Summary
It is the ambition of PRESIST-SEQ to provide a new gold standard in single-cell experimental workflows the cancer research community by developing best practices, standard operating procedures (SOPs), and high-quality FAIR data, with the ultimate aim to empower them to unravel therapeutic resistance. Such, that the community can identify urgently needed markers to predict, prevent, and target tumour resistance. Cancer takes 9.6 million lives each year, 90% of which result from untreatable metastatic relapse occurring after initially (seemingly) effective treatment. Therapeutic resistance is hence a primary cause of cancer death that clinically cannot be predicted, prevented, or treated. Addressing the urgent need for smarter therapeutic strategies is however held back by the lack of standardised experimental approaches that enable studying the biology of residual disease and drug tolerant persister cells in full detail. This need encompasses best practices for single-cell sequencing, advanced modelling techniques using patient-derived organoids and xenografts, and data FAIRification for integrated experiments. To address this need, PERSIST-SEQ brings together globally leading groups in single-cell sequencing technologies, cancer modelling and therapeutic resistance. Furthermore, the consortium has a broad range of clinical samples, cell lines, 3D models (PDX and PDOs) and mice models (GEMMs) at its disposal that can be leveraged to answer a broad range of emerging questions. This positions the consortium excellently to (1) design and standardise single-cell experimental approach to study the biology of therapeutic resistance and (2) initiate the largest single-cell profiling initiative on therapeutic resistance. Importantly, PERSIST-SEQ is organised such that it can quickly adapt to emerging insights and techniques during the project, and that ensures the capture of learnings in manners that stimulate replication of workflows elsewhere.
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| Web resources: | https://cordis.europa.eu/project/id/101007937 |
| Start date: | 01-07-2021 |
| End date: | 30-06-2026 |
| Total budget - Public funding: | 14 031 380,00 Euro - 7 057 980,00 Euro |
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Original description
It is the ambition of PRESIST-SEQ to provide a new gold standard in single-cell experimental workflows the cancer research community by developing best practices, standard operating procedures (SOPs), and high-quality FAIR data, with the ultimate aim to empower them to unravel therapeutic resistance. Such, that the community can identify urgently needed markers to predict, prevent, and target tumour resistance. Cancer takes 9.6 million lives each year, 90% of which result from untreatable metastatic relapse occurring after initially (seemingly) effective treatment. Therapeutic resistance is hence a primary cause of cancer death that clinically cannot be predicted, prevented, or treated. Addressing the urgent need for smarter therapeutic strategies is however held back by the lack of standardised experimental approaches that enable studying the biology of residual disease and drug tolerant persister cells in full detail. This need encompasses best practices for single-cell sequencing, advanced modelling techniques using patient-derived organoids and xenografts, and data FAIRification for integrated experiments. To address this need, PERSIST-SEQ brings together globally leading groups in single-cell sequencing technologies, cancer modelling and therapeutic resistance. Furthermore, the consortium has a broad range of clinical samples, cell lines, 3D models (PDX and PDOs) and mice models (GEMMs) at its disposal that can be leveraged to answer a broad range of emerging questions. This positions the consortium excellently to (1) design and standardise single-cell experimental approach to study the biology of therapeutic resistance and (2) initiate the largest single-cell profiling initiative on therapeutic resistance. Importantly, PERSIST-SEQ is organised such that it can quickly adapt to emerging insights and techniques during the project, and that ensures the capture of learnings in manners that stimulate replication of workflows elsewhere.Status
SIGNEDCall topic
IMI2-2020-20-04Update Date
26-10-2022
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