Monica Baciu-Dragan

Additional information

Publications: 

• oncotreeVIS – An interactive graphical user interface  for visualising mutation tree cohorts (in prep)
  Monica-Andreea Baciu-Drăgan and Niko Beerenwinkel 
  
  Recent developments in single-cell omics technologies have revealed, with increasing precision, the intra- and inter-tumor heterogeneity, including the evolutionary history of tumors and their cell phylogeny, represented by mutation trees. Several mutation tree inference tools can reconstruct the evolutionary relationships among different tumor subclonal populations, which share common genomic events. The inspection of these mutation trees at the cohort level helps understanding the genomic heterogeneity of tumors, an important task in computational oncology and an essential factor for tumor board clinical decisions. We propose oncotreeVIS, a command line tool and interactive graphical user interface for visualising mutation tree cohorts and tree posterior distributions obtained from tree inference tools. Our tool can be run locally for custom user input given in a predefined format, for mutation trees that encode single or joint genomic events such as point mutations, copy number changes or changes in gene expression levels and reflect different modes of tumor evolution governed by selection and by the spatial architecture of the tumor. By default we provide visualisations of several publicly available mutation tree cohorts. OncotreeVIS facilitates the visual inspection of mutation tree clusters and k-nearest neighbours around every mutation tree, together with their corresponding metadata, if available.

• oncotree2vec – A method for embedding and clustering of tumor phylogenetic trees (Bioinformatics, ISMB 2024)
  Monica-Andreea Baciu-Drăgan and Niko Beerenwinkel 
  
  Understanding the genomic heterogeneity of tumors is an important task in computational oncology, especially in the context of finding personalized treatments based on the genetic profile of each patient’s tumor. Tumor clustering that takes into account the temporal order of genetic events, as represented by tumor mutation trees, is a powerful approach for grouping together patients with genetically and evolutionarily similar tumors and can provide insights into discovering tumor subtypes, for more accurate clinical diagnosis and prognosis. Here, we propose oncotree2vec, a method for clustering tumor mutation trees by learning vector representations of mutation trees that capture the different relationships between subclones in an unsupervised manner. Learning low-dimensional tree embeddings facilitates the visualization of relations between trees in large cohorts and can be used for downstream analyses, such as deep learning approaches for single-cell multi-omics data integration. We assessed the performance and the usefulness of our method in three simulation studies, and on two real datasets: a cohort of 43 trees from six cancer types with different branching patterns corresponding to different modes of spatial tumor evolution and a cohort of 123 AML mutation trees.
  https://doi.org/10.1093/bioinformatics/btae214

• 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/10.1101/2020.02.13.20017921

• 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/10.1101/2020.10.12.335919
   

• 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/btw015