Evolutionary dynamics of pancreatic cancer at single-cell and spatial levels

Descripción del proyecto

Descifrar la heterogeneidad genómica en el cáncer

Las células cancerosas se caracterizan por una elevada frecuencia de mutaciones y reordenamientos cromosómicos que conducen a la diversidad y heterogeneidad genéticas. Pruebas recientes sugieren que los reordenamientos genómicos complejos (RGC) pueden impulsar la evolución del adenocarcinoma ductal pancreático (PDAC, por sus siglas en inglés), la forma más común y mortal de cáncer de páncreas. El equipo del proyecto EVO-PDAC, financiado por las Acciones Marie Skłodowska-Curie, pretende aclarar el papel de los RGC, las variantes estructurales y las mutaciones puntuales en la progresión del PDAC. Utilizando técnicas de vanguardia, los investigadores crearán atlas multiómicos de PDAC en diferentes estadios. Los resultados ayudarán a los investigadores a comprender la evolución del PDAC y la aparición de la heterogeneidad intratumoral.

Objetivo

Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer, a disease with a dismal prognosis. PDAC has an especially high tendency of quickly metastasizing and becoming resistant to therapy, and the mechanisms by which this occurs are largely unknown. Recent evidence suggests that saltatory or burst-like evolution mediated by complex genomic rearrangements (CGRs) may be especially relevant in PDAC, altering multiple cancer genes in a single event. However, quantifying CGRs requires specialized techniques (such as Strand-Seq), and therefore the relative importance of CGRs, simple structural variants, and point mutations in PDAC evolution remains unclear. In this project, we will use a genetically engineered mouse model (GEMM) that develops de novo PDAC to longitudinally track PDAC evolution by single-cell and spatial multi-omic methods, using Strand-Seq to accurately detect CGRs. Thus, we aim to create longitudinal single-cell and spatial multi-omic atlases of PDAC at three key evolutionary steps: pre-cancer, therapy-naive PDAC, and post-therapy PDAC. In addition, we will compare our atlases to human PDAC data from the Spatial And Temporal Resolution of Intratumoral Heterogeneity in 3 hard-to-treat Cancers (SATURN3) Consortium. In this way, we will model PDAC evolution at various time points, quantifying the relative importance of gradual vs saltatory evolution and determining how evolvability changes over time. Moreover, we will infer the role of selective pressures from the tumor microenvironment (e.g. immune cells and cancer-associated fibroblasts) in shaping PDAC evolution. Finally, we will explore how all these factors interact with the high intratumoral heterogeneity observed in PDAC. Overall, this project will answer long-standing questions about the evolutionary dynamics of PDAC.