By Steven Grant, MD

2008-01-01

Dr. Grant indicated no relevant conflicts of interest.

Annunziata CM, Davis RE, Demchenko Y, et al. Frequent engagement of the classical and alternative NF-kappaB pathways by diverse genetic abnormalities in multiple myeloma. Cancer Cell. 2007;12:115-30.

The NF-κB pathway involves a family of transcription factors (p50, p52, c-Rel, p65/RelA, and RelB) involved in the regulation of diverse cellular processes, including survival, proliferation, differentiation, and inflammatory responses, among numerous others. This cascade can be subdivided into three components: the classical or canonical pathway, the alternative or non-canonical pathway, and the atypical pathway. In the classical pathway, an inciting stimulus, such as activation of TNF-related cell surface receptors, leads to activation of the IKK complex, which phosphorylates the IκBα protein, resulting in its proteasomal degradation. Under normal conditions, IκBα traps p65/RelA in the cytoplasm; hence, IκBα degradation results in RelA nuclear translocation and DNA binding, culminating in the transcription of numerous NF-κB-dependent genes, including those encoding survival (for example, XIAP, Bcl-xL) and antioxidant proteins (for example, MnSOD2), among others. The NF-κB pathway is hyperactivated in diverse neoplastic diseases, particularly those of hematopoietic origin. For example, human leukemia cells and leukemia stem cells have been shown to require an intact NF-κB pathway for survival. Notably, the survival of multiple myeloma cells appears to be particularly dependent upon NF-κB signaling, and it has long been suggested that targeting the NF-κB pathway might represent a very logical therapeutic strategy in this disease. The success of the proteasome inhibitor bortezomib, which among other actions spares IκBα from proteasomal degradation, in patients with advanced multiple myeloma provides strong support for this notion.

In a recent study appearing in Cancer Cell, Annunziata, et al. surveyed a large number of myeloma cell lines and patient samples for evidence of NF-κB-activating mutations. They found a very high incidence of such mutations (i.e., 15-20 percent), which took multiple forms, including translocations involving or amplifications of NF-κB-activating genes such as NIK, or mutations, deletions, or silencing of NF-κB negative-regulatory genes such as TRAF3 or CYLD. Interestingly, there was a correlation between the presence of such genetic abnormalities and NF-κB hyperactivation with the susceptibility of cells to the antiproliferative and death-inducing effects of agents, such as IKKß inhibitors (e.g., MLN120B), or proteasome inhibitors (e.g., bortezomib). In a companion study in the same journal, Keats, et al. reported a high percentage (e.g., ~20 percent) of such mutations in primary patient samples, particularly those involving TRAF3, many of which were associated with activation of the non-canonical NF-κB pathway.¹

The significance of these studies lies not only in their implications for our understanding of the pathogenesis of multiple myeloma and related hematologic malignancies, but also in their potential to have profound ramifications for attempts to develop more rational targeted therapy in these disorders. In this context, genetic profiling of diffuse lymphocytic B-cell lymphoma (DLBCL) has recently identified specific subtypes (for example, GC vs. ABC), which differ in their response to therapy, and perhaps not coincidentally, their dependence upon the NF-κB pathway.² It is therefore plausible to propose that in the future, genetic profiling of multiple myeloma will not only provide us with important prognostic information, but may also facilitate the development of more rational targeted approaches.

1. Keats JJ, Fonseca R, Chesi M, et al. Promiscuous mutations activate the noncanonical NF-kappaB pathway in multiple myeloma. Cancer Cell. 2007;12:131-44.

2. Bea S, Zettl A, Wright G, et al. Diffuse large B-cell lymphoma subgroups have distinct genetic profiles that influence tumor biology and improve gene-expression-based survival prediction. Blood. 2005;106:3183-90.

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