Opendata, web and dolomites

SCIPER SIGNED

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

Total Cost €

0

EC-Contrib. €

0

Partnership

0

Views

0

 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.

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

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.

Are you the coordinator (or a participant) of this project? Plaese send me more information about the "SCIPER" project.

For instance: the website url (it has not provided by EU-opendata yet), the logo, a more detailed description of the project (in plain text as a rtf file or a word file), some pictures (as picture files, not embedded into any word file), twitter account, linkedin page, etc.

Send me an  email (fabio@fabiodisconzi.com) and I put them in your project's page as son as possible.

Thanks. And then put a link of this page into your project's website.

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

More projects from the same programme (H2020-EU.1.1.)

ChaperoneRegulome (2020)

ChaperoneRegulome: Understanding cell-type-specificity of chaperone regulation

Read More  

CohoSing (2019)

Cohomology and Singularities

Read More  

MOCHA (2019)

Understanding and leveraging ‘moments of change’ for pro-environmental behaviour shifts

Read More