Summary
The composition and molecular features of tumours vary during the course of the disease, and the selection pressure imposed by the environment is a central component in this process. Evolutionary principles have been exploited to explain the genomic aberrations in cancer. However, the phenotypic changes underlying disease progression remain poorly understood. In the past years, I have contributed to identify and characterise the therapeutic implications underlying metabolic alterations that are intrinsic to primary tumours or metastasis. In CancerADAPT I postulate that cancer cells rely on adaptive transcriptional & metabolic mechanisms [converging on a Metabolic Phenotype] in order to rapidly succeed in their establishment in new microenvironments along disease progression. I aim to predict the molecular cues that govern the adaptive properties in prostate cancer (PCa), one of the most commonly diagnosed cancers in men and an important source of cancer-related deaths. I will exploit single cell RNASeq, spatial transcriptomics and multiregional OMICs in order to identify the transcriptional and metabolic diversity within tumours and along disease progression. I will complement experimental strategies with computational analyses that identify and classify the predicted adaptation strategies of PCa cells in response to variations in the tumour microenvironment. Metabolic phenotypes postulated to sustain PCa adaptability will be functionally and mechanistically deconstructed. We will identify therapeutic strategies emanating from these results through in silico methodologies and small molecule high-throughput screening, and evaluate their potential to hamper the adaptability of tumour cells in vitro and in vivo, in two specific aspects: metastasis and therapy response. CancerADAPT will generate fundamental understanding on how cancer cells adapt in our organism, in turn leading to therapeutic strategies that increase the efficacy of current treatments.
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| Web resources: | https://cordis.europa.eu/project/id/819242 |
| Start date: | 01-11-2019 |
| End date: | 31-10-2025 |
| Total budget - Public funding: | 1 999 882,00 Euro - 1 999 882,00 Euro |
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Original description
The composition and molecular features of tumours vary during the course of the disease, and the selection pressure imposed by the environment is a central component in this process. Evolutionary principles have been exploited to explain the genomic aberrations in cancer. However, the phenotypic changes underlying disease progression remain poorly understood. In the past years, I have contributed to identify and characterise the therapeutic implications underlying metabolic alterations that are intrinsic to primary tumours or metastasis. In CancerADAPT I postulate that cancer cells rely on adaptive transcriptional & metabolic mechanisms [converging on a Metabolic Phenotype] in order to rapidly succeed in their establishment in new microenvironments along disease progression. I aim to predict the molecular cues that govern the adaptive properties in prostate cancer (PCa), one of the most commonly diagnosed cancers in men and an important source of cancer-related deaths. I will exploit single cell RNASeq, spatial transcriptomics and multiregional OMICs in order to identify the transcriptional and metabolic diversity within tumours and along disease progression. I will complement experimental strategies with computational analyses that identify and classify the predicted adaptation strategies of PCa cells in response to variations in the tumour microenvironment. Metabolic phenotypes postulated to sustain PCa adaptability will be functionally and mechanistically deconstructed. We will identify therapeutic strategies emanating from these results through in silico methodologies and small molecule high-throughput screening, and evaluate their potential to hamper the adaptability of tumour cells in vitro and in vivo, in two specific aspects: metastasis and therapy response. CancerADAPT will generate fundamental understanding on how cancer cells adapt in our organism, in turn leading to therapeutic strategies that increase the efficacy of current treatments.Status
SIGNEDCall topic
ERC-2018-COGUpdate Date
27-04-2024
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