Summary
Cancers arise through genetic and epigenetic alterations that drive the transformation of single cells into malignant tumors. Among genetic changes, copy number alterations (CNAs) are recurrent chromosomal events that increase or decrease the dosage of specific regions of DNA, can affect up to 30% of a cancer cell genome, and are associated with poor clinical outcomes. Despite their pervasiveness, the functional effects of specific CNAs on cancer phenotypes remain largely unknown, as current approaches cannot faithfully recapitulate the unique properties of these chromosomal alterations. Indeed, CNAs can uniquely affect the expression of hundreds of linked genes and change DNA topology, which in turn can promote intra-tumor heterogeneity as illustrated by random segregation of oncogenes in extra chromosomal DNA (ecDNA). In order to study the functional role of CNAs in cancer, this proposal employs MACHETE, a novel genome engineering toolkit that enables the generation of megabase-sized deletions, gains, and oncogene amplification in ecDNA. Using pancreatic ductal adenocarcinoma (PDAC) as a disease model, we will engineer the major CNAs in this lethal tumor to dissect their role in immune evasion, metastasis, and response to therapy. Additionally, through sequential engineering we will study whether the order of CNA acquisition leads to divergent or convergent phenotypes, a highly relevant yet unexplored aspect of cancer biology. Overall, by combining the MACHETE genome engineering platform with in vivo cancer models and molecular approaches, this proposal will begin to systematically dissect the function of recurrent CNAs in PDAC, with direct implications for therapy. Importantly, the methods and conceptual framework of this proposal are broadly applicable to other cancers and diseases characterized by similar chromosomal alterations, where understanding their underlying biology may lead to a new class of CNA-based clinical targets.
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| Web resources: | https://cordis.europa.eu/project/id/101041659 |
| Start date: | 01-01-2023 |
| End date: | 31-12-2027 |
| Total budget - Public funding: | 1 604 375,00 Euro - 1 604 375,00 Euro |
Cordis data
Original description
Cancers arise through genetic and epigenetic alterations that drive the transformation of single cells into malignant tumors. Among genetic changes, copy number alterations (CNAs) are recurrent chromosomal events that increase or decrease the dosage of specific regions of DNA, can affect up to 30% of a cancer cell genome, and are associated with poor clinical outcomes. Despite their pervasiveness, the functional effects of specific CNAs on cancer phenotypes remain largely unknown, as current approaches cannot faithfully recapitulate the unique properties of these chromosomal alterations. Indeed, CNAs can uniquely affect the expression of hundreds of linked genes and change DNA topology, which in turn can promote intra-tumor heterogeneity as illustrated by random segregation of oncogenes in extra chromosomal DNA (ecDNA). In order to study the functional role of CNAs in cancer, this proposal employs MACHETE, a novel genome engineering toolkit that enables the generation of megabase-sized deletions, gains, and oncogene amplification in ecDNA. Using pancreatic ductal adenocarcinoma (PDAC) as a disease model, we will engineer the major CNAs in this lethal tumor to dissect their role in immune evasion, metastasis, and response to therapy. Additionally, through sequential engineering we will study whether the order of CNA acquisition leads to divergent or convergent phenotypes, a highly relevant yet unexplored aspect of cancer biology. Overall, by combining the MACHETE genome engineering platform with in vivo cancer models and molecular approaches, this proposal will begin to systematically dissect the function of recurrent CNAs in PDAC, with direct implications for therapy. Importantly, the methods and conceptual framework of this proposal are broadly applicable to other cancers and diseases characterized by similar chromosomal alterations, where understanding their underlying biology may lead to a new class of CNA-based clinical targets.Status
SIGNEDCall topic
ERC-2021-STGUpdate Date
09-02-2023
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