Abstract

Pancreatic cancer is one of the most malignant and aggressive forms of cancers that is not amenable to any of the available therapeutics. The vast majority of patients present with highly advanced or metastatic disease with no curative treatment options. Pancreatic cancer's low prognosis rate of five years post diagnosis has been mainly attributed to high metastasis rate and drug resistance. The molecular mechanisms underlying its aggressive nature are mostly unknown and under investigation. The completion of the human genome project at the turn of the century ushered in a new era in which researchers exploited genetic, epigenetic, and proteomic expression profiling methodologies in pancreatic cells, advanced animal models, and patient cohorts leading to a generation of unprecedented amount of datasets. Such analyses have allowed for the evaluations of pancreas samples at much greater molecular depths. However, these efforts have yet to yield any meaningful or tangible outcome. One of the primary reasons for such poor progress is that barring few isolated cases, most of these large-scale expression studies were performed with a reductionist view. The complexity of the disease requires holistic approaches that are expected to allow better understanding of the different components that drive heterogeneous pancreas tumors. In this chapter we highlight the importance of systems and network biology in pancreas cancer diagnostics, prevention, and therapy. We conclude with an example of how systems biology can be helpful in understanding pancreatic cancer heterogeneity, especially in the analyses of highly resistant pancreatic cancer stem-like cells.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.