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 • Universitat de Barcelona UB • Hospital Clinic de Barcelona • University of Arizona • US Department of Health and Human Services
Publication Number
US-11574704-B2
Publication Date
2023-02-07
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 a method for selecting a treatment option for subjects diagnosed 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) by analyzing digital gene expression data obtained from biopsy samples of the subject.
This method involves isolating gene expression products such as RNA from biopsy samples, obtaining digital gene expression data comprising genes from a predefined gene expression signature, generating a weighted average of expression levels to calculate a predictor score, and then classifying the subject's lymphoma type based on the predictor score. Treatment options are then selected and provided based on this classification.
The background highlights the need for more precise molecular methods to identify and classify lymphomas beyond existing systems like WHO classification, which rely on morphology and clinical features but do not fully account for molecular heterogeneity that affects clinical outcomes. Existing classifications such as the cell-of-origin (COO) distinction for DLBCL subtypes (GCB and ABC) require more accurate diagnostic assays to guide targeted treatments and improve predictive biomarker use.
Claims Coverage
The patent includes three independent claims, each defining a method related to treating lymphoma by digital gene expression profiling with specific gene expression signatures and data processing steps.
Method for treating various lymphoma types by digital gene expression analysis
A method comprising isolating RNA gene expression product from a lymphoma biopsy sample; obtaining digital gene expression data for genes in a gene expression signature including at least genes listed in Table 2; optionally obtaining reference gene expression data; calculating a predictor score using a defined equation; classifying the lymphoma type based on the predictor score; and selecting and providing treatment accordingly.
Method for treating diffuse large B cell lymphoma using digital gene expression
A method comprising isolating RNA gene expression product from a DLBCL biopsy; obtaining digital gene expression data for genes in a gene expression signature including at least genes listed in Table 1; optionally obtaining reference data; calculating a predictor score with a specific equation; classifying the DLBCL subtype (ABC or GCB) based on the score; and selecting and providing treatment accordingly.
Method for treating diffuse large B cell lymphoma using a 20-gene expression signature
A method involving isolating RNA from a DLBCL biopsy; obtaining digital gene expression data for genes in a gene expression signature including at least genes listed in Table 3; optionally obtaining reference data; calculating a predictor score as per a defined equation; classifying the lymphoma as ABC or GCB subtype based on the predictor score; and then selecting and providing the appropriate treatment.
The claims cover methods of classifying lymphoma subtypes via digital gene expression signatures derived from biopsy samples and using these classifications to guide treatment selection. Each independent claim specifies the use of particular gene expression signatures and data analysis steps to distinguish among lymphoma types or DLBCL subtypes, directing tailored therapies.
Stated Advantages
Provides a robust, accurate molecular assay for classifying lymphoma subtypes using RNA from routinely collected FFPE biopsy samples.
Allows more precise molecular classification of lymphomas compared to existing immunohistochemical or morphology-based methods.
Enables improved selection of therapeutic methods tailored to lymphoma subtype, potentially avoiding nonproductive or counterproductive treatments.
Exhibits high performance with archival FFPE tissue, ensuring applicability in clinical settings with rapid turnaround times.
Demonstrates high concordance and robustness across independent laboratory sites, supporting reproducibility and reliability.
Documented Applications
Selecting treatment options for patients with ABC DLBCL, GCB DLBCL, PMBL, BL, or MCL based on gene expression profiling.
Molecular subtype classification of diffuse large B cell lymphoma (DLBCL) patients to guide therapy choice including R-CHOP treatment selection.
Use of gene expression profiling on formalin-fixed paraffin embedded tissues for improved lymphoma subtype diagnosis in clinical diagnostic settings.
Application in distinguishing aggressive B cell non-Hodgkin lymphomas (agg-B-NHL) including difficult diagnostic categories such as ABC, GCB, PMBL, BL, and MCL with a single methodology.
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