Top

PrECISE





  • PrECISE

    Personalized Engine for Cancer Integrative Study and Evaluation




  • follow us on
    TWITTER

Welcome to PrECISE


The PrECISE project is a pilot project that combines hypothesis-driven strategies with data-driven analysis in a novel mathematical and computational methodology for the integration of genomic, epigenetic, transcriptomic, proteomic, and clinical data with the goal of risk-stratifying patients and suggesting personalized therapeutic interventions. We have the following specific objectives:

  • Development of a comprehensive computational methodology:
  • To integrate publicly available multi-omics datasets, well-characterized multiple-biopsies cohorts, and literature-driven knowledge powered by the Watson cognitive computer, developed at IBM.


  • Characterization of intra-tumour heterogeneity:
  • We will apply PrECISE to prostate cancer molecular cohorts where multiple biopsies have been generated from each patient.


  • Suggestion of chemotherapy drugs and targeted therapies for each patient:
  • We will investigate molecular mechanisms, identify suitable intervention points for therapy and suggest personalized therapies based on patient’s clonal signatures, and we will validate our predictions in a panel of prostatic cell lines.


  • Development of PrECISE into deployable, easy to use software tool:
  • We will integrate the developed computational modules with the Watson cognitive technology developed at IBM in a user-friendly interface and make PrECISE accessible to the clinical research community.


Welcome to PrECISE


Disclaimer


The information contained in this website and blog is for general information purposes only. The information on the website (and especially in the Blog section) is provided by members of the PrECISE consortium and while we endeavour to keep the information up to date and correct, we make no representations or warranties of any kind, express or implied, about the completeness, accuracy, reliability, suitability or availability with respect to the website or the information, products, services, or related graphics contained on the website for any purpose. Any reliance you place on such information is therefore strictly at your own risk.

In no event will we be liable for any loss or damage including without limitation, indirect or consequential loss or damage, or any loss or damage whatsoever arising from loss of data or profits arising out of, or in connection with, the use of this website.

Through this website you are able to link to other websites which are not under the control of the PrECISE consortium. We have no control over the nature, content and availability of those sites. The inclusion of any links does not necessarily imply a recommendation or endorse the views expressed within them. Please note that this also applies for entries in the Blog section that are made by the individual members of the PrECISE consortium. Such entries may contain information that do not necessarily reflect the view of the whole PrECISE consortium.

Every effort is made to keep the website up and running smoothly.

However, the PrECISE consortium takes no responsibility for, and will not be liable for, the website being temporarily unavailable due to technical issues beyond our control.


tec Technikon Forschungs- und Planungsgesellschaft mBH (Coordinator)
Villach (Austria)

ibm IBM Research GmbH
Rueschlikon (Switzerland)

tuda Technische Universität Darmstadt
Darmstadt (Germany)

ukaachen RWTH Aachen University Hospital
Aachen (Germany)

eth Eidgenoessische Technische Hochschule Zürich
Zuerich (Switzerland)

uzh Universität Zürich
Zuerich (Switzerland)

bcm Baylor College of Medicine
Houston, Texas (USA)

ci Institut Curie
Paris (France)

art Astridbio Technologies KFT
Budapest (Hungary)


Technical Deliverables (Public)

Upcoming:

D4.2 "Catalogue of molecular alterations and dysregulated pathways"

D5.2 "Proteomic data sets in cancer cell lines"

D5.3 "Patient-specific models"

D7.2 "Design and integrate pathway visualization"

Submitted:

D2.2 "Ultra-deep sequencing of prognostic biomarkers"
This report describes an effort to sequence prognostic biomarkers for prostate cancer. We designed a prognostically predictive biomarker panel that targets thirty-six commonly mutated predictive genes and used this panel to profile forty-two Castration Resistant Prostate Cancer (CRPC) patients.

D3.3 "First data-driven reconstruction of context-specific network"
This deliverable contains a description of the algorithms implemented for data-driven reconstruction of context–specific networks. The reconstruction of context–specific networks, e.g., prostate cancer–specific, treatment-specific) can help to better understand and describe complex disease progression, and ultimately reveal unknown interactions fundamental to the generation of new treatment hypotheses. In this work, we present a set of algorithms developed by the partners of the PrECISE consortium, and discuss the results of the analysis of various datasets using these devised algorithms.

D1.1 "Final regulatory network inference"
This report describes graphical user interfaces developed for genomic and clinical data entry for PrECISE.

D7.1 "Data input and input interface"
Prostate specific gene regulatory networks were inferred by integration of RNASeq data measurements from TCGA PRAD and ProCOC cohorts using HIPSTER framework.

D6.2 "Generate multiple punches from each validation sample in D6.1"
This deliverable isolates tissue samples for molecular profiling from the identified samples of the validation cohort (D6.1). The dissemination level of this deliverable is confidential.

D5.1 "Generic model"
Logical models are simple, require in principle no quantitative information, and can be hence applied to large networks combining multiple pathways. This deliverable presents some analyses made on a model, which is derived describing the network dynamics in specific contexts (dependent on initial conditions or perturbations for instance). Some physiological conditions are first simulated in order to validate the model. The validation concerns both the choices made on the topology of the network (players and the interactions), and on those on the logical rules that are associated to each of the variable of the model. Then, some modifications of the wild type are explored, corresponding to mutants. The model still has to be validated on patient or clone profiles, and on data by performing some computations on the activity of pathways in the PC39 patients.

D4.1 "Interactome of molecular interactions in prostate cancer"
The goal of this deliverable was to integrate patient data of the ProCOC cohort into a molecular map specific to prostate cancer. The integrated molecular map is also called network of molecular interactions or interactome. The interactome built from patient data is based on protein measurements of tumor samples from patients. By combining multiple inference methods a data-driven consensus interactome was constructed. To assess the performance of the inference methods we applied the methods to simulated data on known biological networks. To enrich the data driven interaction network, separate interactomes were extracted from publicly available interaction data bases and from publications. We compared the interactomes derived from data and publications to interaction networks from public databases. By combining the interaction networks derived from data, public databases and publications a consensus interactome was built.

D3.2 "Network reconstruction algorithms for MS data"
As modern biological-data measurement technologies have been generating more and more high-dimensional data in recent years, data-driven protein–protein interaction (PPI) network reconstruction has garnered a lot of interest. MS time-course perturbation data is expected to be made available in the course of the PrECISE project. Our goal is to develop network reconstruction algorithms appropriate for this kind of data. This report introduces two approaches that have been the focus of work at TUDA towards network reconstruction from MS data.

D2.1 "Targeted ultra-deep sequencing of cancer-gene loci"
We set out to test our ability to infer mutations and their clonality using 10 castrate-resistant prostate cancer (CRPC) tumor biopsies. Conclusions from studying these biopsies informed methodology for selecting additional tumors for profiling by exome sequencing, for predicting mutations and exome sequencing, and allowed us to devise criteria for estimating mutation cellularity (a necessary step for inferring clonality in WP1). Clonality inference is a building block for prognostic-biomarker inference in WP3, and tumor classification in WP4.

D6.1 "Select patient samples from PC patients and CRPC patients"
This document describes how clinical samples are identified and secured for the project. The dissemination level of this deliverable is confidential.

D3.1 "Computational pipeline to extract prior network information at the proteomic level"
This document gives an overview of the Omnipath database and its accompanying Python module Pypath. Omnipath gathers 54 resources, including 27 high-confidence literature curated signaling resources, providing and easy, unified and convenient entry point to much of the protein interaction knowledge available. Overall, Omnipath and Pypath greatly facilitate the integration and extraction of biological prior knowledge for analysis and model building, and they can be incorporated into wider data processing pipelines. Pypath and Omnipath are available here.

D8.2 "Data Management Plan (DMP)"
The purpose of the DMP is to provide an analysis of the main elements of the data management policy that will be used by the applications with regard to all the datasets that will be generated by the project. The DMP should ensure that most important aspects regarding data management, like metadata generation, data preservation, and responsibilities, are identified in an early stage of the project. This ensures that data is well-managed during the project and also beyond the end of the project. Data which will be generated in the course of the project include output data of random number generators, PUF output data, measurement data, and source code. As the DMP is an incremental tool, it will be adapted in the course of the project.

Ethics Deliverables

D10.1 "HCT Requirement No 2"
Details on cells/tissues type and authorisation by primary owner of data (including references to ethics approval) is provided.

D10.2 "POPD Requirement No 6"
Detailed information on the informed consent procedures that are implemented in regard to the collection, storage and protection of personal data has been submitted.

D10.3 "HCT Requirement No 3"
Details on cells/tissues type are provided, as well as details on the biobank to access it.

D10.4 "POPD Requirement No 5"
Detailed information is provided on the procedures that are implemented for data collection, storage, protection, retention and destruction and confirmation that they comply with national and EU legislation.

D10.5 "POPD Requirement No 4"
This deliverable includes the amendment summarizing the PrECISE project that has been approved by the Cantonal Ethics Committee of Zurich.

D10.6 "NEC Requirement No 8"
The deliverable confirms that the ethical standards and guidelines of Horizon2020 are rigorously applied, regardless of the country in which the research is carried out.

D10.7 "NEC Requirement No 9"
The deliverable provides details on the material which will be imported to/exported from EU.

D10.8 "POPD Requirement No 7"
The deliverable includes explanation why data is not publicly available.

D10.9 "HCT Requirement No 1"
Details on cells/tissues type and ethics approval is provided.



July 2017

11th - 12th of July 2016, Technical Meeting, Prague, Czech Republic

 

July 2016

11th - 12th of July 2016, Technical Meeting, Villach, Austria

 

January 2016

21st - 22nd of January 2016, Kick-Off Meeting, Rueschlikon, Switzerland

Partners

Sign up for e-mail news alerts and get the latest updates related to the PrECISE project.
tensunitdepot.com