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Biomarker discovery by AI-guided, image based single-cell isolation proteomics

Biomarker discovery by AI-guided, image based single-cell isolation proteomics

Objective

Early detection of severe malignancies such as cancer is the most effective way to increase patient survival, but early diagnosis and prediction of treatment outcome critically depend on disease-specific biomarkers. However, molecular and cellular disease heterogeneity provide a ubiquitous and unresolved challenge to this important task, and therefore impede any attempt to develop personalized therapies. Past and current approaches provide “averaged” descriptions of the tumor composition and have shown very limited success to identify biomarkers. This is likely due to the failure of these methods to identify the critical disease promoting cell populations within the tumor. Therefore, I will develop a new workflow that exploits automated microscopic image acquisition and artificial-intelligence-guided image analysis to identify specific cell populations in patient samples. These cells are then individually isolated by laser microdissection, followed by high-sensitivity proteome profiling, to identify proteins that define the identity of individual cells in a given tumor and thus represent the most promising biomarker candidates. To apply my approach to wide array of diseases, I will optimize it for archival biobank tissues (FFPE), the most common form of solid tissues in pathology. Applied to FFPE samples, my approach will allow me to perform both prospective and retrospective studies, correlate disease state and tissue morphology to protein expression and clinical outcome, and map tumor heterogeneity with unprecedented resolution. To achieve this, I will receive world-class training in cutting-edge microscopy and machine learning techniques in my host laboratory, which I complement with my expertise in high-sensitivity proteomics. My new pipeline will offer a highly fertile ground for new biomarker discoveries, inspire and stimulate collaborative research within and outside the host institute and allow me to establish a highly competitive niche for my future career.
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Coordinator

KOBENHAVNS UNIVERSITET

Address

Norregade 10
1165 Kobenhavn

Denmark

Activity type

Higher or Secondary Education Establishments

EU Contribution

€ 207 312

Project information

Grant agreement ID: 846795

Status

Ongoing project

  • Start date

    1 April 2019

  • End date

    31 March 2021

Funded under:

H2020-EU.1.3.2.

  • Overall budget:

    € 207 312

  • EU contribution

    € 207 312

Coordinated by:

KOBENHAVNS UNIVERSITET

Denmark