Genomic Rearrangement in Pancreatic Ductal Tumors
Pancreatic cancer remains the fourth leading cause of cancer-associated mortality in the United States. While prognosis has improved for other major cancers due to early diagnosis, better therapeutic management strategies, and a more comprehensive knowledge of genetic factors, death rates from pancreatic cancer continue to rise.
Approximately 90% of pancreatic cancers are ductal pancreatic adenocarcinomas (PDAC). Only 6% of patients survive five years post-diagnosis. Currently, only 15-20% of pancreatic cancers are diagnosed early enough to benefit from surgical resection, with the majority of tumors having already spread to the surrounding tissues or distant organs. Many somatic mutations have been detected in PDAC, leading to the identification of some key drivers of disease progression, but the involvement of large genomic rearrangements has often been overlooked.
Mayo Clinic researchers, first authors George Vasmatzis, Ph.D., and Fergus Couch, Ph.D., performed mate pair sequencing (MPseq) on genomic DNA from 24 PDAC tumors, including 15 laser-captured microdissected PDAC and 9 patient-derived xenografts, to identify genome-wide rearrangements. The study was published in the Cancer Research journal.
Large genomic rearrangements with intragenic breakpoints altering key regulatory genes involved in PDAC progression were detected in all tumors. SMAD4, ZNF521, and FHIT were among the most frequently hit genes. Conversely, commonly reported genes with copy number gains, including MYC and GATA6, were frequently observed in the absence of direct intragenic breakpoints, suggesting a requirement for sustaining oncogenic function during PDAC progression.
Based on the study results, a wide spectrum of genes was influenced by genomic rearrangements, with many key cancer genes hit directly by intragenic breakpoints. While minimal overlap was observed in genes mutated by rearrangements and point mutations, multiple commonly targeted pathways were identified, indicating the significance of both mutation types in driving PDAC progression.
Overall these results emphasize the needs for integrated data analyses including breakpoint, copy number variation and single nucleotide variant analysis, which together with transcriptome data enable better inference of mechanisms of PDAC progression.