Methods for identifying, diagnosing, and predicting survival of lymphomas
Inventors
Staudt, Louis M. • Wright, George • Dave, Sandeep • Tan, Bruce • Powell, John I. • Wilson, Wyndham • Jaffe, Elaine • Chan, Wing C. • Greiner, Timothy C. • Weisenburger, Dennis • Armitage, James • Fu, Kai • Fisher, Richard I. • Rimsza, Lisa • Miller, Thomas • Grogan, Thomas • Campo, Elias • Bea, Silvia M • Salaverria, Itziar • Lopez-Guillermo, Armando • Montserrat, Emili • Moreno, Victor • Zettl, Andreas • Ott, German • Muller-Hermelink, Hans-Konrad • Rosenwald, Andreas • Vose, Julie • Gascoyne, Randy • Connors, Joseph • Smeland, Erlend • KVALOEY STEIN, null • Kvaløy, Stein • Holte, Harald • Delabie, Jan • Lister, T. Andrew
Assignees
British Columbia Cancer Agency BCCA • Julius Maximilians Universitaet Wuerzburg • Universitat de Barcelona UB • Fundacio Clinic per a la Recerca Biomedica FCRB • Hospital Clinic de Barcelona • Queen Mary University of London • Oslo Universitetssykehus hf • University of Rochester • University of Arizona • University of Nebraska System • US Department of Health and Human Services
Publication Number
US-8131475-B2
Publication Date
2012-03-06
Expiration Date
2024-09-03
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Abstract
Gene expression data provides a basis for more accurate identification and diagnosis of lymphoproliferative disorders. In addition, gene expression data can be used to develop more accurate predictors of survival. The present invention discloses methods for identifying, diagnosing, and predicting survival in a lymphoma or lymphoproliferative disorder on the basis of gene expression patterns. The invention discloses a novel microarray, the Lymph Dx microarray, for obtaining gene expression data from a lymphoma sample. The invention also discloses a variety of methods for utilizing lymphoma gene expression data to determine the identity of a particular lymphoma and to predict survival in a subject diagnosed with a particular lymphoma. This information will be useful in developing the therapeutic approach to be used with a particular subject.
Core Innovation
Gene expression data provides a basis for more accurate identification and diagnosis of lymphoproliferative disorders. Gene expression data can also be used to develop predictors of survival that are more accurate than existing methods. This is achieved by methods for identifying, diagnosing, and predicting survival in a lymphoma or lymphoproliferative disorder based on gene expression patterns.
The invention discloses a novel microarray called the Lymph Dx microarray, which contains probes corresponding to approximately 2,653 genes selected for their utility in identifying lymphoma types and predicting clinical outcomes. This streamlined microarray helps simplify analysis and facilitates the use of gene expression data in lymphoma diagnostics and survival prediction.
Current lymphoma classification systems, including the WHO classification, rely on morphology, immunophenotype, genetic abnormalities, and clinical features. However, patients within the same diagnostic category often have different clinical outcomes due to molecular differences not apparent from morphology. There is a need for more precise methods for identifying and classifying lymphomas based on molecular characteristics to improve patient management.
Mathematical analysis of gene expression data, using methods such as hierarchical clustering and Bayesian analysis, allows for the identification of gene expression signatures associated with specific biological processes, lymphoma subtypes, and survival outcomes. These approaches enable development of multivariate models that predict survival and categorize lymphoma types with quantifiable probabilities, aiding in diagnosis and therapeutic decisions.
Claims Coverage
The patent discloses one independent claim focused on a method for identifying a lymphoma sample as Burkitt lymphoma (BL) using gene expression profiling and Bayesian analysis.
Differentiation of BL using gene expression profiles divided into two gene subsets
The method includes creating lymphoma type pairs where the first type is BL, obtaining gene expression data for genes differentially expressed between BL and other lymphoma types, and dividing this gene set into two subsets: one of c-myc and c-myc target genes, and a second set consisting of the top z genes with the largest difference in expression excluding the first subset.
Use of linear predictor scores based on gene subsets for diagnosis
Generating two series of linear predictor scores for known samples based on expression of each gene subset, obtaining gene expression data from the test sample for these subsets, calculating two linear predictor scores for the sample, and calculating probabilities that the sample belongs to BL using Bayesian analysis of these scores.
The claims focus on a method that uses gene expression data separated into a c-myc related subset and a distinct gene subset with highest differential expression to generate linear predictor scores and calculate probabilistic diagnosis of BL using Bayesian analysis.
Stated Advantages
Provides a more accurate and quantitative method for identifying and diagnosing lymphomas based on gene expression patterns.
Allows prediction of survival outcomes in subjects diagnosed with specific lymphoma types using multivariate models derived from expression data.
Enables better subclassification of lymphomas, including difficult distinctions such as between BL and DLBCL, leading to improved therapeutic decision-making.
The Lymph Dx microarray streamlines gene expression data acquisition by focusing on relevant genes, simplifying analysis and enhancing diagnostic utility.
Documented Applications
Identification and diagnosis of lymphomas and lymphoproliferative disorders using gene expression patterns obtained with the Lymph Dx microarray or other methods.
Prediction of survival outcomes in lymphoma subjects, including follicular lymphoma (FL), diffuse large B-cell lymphoma (DLBCL), and mantle cell lymphoma (MCL), using gene expression signature-based multivariate models.
Classification of lymphoma types and subtypes, including differentiation of Burkitt lymphoma (BL) from diffuse large B-cell lymphoma (DLBCL) and its subgroups (ABC, GCB, PMBL) using Bayesian analysis of gene expression data.
Identification of cyclin D1-negative mantle cell lymphoma based on gene expression profiling distinguishable from other lymphomas.
Refinement of survival prediction models for DLBCL incorporating genomic alterations detected by comparative genomic hybridization alongside gene expression data.
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