Identification of altered cell signaling pathways in stable and progressive chronic lymphocytic leukemia
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ABSTRACT: 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.
Project description:Purified B cells from untreated CLL patients were tested at two time points by liquid chromatography-tandem mass spectrometry. Patients included in the study evolved to either progressive or stable disease . Samples were tested for both groups at diagnosis (Stage Binet A for all patients included), 3 years after diagnosis for the stable disease patients and at the time of progression to stage Binet B/C for those who progressed.
Project description:Objective. B-cell chronic lymphocytic leukemia is a heterogeneous disease with a pronounced variation in the clinical course. With the purpose of identifying genes that could be related to disease progression, we have performed gene expression profiling on B-CLL patients with an indolent disease and patients with a progressive disease with need for therapy. Materials and Methods. We applied the Affymetrix GeneChip technique to 11 B-CLL patients with stable and 10 patients with clinically progressive disease. Supervised and unsupervised clustering methods with different algorithms were used to identify genes that tend to give a distinction between stable and progressive disease. Results. The supervised learning procedures identified groups of genes with a combined power to discriminate samples from progressive and stable disease with 70-90 % accuracy. The gene for protein phosphatase 2 regulatory subunit Bâ (B56) gamma isoform (PPP2R5C) and the gene for retinoblastoma-like 2 (p130) (RBL2) were included among the best discriminators; both genes were down regulated in progressive as compared to stable B-CLL. In a hierarchical clustering analysis based on gene expression pattern three clinical sub-categories could be identified; one with a more severe clinical outcome, a second one with good prognosis and a third one that was intermediate between the other two groups. Conclusions. Our application of microarray analysis on a clinically well defined material has identified a number of genes with combined expression patterns related to stable or progressive disease in general. Unsupervised clustering suggested the existence of subclasses of samples in the progressive group that may be identifiable through gene expression patterns.
Project description:Objective. B-cell chronic lymphocytic leukemia is a heterogeneous disease with a pronounced variation in the clinical course. With the purpose of identifying genes that could be related to disease progression, we have performed gene expression profiling on B-CLL patients with an indolent disease and patients with a progressive disease with need for therapy. Materials and Methods. We applied the Affymetrix GeneChip technique to 11 B-CLL patients with stable and 10 patients with clinically progressive disease. Supervised and unsupervised clustering methods with different algorithms were used to identify genes that tend to give a distinction between stable and progressive disease. Results. The supervised learning procedures identified groups of genes with a combined power to discriminate samples from progressive and stable disease with 70-90 % accuracy. The gene for protein phosphatase 2 regulatory subunit B’ (B56) gamma isoform (PPP2R5C) and the gene for retinoblastoma-like 2 (p130) (RBL2) were included among the best discriminators; both genes were down regulated in progressive as compared to stable B-CLL. In a hierarchical clustering analysis based on gene expression pattern three clinical sub-categories could be identified; one with a more severe clinical outcome, a second one with good prognosis and a third one that was intermediate between the other two groups. Conclusions. Our application of microarray analysis on a clinically well defined material has identified a number of genes with combined expression patterns related to stable or progressive disease in general. Unsupervised clustering suggested the existence of subclasses of samples in the progressive group that may be identifiable through gene expression patterns. Keywords: ordered
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).
Project description:RNA splicing dysregulation is a hallmark of cancers, promoting the onset and progression of disease. In chronic lymphocytic leukemia (CLL), spliceosome mutations leading to aberrant splicing occur in ~20% of patients. However, the underlying mechanism for splicing defects in spliceosome unmutated CLL cases remains elusive. Through an integrative transcriptomic and proteomic analysis, we discover proteins involved in RNA splicing are post-transcriptionally upregulated in CLL cells, resulting in splicing dysregulation. The abundance of splicing (factors) complexes is an independent risk factor and associated with poor prognosis in CLL. Moreover, increased splicing factor expression is highly correlated with METTL3, an RNA methyltransferase that modifies N6-methyladenosine (m6A) on mRNA. METTL3 is essential for cell growth in vitro and in vivo, and controls splicing factor protein expression in a methyltransferase-dependent manner through m6A modification mediated ribosome recycling and decoding process. Our results uncover a novel regulatory axis of METTL3 for splicing dysregulation in CLL and highlight m6A modification as a major contributor to spliceosome mutation-independent splicing defects that lead to CLL progression.
Project description:RNA splicing dysregulation is a hallmark of cancers, promoting the onset and progression of disease. In chronic lymphocytic leukemia (CLL), spliceosome mutations leading to aberrant splicing occur in ~20% of patients. However, the underlying mechanism for splicing defects in spliceosome unmutated CLL cases remains elusive. Through an integrative transcriptomic and proteomic analysis, we discover proteins involved in RNA splicing are post-transcriptionally upregulated in CLL cells, resulting in splicing dysregulation. The abundance of splicing (factors) complexes is an independent risk factor and associated with poor prognosis in CLL. Moreover, increased splicing factor expression is highly correlated with METTL3, an RNA methyltransferase that modifies N6-methyladenosine (m6A) on mRNA. METTL3 is essential for cell growth in vitro and in vivo, and controls splicing factor protein expression in a methyltransferase-dependent manner through m6A modification mediated ribosome recycling and decoding process. Our results uncover a novel regulatory axis of METTL3 for splicing dysregulation in CLL and highlight m6A modification as a major contributor to spliceosome mutation-independent splicing defects that lead to CLL progression.
Project description:RNA splicing dysregulation is a hallmark of cancers, promoting the onset and progression of disease. In chronic lymphocytic leukemia (CLL), spliceosome mutations leading to aberrant splicing occur in ~20% of patients. However, the underlying mechanism for splicing defects in spliceosome unmutated CLL cases remains elusive. Through an integrative transcriptomic and proteomic analysis, we discover proteins involved in RNA splicing are post-transcriptionally upregulated in CLL cells, resulting in splicing dysregulation. The abundance of splicing (factors) complexes is an independent risk factor and associated with poor prognosis in CLL. Moreover, increased splicing factor expression is highly correlated with METTL3, an RNA methyltransferase that modifies N6-methyladenosine (m6A) on mRNA. METTL3 is essential for cell growth in vitro and in vivo, and controls splicing factor protein expression in a methyltransferase-dependent manner through m6A modification mediated ribosome recycling and decoding process. Our results uncover a novel regulatory axis of METTL3 for splicing dysregulation in CLL and highlight m6A modification as a major contributor to spliceosome mutation-independent splicing defects that lead to CLL progression.
Project description:We investigated at two time points a longitudinal cohort of 27 untreated Chronic Lymphocytic Leukemia (CLL) patients with either stable or progressive disease. The sequenced genes included BCOR, EGR2, HIST1H1E, ITPKB, KRAS, MED12, NRAS, RIPK1, SAMHD1, ATM, BIRC3, BRAF, CHD2, DDX3X, DDX3Y, FBXW7, KIT, KLHL6, MAPK1, MYD88, NOTCH1, PIK3CA, POT1, SF3B1, TP53, XPO1 and ZMYM3, which were previously identified as mutated in CLL studies.
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 predictive genomic signature or response to pentostatin, cyclophosphamide, and rituximab, 20 CLL leukemia samples were used in the training set, and 20 CLL leukemia samples were used in the validation set