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Patient Stratification (patient + stratification)

Distribution by Scientific Domains

Life Sciences	50%

Selected Abstracts

Multiple myeloma , an update on diagnosis and treatment

EUROPEAN JOURNAL OF HAEMATOLOGY, Issue 5 2008
Jo Caers
Abstract Multiple myeloma is a plasma cell (PC) malignancy characterized by the accumulation of monoclonal PCs in the bone marrow and the production of large amounts of a monoclonal immunoglobulin or paraprotein. In the past years, new approaches in the diagnosis and treatment were introduced aiming to identify high-risk patients who need proper anti-myeloma treatment. Intensive therapy including autologous hematopoietic stem cell transplantation and the new agents bortezomib, thalidomide, and lenalidomide have improved patients' responses. Further optimalization of the different treatment schedules in well-defined patient groups may prolong their survival. Patient stratification is currently based on patient characteristics, extent of myeloma disease, and associated cytogenetic and laboratory anomalies. More and more gene expression studies are introduced to stratify patients and to individualize therapy. [source]

CLLU1 expression levels predict time to initiation of therapy and overall survival in chronic lymphocytic leukemia

EUROPEAN JOURNAL OF HAEMATOLOGY, Issue 6 2006
Anne Mette Buhl
Abstract:,Objectives:,Chronic lymphocytic leukemia (CLL) is an incurable disease with a highly variable clinical course. IgVH mutational status, chromosomal aberrations, CD38 expression and ZAP-70 expression are prognostic markers in CLL, however, they are not exclusively confined to this disease. We recently identified a novel CLL-specific gene (CLL upregulated gene1, CLLU1) that is exclusively upregulated in CLL cells. Here we describe our evaluation of the prognostic significance of CLLU1 in CLL. Methods:,A cohort of 59 previously untreated CLL patients was studied. We determined the expression levels of two CLLU1 transcripts, cDNA1 and CDS, by quantitative RT-PCR. The relation between CLLU1 expression and time to therapy, overall survival and presence or absence of ZAP-70, CD38, chromosomal aberrations or IgVH mutations in the 59 patients was analyzed. Results:,Analyzed as a continuous, quantitative parameter CLLU1 levels significantly predicted time from diagnosis to initiation of therapy (P , 0.0003) Analyzed as a categorical parameter, by segregation of the patients into groups with cDNA1 or CDS expression above or below the median, the CLLU1 levels significantly predicted time from diagnosis to initiation of therapy (P = 0.001) and predicted overall survival with borderline significance (P , 0.05). Patient stratification according to clinical stage, cytogenetics, IgVH mutational status, ZAP-70 and CD38, demonstrated significantly increased CLLU1 expression in all investigated CLL poor risk groups. CLLU1 expression levels contributed additional prognostic information to ZAP-70-positive patients. Conclusions:,CLLU1 is the first identified CLL specific gene. The CLLU1 mRNA expression level can predict time to initiation of treatment and survival in CLL patients. [source]

High-resolution biomarker discovery: Moving from large-scale proteome profiling to quantitative validation of lead candidates

PROTEOMICS - CLINICAL APPLICATIONS, Issue 10-11 2008
Johannes A. Hewel
Abstract Diverse proteomic techniques based on protein MS have been introduced to systematically characterize protein perturbations associated with disease. Progress in clinical proteomics is essential for personalized medicine, wherein treatments will be tailored to individual needs based on patient stratification using noninvasive disease monitoring procedures to reveal the most appropriate therapeutic targets. However, breakthroughs await the successful development and application of a robust proteomic pipeline capable of identifying and rigorously assessing the relevance of multiple candidate proteins as informative diagnostic and prognostic indicators or suitable drug targets involved in a pathological process. While steady progress has been made toward more comprehensive proteome profiling, the emphasis must now shift from in depth screening of reference samples to stringent quantitative validation of selected lead candidates in a broader clinical context. Here, we present an overview of the emerging proteomic strategies for high-throughput protein detection focused primarily on targeted MS/MS as the basis for biomarker verification in large clinical cohorts. We discuss the conceptual promise and practical pitfalls of these methods in terms of achieving higher dynamic range, higher throughput, and more reliable quantification, highlighting research avenues that merit additional inquiry. [source]

Optimizing flow cytometric DNA ploidy and S-phase fraction as independent prognostic markers for node-negative breast cancer specimens

CYTOMETRY, Issue 3 2001
C.B. Bagwell
Abstract Developing a reliable and quantitative assessment of the potential virulence of a malignancy has been a long-standing goal in clinical cytometry. DNA histogram analysis provides valuable information on the cycling activity of a tumor population through S-phase estimates; it also identifies nondiploid populations, a possible indicator of genetic instability and subsequent predisposition to metastasis. Because of conflicting studies in the literature, the clinical relevance of both of these potential prognostic markers has been questioned for the management of breast cancer patients. The purposes of this study are to present a set of 10 adjustments derived from a single large study that optimizes the prognostic strength of both DNA ploidy and S-phase and to test the validity of this approach on two other large multicenter studies. Ten adjustments to both DNA ploidy and S-phase were developed from a single node-negative breast cancer database from Baylor College (n = 961 cases). Seven of the adjustments were used to reclassify histograms into low-risk and high-risk ploidy patterns based on aneuploid fraction and DNA index optimum thresholds resulting in prognostic P values changing from little (P < 0.02) or no significance to P < 0.000005. Other databases from Sweden (n = 210 cases) and France (n = 220 cases) demonstrated similar improvement of DNA ploidy prognostic significance, P < 0.02 to P < 0.0009 and P < 0.12 to P < 0.002, respectively. Three other adjustments were applied to diploid and aneuploid S-phases. These adjustments eliminated a spurious correlation between DNA ploidy and S-phase and enabled them to combine independently into a powerful prognostic model capable of stratifying patients into low, intermediate, and high-risk groups (P < 0.000005). When the Baylor prognostic model was applied to the Sweden and French databases, similar significant patient stratifications were observed (P < 0.0003 and P < 0.00001, respectively). The successful transference of the Baylor prognostic model to other studies suggests that the proposed adjustments may play an important role in standardizing this test and provide valuable prognostic information to those involved in the management of breast cancer patients. Cytometry (Comm. Clin. Cytometry) 46:121,135, 2001. © 2001 Wiley-Liss, Inc. [source]