The Erasmus MC Bridge-meeting series bridges the gap between Biology, Technology and the Clinic. Each session combines one presentation on biology and one on bioinformatics/technology or a combination of both. The Bridge meeting is organized under the auspices of MolMed and will be chaired by Arne IJpma, dept. of Pathology, Unit Bioinformatics or Harmen van de Werken, Cancer Computational Biology Center. The meeting is organized each second Tuesday of the month and will start at 11.00 a.m. and will end around 12.30 p.m. Afterwards, we will serve our 'interactive’ lunch. After permission is granted by the the speaker, the presentation will be available for download from this intranet site. Please remember that these presentations are only meant within the Erasmus MC. If you wish to share it outside the Erasmus MC, please contact the speaker concerned directly.
|08-01-2019||Wilfred van IJcken||Center for Biomics||New technologies for challenging low input FFPE samples|
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FFPE is the most common tissue preparation method for archiving research biospecimens. Many significant research programs make use of formalin-fixed, paraffin embedded samples. Although FFPE tissue samples are ideal for long-term most often cancer studies, there are several challenges that researchers may encounter using FFPE specimens for molecular research. Due to the formalin treatment the RNA, DNA and protein are cross linked, fragmented and degraded. Therefore FFPE material is considered of low quality and usually only a low amount is available for subsequent molecular analyses. Here, I will present two successful technologies enabling RNA and DNA next gen sequencing analyses on challenging FFPE samples. Characteristics of the methods will be discussed and results shared.
|Peter Valk||Department of Hematology||Molecular Minimal Residual Disease in Acute Myeloid Leukemia|
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BACKGROUND: Patients with acute myeloid leukemia (AML) often reach complete remission, but relapse rates remain high. Next-generation sequencing enables the detection of molecular minimal residual disease in virtually every patient, but its clinical value for the prediction of relapse has yet to be established. METHODS: We conducted a study involving patients 18 to 65 years of age who had newly diagnosed AML. Targeted next-generation sequencing was carried out at diagnosis and after induction therapy (during complete remission). End points were 4-year rates of relapse, relapse-free survival, and overall survival. RESULTS: At least one mutation was detected in 430 out of 482 patients (89.2%). Mutations persisted in 51.4% of those patients during complete remission and were present at various allele frequencies (range, 0.02 to 47%). The detection of persistent DTA mutations (i.e., mutations in DNMT3A, TET2, and ASXL1), which are often present in persons with age-related clonal hematopoiesis, was not correlated with an increased relapse rate. After the exclusion of persistent DTA mutations, the detection of molecular minimal residual disease was associated with a significantly higher relapse rate than no detection (55.4% vs. 31.9%; hazard ratio, 2.14; P<0.001), as well as with lower rates of relapse-free survival (36.6% vs. 58.1%; hazard ratio for relapse or death, 1.92; P<0.001) and overall survival (41.9% vs. 66.1%; hazard ratio for death, 2.06; P<0.001). Multivariate analysis confirmed that the persistence of non-DTA mutations during complete remission conferred significant independent prognostic value with respect to the rates of relapse (hazard ratio, 1.89; P<0.001), relapse-free survival (hazard ratio for relapse or death, 1.64; P=0.001), and overall survival (hazard ratio for death, 1.64; P=0.003). A comparison of sequencing with flow cytometry for the detection of residual disease showed that sequencing had significant additive prognostic value. CONCLUSIONS: Among patients with AML, the detection of molecular minimal residual disease during complete remission had significant independent prognostic value with respect to relapse and survival rates, but the detection of persistent mutations that are associated with clonal hematopoiesis did not have such prognostic value within a 4-year time frame.
|12-02-2019||Robert Kraaij & Carolina Medina-Gomez||Department of Internal Medicine||Gut microbiota in health and disease: the Generation R Study and Rotterdam Study cohorts|
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The omics approach to analyze complex microbiological ecosystems such as that of the human gut has broadened and deepened our understanding of the gut microbiome in health and disease in the past decade. Within Erasmus MC, two large microbiome datasets have been generated from fecal samples collected from 2,111 9-year-old children of the Generation R study and 1,427 adults of the Rotterdam Study. In our presentation, we will address the generation of these datasets, current difficulties in microbiome analyses and ongoing analyses. The potential of these datasets in the field of biomedical research will be emphasized by two different projects of which we will show new, unpublished data. The first links the Streptococcus sp. with the development and severity of osteoarthritis, a degenerative joint disease which is the leading cause of disability in elderly people. The second introduces the MiBioGen consortium, a collaborative effort to determine the role of host genetics in shaping the gut microbiome.
|Pim French||Department of Neurology||Predicting response to EGFR inhibitors|
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Although many tumours depend on EGFR signalling for growth, benefit from EGFR tyrosine kinase inhibitors (TKIs) is restricted to pulmonary adenocarcinoma patients harboring specific activating mutations only. Moreover, not all TKIs provide clinical benefit, even in the context of these ‘sensitive mutations’. We here describe, using high-throughput, high-content and automated imaging analysis, a simple in-vitro assay that can accurately predict which EGFR mutation is sensitive to which TKI. The addition of TKIs to mutated-EGFR expressing cells resulted in a rapid, massive and fully reversible formation of protein aggregates, but only with EGFR-mutations where clinical responses have been documented and only with TKIs with proven clinical benefit. We validated the predictive power of our assay firstly by predicting the sensitivity to TKIs in eleven different EGFR-mutated cell lines. We then show that our assay predicts clinical benefit to TKIs in EGFR-mutated pulmonary adenocarcinoma patients (median survival 7.0 vs 13 months, HR 0.21, P=0.0004). Finally, we predicted clinical responses in patients harboring rare mutations with unknown sensitivity to EGFR TKIs (n=7). All TKIs do however inhibit EGFR phosphorylation and downstream pathway activation, irrespective of the type of mutation present, as shown by western blot, RPPA and RT-qPCR. In summary, we describe an assay that can accurately predict response to targeted therapies in EGFR-mutated tumors and validated this assay in three independent experiments. Our results help guide treatment for EGFR-mutated cancer patients.
|12-03-2019||André Uitterlinden & Jeroen van Rooij||Department of Internal Medicine and/or Department of Neurology||GOALL for personalized medicine: genetic testing with SNP arrays|
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DNA analysis has become an important diagnostic tool in clinical medicine but wide-spread implementation has been hindered by costly techniques and limited knowledge on usefulness, especially for common diseases and traits. DNA arrays have now substantially lowered in price (down to <30 euro’s per DNA sample) and allow robust parallel assessment of large numbers of selected, information-rich DNA variations. Genome Wide Association studies (GWAS) over the past 15 years have generated knowledge that widen the scope of potential applications into the realm of the common diseases that clinicians see on a daily basis. These novel opportunities are recognized internationally and Erasmus MC has a leading position in this groundbreaking change in health care for several reasons which has led to the origin of the Genotyping on ALL patients (GOALL) project. GOALL represents a collaboration between the departments of Internal Medicine, Clinical Genetics, Clinical Chemistry and several other clinical departments, and has recently been selected to be part of Koers23. It envisions such DNA array analysis on eventually all patients that come into Erasmus MC so that its information can be used by clinicians for diagnosis and treatment, but also empowers patients with useful information to manage healthy lives. The outline, challenges, and opportunities of GOALL will be discussed by the prime participants.
|Fernando Rivadeneira||Department of Internal Medicine||Ae-406||N/A|
|09-04-2019||Sacha Gultyaev||Department of Viroscience||Evolution of viruses and RNA structure predictions.|
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A number of viruses have RNA genomes. Viral RNA genomes are characterized by relatively high rate of evolution as compared to DNA genomic sequences. The driving forces and constraints in RNA genome evolution are not only determined by the functions of encoded proteins, but also by higher-order structures formed by RNA molecules. These structures play a number of essential roles in replication of viruses. In this presentation, the computational approaches that we used to predict virus RNA folding on the basis of sequencing data will be described. The examples of predicted conserved functional structures in RNAs of different viruses (in particular, influenza viruses) will be given. A number of these structures and their functions were validated in experiments. Potential applications of RNA structural data for the understanding of virus evolution and development of antiviral strategies will be discussed.
|Floris Groenendijk||Department of Pathology||Tumor Mutational Burden analysis|
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Tumor mutational burden (TMB) has emerged as a promising biomarker to predict response to immune checkpoint inhibitors in a number of solid cancers. TMB is defined as the number of mutations per megabase in the tumor genome. TMB was found to correlate well with the outcome to immunotherapy in patients with advanced melanoma or lung cancer. This has led to the development of several testing platforms that detect TMB. These platforms are increasingly used to stratify patients into clinical trials and are incorporated into regular clinical care as a predictive marker. However, there is a lot of uncertainty on the best way to measure TMB, especially regarding the concordance between various gene panels and cut-offs used by different companies and clinical trials. I will discuss the rationale of TMB as a biomarker and the different approaches to measure TMB. I will share some of the experiences that we have in the Molecular Diagnostics department with TMB measurement, especially on the impact of the bioinformatics analysis pipeline. I will also present the results of an in-silico comparison we have performed to examine the impact of panel design and cut-off variability on concordance between different platforms and whole genome sequencing / exome sequencing based TMB measurements.
|14-05-2019||Arfan Ikram||Department of Epidemiology||Alzheimer’s disease: challenges and opportunities. An epidemiologist’s perspective||Ae-406||N/A|
|Monique Bernsen||Department of Radiology||Pre-clinical Imaging||Ae-406||N/A|
|10-09-2019||David van de Vijver||Department of Viroscience||Ae-406||N/A|
|Tokameh Mahmoudi||Departement of Biochemistry||Organoids||Ae-406||N/A|
|08-10-2019||Jan Hoeijmakers||Department of Molecular Genetics||Ae-406||N/A|
|12-11-2019||Ingrid van der Pluijm & de Graaf van de Laar||Department of Clinical Genetics & Department of Molecular Genetics and Department of Vascular Surgery||Ae-321||N/A|
|David Nieuwenhuijse||Department of Viroscience||Ae-321||N/A|
|10-12-2019||Maarten Fornerod||Department of Cell Biology||Ae-406||N/A|
|Harmen van de Werken||Cancer Computational Biology Center (CCBC) & Department of Urology||Omics of Solid tumors||Ae-406||N/A|