Burkitt's Lymphoma

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Disease Background

Who gets it?
What are the symptoms?
How do we detect it now?
How is it currently treated?
What are the current challenges?
  • Burkitt’s lymphoma is a type of cancer that affects B-cells, which are the cells that make antibodies
  • 30–50% of childhood cancers and <1% of adult cancers are diagnosed as Burkitt’s lymphoma. People who are HIV-positive are 1,000x more likely to get Burkitt’s Lymphoma. The disease has also been linked to the Epstein Barr virus.
  • Burkitt’s lymphoma is characterized by a large tumor in the abdomen or face. Other symptoms include loss of appetite, fatigue, weight loss, and night sweats.
  • Burkitt’s lymphoma responds well to chemotherapy with high survival rates and low recurrence.
  • Burkitt’s lymphoma is detected through an X-ray or CT scan, blood test, cytogenetic analysis, and looking at the tumor cells under a microscope
  • Current treatments generally involve chemotherapy in combination with a drug called Rituximab, both of which target the tumor cells for destruction
  • Patients that have a higher risk of an adverse outcome include >15 years old, already have advanced cancer or a cancer recurrence, high lactate dehydrogenase (LDH) levels, or HIV-positive
  • It is not known why some people respond differently to treatment.
  • It is not known how the Epstein Barr virus makes people susceptible to Burkitt’s lymphoma.
  • All of the protein interactions in the B-cell receptor signaling pathway are not well characterized. How those proteins join and separate is also not known.

Our Approach

Cell State and Dynamics (Research Project 1 - RP1)

Investigators: D. Mitchell Magee, Ph.D. and Brianne Petritis

Collaborators: Garry Nolan, Ph.D. and Parag Mallick, Ph.D. (Stanford University) and Richard Bonneau, Ph.D. (New York University)

As part of the NIH initiated a multidisciplinary effort, Physical Sciences in Oncology (PSOC) program, which aims to gain a better understanding of how cancer develops, we are combining the power of NAPPA with surface plasmon resonance imaging (SPRi) to determine the kinetics of protein-protein interactions in various pathways involved in cancer development. The primary objective of RP1 is the development of computational models that reduce the complexity of molecular and cellular events to a small set of inputs (e.g., genetic background of a cell, environmental context) and outputs (e.g., cell physiology, cell state, likelihood of state change).  We will combine the studies from multiple laboratories to map the signals within a tumor cell with and without therapeutic treatment. Burkitt’s lymphoma is an ideal disease to create a virtual cancer model because the B-cell signaling pathway is relatively well-known and cell lines can be used to study the disease at the cell, tumor, and animal level (see image). Thus far, we have quantitatively characterized the B-cell receptor signaling pathway , which has never been done before for any signaling pathway.

Additional studies will include flow cytometry to assess phosphorylation events and studies of various cancer cell lines at multiple treatment stages, which will be performed in Gary Nolan's laboratory.  The data from these platforms will be analyzed by Rich Bonneau utilizing a variety of analytical tools including Rosetta.  The cumulative efforts will provide analytical methods to model cell signaling pathways involved in tumorigenesis.

 


 

Integrated multi-scale analysis of tumor and host response to therapy (Research Project 4 - RP4)

Investigators:  D. Mitchell Magee, Ph.D. and Marika Hopper

Collaborators: Shan X. Wang, Ph.D. (Stanford University), Scott Lowe, Ph.D. and Cornelius Miething Ph.D. (Memorial Slaon-Kettering Cancer Center), Anand Asthagiri, Ph.D. (Northeastern University), Dan Ruderman, Ph.D. (Applied Minds, Inc), David Agus, Ph.D. (University of Southern California), and Danny Hillis, Ph.D. (Applied Minds, Inc)

Our second PSOC project utilizes nucleic acid programmable protein arrays (NAPPA) to identify autoantibodies that arise during tumor development.  We will first focus on a model lymphoma system and then we will expand these studies to models of lung cancer and acute myelogenous leukemia.  In addition to autoantibodies, select cytokines and other proteins will be monitored using a unique magnetonanosensor platform developed by Shan Wang at Stanford University.

Our results from RP1 and RP4 will be analyzed by Anand Asthagiri and Dan Ruderman for patterns of responsiveness to identify key protein pathways involved in the host response to tumor challenge.  These results will be fed back to the model developers to assess the effect of these pathways on potential therapeutic interventions.  Thus, the integration of the four PSOC research programs will provide fundamental new modalities to predict response to therapy.