• Laurent Excoffier (Keynote)

    The perils of inferring past demography from genomic data

    The talk will review recent attempts at inferring past demography from whole genome data in humans and primates, and present evidence of potential problems due to selection and biased mutational processes

  • Session 6 – Genomics II

      Natalie Davidson. Integrative Analysis of Diverse Transcriptomic Alterations to Identify Cancer-Relevant Genes Across 27 Histotypes
      Kjong Lehmann. Assessing the effect of germline and somatic mutation on gene expression changes in 1,188 human tumours
      Tyler Funnell. Integrated single-nucleotide and structural variation signatures of DNA-repair deficient human cancers
      Harald Voehringer. TensorSignatures: a multidimensional tensor factorization framework for extraction of mutational signatures
      Nuria Lopez-Bigaz (Keynote). Coding and non-coding cancer mutations
  • Session 4 – Imaging

      Iman Hajirasouliha. Classification of Tumor Images using Deep Convolutional Neural Networks
      Yu Fu. Exploring the association between pathology images and genomic data in cancer
      Florian Markowetz (Keynote). Integrating genomics with radiology and pathology
  • Session 2 – Immunotherapy and other translational applications

    Session 2 – Immunotherapy and other translational applications

    • Pauline DepuydtGenomic amplifications and distal 6q loss are novel markers for poor survival in high-risk neuroblastoma patients
    • Shila GhazanfarDCSR: Differential correlation across survival ranking
    • Hanna NajgebauerCELLector: Genomics Guided Selection of Cancer in vitro Models
    • Marta Luksza (Keynote)Predicting cancer evolution from immune interactions
  • Nikolaus Rajewsky (Keynote)

    Single cell sequencing to discover mechanisms of gene regulation
  • Henry Rodriguez

    Introduction to the Proteogenomics challenge-


  • Ruedi Aebersold (Keynote)

    The proteome in context

    The question how genetic variability is translated into phenotypic variability is fundamental in biology and medicine. Powerful genomic technologies now determine variability at a genomic level and at unprecedented speed, accuracy and (low) cost.  Concurrently, life style monitoring devices and improved clinical diagnostic and imaging technologies generate phenotypic data at unprecedented volumes and resolution. However, the molecular mechanisms that translate genotypic variability into phenotypes are poorly understood and it has been generally  challenging to make phenotypic predictions from genomic information alone.

    Most biological processes are catalysed and controlled by proteins. This has led to the notion of “proteogenomics” a term that essentially links genomic variability to proteomic  variability. To date, proteogenomic efforts  have largely focused on the sequence level, using genomic sequences of a specific cell, tissue or organisms to precisely predict which protein and peptide are expected to be detectable in the specific sample.

    In this presentation we extend the notion of proteogenomics from the sequence level to the organization of the proteome into functional modules.  We define the term “proteotype” as a particular instance of a proteome in terms of its protein composition and organization of proteins into functional modules. We will discuss recent advances in SWATH/DIA mass spectrometry that support the fast, accurate and reproducible measurement of proteotypes. We will show with specific case studies  that i) the proteotype is highly modular, ii)  genotypic changes cause complex proteotype changes, particularly  at the level of proteotype organization  and iii) that altered proteotypes affect phenotypes.

    At present the generation of a general model or theory that makes accurate predictions of the effects of genotypic variability on the biochemical state of a cell or organism seems out of reach.  We therefore propose that precise proteotype measurement can serve as a close indicator of the biochemical state of a cell that reflects the response of the cell to (genomic) perturbation and is strong determinant of phenotypes.