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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.

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

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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