The cancer genome contains an evolutionary record of the tumor's past. Mining this record can show us the timelines of tumor evolution. Studying this mutational landscape through neural nets and other computational modeling allows for a more comprehensive view of mutational process, burdens and hierarchical sequence, allowing for clearer definitions of tumor progressions and therapeutic implications. It also opens up opportunities for future early diagnostic approaches.
In-silico Modeling of Tumor Evolution
Noam Auslander, PhD, National institute of Biotechnology Information, NIH, Bethesda, MD, USA
Molecular Archeology of Cancer
Peter Van Loo, PhD, The Francis Crick Institute., London, England, United Kingdom
Objectives:
- Understand how methods of massively parallel sequencing and computational modeling can be used to elucidate and model the temporal sequence of tumor progression and evolution.
- Investigate tumor progression and construct clinically relevant mutational profiles.
- Understand how characterization of tumor evolution can impact clinical care and identify opportunities for future early diagnosis of cancer.
Recording Date: November 20, 2020
Continuing Education Credit Information
CME/CMLE credit: 1.50 hr
Last day to purchase course and CE claim credit: February 16, 2024
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