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dc.contributor.author
Irmisch, Anja
dc.contributor.author
Bonilla, Ximena
dc.contributor.author
Chevrier, Stéphane
dc.contributor.author
Lehmann, Kjong-Van
dc.contributor.author
Singer, Franziska
dc.contributor.author
Toussaint, Nora Christina
dc.contributor.author
Esposito, Cinzia
dc.contributor.author
Mena, Julien
dc.contributor.author
Milani, Emanuela S.
dc.contributor.author
Casanova, Ruben
dc.contributor.author
Stekhoven, Daniel J.
dc.contributor.author
Wegmann, Rebekka
dc.contributor.author
Jacob, Francis
dc.contributor.author
Sobottka, Bettina
dc.contributor.author
Goetze, Sandra
dc.contributor.author
Kuipers, Jack
dc.contributor.author
Sarabia del Castillo, Jacobo
dc.contributor.author
Prummer, Michael
dc.contributor.author
Tuncel, Mustafa
dc.contributor.author
Menzel, Ulrike
dc.contributor.author
Jacobs, Andrea
dc.contributor.author
Engler, Stefanie
dc.contributor.author
Sivapatham, Sujana
dc.contributor.author
Frei, Anja
dc.contributor.author
Gut, Gabriele
dc.contributor.author
Ficek, Joanna
dc.contributor.author
Dummer, Reinhard
dc.contributor.author
Tumor Profiler Consortium
dc.contributor.author
Aebersold, Rudolf
dc.contributor.author
Bacac, Marina
dc.contributor.author
Beerenwinkel, Niko
dc.contributor.author
Beisel, Christian
dc.contributor.author
Bodenmiller, Bernd
dc.contributor.author
Koelzer, Viktor H.
dc.contributor.author
Moch, Holger
dc.contributor.author
Pelkmans, Lucas
dc.contributor.author
Snijder, Berend
dc.contributor.author
Tonay, Markus
dc.contributor.author
Wollscheid, Bernd
dc.contributor.author
Rätsch, Gunnar
dc.contributor.author
Levesque, Mitchell
dc.date.accessioned
2021-03-02T06:26:58Z
dc.date.available
2021-01-22T11:18:46Z
dc.date.available
2021-02-26T06:55:14Z
dc.date.available
2021-03-02T06:26:58Z
dc.date.issued
2020-02-14
dc.identifier.other
10.1101/2020.02.13.20017921
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/464734
dc.description.abstract
Recent technological advances allow profiling of tumor samples to an unparalleled level with respect to molecular and spatial composition as well as treatment response. We describe a prospective, observational clinical study performed within the Tumor Profiler (TuPro) Consortium that aims to show the extent to which such comprehensive information leads to advanced mechanistic insights of a patient’s tumor, enables prognostic and predictive biomarker discovery, and has the potential to support clinical decision making. For this study of melanoma, ovarian carcinoma, and acute myeloid leukemia tumors, in addition to the emerging standard diagnostic approaches of targeted NGS panel sequencing and digital pathology, we perform extensive characterization using the following exploratory technologies: single-cell genomics and transcriptomics, proteotyping, CyTOF, imaging CyTOF, pharmacoscopy, and 4i drug response profiling (4i DRP). In this work, we outline the aims of the TuPro study and present preliminary results on the feasibility of using these technologies in clinical practice showcasing the power of an integrative multi-modal and functional approach for understanding a tumor’s underlying biology and for clinical decision support.Competing Interest StatementThe authors have declared no competing interest.Clinical TrialBASEC-Nr.2018-02050Funding StatementThe study described in this paper is the result of a jointly-funded effort between several academic institutions (The University of Zurich, The University of Zurich Hospital, The Swiss Federal Institute of Technology in Zurich, The University of Basel Hospital, and The University of Basel), as well as F. Hoffmann-La Roche AG.Author DeclarationsAll relevant ethical guidelines have been followed; any necessary IRB and/or ethics committee approvals have been obtained and details of the IRB/oversight body are included in the manuscript.YesAll necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesThe manuscript details a prospetive outlook for a study that is currently underway. However, the data will be made available upon study completion and publication.
en_US
dc.language.iso
en
en_US
dc.publisher
Cold Spring Harbor Laboratory Press
en_US
dc.title
The Tumor Profiler Study: Integrated, multi-omic, functional tumor profiling for clinical decision support
en_US
dc.type
Working Paper
ethz.journal.title
medRxiv
ethz.size
37 p.
en_US
ethz.publication.place
Cold Spring Harbor, NY
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02661 - Institut für Maschinelles Lernen / Institute for Machine Learning::09568 - Rätsch, Gunnar / Rätsch, Gunnar
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02030 - Dep. Biologie / Dep. of Biology::02539 - Institut für Molecular Health Sciences / Institute of Molecular Health Sciences
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02030 - Dep. Biologie / Dep. of Biology::02539 - Institut für Molecular Health Sciences / Institute of Molecular Health Sciences::09735 - Bodenmiller, Bernd / Bodenmiller, Bernd
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02661 - Institut für Maschinelles Lernen / Institute for Machine Learning::09568 - Rätsch, Gunnar / Rätsch, Gunnar
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02030 - Dep. Biologie / Dep. of Biology::02539 - Institut für Molecular Health Sciences / Institute of Molecular Health Sciences::09735 - Bodenmiller, Bernd / Bodenmiller, Bernd
ethz.relation.isPreviousVersionOf
handle/20.500.11850/524767
ethz.date.deposited
2021-01-22T11:18:54Z
ethz.source
BATCH
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2021-02-26T06:55:23Z
ethz.rosetta.lastUpdated
2022-03-29T05:31:43Z
ethz.rosetta.versionExported
true
ethz.COinS
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