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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.

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

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

Map

 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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