Monica Baciu-Dragan

Additional information

Publications: 

• The tumor profiler study: integrated, multi-omic, functional tumor profiling for clinical decision support (medRxiv, 2021)
  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 A Tuncel, Ulrike Menzel, Andrea Jacobs, Stefanie Engler, Sujana Sivapatham, Anja L Frei, Gabriele Gut, Joanna Ficek, Nicola Miglino, Melike Ak, Faisal S Al-Quaddoomi, Jonas Albinus, Ilaria Alborelli, Sonali Andani, Per-Olof Attinger, Daniel Baumhoer, Beatrice Beck-Schimmer, Lara Bernasconi, Anne Bertolini, Natalia Chicherova, Maya D'Costa, Esther Danenberg, Natalie Davidson, Monica-Andreea Drăgan, Martin Erkens, Katja Eschbach, André Fedier, Pedro Ferreira, Bruno Frey, Linda Grob, Detlef Günther, Martina Haberecker, Pirmin Haeuptle, Sylvia Herter, Rene Holtackers, Tamara Huesser, Tim M Jaeger, Katharina Jahn, Alva R James, Philip M Jermann, André Kahles, Abdullah Kahraman, Werner Kuebler, Christian P Kunze, Christian Kurzeder, Sebastian Lugert, Gerd Maass, Philipp Markolin, Julian M Metzler, Simone Muenst, Riccardo Murri, Charlotte KY Ng, Stefan Nicolet, Marta Nowak, Patrick GA Pedrioli, Salvatore Piscuoglio, Mathilde Ritter, Christian Rommel, María L Rosano-González, Natascha Santacroce, Ramona Schlenker, Petra C Schwalie, Severin Schwan, Tobias Schär, Gabriela Senti, Vipin T Sreedharan, Stefan Stark, Tinu M Thomas, Vinko Tosevski, Marina Tusup, Audrey Van Drogen, Marcus Vetter, Tatjana Vlajnic, Sandra Weber, Walter P Weber, Michael Weller, Fabian Wendt, Norbert Wey, Mattheus HE Wildschut, Shuqing Yu, Johanna Ziegler, Marc Zimmermann, Martin Zoche, Gregor Zuend, Rudolf Aebersold, Marina Bacac, Niko Beerenwinkel, Christian Beisel, Bernd Bodenmiller, Reinhard Dummer, Viola Heinzelmann-Schwarz, Viktor H Koelzer, Markus G Manz, Holger Moch, Lucas Pelkmans, Berend Snijder, Alexandre PA Theocharides, Markus Tolnay, Andreas Wicki, Bernd Wollscheid, Gunnar Rätsch, Mitchell P Levesque
  
  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.
  https://doi.org/10.1101/2020.02.13.20017921call_made

• Within-patient genetic diversity of SARS-CoV-2 (bioRxiv, 2020)
  Kuipers, Jack; Batavia, Aashil A.; Jablonski, Kim Philipp; Bayer, Fritz; Borgsmüller, Nico; Dondi, Arthur; Drăgan, Monica-Andreea; Ferreira, Pedro; Jahn, Katharina; Lamberti, Lisa; Pirkl, Martin; Posada Cespedes, Susana; Topolsky, Ivan; Nissen, Ina; Santacroce, Natascha; Burcklen, Elodie; Schär, Tobias; Capece, Vincenzo; Beckmann, Christiane; Kobel, Olivier; Noppen, Christoph; Redondo, Maurice; Nadeau, Sarah Ann; Seidel, Sophie; Santamaria de Souza, Noemie; Beisel, Christian; Stadler, Tanja; Beerenwinkel, Niko 
  
  SARS-CoV-2, the virus responsible for the current COVID-19 pandemic, is evolving into different genetic variants by accumulating mutations as it spreads globally. In addition to this diversity of consensus genomes across patients, RNA viruses can also display genetic diversity within individual hosts, and co-existing viral variants may affect disease progression and the success of medical interventions. To systematically examine the intra-patient genetic diversity of SARS-CoV-2, we processed a large cohort of 3939 publicly-available deeply sequenced genomes with specialised bioinformatics software, along with 749 recently sequenced samples from Switzerland. We found that the distribution of diversity across patients and across genomic loci is very unbalanced with a minority of hosts and positions accounting for much of the diversity. For example, the D614G variant in the Spike gene, which is present in the consensus sequences of 67.4% of patients, is also highly diverse within hosts, with 29.7% of the public cohort being affected by this coexistence and exhibiting different variants. We also investigated the impact of several technical and epidemiological parameters on genetic heterogeneity and found that age, which is known to be correlated with poor disease outcomes, is a significant predictor of viral genetic diversity.
  https://doi.org/10.1101/2020.10.12.335919call_made
   

• GeneValidator: Identify problems with protein-coding gene predictions (Bioinformatics, 2016)
  Drăgan, Monica-Andreea; Moghul, Ismail; Priyam, Anurag; et al. 
  
  Summary: Genomes of emerging model organisms are now being sequenced at very low cost. However, obtaining accurate gene predictions remains challenging: even the best gene prediction algorithms make substantial errors and can jeopardize subsequent analyses. Therefore, many predicted genes must be time-consumingly visually inspected and manually curated. We developed GeneValidator (GV) to automatically identify problematic gene predictions from newly sequenced genomes.
  https://doi.org/10.1093/bioinformatics/btw015call_made