Instructor: David Patterson

2 Units, Course Control Number: 27268

A provocative hypothesis is that the massive growth of online digital descriptions of tumor cell genomes will enable computer scientists to help make breakthroughs in cancer treatment, perhaps even within the next few years.

The goal of this course to become familiar with the challenges of helping cancer patients using genomics from a computer science perspective. The course will involve reading introductory material as well as some more advanced papers, interacting with experts as guest lecturers, and then doing a related project that will initially explore the opportunities at the intersection between cancer genomics and computing.

Course Background and Motivation

Medicine has traditionally relied on physicians to make complex assessments of patients based on a combination of history, observed phenomena (physical exam), laboratory values (a set of numbers), slides, and imaging data. Specialties have formed (pathology and radiology) to facilitate patient decision-making by interpreting the more complex tests.  Over the next several years, sequencing technologies will begin to make their way into medicine, offering the most complex tests available.  This advance brings a new type of data with tremendous promise to help elucidate physiological and pathological functions within the body, as well as to make more informed decisions about patient care.  The cost of genome sequencing is projected to fall within range where it may be used for diagnostic and treatment purposes within the next 2 years.  Due to the overwhelming amount of information returned by these tests, direct human interpretation is not feasible, and therefore will have to be guided by computational methods and visualization.

The use of sequencing information has debuted in cancer.  There  are currently almost 12 million people in the United States who live with cancer.  Of these, over 500,000 are expected to die this year.  Over the past several years, limited sequence information has begun to guide therapy and improve patient survival. Studies have shown that subpopulations of patients with a specific genetic defect (e.g. FLT3, BCR-ABL) can go into complete remission with inhibitors (drugs) affecting the corresponding mutated protein or a downstream signaling molecule.  There is also evidence to suggest that combination therapy targeting multiple related pathways may be more effective than individual drugs.  It may be possible to significantly improve the lives of patients with cancer by rational analysis of genomic changes that occur over the course of the disease.  This can only be accomplished by machines due to the amount of information involved.

The goal of this course to become familiar with the challenges of using genomics to help cancer patients from a computer science perspective. The course will involve reading introductory material as well as some more advanced papers, interacting with guest speakers, and then doing a related project in this space.