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A Genomic Approach to Improve Prognosis and Predict Therapeutic Response in Chronic Lymphocytic Leukemia (Duke_VA)

ABSTRACT: Chronic lymphocytic leukemia (CLL) is a heterogeneous malignancy, characterized by a variable clinical course. While clinical and laboratory parameters are increasingly being used to refine prognosis, they do not accurately predict response to commonly used therapy. We used gene expression profiling to generate and further refine prognostic and predictive markers. Genomic signatures that reflect progressive disease and responses to chemotherapy or chemo-immunotherapy were created using cancer cell lines and patient leukemia samples. We validated these signatures using independent clinical data from four separate cohorts representing a total of 301 CLL patients. A prognostic genomic signature created from patient leukemic cell gene expression data coupled with clinical parameters could statistically differentiate patients with stable or progressive disease in the training dataset. The progression signature was then validated in two independent datasets, demonstrating a capacity to accurately identify patients at risk for progressive disease. In addition, two distinct genomic signatures that predict response to chlorambucil or pentostatin, cyclophosphamide, and rituximab were also generated and were shown to accurately distinguish responding and non-responding CLL patients. Microarray analysis of CLL patients’ lymphocytes can be used to refine prognosis and predict response to different therapies. These results have direct implications for standard and investigational therapeutics in CLL patients. Keywords: Gene Expression Profiling of two phenotypic states

ORGANISM(S): Homo sapiens

PROVIDER: GSE10138 | GEO | 2009/10/31

SECONDARY ACCESSION(S): PRJNA108983

REPOSITORIES: GEO

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Transcription profiling of human chronic lymphocytic leukemia samples from patients with stable or progressive disease

Project description:Chronic lymphocytic leukemia (CLL) is a heterogeneous malignancy, characterized by a variable clinical course. While clinical and laboratory parameters are increasingly being used to refine prognosis, they do not accurately predict response to commonly used therapy. We used gene expression profiling to generate and further refine prognostic and predictive markers. Genomic signatures that reflect progressive disease and responses to chemotherapy or chemo-immunotherapy were created using cancer cell lines and patient leukemia samples. We validated these signatures using independent clinical data from four separate cohorts representing a total of 301 CLL patients. A prognostic genomic signature created from patient leukemic cell gene expression data coupled with clinical parameters could statistically differentiate patients with stable or progressive disease in the training dataset. The progression signature was then validated in two independent datasets, demonstrating a capacity to accurately identify patients at risk for progressive disease. In addition, two distinct genomic signatures that predict response to chlorambucil or pentostatin, cyclophosphamide, and rituximab were also generated and were shown to accurately distinguish responding and non-responding CLL patients. Microarray analysis of CLL patientsâ lymphocytes can be used to refine prognosis and predict response to different therapies. These results have direct implications for standard and investigational therapeutics in CLL patients. Experiment Overall Design: For the prognostic genomic signature, 68 CLL leukemia samples were used (36 from patients with stable disease and 32 from patients with progressive disease).

2009-11-11 | E-GEOD-10138 | biostudies-arrayexpress

Transcription profiling of human chronic lymphocytic leukemia samples to improve prognosis and predict therapeutic response

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2009-11-11 | E-GEOD-10137 | biostudies-arrayexpress

A Genomic Approach to Improve Prognosis and Predict Therapeutic Response in Chronic Lymphocytic Leukemia (Mayo_Ohio)

Project description:Chronic lymphocytic leukemia (CLL) is a heterogeneous malignancy, characterized by a variable clinical course. While clinical and laboratory parameters are increasingly being used to refine prognosis, they do not accurately predict response to commonly used therapy. We used gene expression profiling to generate and further refine prognostic and predictive markers. Genomic signatures that reflect progressive disease and responses to chemotherapy or chemo-immunotherapy were created using cancer cell lines and patient leukemia samples. We validated these signatures using independent clinical data from four separate cohorts representing a total of 301 CLL patients. A prognostic genomic signature created from patient leukemic cell gene expression data coupled with clinical parameters could statistically differentiate patients with stable or progressive disease in the training dataset. The progression signature was then validated in two independent datasets, demonstrating a capacity to accurately identify patients at risk for progressive disease. In addition, two distinct genomic signatures that predict response to chlorambucil or pentostatin, cyclophosphamide, and rituximab were also generated and were shown to accurately distinguish responding and non-responding CLL patients. Microarray analysis of CLL patients’ lymphocytes can be used to refine prognosis and predict response to different therapies. These results have direct implications for standard and investigational therapeutics in CLL patients. Keywords: Gene Expression Profiling of two phenotypic states (responders versus progressors)

2009-10-31 | GSE10137 | GEO

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Transcription profiling of human B-cell chronic lympocytic leukemia patients in indolent or progressive phase

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2007-08-25 | E-GEOD-2403 | biostudies-arrayexpress

Identification of altered cell signaling pathways in stable and progressive chronic lymphocytic leukemia

Project description:Chronic lymphocytic leukemia (CLL) is characterized by significant biological and clinical heterogeneity. This study was designed to explore CLL B-cells’ proteomic profile in order to identify biological processes affected at an early stage and during disease evolution as stable or progressive. Purified B cells from 11 untreated CLL patients were tested at two time points by liquid chromatography-tandem mass spectrometry. Patients included in the study evolved to either progressive (n=6) or stable disease (n=5). First, at an early stage of the disease (Binet stage A), based on the relative abundance levels of 389 differentially expressed proteins (DEP), samples were separated into stable and progressive clusters with the main differentiating factor being RNA splicing pathway. Next, in order to test how the DEPs affect RNA splicing, a RNA-Seq study was conducted for 4 Stable and 4 Progressive CLL patients, showing 4217 differentially spliced genes between the two clusters. Distinct longitudinal evolutions were observed with predominantly proteomic modifications in the stable CLL group and spliced genes in the progressive CLL group. Splicing events were shown to be 6 times more frequent in the progressive CLL group. The main aberrant biological processes controlled by DEP and spliced genes in the progressive group were cytoskeletal organization, Wnt/β-catenin signaling, mitochondrial and inositol phosphate metabolism with a downstream impact on CLL B-cell survival and migration. This study suggests that proteomic profiles at the early stage of CLL can discriminate progressive from stable disease and that RNA splicing dysregulation underlies CLL evolution, which opens new perspectives in terms of biomarkers and therapy.

2021-06-05 | GSE176141 | GEO

Expression data from untreated CLL patients

Project description:The clinical course of patients with chronic lymphocytic leukemia (CLL) is heterogeneous. Several prognostic factors have been identified that can stratify patients into groups that differ in their relative tendency for disease progression and/or survival. Here, we pursued a subnetwork-based analysis of gene expression profiles to discriminate between groups of patients with disparate risks for CLL progression. From an initial cohort of 130 patients, we identified 38 prognostic subnetworks that could predict the relative risk for disease progression requiring therapy from the time of sample collection, more accurately than established markers. The prognostic power of these subnetworks then was validated on two other cohorts of patients. We noted reduced divergence in gene expression between leukemia cells of CLL patients classified at diagnosis with aggressive versus indolent disease over time. The predictive subnetworks vary in levels of expression over time but exhibit increased similarity at later timepoints prior to therapy, suggesting that degenerate pathways apparently converge into common pathways that are associated with disease progression. As such, these results have implications for understanding cancer evolution and for the development of novel treatment strategies for patients with CLL. Leukemia cells were isolated from blood samples of CLL patients enrolled in the MILE study who had not received prior therapy for CLL, as per the MILE protocol22. Expression data were gathered from samples found to have a CLL cell population with greater than 90% CD5+CD19+, as accessed via flow cytometry. Total RNA was isolated and hybridized to Affymetrix HG-U133+2 GeneChips. This study is about the time to treatment, ie., prognosis instead of diagnosis.

2012-07-25 | E-GEOD-39671 | biostudies-arrayexpress

Expression data from untreated CLL patients

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Personalized medicine in psoriasis: developing a genomic classifier to predict histological response to Alefacept

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