The Tumor Profiler Study: Integrated, multi-omic, functional tumor profiling for clinical decision support
By
Anja Irmisch,
Ximena Bonilla,
Stéphane Chevrier,
Kjong-Van Lehmann,
Franziska Singer,
Nora C Toussaint,
Cinzia Esposito,
Julien Mena,
Emanuela S Milani,
Ruben Casanova,
Daniel J Stekhoven,
Rebekka Wegmann,
Francis Jacob,
Bettina Sobottka,
Sandra Goetze,
Jack Kuipers,
Jacobo Sarabia del Castillo,
Michael Prummer,
Mustafa Tuncel,
Ulrike Menzel,
Andrea Jacobs,
Stefanie Engler,
Sujana Sivapatham,
Anja Frei,
Gabriele Gut,
Joanna Ficek,
Reinhard Dummer,
Tumor Profiler Consortium,
Rudolf Aebersold,
Marina Bacac,
Franziska Singer,
Christian Beisel,
Bernd Bodenmiller,
Viktor H Koelzer,
Holger Moch,
Lucas Pelkmans,
Berend Snijder,
Markus Tolnay,
Bernd Wollscheid,
Gunnar Rätsch,
Mitchell Paul Levesque
Posted 14 Feb 2020
medRxiv DOI: 10.1101/2020.02.13.20017921
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 patients 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 tumors underlying biology and for clinical decision support.
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