Proteomic Analysis of Apoptotic Pathways Reveals Prognostic Factors in Follicular Lymphoma

Abstract

Follicular lymphoma (FL) is the second most common non-Hodgkin's lymphoma and generally is incurable. Reliable prognostic markers to differentiate patients who progress rapidly from those who survive for years with indolent disease have not been established. Most cases overexpress Bcl-2, but the pathogenesis of FL remains incompletely understood. To determine whether a proteomic approach could help overcome these obstacles, we procured lymphoid follicles from 20 cases of FL and 15 cases of benign follicular hyperplasia (FH) using laser capture microdissection. Lysates were spotted on reverse-phase protein microarrays and probed with 21antibodies to proteins in the intrinsic apoptotic pathway, including those specific for posttranslational modifications such as phosphorylation. A panel of three antibodies [phospho-Akt(Ser473), Bcl-2, and cleaved poly(ADP-ribose) polymerase] segregated most cases of FL from FH. PhosphoAkt(Ser473) and Bcl-2 were significantly increased in FL (P = 0.001and P < 0.0001, respectively). Additionally, the Bcl-2/Bak ratio completely segregated FL from FH. High ratios of Bcl-2/Bak and Bcl-2/Bax were associated with early death from disease with differences in median survival times of 7.3 years (P = 0.0085) and 3.8 years (P = 0.018), respectively. Using protein microarrays, we identified candidate proteins that may signify clinically relevant molecular events in FL. This approach showed significant changes at the posttranslational level, including Akt phosphorylation, and suggested new prognostic markers, including the Bcl-2/Bak and Bcl-2/Bax ratios. Proteomic end points should be incorporated in larger, multicenter trials to validate the clinical utility of these protein microarray findings.

Posttranslational Protein Modifications

“…With the advent of a vast repertoire of antibodies specific to defined phosphorylated sites of proteins participating in signaling pathways, innovative protein microarray platforms are being developed to characterize tumors by quantifying the phosphorylation status of many signaling proteins (5). This type of analysis allows one to determine the activation status of multiple signaling pathways providing valuable insights into establishing an individualized targeted therapy for the patient (35,36). This analysis, in principal, could be continued following the initial stages of treatment to monitor whether the therapy is effective and provide follow-up strategies to modify treatment for recurrent tumors.…”

Preanalytical variables and phosphoepitope expression in FFPE tissue: quantitative epitope assessment after variable cold ischemic time

“…[5][6][7][8][9] Thus, identification of activated pathways in tumor specimens is an essential element in efficient cancer diagnosis and treatment. [6][7][8][9][10][11] Protein phosphorylation is a dynamic balance between two opposite events: phosphorylation by highly specific kinases and dephosphorylation by less-specific phosphatases. [12][13][14] To utilize phosphoproteins as markers of cellular signaling activity and tumor proliferation, 6,8,9 it is crucial to assess their expression levels in tissue specimens that reflect the in vivo status as accurately as possible.…”

Array-based proteomics: mapping of protein circuitries for diagnostics, prognostics, and therapy guidance in cancer

“…Arraying each lysate in a dilution curve series is critical for ensuring that at least part of the assay can be assessed within the linear range of a particular antibody-antigen reaction and also allows for more accurate quantification. The technique has been shown to be robust in terms of intra-and inter-array variation (CV less than 10%), and with a sensitivity of less than 50 fg/µl [36,37,39]. The latter depends significantly on the affinity of the probing antibody.…”

“…In order to carry out comparisons between arrays, bridging cases have been used [39]. These are lysates that are spotted onto several arrays, thus providing a common reference to which cases from each array can be standardized and in this manner providing inter-array comparisons.…”

“…This technique has previously been used for data mining of both cDNA [52,53] and tissue microarrays [54] to determine transcriptional fingerprints underlying phenotypic variations. Moreover, it has been applied to protein microarrays, where components accurately segregated follicular lymphoma from follicular hyperplasia [39].…”