Ximena Bonilla, MD, PhD
- ximena.bonilla@ inf.ethz.ch
Biomedical Informatics Group
- SHM 26 B 4
I’m interested in the study of diseases as complex systems using multidisciplinary approaches in the areas of 'omics, medicine, and analytical sciences. I focus on the understanding of key molecular pathways and the identification of drug targets that can ultimately impact patient management, survival, and quality of life.
I seek and enjoy cross-disciplinary experience and working in translational projects.
I obtained my PhD in human genetics at the University of Geneva as part of the NCCR-Frontiers in Genetics program (laboratory of Stylianos E. Antonarakis) for the genomic characterization of basal cell carcinoma of the skin, while also working in Mendelian genomics and somatic mosaicism as a disease mechanism. My background is in genetic diagnosis (MSc, Medical genetics, University of Glasgow, Scotland) and medicine (MD, General medicine, Universidad Autónoma de Coahuila, México). I joined the Rätsch lab in September 2018.
Abstract Abstract Motivation Recent technological advances have led to an increase in the production and availability of single-cell data. The ability to integrate a set of multi-technology measurements would allow the identification of biologically or clinically meaningful observations through the unification of the perspectives afforded by each technology. In most cases, however, profiling technologies consume the used cells and thus pairwise correspondences between datasets are lost. Due to the sheer size single-cell datasets can acquire, scalable algorithms that are able to universally match single-cell measurements carried out in one cell to its corresponding sibling in another technology are needed. Results We propose Single-Cell data Integration via Matching (SCIM), a scalable approach to recover such correspondences in two or more technologies. SCIM assumes that cells share a common (low-dimensional) underlying structure and that the underlying cell distribution is approximately constant across technologies. It constructs a technology-invariant latent space using an auto-encoder framework with an adversarial objective. Multi-modal datasets are integrated by pairing cells across technologies using a bipartite matching scheme that operates on the low-dimensional latent representations. We evaluate SCIM on a simulated cellular branching process and show that the cell-to-cell matches derived by SCIM reflect the same pseudotime on the simulated dataset. Moreover, we apply our method to two real-world scenarios, a melanoma tumor sample and a human bone marrow sample, where we pair cells from a scRNA dataset to their sibling cells in a CyTOF dataset achieving 93% and 84% cell-matching accuracy for each one of the samples respectively.
Authors Stefan G. Stark, Joanna Ficek, Francesco Locatello, Ximena Bonilla, Stéphane Chevrier, Franziska Singer, Tumor Profiler Consortium, Gunnar Rätsch, Kjong-Van Lehmann
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 patient9s 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 tumor9s underlying biology and for clinical decision support.
Authors Anja Irmisch, Ximena Bonilla, Stephane Chevrier, Kjong-Van Lehmann, Franziska Singer, Nora 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, Rene Holtackers, Gabriele Gut, Joanna Ficek, Reinhard Dummer, Rudolf Aebersold, Marina Bacac, Niko Beerenwinkel, Christian Beisel, Bernd Bodenmiller, Viktor H Koelzer, Holger Moch, Lucas Pelkmans, Berend Snijder, Markus Tolnay, Bernd Wollscheid, Gunnar Raetsch, Mitchell P Levesque, Tumor Profiler Consortium