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 S. • Chan, Wing C. • Greiner, Timothy C. • Weisenburger, Dennis • Armitage, James • Fu, Kai • Fisher, Richard I. • Rimsza, Lisa M. • Miller, Thomas • Grogan, Thomas • Campo Guerri, Elias • Bea, Silvia M. • Salaverria, Itziar • Lopez-Guillermo, Armando • Montserrat, Emilio • Moreno, Victor • Zettl, Andreas • Ott, German • Muller-Hermelink, Hans-Konrad • Rosenwald, Andreas • Vose, Julie • Gascoyne, Randy • Connors, Joseph • Smeland, Erlend B. • Kvaloy, 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-10697975-B2
Publication Date
2020-06-30
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. The invention discloses methods for identifying, diagnosing, and predicting survival of lymphomas or lymphoproliferative disorders on the basis of gene expression patterns, including the use of a novel microarray named the Lymph Dx microarray for obtaining gene expression data from lymphoma samples.
The invention also provides methods for generating survival predictors for particular lymphoma types. Gene expression data obtained from diagnosed biopsy samples are analyzed to identify gene expression signatures associated with longer or shorter survival. These signatures are averaged to obtain signature values used to build multivariate survival predictors. Specific models with equations are disclosed for predicting survival in follicular lymphoma, diffuse large B cell lymphoma (DLBCL), mantle cell lymphoma (MCL), and methods combining gene expression data with chromosomal gain or amplification information.
Furthermore, the invention includes diagnostic methods for determining lymphoma type by identifying genes differentially expressed between lymphoma types, calculating scale factors, and using linear predictor scores with Bayesian analysis to calculate probabilities of a sample belonging to specific lymphoma types. The classification methods include several pairwise models to optimize gene selection and prediction accuracy, and overlapping gene sets are considered by subdivision into gene expression signature categories.
The problem being solved is that existing classification systems for lymphomas such as the WHO classification, although useful, do not sufficiently account for molecular differences that result in different clinical outcomes among patients diagnosed with the same lymphoma category. More precise methods for identifying, classifying, and predicting survival of lymphomas based on molecular characteristics are needed to guide optimized therapeutic approaches.
Claims Coverage
The patent includes a set of inventive features primarily related to methods for treating diffuse large B cell lymphoma (DLBCL) by assessing gene expression and chromosomal alterations.
Gene expression assessment for DLBCL treatment
Perform isolating gene expression product from a DLBCL biopsy sample and determining an average gene expression level of specified genes for prognosis and treatment decisions.
Chromosomal analysis for prognosis refinement
Use comparative genomic hybridization to analyze DLBCL biopsy samples for gains or amplifications in the 3p11-p12 region of chromosome 3 to predict a less favorable outcome.
Cytogenetic analysis of chromosome 3 region
Perform cytogenetic analysis of the 3p11-p12 region of chromosome 3 in DLBCL biopsy samples to detect gains or amplifications associated with prognosis.
Polymerase chain reaction for chromosomal gain detection
Conduct polymerase chain reaction (PCR) assays on DLBCL biopsy samples to detect gain or amplification in the 3p11-p12 region of chromosome 3, including real-time quantitative PCR.
The claims focus on diagnostic and prognostic methods combining gene expression profiling and chromosomal analyses, especially of the 3p11-p12 region of chromosome 3, to guide therapeutic decisions for DLBCL patients.
Stated Advantages
More accurate identification and diagnosis of lymphoproliferative disorders through gene expression data.
Development of more accurate predictors of survival in lymphoma patients.
Improved selection and application of therapeutic methods based on precise lymphoma subtype classification and survival prediction.
Reduced complexity in gene expression analysis due to a novel microarray with a focused set of genes useful for diagnosis and survival prediction.
Statistical robustness of classification and survival prediction through the use of Bayesian analysis and cross-validation techniques.
Documented Applications
Identifying and classifying lymphomas and lymphoproliferative disorders based on gene expression profiles.
Generating multivariate survival prediction models for follicular lymphoma, DLBCL, and mantle cell lymphoma.
Distinguishing lymphoma subtypes such as ABC DLBCL, GCB DLBCL, PMBL, Burkitt lymphoma, cyclin D1-negative mantle cell lymphoma, and others using distinct molecular signatures.
Refining survival predictions for DLBCL patients by integrating gene expression signatures and chromosomal gain or amplification data.
Guiding therapeutic approach selection for lymphoma patients based on molecular classification and survival prediction results.
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