Jill Mesirov PhD

Prior to Sanford Burnham Prebys,  Mesirov was associate vice chancellor for computational health sciences at UC San Diego and co-lead of the Structural and Functional Genomics Program at UC San Diego Moores Cancer Center, which emphasizes the development and use of high throughput structural genomic data to guide basic cancer research and clinical applications.

Mesirov also serves on the pediatric neuro-oncology tumor board at Rady Children’s Hospital, which matches children with brain and spinal cord tumors with the most effective, targeted, genome-based therapies.

Mesirov is a mathematician by training, with a B.A. from the University of Pennsylvania and master’s and doctoral degrees from Brandeis University in Massachusetts. After early exposure and experience in high-performance computing, she joined the Broad Institute of Massachusetts Institute of Technology and Harvard to study and advance the use of genomics data, which at the time was an emerging discipline, ultimately becoming associate director and chief informatics officer.

Previous to Broad, Mesirov served as manager of computational biology and bioinformatics in the Healthcare/Pharmaceutical Solutions Organization and as director of research at Thinking Machines Corporation. She also held positions in the mathematics department at UC Berkeley and at the Institute for Defense Analyses, where she conducted work in cryptology and speech and designed and implemented efficient computer algorithms.

Mesirov is former president of the Association for Women in Mathematics; served as associate executive director of the American Mathematical Society and is a fellow of the American Association for the Advancement of Science, the American Mathematical Society and the International Society for Computational Biology.

She has also served on several advisory boards and as an editor on journals in computational science and applied mathematics. She has authored or co-authored more than 300 journal articles and technical reports.

Machine learning and cancer genomics

The Mesirov lab studies cancer genomics by applying machine-learning methods to sequencing data from patient tumors. By analyzing large datasets capturing tissue structure and function at a cellular level, the research team is advancing our understanding of the unique characteristics underlying different tumor subtypes.

Knowing what drives how tumors will respond to potential treatments and the varying risks of resistance or recurrence enables physicians and patients to make more informed decisions. Demystifying differences among tumor subtypes also can uncover potential vulnerabilities that can be targeted to develop new treatments.

The Mesirov lab’s overall goal is to help build the foundation for precision oncology, a clinical approach where routine sequencing-based diagnostics aid doctors in prescribing therapies tailored to each tumor subtype.

Sharing software tools to accelerate biomedical research

The Mesirov lab also is committed to the development of practical, accessible software tools to assist the general biomedical research community in conducting data-intensive research.

These include widely used open-access platforms such as GenePattern, Gene Set Enrichment Analysis with its Molecular Signature Database, and the Integrative Genomics Viewer, which allow researchers worldwide to analyze and visualize complex and massive genomic datasets without needing advanced programming skills. Tools shared by the Mesirov lab now support more than one million users in 100 countries.