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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.

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