Project description
Neuroblastoma: hitting the bull’s eye with more than one ‘bullet’
Cancer stands a leading cause of illness and death globally, with the incidence increasing across many types. A key factor contributing to the failure of therapies, drug resistance and cancer’s lethality is intratumour heterogeneity – the existence of different cell populations with different molecular and phenotypical profiles within the same solid tumour. The ERC-funded Precision Lethality project aims to leverage cell barcoding ¬¬– tagging individual cells of interest with unique genetic identifiers or barcodes – and deep learning to address this challenge. Insight should help the team to identify and verify new drugs for combinatorial drug treatments to combat neuroblastoma, the most common extracranial solid paediatric tumour.
Objective
Tumors are heterogeneous in nature due to genetic instabilitTumors are heterogeneous in nature due to genetic instability, ongoing selection, and variable microenvironments with local adaptation. Many tumors thus necessitate combinatorial drug treatments to reach all cells. This is true also for neuroblastoma, the most common extracranial solid pediatric tumor. Despite the well-known importance of clonal heterogeneity, most in vitro drug combination strategies, including commonly used synergistic interaction, do not quantify subclonal drug response. Here recently developed cell barcoding strategies will be used to monitor survival of thousands of individual subclones in neuroblastoma organoids, under different drug perturbations. This lineage tracing strategy allows identification of cell populations that survive a specific drug, and to find other drugs that specifically target those cell populations. I call this strategy Precision Lethality.
In this research program two different precision lethality methodologies will be developed, one where cell barcoding is used to identify drug combinations with low cross-resistance, and one that uses the possibility to express the barcode so it can be captured by single cell RNA sequencing. This allows assessing the transcriptional state before drug perturbation of cells that are known to be either drug resistant or drug sensitive. Combined with publicly available drug sensitivity data repositories, this will be used to construct a deep learning model to predict drug sensitivities of individual cells.
Successful completion of this multi-disciplinary research program will identify and verify new drugs to complement standard of care consolidation therapy for neuroblastoma. I also envision that these studies will showcase to the broader cancer research community how cell barcoding can be used as a drug combination strategy to overcome clonal heterogeneity, which would increase the chances of finding curative cancer treatments.
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.
- medical and health sciences clinical medicine oncology
- natural sciences biological sciences genetics RNA
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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)
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Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
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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.
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Call for proposal
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Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2023-STG
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171 77 STOCKHOLM
Sweden
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