Prostate cancer diagnosis and outcome prediction by expression analysis

a technology of expression analysis and prostate cancer, applied in the field of prostate cancer diagnosis and outcome prediction by expression analysis, can solve the problems of difficult classification of individual samples into particular disease classes, ineffective or harmful treatment, incorrect diagnosis and treatment, etc., and achieve the effect of reducing levels, less effective, and reducing levels

Inactive Publication Date: 2006-01-12
WHITEHEAD INST FOR BIOMEDICAL RES +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The classification of a sample from an individual into particular disease classes has often proven to be difficult, incorrect, or equivocal.
Inaccurate results can lead to incorrect diagnoses and potentially ineffective or harmful treatment.
A critical issue in the care of men with prostate cancer is to improve the risk stratification of patients with intermediate risk disease.
However, while these measures can successfully distinguish between men at low, intermediate, and high risk for tumor recurrence following local therapy, they are less successful in helping guide therapy for the majority of men falling into the intermediate risk group.

Method used

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  • Prostate cancer diagnosis and outcome prediction by expression analysis
  • Prostate cancer diagnosis and outcome prediction by expression analysis
  • Prostate cancer diagnosis and outcome prediction by expression analysis

Examples

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example 1

Sample Identification

[0163] From 1995 to 1997, samples of prostate tumors and non-tumor prostate tissue (normal prostate tissue) were collected from consented patients undergoing radical prostatectomy at the Brigham and Women's Hospital (Boston, Mass.). Samples were embedded in optimal cutting temperature (OCT) solution, snap-frozen, and stored in liquid nitrogen. Two hundred thirty-five (235) tumor samples were cryosectioned and histologically reviewed by an experienced prostate pathologist. Sixty-five samples (27.7%) with tumor present on opposing sides of the sample that also had available corresponding normal tissue were included for further analysis. All tumor samples were prospectively reviewed by the same pathologist for Gleason score (described below) and all tumor and normal samples were reviewed to quantify the proportion of the sample comprised of tumor epithelium, normal epithelial, stromal, inflammatory and / or urothelial cells (when present). The original surgical path...

example 2

Preparation of Samples for Microarray Hybridization and Measurement of Gene Expression

[0165] High-quality oligonucleotide based expression data was obtained from 52 prostate tumors and 50 prostate samples lacking detectable tumor (referred to as “normal prostate” here forward) as follows. Total RNA was extracted from the OCT-embedded specimens after tissue homogenization (with a Polytron PT 2100 tissue homogenizer) using Trizol reagent (Life Technologies, Gaithersberg, Va.). During all processing, the thawing of specimens was minimized so as to limit RNA degradation. In two large batches, using pooled reagents and established methods (Golub, et al., Science 286: 531-537 (1999)), labeled cRNA (referred to as “target”) was synthesized for each sample from a minimum of 10 micrograms of total RNA. Seven replicate RNA samples (5 tumors and 2 normal samples) with excess RNA were included to assess expression variability introduced by sample preparation and hybridization. Four replicate s...

example 3

Early Expression Analysis: Quality Assessment, Scaling, Filtering, and Statistical Methods

[0167] Gene expression files where overall microarray staining intensity, the percentage of genes detected, or the mean average difference were 2 standard deviations outside the mean level of the dataset were excluded. To minimize the effect of technical variation on subsequent analysis, expression files from each sample included in subsequent experiments were scaled together (also referred to as “normalized”). Files were scaled by multiplying the average difference of each gene by the ratio of the mean average difference for all genes on the sample array and the mean average difference of the selected reference microarray representing the median value for the mean average difference of all arrays.

[0168] To exclude genes with minimal variation, the average difference values were set at lower (10) and upper thresholds (16000) and genes without variation (<5-fold between any two samples) across...

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Abstract

Methods identifying prostate cancer, methods for prognosing and diagnosing prostate cancer, methods for identifying a compound that modulates prostate cancer development, methods for determining the efficacy of a prostate cancer therapy, and oligonucleotide microarrays containing probes for genes involved in prostate cancer development are described.

Description

RELATED APPLICATION [0001] This application is a continuation of U.S. patent application Ser. No. 10 / 325,457, filed on Dec. 19, 2002, which claims the benefit of U.S. Provisional Application No. 60 / 343,448, filed Dec. 21, 2001. The entire teachings of the above application are incorporated herein by reference.GOVERNMENT SUPPORT [0002] The invention was supported, in whole or in part, by a grant NIH 1U01CA84995 from the National Institutes of Health. The Government has certain rights in the invention.BACKGROUND OF THE INVENTION [0003] Classification of biological samples from individuals is not an exact science. In many instances, accurate diagnosis and safe and effective treatment of a disorder depend on being able to discern biological distinctions among cell or tissue samples from a particular area of the body, such as prostate cancer samples and normal prostate samples. The classification of a sample from an individual into particular disease classes has often proven to be diffic...

Claims

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Application Information

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Patent Type & Authority Applications(United States)
IPC IPC(8): C12Q1/68
CPCC12Q1/6886C12Q2600/158C12Q2600/112C12Q2600/118C12Q2600/136G01N2800/52
Inventor GOLUB, TODD R.FEBBO, PHILLIP G.ROSS, KENNETH N.SELLERS, WILLIAM R.
Owner WHITEHEAD INST FOR BIOMEDICAL RES
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