Objective
High-grade serous ovarian cancer (HGSOC), which is the most aggressive type of ovarian cancer, is characterized by the mutation of gene TP53 and extensive copy number variations (CNVs). HGSOC tumors typically show an initial favourable response to standard treatments, however they often acquire resistance and relapse. Currently, there is a need for new therapeutic approaches to combat emerging drug-resistant subpopulations. Although, the scarcity of common targetable oncogenic mutations has complicated the development of directed therapies, the study of CNVs offers a promising opportunity to find new mechanisms of resistance and develop alternative treatments, as it has been described how CNVs can model clonal fitness and therapeutic resistance in other types of cancer.
Here, I will explore the impact of the CNVs on the treatment response of HGSOC patients and their distal effects on the transcriptome using convolutional neural networks. This complex machine learning model will be trained with the largest available longitudinal cohort of HGSOC samples at the single-cell resolution and will reveal which CNVs, and their specific combinations, have a relevant role in shaping the HGSOC tumours upon treatment. Employing a systems biology approach I will identify convergent phenotypes within these relevant CNV profiles and then validate their effects on treatment using data from both external cohorts and drug-treated patient derived organoid models. This novel approach enables revealing resistance mechanisms driven by complex genotypes, and thus allows finding specific vulnerabilities to combat emerging resistance in HGSOC.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences biological sciences genetics RNA transcriptomes
- natural sciences biological sciences genetics mutation
- medical and health sciences clinical medicine oncology
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
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.
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.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-MSCA-2021-PF-01
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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.
00014 HELSINGIN YLIOPISTO
Finland
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.