Centre for Ecological Research - Institute of Evolution
The course was held for the first time 2023-2024 autumn quarter with 12 students finishing. It might be back in an updated form in the future.
Semesters Taught: Fall 2023-2024.
Cancer is a disease caused by the acquisition of genetic alterations in healthy human cells. Understanding this evolutionary process called cancer progression is hindered by the vast number of cancer driver genes and the even higher complexity of their combinations, not just across different cancer types, but across tumors of the same cancer or even subclones of cells within the same tumor. While sequencing biopsies from patients’ tumors is useful, we need tools to conduct controlled experiments, measuring the phenotypes of different cancer genotypes in vitro (cell lines) and in vivo (usually in mouse experimental models). Our ability to do just that was recently tremendously improved thanks to two revolutions in molecular biology: 1) CRISPR-Cas9 genome editing technology has fundamentally changed the way we can introduce mutations. It works so well that a whole field basically stopped whatever old technique they were using and switched to using CRISPR overnight. 2) The technology of next generation sequencing and hence the ease and price of sequencing DNA has been improving at an exponential rate comparable to Moore’s Law in IT (doubling of “computing power” every two years). These two advancements have fundamental effects on biological research reaching far beyond cancer genetics. They enable scientists to generate so much data in a year, that would have taken a lifetime for hundreds of researchers only a couple decades ago. The big data generated by these experimental screens in turn require the use of more advanced computational tools than just an excel sheet. During the course, after a short introduction to CRISPR screens and programming in R language, you will form small teams and work on your own projects, formulating biological questions relevant to cancer biology, answering them by analyzing published datasets and/or designing your own CRISPR screens for a hypothetical experiment to test novel hypotheses.