Pathological diagnosis is perhaps most important to the oncologist to the extent that it can inform about prognosis and treatment choice. Current diagnostic categorization of a lymphoma as DLBL relies on a fairly small number of data, including cell surface markers, nuclear and cytoplasmic appearance, and tissue morphology. When these data lead to the diagnosis of DLBL, the oncologist is left with a diagnostic grouping that includes those who will die of unresponsive disease in the first 6 months after diagnosis despite the most aggressive treatment approaches, and those who will rapidly obtain and maintain a durable complete remission after administration of anthracycline-based combination chemotherapy. It seems odd to call two diseases that behave so differently by the same name.
In an attempt to better divide the heterogeneous group of diseases encompassed by the label DLBL, Shipp and colleagues developed the IPI. The IPI uses just four pieces of clinical and laboratory data to further subclassify DLBL into four groups. While this formulation does provide a useful refinement of prognosis, it still falls short of the ideal predictor: a predictor that would definitively determine, prior to a particular therapy, whether that therapy will work.
While the ideal predictor may be unattainable in practice, attempts are being made to improve prognostic prediction using the many bits of molecular data provided by GEP. Two groups, one based at the National Cancer Institute and one based at the Dana-Farber Cancer Institute, have published results of applying GEP to lymphoma samples for which clinical data were available. In both cases, predictors generated by GEP were able to identify new subclasses of lymphomas and also to further refine prognosis even within IPI subgroups. Furthermore, when prognosis is predicted by a molecular signature, the molecules involved in that signature can be immediately identified as potential targets of anti-cancer therapy, a feat not possible when prognosis is determined by purely clinical criteria. The Dana-Farber group identified protein kinase C-P as such a target, and clinical trials incorporating a PKC-P inhibitor in DLBL are under way.
GEP potentially places tens of thousands of bits of data at the disposal of the pathologist and oncologist. As experience with this fascinating technology grows, its use in prognosis and therapeutic development will only improve.
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