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SCIPER SIGNED

Studying Cancer Individuality by Personal and Predictive Drug Screening and Differential OMICs

Total Cost €

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EC-Contrib. €

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Partnership

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 SCIPER project word cloud

Explore the words cloud of the SCIPER project. It provides you a very rough idea of what is the project "SCIPER" about.

validation    relevance    tools    clinical    amenable    internal    ones    therapies    aggressive    throughput    microscopy    types    inference    malignancies    quantify    multicellular    individuality    combine    causal    govern    multiplexed    medicine    burdens    precision    predictive    automated    sequencing    interventional    ex    hematologic    critically    cell    proteomic    memory    exposure    ineffective    treatment    computational    endangers    integration    individual    learning    receive    governing    cellular    population    mechanistic    incompletely    rna    prior    neutralizing    reaching    approval    patients    confocal    machine    omics    single    vivo    convolutional    hundreds    comparisons    molecular    phenotypic    powerful    drug    harmful    image    physiological    cancer    patient    alone    platform    trial    malignant    multiclass    reveals    culturing    preserve    screening    neural    healthy    cells    biopsies    first    healthcare    disentangles    led    sub    maximize    principles    confounding    sorting    autonomous    immunofluorescence    profiling    enabled    network    small    lives    determinants    prevents   

Project "SCIPER" data sheet

The following table provides information about the project.

Coordinator
EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH 

Organization address
address: Raemistrasse 101
city: ZUERICH
postcode: 8092
website: https://www.ethz.ch/de.html

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Switzerland [CH]
 Total cost 1˙500˙000 €
 EC max contribution 1˙500˙000 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2018-STG
 Funding Scheme ERC-STG
 Starting year 2018
 Duration (year-month-day) from 2018-11-01   to  2023-10-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH CH (ZUERICH) coordinator 1˙500˙000.00

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 Project objective

The cellular and molecular systems that determine drug responses in cancer are complex, highly individual, and incompletely understood. As a result, many cancer patients receive ineffective or even harmful therapies, which endangers lives, burdens healthcare systems, and prevents new therapies from reaching clinical approval.

To address this problem, we are developing a platform that measures hundreds of ex vivo drug responses from small patient biopsies by immunofluorescence, automated confocal microscopy, single-cell image analysis, and machine learning. We preserve cellular memory and maximize physiological relevance by not culturing or sorting cells prior to drug exposure. Sub-cellular, single-cell, and cell population-wide image analysis reveals on-target drug responses and disentangles multicellular ones. In a first interventional clinical trial, this phenotypic information alone led to strongly improved treatment of patients with aggressive hematologic malignancies.

Enabled by this high-throughput, predictive, and phenotypic information, I here propose to identify the molecular and cellular systems that govern treatment response individuality in cancer. (Aim 1) We will combine drug response profiling with RNA sequencing and proteomic measurements of malignant and healthy cells from the same biopsies. Critically, the patient-internal comparisons in both screening and OMICs allow neutralizing complex confounding factors. (Aim 2) New multiplexed immunofluorescence and convolutional neural network-based analyses will identify multiclass cell-types and -states, and quantify non-cell-autonomous responses. (Aim 3) Computational integration and causal inference will identify the molecular determinants and governing principles of drug response individuality in cancer, amenable to further validation. This proposal will thus improve our mechanistic understanding of cancer individuality and develop powerful new tools for OMICs-based precision medicine.

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The information about "SCIPER" are provided by the European Opendata Portal: CORDIS opendata.

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