Summary
Tumours evolve, transforming from early-stage curable disease into treatment-refractory, deadly cancer. Therapy resistance is arguably the biggest problem in oncology today, and much of it remains unexplained.
The central hypothesis of this proposal is that a large proportion of unexplained drug resistance is due to heritable epigenetic alterations, and non-heritable transcriptional plasticity in cancer cells. I refer to these mechanisms as the dark matter of cancer evolution. Genetic, epigenetic and transcriptional adaptation, together with changes in the tumour microenvironment, may happen at the same time in the same tumour. Lack of knowledge of these mechanisms hinders the development of new treatments strategies. Tackling drug resistance requires a unique combination of clinical cohorts, experimental models, evolutionary biology and computational methods.
I will map and quantify the mechanisms and evolutionary dynamics of genetic and non-genetic drug resistance at unprecedented scale. I will focus on colorectal cancer, the third most common cancer and second leading cause of cancer-related death worldwide. I will use patient-derived organoid models, matched to clinical cohorts followed longitudinally. I will measure organoid evolution under the pressure of cancer drugs, with and without the tumour microenvironment. I will track cell lineages with lentiviral barcoding and perform longitudinal single cell multi-omics, measuring genomes, epigenomes and transcriptomes of the same cell. I will interpret the results within a unique computational framework that brings together evolutionary theory with machine learning to measure, predict and control resistance.
This project will identify new mechanisms and dynamics of cancer drug resistance, deliver new predictive models, and find novel collateral drug sensitivities. This will allow designing rational drug combinations and schedules that will prevent or delay resistance, drastically improving patient outcome.
The central hypothesis of this proposal is that a large proportion of unexplained drug resistance is due to heritable epigenetic alterations, and non-heritable transcriptional plasticity in cancer cells. I refer to these mechanisms as the dark matter of cancer evolution. Genetic, epigenetic and transcriptional adaptation, together with changes in the tumour microenvironment, may happen at the same time in the same tumour. Lack of knowledge of these mechanisms hinders the development of new treatments strategies. Tackling drug resistance requires a unique combination of clinical cohorts, experimental models, evolutionary biology and computational methods.
I will map and quantify the mechanisms and evolutionary dynamics of genetic and non-genetic drug resistance at unprecedented scale. I will focus on colorectal cancer, the third most common cancer and second leading cause of cancer-related death worldwide. I will use patient-derived organoid models, matched to clinical cohorts followed longitudinally. I will measure organoid evolution under the pressure of cancer drugs, with and without the tumour microenvironment. I will track cell lineages with lentiviral barcoding and perform longitudinal single cell multi-omics, measuring genomes, epigenomes and transcriptomes of the same cell. I will interpret the results within a unique computational framework that brings together evolutionary theory with machine learning to measure, predict and control resistance.
This project will identify new mechanisms and dynamics of cancer drug resistance, deliver new predictive models, and find novel collateral drug sensitivities. This will allow designing rational drug combinations and schedules that will prevent or delay resistance, drastically improving patient outcome.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101125077 |
| Start date: | 01-03-2024 |
| End date: | 28-02-2029 |
| Total budget - Public funding: | 1 995 582,00 Euro - 1 995 582,00 Euro |
Cordis data
Original description
Tumours evolve, transforming from early-stage curable disease into treatment-refractory, deadly cancer. Therapy resistance is arguably the biggest problem in oncology today, and much of it remains unexplained.The central hypothesis of this proposal is that a large proportion of unexplained drug resistance is due to heritable epigenetic alterations, and non-heritable transcriptional plasticity in cancer cells. I refer to these mechanisms as the dark matter of cancer evolution. Genetic, epigenetic and transcriptional adaptation, together with changes in the tumour microenvironment, may happen at the same time in the same tumour. Lack of knowledge of these mechanisms hinders the development of new treatments strategies. Tackling drug resistance requires a unique combination of clinical cohorts, experimental models, evolutionary biology and computational methods.
I will map and quantify the mechanisms and evolutionary dynamics of genetic and non-genetic drug resistance at unprecedented scale. I will focus on colorectal cancer, the third most common cancer and second leading cause of cancer-related death worldwide. I will use patient-derived organoid models, matched to clinical cohorts followed longitudinally. I will measure organoid evolution under the pressure of cancer drugs, with and without the tumour microenvironment. I will track cell lineages with lentiviral barcoding and perform longitudinal single cell multi-omics, measuring genomes, epigenomes and transcriptomes of the same cell. I will interpret the results within a unique computational framework that brings together evolutionary theory with machine learning to measure, predict and control resistance.
This project will identify new mechanisms and dynamics of cancer drug resistance, deliver new predictive models, and find novel collateral drug sensitivities. This will allow designing rational drug combinations and schedules that will prevent or delay resistance, drastically improving patient outcome.
Status
SIGNEDCall topic
ERC-2023-COGUpdate Date
12-03-2024
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