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

Total Cost €


EC-Contrib. €






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

Project "SCIPER" data sheet

The following table provides information about the project.


Organization address
address: Raemistrasse 101
postcode: 8092

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


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 


 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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