Integration of Multi-omics Data for Prediction of Metabolic Traits

<p>In biomarker research, the goal is to construct an prediction rule on the basis of a small number of predictors. Formally, this means representing a macro-level response as a function of molecular features (DNA variants, transcript or protein abundancies) with minimal error. The aim is to develop a framework for selection of a composite biomarker: an ensemble of small number of predictors, that is able to predict the macro-level response.</p> <p>To benchmark the process of construction of the composite biomarker, we use a mouse model. Mouse model has an advantage<br> over human samples, as many confounding factors are controlled. Here we use measurements of 35 murine strains<br> from the BXD recombinant inbred strain panel exposed to high-fat and chow diets. As explanatory variable set we use molecular profile of liver, and as response variables, we have selected 7 phenotypic traits related to metabolism.</p> <p> </p> <p><strong>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 668858. This work was supported (in part) by the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract number 15.0324-2. The opinions expressed and arguments employed therein do not necessarily reflect the official views of the Swiss Government.</strong><br>  </p

Proteome heterogeneity in benign and malignant prostate tissue

<p>Intra-tumor heterogeneity (ITH) has been characterized at the morphologic and genomic level. However, it is unclear how genomic heterogeneity is translated into functional proteome ITH. We addressed this question by performing a multi-region proteomic analysis of 60 biopsy-scale tissue samples from three prostate cancer patients using pressure cycling technology (PCT) and SWATH mass spectrometry. We quantified the degree of ITH for 1,906 proteins in malignant and benign tissue. The majority of proteins displayed a relatively low degree of ITH and benign tissue exhibited generally more complex patterns of ITH than malignant tissue. Further, we developed an ITH-corrected protein fold-change measure and demonstrated in an independent patient cohort that this new measure rescued potentially clinically relevant protein markers and stratified patients. This study established a strategy for quantifying proteome-scale ITH, generated a data resource of the proteomic ITH in prostate cancers, and demonstrated the value of considering ITH for tumor characterization.</p> <p><strong>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 668858. This work was supported (in part) by the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract number 15.0324-2. The opinions expressed and arguments employed therein do not necessarily reflect the official views of the Swiss Government.</strong></p> <p> </p