Technical Deliverables (Public)
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"
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.
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.