Methods for identifying, diagnosing, and predicting survival of lymphomas
Inventors
Staudt, Louis M. • Wright, George W. • Dave, Sandeep • Tan, Bruce K.
Assignees
US Department of Health and Human Services
Publication Number
US-10370715-B2
Publication Date
2019-08-06
Expiration Date
2024-09-03
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Abstract
The invention provides methods for identifying, diagnosing, and predicting survival in a lymphoma or lymphoproliferative disorder on the basis of gene expression patterns. The invention provides a microarray for obtaining gene expression data from a lymphoma sample. The invention also provides 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, which is useful in developing an appropriate therapeutic approach.
Core Innovation
The invention provides methods for identifying, diagnosing, and predicting survival in lymphomas or lymphoproliferative disorders based on gene expression patterns. It discloses a novel microarray, termed the Lymph Dx microarray, containing approximately 2,653 genes for obtaining gene expression data from lymphoma biopsy samples. This microarray reduces complexity by eliminating genes less useful for lymphoma type identification and survival prediction, facilitating streamlined analysis of gene expression data.
The invention further includes diverse methods utilizing gene expression data to determine lymphoma identity and predict subject survival, aiding in developing appropriate therapeutic approaches. The methods involve detecting gene expression signatures, averaging gene expression levels within these signatures, and calculating survival predictor scores via multivariate models using specific gene expression signatures linked to lymphoma subtypes such as follicular lymphoma (FL), diffuse large B cell lymphoma (DLBCL), and mantle cell lymphoma (MCL).
The problem solved addresses the inadequacy of previous lymphoma classification systems based on morphology, immunophenotype, clinical and genetic factors, which, although useful, often fail to accurately predict clinical outcomes due to molecular differences unobservable through morphological analyses. There was a need for more precise, predictive, and specific methods to identify, classify, and prognosticate lymphomas based on molecular characteristics using gene expression data, methods which this invention fulfills.
Claims Coverage
The patent includes five independent claims covering methods for treating DLBCL based on gene expression-derived survival predictor scores, use of microarray technology, and specific gene compositions within gene expression signatures.
Method of treating DLBCL based on survival predictor scores
A method of treating a subject with DLBCL involving (1) determining a first survival predictor score by measuring gene expression levels for genes in ABC DLBCL high, lymph node, and MHC class II gene expression signatures from a biopsy, averaging expression levels within each signature, and calculating a survival predictor score by the equation: [0.586*(ABC DLBCL high signature value)] - [0.468*(lymph node signature value)] - [0.336*(MHC class II signature value)], where a higher score indicates worse survival; (2) determining a second survival predictor score similarly; (3) comparing the two scores; (4) determining poor prognosis if the second score is higher; and (5) treating the subject accordingly.
Use of microarray for gene expression data acquisition
Obtaining gene expression data for the method of treating DLBCL by using a microarray.
Genes comprising the ABC DLBCL high gene expression signature
The ABC DLBCL high gene expression signature includes at least one gene selected from UNIQIDs: 1134271, 1121564, 1119889, 1133300, 1106030, 1139301, 1122131, 1114824, 1100161, and 1120129.
Genes comprising the lymph node gene expression signature
The lymph node gene expression signature includes at least one gene selected from UNIQIDs: 1097126, 1120880, 1098898, 1123376, 1128945, 1130994, 1124429, 1099358, 1130509, 1095985, 1123038, 1133700, 1122101, and 1124296.
Genes comprising the MHC class II gene expression signature
The MHC class II gene expression signature includes at least one gene selected from UNIQIDs: 1123127, 1136777, 1137771, 1134281, 1136573, and 1132710.
The independent claims focus on therapeutic methods for DLBCL integrating gene expression data from specified gene expression signatures, microarray-based data acquisition, and defining the gene composition of these signatures to generate survival predictor scores predictive of prognosis.
Stated Advantages
Provides more accurate and predictive methods for lymphoma identification, classification, and survival prediction based on gene expression patterns.
Enables development of efficient microarrays containing focused gene sets improving analysis specificity and reducing complexity.
Facilitates improved selection and application of therapeutic strategies tailored to molecular lymphoma classification and predicted survival outcomes.
Allows identification of lymphoma subtypes indistinguishable by morphology, aiding precise diagnosis and prognosis.
Improves patient management by enabling prediction of clinical outcomes and response to treatment.
Documented Applications
Identifying and diagnosing various lymphoma types and subtypes including follicular lymphoma (FL), diffuse large B cell lymphoma (DLBCL) and its subtypes (ABC, GCB, PMBL), mantle cell lymphoma (MCL), small lymphocytic lymphoma (SLL), follicular hyperplasia (FH), primary mediastinal B cell lymphoma (PMBL), mucosa-associated lymphoid tissue lymphoma (MALT), lymphoplasmacytic lymphoma, post-transplant lymphoproliferative disorder (PTLD), nodal marginal zone lymphoma (NMZ), Burkitt lymphoma (BL) and others.
Predicting survival in subjects diagnosed with specific lymphoma types by calculating survival predictor scores based on gene expression data.
Utilizing microarrays and RT-PCR for quantitative gene expression measurement in lymphoma samples.
Developing multivariate gene expression signature-based models for lymphoma classification and prognosis.
Using Bayesian analysis to assign probabilities that a given lymphoma sample belongs to certain lymphoma types and subtypes.
Treatment decision-making for DLBCL patients based on comparison of survival predictor scores derived from gene expression data.
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