N., Jonkers, Jos, and de Visser, Karin E.

Published
2019
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31. Immune induction strategies in metastatic triple-negative breast cancer to enhance the sensitivity to PD-1 blockade: the TONIC trial

Author

Voorwerk, Leonie, Slagter, Maarten, Horlings, Hugo M., Sikorska, Karolina, van de Vijver, Koen K., de Maaker, Michiel, Nederlof, Iris, Kluin, Roelof J. C., Warren, Sarah, Ong, SuFey, Wiersma, Terry G., Russell, Nicola S., Lalezari, Ferry, Schouten, Philip C., Bakker, Noor A. M., Ketelaars, Steven L. C., Peters, Dennis, Lange, Charlotte A. H., van Werkhoven, Erik, van Tinteren, Harm, Mandjes, Ingrid A. M., Kemper, Inge, Onderwater, Suzanne, Chalabi, Myriam, Wilgenhof, Sofie, Haanen, John B. A. G., Salgado, Roberto, de Visser, Karin E., Sonke, Gabe S., Wessels, Lodewyk F. A., Linn, Sabine C., Schumacher, Ton N., Blank, Christian U., and Kok, Marleen

Published
2019
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32. A network analysis to identify mediators of germline-driven differences in breast cancer prognosis

Author

Escala-Garcia, Maria, Abraham, Jean, Andrulis, Irene L., Anton-Culver, Hoda, Arndt, Volker, Ashworth, Alan, Auer, Paul L., Auvinen, Päivi, Beckmann, Matthias W., Beesley, Jonathan, Behrens, Sabine, Benitez, Javier, Bermisheva, Marina, Blomqvist, Carl, Blot, William, Bogdanova, Natalia V., Bojesen, Stig E., Bolla, Manjeet K., Børresen-Dale, Anne-Lise, Brauch, Hiltrud, Brenner, Hermann, Brucker, Sara Y., Burwinkel, Barbara, Caldas, Carlos, Canzian, Federico, Chang-Claude, Jenny, Chanock, Stephen J., Chin, Suet-Feung, Clarke, Christine L., Couch, Fergus J., Cox, Angela, Cross, Simon S., Czene, Kamila, Daly, Mary B., Dennis, Joe, Devilee, Peter, Dunn, Janet A., Dunning, Alison M., Dwek, Miriam, Earl, Helena M., Eccles, Diana M., Eliassen, A. Heather, Ellberg, Carolina, Evans, D. Gareth, Fasching, Peter A., Figueroa, Jonine, Flyger, Henrik, Gago-Dominguez, Manuela, Gapstur, Susan M., García-Closas, Montserrat, García-Sáenz, José A., Gaudet, Mia M., George, Angela, Giles, Graham G., Goldgar, David E., González-Neira, Anna, Grip, Mervi, Guénel, Pascal, Guo, Qi, Haiman, Christopher A., Håkansson, Niclas, Hamann, Ute, Harrington, Patricia A., Hiller, Louise, Hooning, Maartje J., Hopper, John L., Howell, Anthony, Huang, Chiun-Sheng, Huang, Guanmengqian, Hunter, David J., Jakubowska, Anna, John, Esther M., Kaaks, Rudolf, Kapoor, Pooja Middha, Keeman, Renske, Kitahara, Cari M., Koppert, Linetta B., Kraft, Peter, Kristensen, Vessela N., Lambrechts, Diether, Le Marchand, Loic, Lejbkowicz, Flavio, Lindblom, Annika, Lubiński, Jan, Mannermaa, Arto, Manoochehri, Mehdi, Manoukian, Siranoush, Margolin, Sara, Martinez, Maria Elena, Maurer, Tabea, Mavroudis, Dimitrios, Meindl, Alfons, Milne, Roger L., Mulligan, Anna Marie, Neuhausen, Susan L., Nevanlinna, Heli, Newman, William G., Olshan, Andrew F., Olson, Janet E., Olsson, Håkan, Orr, Nick, Peterlongo, Paolo, Petridis, Christos, Prentice, Ross L., Presneau, Nadege, Punie, Kevin, Ramachandran, Dhanya, Rennert, Gad, Romero, Atocha, Sachchithananthan, Mythily, Saloustros, Emmanouil, Sawyer, Elinor J., Schmutzler, Rita K., Schwentner, Lukas, Scott, Christopher, Simard, Jacques, Sohn, Christof, Southey, Melissa C., Swerdlow, Anthony J., Tamimi, Rulla M., Tapper, William J., Teixeira, Manuel R., Terry, Mary Beth, Thorne, Heather, Tollenaar, Rob A. E. M., Tomlinson, Ian, Troester, Melissa A., Truong, Thérèse, Turnbull, Clare, Vachon, Celine M., van der Kolk, Lizet E., Wang, Qin, Winqvist, Robert, Wolk, Alicja, Yang, Xiaohong R., Ziogas, Argyrios, Pharoah, Paul D. P., Hall, Per, Wessels, Lodewyk F. A., Chenevix-Trench, Georgia, Bader, Gary D., Dörk, Thilo, Easton, Douglas F., Canisius, Sander, and Schmidt, Marjanka K.

Published
2020
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35. Comparative oncogenomics identifies combinations of driver genes and drug targets in BRCA1-mutated breast cancer

Author

Annunziato, Stefano, de Ruiter, Julian R., Henneman, Linda, Brambillasca, Chiara S., Lutz, Catrin, Vaillant, François, Ferrante, Federica, Drenth, Anne Paulien, van der Burg, Eline, Siteur, Bjørn, van Gerwen, Bas, de Bruijn, Roebi, van Miltenburg, Martine H., Huijbers, Ivo J., van de Ven, Marieke, Visvader, Jane E., Lindeman, Geoffrey J., Wessels, Lodewyk F. A., and Jonkers, Jos

Published
2019
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38. Spatial relationships in the urothelial and head and neck tumor microenvironment predict response to combination immune checkpoint inhibitors

Author

Gil-Jimenez, Alberto, primary, van Dijk, Nick, additional, Vos, Joris L., additional, Lubeck, Yoni, additional, van Montfoort, Maurits L., additional, Peters, Dennis, additional, Hooijberg, Erik, additional, Broeks, Annegien, additional, Zuur, Charlotte L., additional, van Rhijn, Bas, additional, Vis, Daniel J., additional, van der Heijden, Michiel S., additional, and Wessels, Lodewyk F. A., additional

Published
2023
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39. Abstract 5786: Spatial relationships in the tumor microenvironment predict response to immune checkpoint inhibitors in urothelial and head and neck cancer

Author

Gil-Jimenez, Alberto, primary, van Dijk, Nick, additional, Lubeck, Yoni, additional, van Montfoort, Maurits L., additional, Peters, Dennis, additional, Hooijberg, Erik, additional, Broeks, Annegien, additional, Vos, Joris L., additional, Zuur, Charlotte L., additional, van Rhijn, Bas, additional, Vis, Daniel J., additional, van der Heijden, Michiel S., additional, and Wessels, Lodewyk F., additional

Published
2023
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40. Abstract 3488: Truncated FGFR2 - a clinically actionable oncogene in multiple cancers

Author

Zingg, Daniel Kaspar, primary, Bhin, Jinhyuk, additional, Yemelyanenko, Julia, additional, Kas, Sjors M., additional, Lutz, Catrin, additional, Lin, Chi-Chuan, additional, Klarenbeek, Sjoerd, additional, Lee, Jessica K., additional, Silverman, Ian M., additional, Annunziato, Stefano, additional, Ven, Marieke van de, additional, Ali, Siraj M., additional, Burn, Timothy C., additional, Ganesan, Shridar, additional, Wessels, Lodewyk F., additional, and Jonkers, Jos, additional

Published
2023
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49. Supplementary Information from Pretreatment microRNA Expression Impacting on Epithelial-to-Mesenchymal Transition Predicts Intrinsic Radiosensitivity in Head and Neck Cancer Cell Lines and Patients

Author

de Jong, Monique C., primary, ten Hoeve, Jelle J., primary, Grénman, Reidar, primary, Wessels, Lodewyk F., primary, Kerkhoven, Ron, primary, te Riele, Hein, primary, van den Brekel, Michiel W.M., primary, Verheij, Marcel, primary, and Begg, Adrian C., primary

Published
2023
Full Text
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