Gene expression profi ling of acute myeloid leukemia

Hematopoïese, of de vorming van functionele bloedcellen, is een proces wat plaats vindt in het beenmerg. Hematopoïetische stamcellen ondergaan cycli van deling en differentiatie waarin de functionele eindcellen, zoals rode bloedcellen, bloedplaatjes en witte bloedcellen, worden gevormd. Leukemie is een ziekte waarbij de stamcellen abnormale processen van deling in combinatie met een stop van de differentiatie ondergaan, waardoor er de vorming van functionele eindcellen wordt belemmerd. In het geval van acute myeloïde leukemie (AML) is er een afwijking in de tak van bloedcelvorming waar onder andere rode bloedcellen, bloedplaatjes en granulocyten worden gevormd. De ontsporing van hematopoïetische stamcellen met AML als gevolg wordt veroorzaakt door abnormaliteiten in het genoom, zoals chromosomale fusies, deleties en mutaties. De klinische prognose wordt momenteel bepaald aan de hand van de aan- of afwezigheid van (combinaties van) abnormaliteiten. Het belangrijkste gevolg van genomische afwijkingen is de abnormale transcriptie van genen naar mRNA. Met behulpvan gen expressie profilering, door middel van microarrays, kunnen de transcriptie niveaus van duizenden genen simultaan worden bepaald. In hoofdstuk 2 is een onderzoek beschreven waarin met gen expressie profilering is toegepast op 285 beenmerg monsters van de novo AML patiënten, voor het bepalen van prognose. Verschillende bekende prognostische groepen, zoals t(8;21) en inv(16) konden worden geidentificeerd, alsmede een nieuwe prognostisch relevante groep van patiënten met een relatief slechte prognose (cluster 10).Hoofdstuk 2 laat zien dat gen expressie profilering in staat is om de huidige technieken voor het bepalen van prognose te vervangen, en prognose te verbeteren.Roeland George Willehad Verhaak was born in Wijchen, the Netherlands, on September 29 1976. After fi nishing his VWO education at the Kottenpark College in Enschede in 1996, he started a curriculum Biomedical Health Sciences at the Catholic University Nijmegen (KUN, currently Radboud University). As part of this education, he followed majors in pathobiology and toxicology, and a minor in computer science. A toxicology internship, titled ‘Mitochondrial toxicity of nuclease reverse transcriptase inhibitors, was completed at the Department of Pharmacology and Toxicology of the KUN under supervision of Dr. Roos Masereeuw. A second intership project, ‘Development of a diagnostic marker of multiple sclerosis’, was completed at the Department of Biochemistry, under supervision of Dr. Rinie van Boekel en Prof.dr. W. Van Venrooij. He obtained his Masters–degree in August 2000. After having started a project at the Department of Medical Informatics of the KUN in October 2000 in which he worked on structuring of temporal data, he switched to the bioinformatics company Dalicon BV in April 2002. At Dalicon, he worked as software engineer, with a particular focus at the database system SRS. In April 2003 he started a PhD-project at the Department of Hematology at the Erasmus MC in the lab of Prof.dr. Bob Löwenberg, supervised by Dr. Peter Valk. This work has been described in this thesis. From March 2006 until June 2006, he was a visiting scientist of the Department of Biostatistics and Computational Biology of the Dana-Farber Cancer Institute in Boston, supervised by Prof.dr. John Quackenbush. The author wil continue his academic career at the Broad Institute in Boston, a research collaboration of MIT, Harvard and its affiliated hospitals, and the Whitehead Institute

Glioma through the looking GLASS: molecular evolution of diffuse gliomas and the Glioma Longitudinal Analysis Consortium.

Adult diffuse gliomas are a diverse group of brain neoplasms that inflict a high emotional toll on patients and their families. The Cancer Genome Atlas and similar projects have provided a comprehensive understanding of the somatic alterations and molecular subtypes of glioma at diagnosis. However, gliomas undergo significant cellular and molecular evolution during disease progression. We review the current knowledge on the genomic and epigenetic abnormalities in primary tumors and after disease recurrence, highlight the gaps in the literature, and elaborate on the need for a new multi-institutional effort to bridge these knowledge gaps and how the Glioma Longitudinal Analysis Consortium (GLASS) aims to systemically catalog the longitudinal changes in gliomas. The GLASS initiative will provide essential insights into the evolution of glioma toward a lethal phenotype, with the potential to reveal targetable vulnerabilities and, ultimately, improved outcomes for a patient population in need

Reconstructing the molecular life history of gliomas.

At the time of their clinical manifestation, the heterogeneous group of adult and pediatric gliomas carries a wide range of diverse somatic genomic alterations, ranging from somatic single-nucleotide variants to structural chromosomal rearrangements. Somatic abnormalities may have functional consequences, such as a decrease, increase or change in mRNA transcripts, and cells pay a penalty for maintaining them. These abnormalities, therefore, must provide cells with a competitive advantage to become engrained into the glioma genome. Here, we propose a model of gliomagenesis consisting of the following five consecutive phases that glioma cells have traversed prior to clinical manifestation: (I) initial growth; (II) oncogene-induced senescence; (III) stressed growth; (IV) replicative senescence/crisis; (V) immortal growth. We have integrated the findings from a large number of studies in biology and (neuro)oncology and relate somatic alterations and other results discussed in these papers to each of these five phases. Understanding the story that each glioma tells at presentation may ultimately facilitate the design of novel, more effective therapeutic approaches. Acta Neuropathol 2018 May; 135(5):649-670

Integrative genomic analyses reveal clinically relevant long non-coding RNA in human cancer

Despite growing appreciations of the importance of long non-coding RNA (lncRNA) in normal physiology and disease, our knowledge of cancer-related lncRNA remains limited. By repurposing microarray probes, we constructed the expression profile of 10,207 lncRNA genes in approximately 1,300 tumors over four different cancer types. Through integrative analysis of the lncRNA expression profiles with clinical outcome and somatic copy number alteration (SCNA), we identified lncRNA that are associated with cancer subtypes and clinical prognosis, and predicted those that are potential drivers of cancer progression. We validated our predictions by experimentally confirming prostate cancer cell growth dependence on two novel lncRNA. Our analysis provided a resource of clinically relevant lncRNA for development of lncRNA biomarkers and identification of lncRNA therapeutic targets. It also demonstrated the power of integrating publically available genomic datasets and clinical information for discovering disease associated lncRNA

Perspective of mesenchymal transformation in glioblastoma.

Despite aggressive multimodal treatment, glioblastoma (GBM), a grade IV primary brain tumor, still portends a poor prognosis with a median overall survival of 12-16 months. The complexity of GBM treatment mainly lies in the inter- and intra-tumoral heterogeneity, which largely contributes to the treatment-refractory and recurrent nature of GBM. By paving the road towards the development of personalized medicine for GBM patients, the cancer genome atlas classification scheme of GBM into distinct transcriptional subtypes has been considered an invaluable approach to overcoming this heterogeneity. Among the identified transcriptional subtypes, the mesenchymal subtype has been found associated with more aggressive, invasive, angiogenic, hypoxic, necrotic, inflammatory, and multitherapy-resistant features than other transcriptional subtypes. Accordingly, mesenchymal GBM patients were found to exhibit worse prognosis than other subtypes when patients with high transcriptional heterogeneity were excluded. Furthermore, identification of the master mesenchymal regulators and their downstream signaling pathways has not only increased our understanding of the complex regulatory transcriptional networks of mesenchymal GBM, but also has generated a list of potent inhibitors for clinical trials. Importantly, the mesenchymal transition of GBM has been found to be tightly associated with treatment-induced phenotypic changes in recurrence. Together, these findings indicate that elucidating the governing and plastic transcriptomic natures of mesenchymal GBM is critical in order to develop novel and selective therapeutic strategies that can improve both patient care and clinical outcomes. Thus, the focus of our review will be on the recent advances in the understanding of the transcriptome of mesenchymal GBM and discuss microenvironmental, metabolic, and treatment-related factors as critical components through which the mesenchymal signature may be acquired. We also take into consideration the transcriptomic plasticity of GBM to discuss the future perspectives in employing selective therapeutic strategies against mesenchymal GBM

SNPExpress: integrated visualization of genome-wide genotypes, copy numbers and gene expression levels

Background: Accurate analyses of comprehensive genome-wide SNP genotyping and gene expression data sets is challenging for many researchers. In fact, obtaining an integrated view of both large scale SNP genotyping and gene expression is currently complicated since only a limited number of appropriate software tools are available. Results: We present SNPExpress, a software tool to accurately analyze Affymetrix and Illumina SNP genotype calls, copy numbers, polymorphic copy number variations (CNVs) and Affymetrix gene expression in a combinatorial and efficient way. In addition, SNPExpress allows concurrent interpretation of these items with Hidden-Markov Model (HMM) inferred Loss-of-Heterozygosity (LOH)- and copy number regions. Conclusion: The combined analyses with the easily accessible software tool SNPExpress will not only facilitate the recognition of recurrent genetic lesions, but also the identification of critical pathogenic genes