This webinar will demonstrate a new approach that combines precise FFPE tumor isolation with extraction-free DNA/RNA library preparation to minimize material losses and reduce the amount of tissue input required for NGS analysis.
The need to process small quantities of solid tumor specimens is increasing as early detection strategies become more effective and less invasive biopsy strategies are adopted. Moreover, the rapidly changing landscape of molecular testing points towards a need for minimizing sample input and preservation of sample for future testing. Processing small areas of dissected tumor can be challenging as traditional manual macrodissection and purification methods include multiple steps during which material can be lost.
In this webinar, Bryan Lo, Medical Director of the Molecular Oncology Diagnostics Lab at the Ottawa Hospital, will present a new approach that combines automated tissue dissection with NGS library prepared directly from fragments of dissected tissue.
Dr. Lo will discuss a study that demonstrated that gene expression profiling of pancreatic cancer and precursor lesions extracted by automated tissue dissection system yielded highly correlated data across tissue samples. Furthermore, mutation screening of dissected tissue fragments from melanoma and colorectal cancer showed high correlation between libraries prepared from an extraction-free method vs extracted DNA.
Taken together, these findings demonstrate that an automated tissue dissection approach joined with extraction-free library preparation can help efficiently process extremely small samples that are otherwise too challenging for standard NGS analysis.
Attendees of this webinar will learn the following:
- Current challenges with isolating precise tumor areas of interest from FFPE tissue samples
- How a high-performance, automated tissue dissection system can extract challenging tumor tissue fragments precisely and consistently
- How to subsequently prepare NGS libraries using extraction-free methods to further reduce the risk of sample loss