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Diagnosis, prognosis and identification of potential therapeutic targets of multiple myeloma based on gene expression profiling

a gene expression and multiple myeloma technology, applied in the field of cancer research, can solve the problems of slow progress in understanding the biology and genetics of multiple myeloma and advancing therapy, and the death of most patients, and it is difficult to establish correlations between genetic abnormalities and clinical outcomes

Inactive Publication Date: 2008-09-25
BIOVENTURES LLC
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

The patent text describes a method for profiling gene expression in multiple myeloma and identifying genes that are differentially expressed in different subgroups of the disease. This can be used for diagnosis and treatment of multiple myeloma. The method also suggests a developmental stage-based classification system for multiple myeloma and a method for controlling bone loss in individuals with the disease. The technical effects of this patent include improved diagnosis and treatment of multiple myeloma and a better understanding of the disease's biology.

Problems solved by technology

Although initial transformation events leading to the development of multiple myeloma are thought to occur at a post-germinal center stage of development as suggested by the presence of somatic hypermutation of IGV genes, progress in understanding the biology and genetics of and advancing therapy for multiple myeloma has been slow.
Multiple myeloma cells are endowed with a multiplicity of anti-apoptotic signaling mechanisms that account for their resistance to current chemotherapy and thus the ultimately fatal outcome for most patients.
Given this “genetic chaos” it has been difficult to establish correlations between genetic abnormalities and clinical outcomes.
However, even with the most comprehensive analysis of laboratory parameters, such as b2-microglobulin (b2M), C-reactive protein (CRP), plasma cell labeling index (PCLI), metaphase karyotyping, and FISH, the clinical course of patients afflicted with multiple myeloma can only be approximated, because no more than 20% of the clinical heterogeneity can be accounted for.
The molecular basis of monoclonal gammopathy of undetermined significance and multiple myeloma are not very well understood and it is not easy to differentiate the two disorders.
Especially in early phases of multiple myeloma, the differential diagnosis is associated with a certain degree of uncertainty.
Thus, the prior art is deficient in methods of differential diagnosing and identifying distinct and prognostically relevant clinical subgroups of multiple myeloma.

Method used

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  • Diagnosis, prognosis and identification of potential therapeutic targets of multiple myeloma based on gene expression profiling
  • Diagnosis, prognosis and identification of potential therapeutic targets of multiple myeloma based on gene expression profiling
  • Diagnosis, prognosis and identification of potential therapeutic targets of multiple myeloma based on gene expression profiling

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example 1

Cell Isolation and Analysis

[0070]Samples for the following studies included plasma cells from 74 newly diagnosed cases of multiple myeloma, 5 subjects with monoclonal gammopathy of undetermined significance, 7 samples of tonsil B lymphocytes (tonsil BCs), 11 samples of tonsil plasma cells (tonsil PCs), and 31 bone marrow PCs derived from normal healthy donor. Multiple myeloma cell lines (U266, ARP1, RPM1-8226, UUN, ANBL-6, CAG, and H929 (courtesy of P. L. Bergsagel) and an Epstein-Barr virus (EBV)-transformed B-lymphoblastoid cell line (ARH-77) were grown as recommended (ATCC, Chantilly, Va.).

[0071]Tonsils were obtained from patients undergoing tonsillectomy for chronic tonsillitis. Tonsil tissues were minced, softly teased and filtered. The mononuclear cell fraction from tonsil preparations and bone marrow aspirates were separated by a standard Ficoll-Hypaque gradient (Pharmacia Biotech, Piscataway, N.J.). The cells in the light density fraction (S.G.≦1.077) were resuspended in cel...

example 2

Preparation of Labeled cRNA and Hybridization to High-Density Microarray

[0075]Total RNA was isolated with RNeasy Mini Kit (Qiagen, Valencia, Calif.). Double-stranded cDNA and biotinylated cRNA were synthesized from total RNA and hybridized to GENECHIP® HUGENEFL® microarrays (Affymetrix, Santa Clara, Calif.), which were washed and scanned according to procedures developed by the manufacturer. The arrays were scanned using Hewlett Packard confocal laser scanner and visualized using Affymetrix 3.3 software (Affymetrix). Arrays were scaled to an average intensity of 1,500 and analyzed independently.

example 3

Genechip Data Analysis

[0076]To efficiently manage and mine high-density oligonucleotide DNA microarray data, a new data-handling tool was developed. GENECHIP®—derived expression data was stored on an MS SQL Server. This database was linked, via an MS Access interface called Clinical Gene-Organizer to multiple clinical parameter databases for multiple myeloma patients. This Data Mart concept allows gene expression profiles to be directly correlated with clinical parameters and clinical outcomes using standard statistical software. All data used in the present analysis were derived from Affymetrix 3.3 software. GENECHIP® 3.3 output files are given (1) as an average difference (AD) that represents the difference between the intensities of the sequence-specific perfect match probe set and the mismatch probe set, or (2) as an absolute call (AC) of present or absent as determined by the GENECHIP® 3.3 algorithm. Average difference calls were transformed by the natural log after substitutin...

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Abstract

Provided herein are methods for diagnosing and treating multiple myeloma based on statistical analysis of and subsequent increasing / inhibiting expression of subgroups of plasma cells and B cell genes. Also provided are methods for a developmental stage-based classification for multiple myeloma using hierarchical clustering analysis of plasma cell and B cell nucleic acids and for discriminating among normal, hyperplastic and malignant using gene expression array data and statistical analysis thereof. In addition methods for determining the risk of developing bone disease in a test individual by examining expression levels of a WNT signaling antagonist, such as DKK1, are provided. A kit comprising anti-DKK1 antibodies and detection reagents for measuring DKK1 protein levels also is provided.

Description

CROSS-REFERENCE TO RELATED APPLICATION[0001]This is a divisional application of U.S. Ser. No. 10 / 454,263, filed Jun. 4, 2003, which is a continuation-in-part of U.S. Ser. No. 10 / 409,004, filed Apr. 8, 2003, which is a continuation-in-part of U.S. Ser. No. 10 / 289,746, filed Nov. 7, 2002, which claims benefit of provisional patent applications U.S. Ser. No. 60 / 348,238, filed Nov. 7, 2001, U.S. Ser. No. 60 / 355,386, filed Feb. 8, 2002, and U.S. Ser. No. 60 / 403,075, filed Aug. 13, 2002, now all abandoned.FEDERAL FUNDING LEGEND[0002]This invention was produced in part using funds obtained through a grant from the National Cancer Institute. Consequently, the federal government has certain rights in this invention.BACKGROUND OF THE INVENTION[0003]1. Field of the Invention[0004]The present invention relates generally to the field of cancer research. More specifically, the present invention relates to gene expression profiling of plasma cells from patients with multiple myeloma or monoclonal ...

Claims

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Application Information

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Patent Type & Authority Applications(United States)
IPC IPC(8): C40B30/04C12Q1/68G01N33/53G01N33/48G01N33/50G06F19/00
CPCC12Q1/6837C12Q2600/112C12Q2600/158C12Q1/6886Y02A90/10
Inventor SHAUGHNESSY, JOHN D.BARLOGIE, BARTZHAN, FENGHUANG
Owner BIOVENTURES LLC
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