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PhD Course in Genomic Medicine 2016

Enlarged view: The medical research location Zurich

Abstract

Genomic technologies are not only changing the way biomedical research is performed, but they also have an increasing impact on medical diagnostics and treatment. However, using genomic data in a clinical setting is challenging. It requires an interdisciplinary approach, including knowledge of the measurement technologies, of computational and statistical methods for analyzing the data they produce, and of clinical procedures to translate the results into improved healthcare.

This 1-week PhD block course on "Genomic Medicine" has been designed to help preparing the next generation of clinical and biomedical researchers for this transformation. It addresses clinical as well as basic biomedical and computational scientists. The goal of the course is to provide a broad overview of Genomic Medicine and to prepare participants to contribute to Genomic Medicine in interdisciplinary teams.

Content

The course covered the following topics:

  • Omics technologies
  • Computational and statistical analysis of omics data
  • Biomarker discovery
  • Reproducibility
  • Genomic clinical trials
  • Precision medicine: genome-based treatment optimization
  • Ethical and legal aspects of Genomic Medicine
  • Commercial applications of Genomic Medicine
  • Genomic Medicine case studies

Prerequisites

None.

Credit Points

Upon successful completion of the performance assessment, students received 2 ECTS credit points.

Performance Assessment

During the week students worked in small groups on selected Genomic Medicine research papers. They solved tasks, presented and discussed their results on the last day in front of the plenum. Find the list of student tasks at the end of this page.

Date & Location

Mon, November 21 to Fri, November 25, 2016

ETH Zurich ML H 37.1 and in University Hospital Zurich

Lecturers

Student Tasks

Task Titles & Papers

  1. Reproducibility, Baggerly & Coombs, 2009
  2. Genome-wide Association Studies, Bush & Moore, 2012
  3. Ethics of Genome Editing (CRISPR/Cas9), Cyranoski, 2015 and Lander, 2015 - external pageNature News, 2015
  4. Clinical Trial, Eggermont et al., 2015
  5. Molecular Profiling of Omics Data, Crosetto et al., 2015
  6. Genomic Data Analysis, Fischer et al., 2014
  7. Cancer Personalized Medicine, Simon & Roychowdhury, 2015
  8. Genomics Expert Systems in HIV, Zazzi et al., 2011 and Rosen-Zvi et al., 2008
  9. Companion Diagnostics, Scher et al., 2011

Other resources

Literature

  • Baggerly, K. A., & Coombes, K. R. (2009). Deriving chemosensitivity from cell lines: Forensic bioinformatics and reproducible research in high-throughput biology. The Annals of Applied Statistics, 1309-1334.
  • Bush, W. S., & Moore, J. H. (2012). Chapter 11: Genome-wide association studies. PLoS Comput Biol, 8(12), e1002822.
  • Crosetto, N., Bienko, M., & van Oudenaarden, A. (2015). Spatially resolved transcriptomics and beyond. Nature Reviews Genetics, 16(1), 57-66.
  • Cyranoski, D. (2015). Ethics of embryo editing divides scientists. Nature, 519(7543), 272-272.
  • Eggermont, A. M., Chiarion-Sileni, V., Grob, J. J., Dummer, R., Wolchok, J. D., Schmidt, H., ... & Testori, A. (2015). Adjuvant ipilimumab versus placebo after complete resection of high-risk stage III melanoma (EORTC 18071): a randomised, double-blind, phase 3 trial. The Lancet Oncology, 16(5), 522-530.
  • Fischer, A., Vázquez-García, I., Illingworth, C. J., & Mustonen, V. (2014). High-definition reconstruction of clonal composition in cancer. Cell reports, 7(5), 1740-1752.
  • Lander, E. S. (2015). Brave New Genome. New England Journal of Medicine, 373(1), 5-8.
  • Rosen-Zvi, M., Altmann, A., Prosperi, M., Aharoni, E., Neuvirth, H., Sönnerborg, A., ... & Lengauer, T. (2008). Selecting anti-HIV therapies based on a variety of genomic and clinical factors. Bioinformatics, 24(13), i399-i406.
  • Scher, H. I., Nasso, S. F., Rubin, E. H., & Simon, R. (2011). Adaptive clinical trial designs for simultaneous testing of matched diagnostics and therapeutics. Clinical Cancer Research, 17(21), 6634-6640.
  • Simon, R., & Roychowdhury, S. (2013). Implementing personalized cancer genomics in clinical trials. Nature reviews Drug discovery, 12(5), 358-369.
  • Zazzi, M., Kaiser, R., Sönnerborg, A., Struck, D., Altmann, A., Prosperi, M., ... & Incardona, F. (2011). Prediction of response to antiretroviral therapy by human experts and by the EuResist data‐driven expert system (the EVE study). HIV medicine, 12(4), 211-218.
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