25 February 2016
Pancreatic cancer is the fourth most common cause of cancer death in Western societies. With a very low five-year survival rate of <5%, there is an urgent need to better understand the cancer’s underlying mechanisms and to develop new therapies.
Now, a breakthrough study of over 450 pancreatic cancer genomes, published today in Nature, has shown that pancreatic cancer is in fact four distinct diseases, which may be differentially susceptible to particular therapies.
The research, which was led by Australian researchers from the Australian Pancreatic Cancer Genome Initiative (APGI) and the Garvan Institute of Medical Research, along with the University of Melbourne and the University of Queensland, also defines ten genetic pathways that are key to the transformation of normal pancreatic tissue into cancer.
Study participants were recruited through the Garvan-based Australian Pancreatic Cancer Genome Initiative (APGI), a part of the International Cancer Genome Consortium. The authors carried out comprehensive genomic analysis, including whole genome sequencing and RNA expression profiling, on 456 pancreatic cancers of study participants.
The analysis identified four distinct tumour subtypes – ‘squamous’, ‘pancreatic progenitor’, ‘immunogenic’ and ‘ADEX’ (aberrantly differentiated endocrine exocrine). Importantly, the four subtypes were associated with distinct histopathological characteristics and differential outcomes.
Amber Johns, of APGI and Garvan, says, “This paper marks a huge step forward in our understanding of pancreatic cancer. We have previously shown that pancreatic cancer is not one disease but several – but now we have identified distinct subgroups in detail, along with the genetic drivers that underpin them.
“This research is critical to the development of precision medicine for pancreatic cancer – essentially, it has the potential to take the guesswork out and make it possible to customise treatment approaches for individual patients.
“For APGI, the challenge now is to develop strategies to apply these findings in real-life clinical settings. We are working actively on ways to use big datasets, such as one highlighted in this paper, to improve outcomes for patients with pancreatic cancer.”