Cancer patients can now get their tumors sequenced, but the multitudes of genetic variations
in tumors makes it difficult to identify the cancer-promoting “driver” mutations from the
relatively innocuous “passenger” mutations. These variations are too numerous and resource-
intensive to study individually, and computational algorithms remain unacceptably inaccurate
for routine use in the clinic. Many cancer patients and carriers of variations in cancer-related
genes are left wondering how their variants will affect their health. Thus, there is a critical need
for connecting sequence information with clinical outcome, since it will allow us to take actions
to help these individuals.
I will fill this need by simultaneously measuring the consequences of every possible missense
variant of a cancer protein. I will focus on the tumor suppressor PTEN, mutated in diverse
cancers including prostate cancer, glioblastoma, and endometrial cancer. Working with my
mentor, Dr. Douglas Fowler, I will create comprehensive datasets describing how each PTEN
variant influences various oncogenic processes, such as uncontrolled cell proliferation and
survival. Subsequently, working with my co-mentor Dr. Brian Shirts, I will develop an algorithm
that accurately predicts the pathogenic consequences of variations observed in the clinic.
The information I collect on PTEN will allow clinicians to distinguish cancer “driver” from
“passenger” mutations, which help them to accurately assess patient disease risk and tailor
patient-specific treatment regiments. The tools and strategies I develop will be applicable to
other tumor suppressor proteins, and will create a framework for fully harnessing the power of
cancer genomics to attack cancer.