An NCI-designated Cancer Center since 1983.
The Jackson Laboratory (JAX) has received $2.5 million from The Mark Foundation for Cancer Research to study in mice the influence of host genetics on response to immunotherapy. The goal of this project is to generate insights that will empower future decisions about the best treatments for cancer patients based on their genetic backgrounds.
JAX postdoctoral associate Kira Young, Ph.D., has received the ASH Scholar Award from the American Society for Hematology to support her research studying “old blood.” Young is using the award to understand the different types of white blood cells that make up the immune system and how they change as we age.
JAX Professor Karolina Palucka is seeking new ways to harness the body's natural defenses against cancer.
JAX Professors Roel Verhaak and Ching Lau are leading research programs into glioblastoma with awards from the National Cancer Institute as part of a large national research consortium.
Armed with new funding from the V Foundation, Olga Anczuków-Camarda is uncovering genetic changes in the breast, paving the road to early cancer detection and prevention.
JAX postdoctoral associate Frederick Varn, Ph.D., has received a prestigious fellowship from the Jane Coffin Childs Memorial Fund for Medical Research.
C.C. Little’s pursuit of cancer genetics through genetically defined laboratory mice that would lay the groundwork for nine decades of advances in cancer diagnosis and treatment.
JAX has signed a research agreement for up to $4.2 million with Sanofi to identify new targets to treat triple-negative breast cancer and ovarian carcinoma.
Recently developed outbred mouse populations, such as the diversity outbred (DO) mice at The Jackson Laboratory (JAX), have created research options that parallel or even exceed human genetic diversity. Research with DO mice offers a full range of genetic diversity, and therefore more generalizability of responses across populations.
JAX researchers have developed a new computational algorithm that is capable of modeling the effects of both stochastic gene expression and cell-to-cell variability in a cell population.