Nature CommunicationsCancer BiologyPeer-reviewed journal article
Clonal evolutionary analysis reveals patterns of malignant transformation in pancreatic cancer from Intraductal Papillary Mucinous IPMN Neoplasms (IPMN)
Antonio PeaiD, Xiaotong T. HeiD, Rosie Upstill-Goddard, Claudio LuchiniiD, Leonor Patricia Schubert Santana, Stephan B. DreyeriD, Fraser Duthie, Nigel Balfour JamiesoniD, Colin J. McKay, Euan J. Dickson, Alessandra PulvirentiiD, Selma RebusiD, Genomics Innovation Alliance, Scottish Genome Partnership, Paola Piccoli, Nicola Sperandio, Rita T. LawloriD, Michele Milella³, Fieke E. M. FroelingiD, Roberto SalviaiD, Aldo ScarpaiD, Andrew V. Bainkin, David C. Wedge, David K. Chang
Abstract
Intraductal papillary mucinous neoplasms (IPMNs) are critical precursors to pancreatic ductal adenocarcinoma (PDAC), a highly lethal cancer due to late detection and rapid progression. Using multi-region whole-genome and transcriptome sequencing, we traced the evolution of PDAC from IPMN, constructing detailed phylogenetic trees to provide insights into subclonal architectures and progression pathways. Our analysis identified two distinct evolutionary trajectories: one driven by a single ancestral clone, and another involving multiple independent ancestral clones, potentially influencing the timing and nature of PDAC onset. We further explored the roles of mutational signatures and structural variants (SVs) in promoting clonal evolution. Complementing these genomic findings, our transcriptomic analysis revealed unique gene expression profiles and variations in the immune landscape, correlating with the different progression stages from IPMN to PDAC. These insights reveal the complex molecular dynamics of IPMN progression to PDAC, highlighting the need to refine early detection and treatment strategies.
- organism: human
- assay: whole-genome sequencing
- assay: bulk RNA-seq
- research focus: chromatin & epigenetic regulation
- research focus: clustering
- research focus: gene regulatory network inference
- approach: statistical model
- approach: genetic perturbation / CRISPR
- Citations
- 2
- FWCI
- 4.33
- Field percentile
- top 7%
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