Diagnostic Plasma-Derived Proteomic Biomarkers of Aggressive Diffuse Large B-Cell Lymphoma: Preliminary Data Based On the 075 French GEOLAMS Multicentric and Prospective Trial.

Abstract 2933

Poster Board II-909

The challenge of clinical proteomic is to link protein expression profile variations to specific disease phenotypes and identify relevant biomarkers in order to develop straightforward diagnostic or prognosis tools. Blood, a tissue that interfaces with virtually every part of the body, is considered to be a deep source of native and secreted diagnostic analytes. Despite this great potential, the first decade of the proteomics era met with little success, not only because of the vast and complex nature of the proteome but also due to proteins dynamic range and complex degradation pathways, and to the heterogeneity of plasma protein profiles in the human population. Altogether, progress in clinical proteomics will reside in the elaboration of standardized preanalytic procedures, cross-comparisons between samples and independent validation.

In aggressive diffuse large B-cell lymphomas (DLBCL), diagnostic and prognostic biomarkers are mandatory to optimize treatment, and include patients in future trials. The aim of the present study was first to identify diagnostic blood biomarkers of DLBCL based on the 075 French GOELAMS ongoing trial -which involved adults younger than 60 suffering from an aggressive form of DLBCL- that randomized patients between CHOP-14 Rituximab or intensive chemotherapy plus Rituximab including autologous stem-cell support. This protocol was built after our group demonstrated the high efficiency of high-dose chemotherapy and autologous stem-cell transplantation as frontline therapy in this disease compared to conventional CHOP (New Engl J Med, 2004). In this study, 200 patients were compared to 100 controls matched for sex and age. Well-defined pre-analytic steps were applied and plasma was collected, at the time of diagnosis, on the specific anti-proteasic P100v1.1 tube from Becton Dickinson. All samples were centralized and aliquoted in a unique platform prior to analytic steps. The whole series of 300 samples was randomly assigned on chips to be analyzed with SELDI-TOF/MS using three different chemistry protocols (CM10, Q10 & IMAC30) and two beam intensities (2000 and 4000, respectively). There was a longer delay in the process of patient's samples before plasma isolation compared to controls; this time had to be considered since it could participate to the protein degradation process and lead to proteomic modifications. Statistical analyses were implemented with the R package software [R development core team. R: A Language for Environmental and Statistical Computing. Vienna: R Foundation, 2008.]. Univariate analyses comparison resulted in 185 peaks differential of the case-control status (t-test, FDR=5%). Multivariate analyses were then performed according to chemistry and beam intensity using stepforward logistic regression. This resulted in 78 peaks related to DLBCL diagnosis. In order to reduce dimension, partial least square regression [A. L. Boulesteix and K. Strimmer (2005). Predicting Transcription Factor Activities from Combined Analysis of Microarray and ChIP Data: A Partial Least Squares Approach] was applied, resulting in two components corresponding to a weighted sum of the 78 peaks. These two components were introduced as covariates in logistic regression so that the 78 peaks could be ranked according to their global coefficient, allowing then top peaks to be studied. Sparse partial least square was also considered as another approach to reduce dimension and select peaks among the 78 identified. These two approaches were compared and proteins studied in greater detail.

Altogether, this study allowed to identify promising candidate cancer biomarkers that are currently being validated through the analysis of additional plasma issued from other types of lymphoma (follicular, mantle cell and low burden DLBCL) and non-cancerous septic patients. Highly specific peak combinations will be considered before peptide characterization in order to end up with a diagnostic set of proteins useful for the diagnosis and management of aggressive DLBCLs.