Method for subtyping lymphoma types by means of expression profiling
Inventors
Staudt, Louis M. • Wright, George W. • Scott, David William • Connors, Joseph M. • Gascoyne, Randy D. • Rimsza, Lisa • Guerri, Elias Campo • Tubbs, Raymond • Greiner, Timothy C. • Cook, James Robert • Fu, Kai • Williams, Paul Michael • LIH, Chih-Jian • Jaffe, Elaine S. • Braziel, Rita M. • Rosenwald, Andreas • Smeland, Erlend B. • Chan, Wing C. • Ott, German • Delabie, Jan • Weisenburger, Dennis
Assignees
British Columbia Cancer Agency BCCA • Julius Maximilians Universitaet Wuerzburg • Universitat de Barcelona UB • Hospital Clinic de Barcelona • Oslo Universitetssykehus hf • Cleveland Clinic Foundation • Oregon Health and Science University • University of Arizona • University of Nebraska System • US Department of Health and Human Services
Publication Number
US-10607717-B2
Publication Date
2020-03-31
Expiration Date
2034-11-05
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Abstract
The invention is directed to methods for selecting a treatment option for an activated B cell-like diffuse large B cell lymphoma (ABC DLBCL) subject, a germinal center B cell-like diffuse large B cell lymphoma (GCB DLBCL) subject, a primary mediastinal B cell lymphoma (PMBL) subject, a Burkitt lymphoma (BL) subject, or a mantle cell lymphoma (MCL) subject by analyzing digital gene expression data obtained from the subject, e.g., from a biopsy sample.
Core Innovation
The invention provides methods for selecting a treatment option for subjects with various lymphoma types, including activated B cell-like diffuse large B cell lymphoma (ABC DLBCL), germinal center B cell-like diffuse large B cell lymphoma (GCB DLBCL), primary mediastinal B cell lymphoma (PMBL), Burkitt lymphoma (BL), and mantle cell lymphoma (MCL). The method involves isolating a gene expression product from a biopsy sample, obtaining digital gene expression data comprising genes from a gene expression signature, generating a weighted average of expression levels to obtain a signature value, calculating a predictor score, classifying the subject based on the predictor score, and selecting and providing a treatment option accordingly.
The problem being solved arises from the limitations of existing lymphoma classification systems, including the WHO classification, which, although useful, often result in patients within the same diagnostic categories experiencing different clinical outcomes due to molecular differences not observable by tumor morphology analysis. Specifically, diffuse large B cell lymphoma (DLBCL) subtypes such as GCB and ABC require more precise diagnostic assays to better qualify patients for targeted clinical trials and to serve as predictive biomarkers. The invention addresses the need for more accurate, molecular characteristic-based lymphoma identification and classification methods.
The invention uses novel gene expression signatures based on digital gene expression data obtained from biopsy samples, including formalin-fixed and paraffin-embedded tissue, to classify lymphoma types. It involves the use of digital gene expression assays such as the NanoString NCOUNTER™ assay and approaches like serial analysis of gene expression (SAGE), SuperSAGE, digital northern analysis, and RNA-seq. Specific gene arrays comprising hundreds of genes, as well as a focused 20 gene array derived from prior studies, serve as the molecular basis for creating predictive models distinguishing lymphoma subtypes, which facilitates treatment selection.
Claims Coverage
The patent contains multiple independent claims detailing methods for diagnosing lymphoma subtypes and selecting treatments based on digital gene expression data, employing specific gene expression signatures and analytical models.
Method for treating lymphoma subtypes based on digital gene expression data
A method involving isolating RNA gene expression products from a lymphoma subject biopsy, obtaining digital gene expression data comprising a specific gene expression signature, generating weighted expression averages, calculating predictor scores, classifying the lymphoma subtype as one of ABC DLBCL, GCB DLBCL, PMBL, BL, or MCL, selecting a treatment option based on classification, and providing the treatment.
Isolation of gene expression product from FFPE biopsy samples
Obtaining gene expression products specifically from formalin-fixed and paraffin-embedded biopsy samples, enabling reliable analysis of archival tissue.
Use of digital gene expression assays with color-coded probes
Employing assays that use color-coded probe pairs for sensitive and precise digital detection of RNA targets, like the NanoString NCOUNTER™ system, avoiding the need for reverse transcription and PCR amplification.
The claims collectively cover innovative methods for subclassifying lymphomas by analyzing digital gene expression signatures from biopsy samples, particularly formalin-fixed paraffin-embedded tissues, and utilizing the molecular subtype classification to guide therapeutic decisions.
Stated Advantages
Provides more accurate and precise classification of lymphoma types based on molecular characteristics, enabling better prediction of clinical outcomes.
Enables robust and reproducible subtype classification using formalin-fixed and paraffin-embedded biopsy samples, which are routinely collected.
Facilitates rapid turnaround time for clinical decision-making, with high concordance between independent testing sites, indicating robustness and portability.
Improves accuracy of classification compared to existing immunohistochemical algorithms, with significantly lower misclassification rates.
Supports selection of optimal treatment regimens tailored to specific lymphoma subtypes, improving therapeutic efficacy and avoiding ineffective or counterproductive therapies.
Documented Applications
Selecting treatment options for subjects diagnosed with ABC DLBCL, GCB DLBCL, PMBL, BL, or MCL based on molecular classification derived from digital gene expression profiling.
Classification of diffuse large B cell lymphoma (DLBCL) as the ABC or GCB subtype to guide appropriate therapy selection, such as R-CHOP therapy for GCB DLBCL subjects.
Use in clinical diagnostics to subtype lymphomas using formalin-fixed and paraffin-embedded tissue biopsy samples via digital gene expression profiling techniques.
Adjunct to existing diagnostic methods including immunohistochemistry, flow cytometry, FISH, and viral diagnostics, providing enhanced molecular characterization.
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