Survival predictor for diffuse large B cell lymphoma
Inventors
Rimsza, Lisa M. • Lister, Andrew T. • Chan, Wing C. • Weisenburger, Dennis • Delabie, Jan • Smeland, Erlend B. • Holte, Harald • Kvaløy, Stein • Braziel, Rita M. • Fisher, Richard I. • Jares, Pedro • Lopez-Guillermo, Armando • Campo Guerri, Elias • Jaffe, Elaine S. • Lenz, Georg • Wilson, Wyndham H. • Wright, George W. • Dave, Sandeep S. • Staudt, Louis M. • Gascoyne, Randy D. • Connors, Joseph M. • Muller-Hermelink, Hans-Konrad • Rosenwald, Andreas • Ott, German
Assignees
British Columbia Cancer Agency BCCA • Julius Maximilians Universitaet Wuerzburg • Universitat de Barcelona UB • Hospital Clinic de Barcelona • Queen Mary University of London • Oslo Universitetssykehus hf • Oregon Health and Science University • University of Rochester • University of Arizona • University of Nebraska System • US Department of Health and Human Services
Publication Number
US-11028444-B2
Publication Date
2021-06-08
Expiration Date
2029-06-05
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Abstract
The invention provides methods and materials related to a gene expression-based survival predictor for DLBCL patients.
Core Innovation
The invention provides methods and materials related to a gene expression-based survival predictor for patients suffering from diffuse large B cell lymphoma (DLBCL), including those treated with chemotherapy combined with the administration of Rituximab, the current standard of care. The predictor utilizes gene expression profiles from DLBCL biopsy samples, focusing on specific gene expression signatures such as germinal center B cell (GCB), stromal-1, and stromal-2 signatures, to calculate a survival predictor score that correlates with patient survival outcomes.
The problem addressed by the invention is the molecular heterogeneity of DLBCL and the lack of prognostically significant methods for distinguishing among DLBCL subtypes in patients treated with R-CHOP therapy. Existing subtyping and prognostic approaches, such as immunohistochemistry and gene expression profiling, while previously useful in CHOP-treated patients, lost prognostic relevance in the Rituximab-treated population. Thus, there is a need for novel methods that provide accurate survival prediction and prognostication for DLBCL patients receiving R-CHOP.
The invention solves this need by developing a multivariate model based on gene expression signatures from DLBCL biopsy samples to predict survival outcomes. Signature values derived from the expression averages of genes in the GCB, stromal-1, and stromal-2 signatures are combined using specific scale factors and offset terms to compute a survival predictor score. This score correlates inversely with patient survival: lower scores indicate favorable outcomes, while higher scores indicate poorer outcomes. Additionally, the invention provides methods for generating survival estimate curves correlating survival probability with time after treatment, and methods for selecting patients for antiangiogenic therapy based on stromal-2 signature expression values.
Claims Coverage
The claims define four main inventive features related to methods for evaluating and treating DLBCL patients based on gene expression profiles.
Method of evaluating a subject for antiangiogenic therapy of DLBCL based on gene expression profiling
This feature involves isolating gene expression product from DLBCL biopsy samples and obtaining a gene expression profile detecting expression levels for selected genes including VWF, CD31 (PECAM1), EGFL7, MMRN2, GPR116, SPARCL, KDR, Grb10, integrin alpha 9, TEK, ROBO4, ERG, CAV1, CAV2, and EHD2. A signature value is determined from the gene expression profile and compared to a standard value. Antiangiogenic therapy is indicated if the subject's signature value is higher than the standard.
Use of signature value as an average of expression levels
This feature specifies that the signature value corresponds to the average of the expression levels of the genes detected in the gene expression profile.
Gene expression profiling specifying nucleotide sequence identifiers
This feature specifies that obtaining a gene expression profile involves detecting expression levels for genes corresponding to a listed series of nucleotide sequence accession numbers including NM_014601, NM_017789, NM_000484, and others as enumerated in the claim, covering the genes typically comprising the stromal-2 signature.
Signature value defined as the average expression of specified gene set
This feature further defines that the signature value corresponds to the average of expression levels of all genes in the gene expression profile corresponding to the nucleotide sequences listed.
The claims collectively cover methods for using gene expression profiling of specific angiogenesis- and stromal-related genes in DLBCL biopsy samples to evaluate candidacy for antiangiogenic therapy and to treat patients accordingly, where gene expression signature values are averaged and compared to standard references to guide treatment decisions.
Stated Advantages
The survival predictor provides a quantitative and prognostically significant method to distinguish among molecular subtypes of DLBCL treated with R-CHOP, improving survival predictions.
It enables identification of patients with inferior predicted outcomes who may benefit from alternative treatments or clinical trial enrollment.
The predictor score facilitates comparison among patient cohorts in clinical trials by providing a molecularly defined characterization of tumor biology.
The gene expression-based model is statistically independent of established clinical prognostic indices, offering complementary prognostic information.
The stromal score correlates with tumor blood vessel density, enabling selection of patients likely to benefit from antiangiogenic therapy.
Documented Applications
Predicting survival outcomes for DLBCL patients after R-CHOP therapy using gene expression profiles from tumor biopsies.
Stratifying DLBCL patients based on molecular subtypes and survival predictor scores to guide individualized treatment plans.
Selecting DLBCL patients for antiangiogenic therapy based on elevated stromal-2 gene expression signature values.
Using targeted arrays comprising probes for GCB, stromal-1, and stromal-2 gene expression signatures to obtain gene expression profiles for survival prediction and therapy decisions.
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