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Genome-wide surveys and functional analysis of pancreatic cancer metastasis drivers

Project description

Investigating the molecular basis of pancreatic cancer metastasis

The EU-funded PACA-MET project will investigate the genetic and molecular basis of metastasis in pancreatic ductal adenocarcinoma (PDAC). Scientists will employ sequencing and genome-wide screens to study the genes and pathways that drive metastasis in mouse models of the disease. The project will validate newly discovered genes using human patient PDAC cohorts, while functional characterisation of these genes at the organism level will help dissect the complexity of the metastatic cascade. PACA-MET is expected to identify key molecular networks implicated in PDAC metastasis and unveil novel therapeutic targets against this aggressive malignancy.

Objective

Metastasis is the major cause of death in pancreatic ductal adenocarcinoma (PDAC). International sequencing efforts on >800 human primaries gave comprehensive insights into PDAC genetics. In contrast, equivalent studies for “metastasis genetics” were not possible, largely because of a lack of metastatic tissue resources, particularly of treatment-naive ones. Another bottleneck is the scarcity of adequate experimental models recapitulating the multi-step nature of metastasis. As a consequence, the molecular basis of metastasis remains poorly understood.

We developed unique resources and tools for metastasis research and propose to use them at three levels to systematically interrogate the molecular underpinnings of PDAC metastasis.

We will first perform complementary genome-scale surveys for genes and pathways driving metastasis and metastatic organotropism. We will (i) sequence our unique, largely unpublished resource of 1200 metastatic mouse PDAC, (ii) will perform genome-wide in vivo metastasis screens using transposon tools and approaches, which we pioneered in mice, and (iii) will perturb the human metastasis transcriptome and epigenome.

Second, we will validate newly discovered genes using human PDAC cohorts, and through functional studies in mice. We will deploy next-generation metastasis models based on advanced somatic genome engineering. They allow rapid functional studies at an organismal level, thus capturing the complexity of the metastatic cascade.

Third, building on our recent discovery of two prototype PDAC metastasis drivers, we will perform in depth mechanistic studies to identify underlying molecular networks and vulnerabilities.

This work will unravel - for the first time - comprehensive genetic and functional landscapes of PDAC metastasis. PACA-MET thus promises to uncover fundamental novel biological principles and identify therapeutic targets for one of biggest challenges in medicine.

Fields of science (EuroSciVoc)

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Programme(s)

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Topic(s)

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Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

ERC-COG - Consolidator Grant

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) ERC-2018-COG

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Host institution

KLINIKUM DER TECHNISCHEN UNIVERSITÄT MÜNCHEN (TUM KLINIKUM)
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 1 995 875,00
Address
ISMANINGER STRASSE 22
81675 MUENCHEN
Germany

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Region
Bayern Oberbayern München, Kreisfreie Stadt
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 1 995 875,00

Beneficiaries (1)

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