Biomarker Subnetwork for Diagnosis, Prognosis, and Treatment of Chronic Lymphocytic Leukemia

Background: The course of chronic lymphocytic leukemia (CLL) is highly variable. Patients with aggressive disease require immediate treatment, whereas patients with an indolent form of the disease need not consider aggressive therapy options for years. This dichotomy of prognosis creates a need for an effective method for distinguishing indolent CLL from more advanced CLL. To date, several biomarkers have been developed for the diagnosis of CLL. However, none of the established markers provide a definitive biomarker that can universally detect and distinguish the aggressive from the indolent form of CLL. Therefore, a strong need remains for a method of using biomarkers to effectively detect CLL, determine differential diagnosis, forecast prognosis, and determine treatment needs of CLL patients.

Technology Description: This invention provides a molecular differential diagnosis/prognosis tool that assigns patients to “aggressive” (high-risk) or “indolent” (low-risk) groups based on their gene expression and correlated to the treatment-free survival from the date of sample collection. This technology is based on the seminal discovery that a panel of protein biomarkers is differentially expressed in CLL patients at distinct stages of the disease, allowing for more accurate determination of disease progression. More specifically, this invention provides a molecular classification procedure based on the activity levels of stage-specific, disease-associated modules in protein networks.

Advantages:

  • Links known cancer-susceptibility genes to differentially expressed biomarkers.
  • Examination of biomarker networks reveals the molecular mechanisms important to CLL progression.
  • Network-based classification achieves a higher accuracy in predicting the duration of treatment-free survival in newly diagnosed patients than commonly used prognostic factors or conventional gene-array analysis.

Applications:

  • This invention could be used to diagnose CLL and to determine individual patient prognosis.
  • In addition to initial determination of patient prognosis, this technology can be used to monitor the progression of disease in previously diagnosed patients in order to alter therapy regimens at the most appropriate stage (early stage of transition toward the aggressive form), thereby improving the clinical outcome for the patient.
  • Identified subnetwork markers can be used to build a database used in clinical routines for predicting a patient’s specific need for treatment.
  • This technology provides a “net-class” protein network that could be used to develop a prognosis software (algorithm) with far reaching implications for accurate interpretation of molecular patient profiles.
  • Elucidation of the molecular mechanisms important to CLL progression provides a collection of new targets for drug development.
  • Subnetwork identification may provide further insight into potentially efficacious combinations of therapeutic options for adjunctive therapy.

State of Development: This technology is offered exclusively or nonexclusively for U.S. and/or certain foreign countries. A commercial sponsor for potential future research is sought.

Related Materials:

  • Inventor Information—Thomas J. Kipps, UC San Diego Professor of Medicine, Deputy Director Research Operations, UC San Diego Rebecca and John Moores UCSD Cancer Center, Director, CLL Research Consortium.
  • HY Chuang, E Lee, YT Liu, D Lee, and T Ideker; Molecular Systems Biology 3:140; October 2007; “Network-based classification of breast cancer metastasis.”

Case Number: SD2009-180


Inquiries To: invent@ucsd.edu