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<p class="MsoNormal">Please attached information on a new opportunity for a PhD position in Bioinformatics/Systems Biology within the Marie S. Curie ETN PhD training program GLIO-TRAIN, investigating at a systems levels molecular and biological difference in
glioblastoma between extreme. As you may know, these positions are well remunerated, and a structured program will be delivered.
<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Regards<o:p></o:p></p>
<p class="MsoNormal">Jochen Prehn<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Attached some information:<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Project : Systems-based integration and analysis of a deeply phenotyped GBM cohort correlating to ‘extreme’ and ‘poor’ responding patients
<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Location: Royal College of Surgeons in Ireland Principle Investigator: Prof Jochen Prehn (prehn@rcsi.ie)<o:p></o:p></p>
<p class="MsoNormal">Collaborators: Dr Alexander Kel (geneXplain, Germany), Dr Ahmed Idbaih (ICM Institute for Brain and Spinal Cord, France), Prof Diether Lambrechts (VIB Leuven).
<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Project Summary: The project will molecularly phenotype and functionally interrogate patient cohorts correlating to ‘extreme’ and ‘poor’ GBM responders. The strategy will employ the process of ‘natural pre-selection’ to identify key differences
in the underlying biology facilitating identification of new functional master drivers. Natural pre-selection been successful in describing metabolic aberrations leading to diabetes and obesity, but has thus far not been employed in GBM. To identify novel
GBM resistance mechanisms, the PhD student 9, hosted at RCSI will focus on deep phenotyping a cohort of gender balanced n=100 GBM patients grouped according to favourable or unfavourable clinical outcome despite similar (histo)pathological features. The PhD
student will be seconded to UPMC for 3 months to identify and prepare a collection of n=100 GBM samples for subsequent analysis. On return to RCSI, (s)he will commence molecular subtyping of this cohort of ‘poor’ and ‘extreme’ responders to include WES, RNA
seq, metabolomic profiling (NMR<o:p></o:p></p>
<p class="MsoNormal">spectroscopy) and reverse phase protein array -based protein and phosphoprotein analysis (>150 validated antibodies). The PhD student will employ deterministic models developed by RCSI to quantitatively identify key biological differences
between ‘poor’ and ‘extreme’ responders, and to relate these differences to GBM subtypes. Subsequently during a 5 month secondment to SME beneficiary GEX, PhD student 9 will link transcriptomic profiles to master transcription regulators and upstream, targetable
signalling pathways, Back at RCSI, (s)he will integrate outputs from molecular profiling, deterministic systems modelling and GEX platform analysis to define specific, targetable master drivers of ‘poor’ and ‘extreme’ responders and their interaction with
key signalling pathways and GBM subtypes. <o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Specific Requirements: <o:p></o:p></p>
<p class="MsoNormal"> • MSc or BSc in Bioinformatics / Human Genetics / (Bio)Engineering or a related discipline with preference given to those candidates with experience in Tumour Biology/Oncology.<o:p></o:p></p>
<p class="MsoNormal">• Experience with Analysis of Genomics Data / Next Generation Sequencing (RNA Seq, Shallow Sequencing etc) or Proteomics data.
<o:p></o:p></p>
<p class="MsoNormal"> • Programming skills and/or Statistical Analysis Methods are of advantage
<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">References:<o:p></o:p></p>
<p class="MsoNormal">Raoof R, Jimenez-Mateos EM, Bauer S, Tackenberg B, Rosenow F, Lang J, Onugoren MD, Hamer H, Huchtemann T, Körtvélyessy P, Connolly NMC, Pfeiffer S, Prehn JHM, Farrell MA, O'Brien DF, Henshall DC, Mooney C. Cerebrospinal fluid microRNAs
are potential biomarkers of temporal lobe epilepsy and status epilepticus. Sci Rep. 2017 Jun 12;7(1):3328 Tivnan A, Heilinger T, Ramsey JM, O'Connor G, Pokorny JL, Sarkaria JN, Stringer BW, Day BW, Boyd AW, Kim EL, Lode HN, Cryan SA, Prehn JH. Anti-GD2-ch14.18/CHO
coated nanoparticles mediate glioblastoma (GBM)-specific delivery of the aromatase inhibitor, Letrozole, reducing proliferation, migration and chemoresistance in patient-derived GBM tumor cells. Oncotarget. 2017 Mar 7;8(10):16605-16620.
<o:p></o:p></p>
<p class="MsoNormal">Haunsberger SJ, Connolly NM, Prehn JH. miRNAmeConverter: an R/Bioconductor package for translating mature miRNA names to different miRBase versions. Bioinformatics. 2017 Feb 15;33(4):592-593.
<o:p></o:p></p>
<p class="MsoNormal">Kinsella S, Prehn JH. In the Middle of a Chain Interaction. Mol Cell. 2016 Oct 20;64(2):217-218.
<o:p></o:p></p>
<p class="MsoNormal">Salvucci M, Würstle ML, Morgan C, Curry S, Cremona M, Lindner AU, Bacon O, Resler AJ, Murphy ÁC, O'Byrne R, Flanagan L, Dasgupta S, Rice N, Pilati C, Zink E, Schöller LM, Toomey S, Lawler M, Johnston PG, Wilson R, Camilleri-Broët S, Salto-Tellez
M, McNamara DA, Kay EW, Laurent-Puig P, Van Schaeybroeck S, Hennessy BT, Longley DB, Rehm M, Prehn JH. A Stepwise Integrated Approach to Personalized Risk Predictions in Stage III Colorectal Cancer. Clin Cancer Res. 2017 Mar 1;23(5):1200-1212.<o:p></o:p></p>
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