Molecular Signatures to Improve Diagnosis, Prognostication and Identification of Oncogenic Pathways in Peripheral T and NK Cell Lymphoma.

Background: Peripheral T-cell lymphoma (PTCL) consists of an uncommon and heterogeneous group of lymphomas that are often challenging to diagnose and classify. Since most patients also have a poor survival with standard multiagent chemotherapy, more effective therapeutic approaches are needed to improve patient outcome.

Table1:

Methods: A mRNA profiling study using Affymetrix HGU133+2 arrays on 143 cases of PTCL and NK-cell lymphoma (NKCL) from the International Peripheral T-cell Lymphoma Project, was conducted on pre-treatment biopsies. These included the following pathologically classified cases (Table 1). In addition, we also profiled nine NK cell lines, seven T cell lines, normal resting and activated CD4+ and CD8+ T cells and resting and IL2- activated NK cells from healthy individuals. BRB-ArrayTools was used to develop gene classifiers for the major PTCL entities and survival predictors for AITL based on gene expression data.

Results: We have identified key molecular signatures for PTCL and NKCL that have allowed us to construct a robust classifier for AITL (207 transcripts), ALK+ ALCL (94), ATLL (225) and NKCL (127). PTCL-U group may have 3 or 4 molecular subgroups and additional studies with more cases, are necessary to further define this group. Misclassified cases were identified and re-assigned to the molecularly defined entities, including re-assigning of 9/44 PTCL-U to AITL. We have confirmed the enriched expression of genes identified in follicular helper T-cells in AITL, suggesting that AITL is derived from this T-cell subset. A number of oncogenic pathways (e.g. NF-κB, HIF-a,VEGF, IL6) and tumor/host interactions that contributed to local tumor-induced immunosuppression (e.g. TGF-b), were identified in AITL. A molecular predictor of outcome was developed for AITL and validated by leave one-out-cross validation. Since PTCL is an uncommon disease, future studies will require the collaboration of multiple large clinical groups with tissue resources for both discovery and validation.

Conclusion: This study has demonstrated that GEP will allow the construction of robust and biologically-meaningful classifiers for PTCL, and prognosticators can be derived for well-defined entities with a sufficient number of cases. GEP will also allow us to identify therapeutically-relevant oncogenic pathways and tumor/host interactions that may lead to improvement in the therapy and outcome of patients with PTCL and NKCL. (This study is a part of the International T-cell Lymphoma Project)