A Genomic Approach to Improve Prognosis and Predict Therapeutic Response in Chronic Lymphocytic Leukemia
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE10139 | GEO | 2009/10/31
SECONDARY ACCESSION(S): PRJNA108687
REPOSITORIES: GEO
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Project description:Chronic lymphocytic leukemia (CLL) is a B-cell malignancy characterized by a variable clinical course. Several parameters have prognostic capabilities but are associated with altered response to therapy in only a small subset of patients.We used gene expression profiling methods to generate predictors of therapy response and prognosis. Genomic signatures that reflect progressive disease and responses to chemotherapy or chemoimmunotherapy were created using cancer cell lines and patient leukemia cell samples. We validated and applied these three signatures to independent clinical data from four cohorts, representing a total of 301 CLL patients.A genomic signature of prognosis created from patient leukemic cell gene expression data coupled with clinical parameters significantly differentiated patients with stable disease from those with progressive disease in the training data set. The progression signature was validated in two independent data sets, showing a capacity to accurately identify patients at risk for progressive disease. In addition, genomic signatures that predict response to chlorambucil or pentostatin, cyclophosphamide, and rituximab were generated and could accurately distinguish responding and nonresponding CLL patients.Thus, microarray analysis of CLL lymphocytes can be used to refine prognosis and predict response to different therapies. These results have implications for standard and investigational therapeutics in CLL patients.
| S-EPMC2783430 | biostudies-literature
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
2009-10-31 | GSE10138 | GEO
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
Project description:This SuperSeries is composed of the following subset Series:; GSE10137: A Genomic Approach to Improve Prognosis and Predict Therapeutic Response in Chronic Lymphocytic Leukemia (Mayo_Ohio); GSE10138: A Genomic Approach to Improve Prognosis and Predict Therapeutic Response in Chronic Lymphocytic Leukemia (Duke_VA) Experiment Overall Design: Refer to individual Series
2009-11-09 | E-GEOD-10139 | biostudies-arrayexpress
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
2009-11-11 | E-GEOD-10137 | biostudies-arrayexpress
Project description:Heterogeneity of CD20 expression exists in chronic lymphocytic leukemia (CLL), therefore, we explored the prognostic significance of CD20 expression in Chinese patients with CLL. Multiparameter flow cytometry was used to detect the expression of CD20 in CD5(+) CD19(+) cells. In 172 CLL patients, the median expression percent of CD20 was 97.82% (range, 0-100), and the median mean fluorescence intensity (MFI) of CD20 in CLL cells was 731.45 (range, 0.00-9071.90). The percentage of CD20(+) cells in the patient group with mutated variable region of immunoglobulin genes (IGHV) was higher than in the non-mutant IGHV group (mean, 92.1% vs 80.4%, P < 0.001). There were no differences in the MFI of CD20(+) cells in all prognostic factor groups. Representation of the data using a receiver operating characteristic plot reflected separation between the two IGHV groups, with an area under the curve of 0.661 (95% confidence interval, 0.569-0.753). At the cut-off value of 60.3% for percentage of CD20, the sensitivity and specificity were 90.00% and 38.46%, respectively. Patients whose percentage of CD20 antigen was above 60.3% had longer treatment-free survival (hazard ratio, 0.452; 95% confidence interval, 0.232-0.884, P = 0.020). Percentage and MFI of CD20 were the variables not associated with treatment-free survival by multivariate Cox regression analysis (P < 0.05). High level of CD20 expression in de novo CLL appears to be associated with a good prognosis.
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Project description:This article discusses recent advances in genomic approaches used to understand chronic lymphocytic leukemia. Tools for analyzing DNA-level lesions are described, data obtained from these various platforms summarized, and the clinical relevance of these findings discussed.
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Project description:Chronic lymphocytic leukemia (CLL) has a variable clinical course. Presence of specific genomic aberrations has been shown to impact survival outcomes and can help categorize CLL into clinically distinct subtypes. We studied 178 CLL patients enrolled in a prospective study at the University of Michigan, of whom 139 and 39 were previously untreated and previously treated, respectively. We obtained unbiased, high-density, genome-wide measurements of subchromosomal copy number changes in highly purified DNA from sorted CD19(+) cells and buccal cells using the Affymetrix 50kXbaI SNP array platform (Santa Clara, CA). Genomic complexity scores were derived and correlated with the surrogate clinical end points time to first therapy (TTFT) and time to subsequent therapy (TTST): measures of disease aggressiveness and/or therapy efficaciousness. In univariate analysis, progressively increasing complexity scores in previously untreated CLL patients identified patients with short TTFT at high significance levels. Similarly, TTST was significantly shorter in pretreated patients with high as opposed to low genomic complexity. In multivariate analysis, genomic complexity emerged as an independent risk factor for short TTFT and TTST. Finally, algorithmic subchromosomal complexity determination was developed, facilitating automation and future routine clinical application of CLL whole-genome analysis.
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Project description:The last several years have witnessed a paradigm shift in the management of patients with chronic lymphocytic leukemia (CLL). The course of this very heterogeneous disease, traditionally treated with chemotherapeutic agents usually in combination with rituximab, typically has been characterized by remissions and relapses, and survival times vary greatly, depending on intrinsic biological attributes of the leukemia. The developments of the last few years have been transformative, ushering in an era of novel, molecularly targeted therapies, made possible by extensive efforts to elucidate the biology of the disease that predated the new targeted drugs. Thus, successful therapeutic targeting of the B-cell receptor signaling pathway and of the Bcl-2 anti-apoptotic protein with small molecules has now made chemotherapy-free approaches possible, hopefully mitigating the risk of development of therapy-related myeloid neoplasms and making eventual cure of CLL with the use of optimal drug combinations a realistic goal. Most importantly, these therapies have demonstrated unprecedented efficacy in patients with deletion 17p/TP53 mutation, a subset that historically has been very difficult to treat. However, as we gain more experience with the newer agents, unique safety concerns and resistance mechanisms have emerged, as has the issue of cost, as these expensive drugs are currently administered indefinitely. Accordingly, novel laboratory-based strategies and clinical trial designs are being explored to address these issues. The availability of whole exome/genome sequencing has given us profound insights into the mutational landscape of CLL. In this article, we highlight some of the most impactful advances since this topic was last reviewed in this journal.
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Project description:Treatment outcome of chronic lymphocytic leukemia (CLL) has considerably improved since the introduction of fludarabine (F) as part of the standard therapy. Nevertheless, refractoriness to fludarabine occurs in a significant number of patients and is associated with an unfavorable prognosis. Important risk factors are 17p deletion and/or mutation of TP53. For this subgroup the CD52 antibody alemtuzumab (A) presents a new treatment approach and has already been approved. Meanwhile we have to face also refractoriness to alemtuzumab. Importantly, the monoclonal CD20 antibody ofatumumab has now shown efficacy in F and A double-refractory CLL. The next generation CD20 antibody GA-101 is currently compared to rituximab (R) and will possibly be its more potent successor. Further B-cell antigens are targeted by lumiliximab (CD23), TRU-016 (CD37) and blinatumomab (CD19). Apart from monoclonal antibody therapies, a great number of small molecules are examined for the treatment of refractory and relapsed CLL. Most of these agents aim to overcome apoptosis resistance in CLL cells or influence the microenvironment. Typical targets are regulators of the cell cycle and antiapoptotic molecules like the members of the Bcl-2 family. Up to now the most promising agents appear to be flavopiridol and lenalidomide among others.
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