PI: Dr. Patrick Kemmeren
Mechanisms of genetic interactions in pediatric cancer
Cancers arise and progress through the acquirement of combinations of mutations. Genetic interactions are specific combinations of mutations that have unpredictable phenotypic consequences. Thorough understanding of genetic interactions, their underlying mechanisms and relation to pediatric cancer is crucial for deciphering cancer predisposition, onset and progression as well as for developing precision medicine approaches. Two genetic interaction types are of particular importance for cancer. Detailed understanding of cooperative interactions will aid in elucidating general principles governing cancer onset and progression. Understanding mutually exclusive interactions is pivotal for developing more effective cancer drugs. We investigate the role of genetic interactions and their contribution to different pediatric cancers. In particular, we focus on cooperative and mutually exclusive interactions between genes, pathways and processes altered in pediatric cancers as exhibited in cancer genomes of patients. We also include common variants associated with pediatric cancer and propose mechanistic models for cooperative and mutually exclusive interactions using a combination of bioinformatics, systems biology and wet-lab follow up verification and validation experiments.
Biobanking and genomics BIG data for diagnostics and research
The availability of a plethora of genomics technologies have drastically changed many life sciences, including cancer research, into a data-driven research field. Uniform and systematic access to these data and the underlying patient samples is essential for modern pediatric oncology research. For diagnostic purposes whole exome sequencing (WES) and RNA sequencing will be performed as standard of care. For research purposes, additional whole genome sequencing (WGS) and DNA methylation profiling will be performed. In both scenarios, primary patient material and research derived materials will be stored in a biobank and characterized at the molecular level through WES, WGS, RNA-seq and DNA methylation analyses. Together with the molecular diagnostics lab and Holstege lab , we are developing an integrated platform for sample tracking, data management, data integration and data analytics of these genomics BIG data.
Classification of pediatric tumor (sub)types
Pediatric cancers types are usually diagnosed based on histopathological features. These cancer types however frequently display clinically heterogenous behavior. Improved classification of pediatric cancer subtypes can lead to targeted therapeutic strategies and ultimately lead to an increase in survival. For diagnostic purposes, DNA methylation profiling in addition to whole exome sequencing and RNA sequencing is used for brain tumors. The aim is to perform DNA methylation profiling for solid tumors and potentially hematological tumors in the near future as well. Together with the molecular diagnostics lab and Holstege lab, we are currently developing DNA methylation analyses pipelines for both diagnostic and research purposes to discover and predict distinct tumor subtypes for improved cancer treatment.
Bioinformatics expertise core
The Bioinformatics expertise core consists of a number of bioinformaticians, computational scientists, bioinformatic analysts and data scientists that are part of the different research groups located within the Princess Máxima Center. Our aims are to create an expertise group of people all working in bioinformatics or systems biology, using a common set of rules and guidelines to facilitate code sharing and reuse as well as coordinating the core bioinformatics infrastructure needs for our research activities. These efforts are coordinated by the Kemmeren group and includes bioinformaticians and computational biologists from the Holstege, Kuiper, den Boer, Meijerink and van Boxtel groups.
A high-resolution gene expression atlas of epistasis between gene-specific transcription factors exposes potential mechanisms for genetic interactions. Sameith K, Amini S, Groot Koerkamp MJ, van Leenen D, Brok M, Brabers N, Lijnzaad P, van Hooff SR, Benschop JJ, Lenstra TL, Apweiler E, van Wageningen S, Snel B, Holstege FC, Kemmeren P. BMC Biology. 2015 Dec 23;13(1):112. PubMed PMID: 26700642
Large-scale genetic perturbations reveal regulatory networks and an abundance of gene-specific repressors. Kemmeren P, Sameith K, Pasch LA van de, Benschop JJ, Lenstra TL, Margaritis T, O'Duibhir E, Apweiler E, Wageningen S van, Ko CW, Heesch S van, Kashani MM, Ampatziadis-Michailidis G, Brok MO, Brabers NA, Miles AJ, Bouwmeester D, Hooff SR van, Bakel H van, Sluiters E, Bakker LV, Snel B, Lijnzaad P, Leenen D van, Groot Koerkamp MJ, Holstege FC Cell. 2014 Apr 24;157(3):740-52. PubMed PMID: 24766815
Yeast glucose pathways converge on the transcriptional regulation of trehalose biosynthesis. Apweiler E, Sameith K, Margaritis T, Brabers N, van de Pasch L, Bakker LV, van Leenen D, Holstege FC, Kemmeren P. BMC Genomics. 2012 Jun 14;13:239. PubMed PMID: 22697265
Functional overlap and regulatory links shape genetic interactions between signaling pathways. Wageningen S van, Kemmeren P, Lijnzaad P, Margaritis T, Benschop JJ, Castro IJ de, Leenen D van, Groot Koerkamp MJ, Ko C, Miles AJ, Brabers NAC, Brok MO, Lenstra TL, Fiedler D, Fokkens L, Aldecoa R, Apweiler E, Taliadouros V, Sameith K, Pasch LAL van de, Hooff SR van, Bakker LV, Krogan NJ, Snel B, Holstege FCP Cell. 2010 Dec 10;143(6):991-1004. PubMed PMID: 21145464
A consensus of core protein complex compositions for Saccharomyces cerevisiae. Benschop JJ, Brabers N, van Leenen D, Bakker LV, van Deutekom HW, van Berkum NL, Apweiler E, Lijnzaad P, Holstege FC, Kemmeren P. Molecular Cell. 2010 Jun 25;38(6):916-28. PubMed PMID: 20620961