Test to detect NF-kB2 breaks to guide treatment with proteasome inhibitors and its use with other therapeutic agents.
Multiple myeloma (MM) and lymphomas are late-stage B-cell malignancies. Novel agents targeting the proteasome (bortezomib) have shown promising results in relapsed and previously untreated patients. Existing technologies for predicting patient response to bortezomib are based on a variety of clinical factors such as age, race, family history, and tumor burden. Difficulties in identifying these clinical factors, combinations of these factors, and applying them to the correct target populations can lead patients to get unnecessary and suboptimal treatment. With the inaccuracy of current methods for predicting patient response to bortezomib coupled with increasing cases of MM among certain target populations, there is an increased need for a more accurate methodology.
Emory researchers have developed a novel technology that uses the NF-kB2 break apart fluorescent in situe hybridization to predict patient response to bortezomib treatment for MM and lymphomas. The NF-kB2 3' end is a region of the NF-kB2 gene that is involved in regulating bortezomib sensitivity. Patients who lack the NF-kB2 3' end are less sensitive to bortezomib treatment and therefore are more likely to experience treatment failure. The technology uses a combination of in vitro models and clinical data to predict patient response to bortezomib treatment. The in vitro models demonstrate that cells with low NF-kB2 levels or cells expressing NF-kB2-siRNA are less sensitive to bortezomib. The clinical data show that patients with the presence of the NF-kB2 3' end have a greater chance of responding to bortezomib (60-95%) vs those who lacked the NF-kB2 3' end (30-40%). The loss of the NF-kB2 3'end may be associated with NF-kB2 rearrangements or splicing variants. These genetic changes may lead to the production of NF-kB2 proteins that are less sensitive to bortezomib. This novel technology has the potential to improve the accuracy of patient response prediction for bortezomib treatment, which could help ensure that patients are only given bortezomib if they are likely to benefit from it and also help avoid unnecessary treatment in patients who are unlikely to respond.
The technology is currently in the clinical stage of development.