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Reagent sets and gene signatures for renal tubule injury

a technology of gene signatures and reagent sets, applied in the field of reagent sets and gene signatures for renal tubule injury, can solve the problems of renal failure among hospitalized patients, necrotic lesions of the tubules, expensive and long studies, etc., and achieve the effect of increasing the average performance of a depleted s

Inactive Publication Date: 2006-09-07
ENTELOS INC
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

[0011] In one embodiment, the reagent sets of the invention comprise polynucleotides or polypeptides representing genes comprising a random selection of at least about 10% of the genes from Table 4, wherein the addition of said randomly selected genes to a fully depleted gene set for the renal tubule injury classification question increases the average logodds ratio of the linear classifiers generated by the depleted set to at least about 2.5. In another embodiment, a random selection of at least 20% of the genes from Table 4, wherein the addition of said randomly selected genes to a fully depleted gene set for the renal tubule injury classification question increases the average logodds ratio of the linear classifiers generated by the depleted set to at least about 3.3. In another embodiment, a random selection of at least 40% of the genes from Table 4, wherein the addition of said randomly selected genes to a fully depleted gene set for the renal tubule injury classification question increases the average logodds ratio of the linear classifiers generated by the depleted set to at least about 4.0. In other embodiments, reagent sets of the present invention comprise random selections of at least about 5%, 30%, 50%, 60%, 70%, 80%, 90%, or even 99% of the genes from Table 4, each which are capable of substantially increasing the average performance of a depleted set for generating classifiers RTI.
[0012] In one embodiment, the invention provides a reagent set for classifying renal tubule injury comprising a set of polynucleotides or polypeptides representing a plurality of genes selected from Table 4, wherein the addition of a random selection of at least 10% of said plurality of genes to the fully depleted set for the renal tubule injury classification question increases the average logodds ratio of the linear classifiers generated by the depleted set by at least 2-fold. In another embodiment, the reagent set includes at least 40% of said plurality of genes to the fully depleted set for the renal tubule injury classification question increases the average logodds ratio of the linear classifiers generated by the depleted set by at least 3-fold.

Problems solved by technology

Necrotic lesions of the tubules can arise as a consequence of septic, toxic or ischemic insult, and is a frequent cause of renal failure among hospitalized patients.
However, histopathological or clinical indications of kidney injury are not readily apparent in the early course of treatment, thus necessitating expensive and lengthy studies.

Method used

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  • Reagent sets and gene signatures for renal tubule injury
  • Reagent sets and gene signatures for renal tubule injury
  • Reagent sets and gene signatures for renal tubule injury

Examples

Experimental program
Comparison scheme
Effect test

example 1

Construction of Chemogenomic Reference Database (DrugMatrix™)

[0110] This example illustrates the construction of a large multivariate chemogenomic dataset based on DNA microarray analysis of rat tissues from over 580 different in vivo compound treatments. This dataset was used to generate RTI signatures comprising genes and weights which subsequently were used to generate a necessary set of highly responsive genes that may be incorporated into high throughput diagnostic devices as described in Examples 2-7.

[0111] The detailed description of the construction of this chemogenomic dataset is described in Examples 1 and 2 of Published U.S. Pat. Appl. No. 2005 / 0060102 A1, published Mar. 17, 2005, which is hereby incorporated by reference for all purposes. Briefly, in vivo short-term repeat dose rat studies were conducted on over 580 test compounds, including marketed and withdrawn drugs, environmental and industrial toxicants, and standard biochemical reagents. Rats (three per group) w...

example 2

Preparation of a Chemogenomic Dataset for Late-Onset Renal Tubule Injury

[0115] This example describes methods used to prepare a chemogenomic dataset (i.e., a positive training set) for use deriving a signature for renal tubule injury (i.e., late-onset nephrotoxicity).

[0116] Overview

[0117] 28-day repeat dose studies were conducted on known nephrotoxicants. Doses were chosen that would not cause histological or clinical evidence of renal tubular injury after 5 days of dosing, but would cause histological evidence of tubular injury after 28 days of dosing. Animals were assigned to groups such that mean body weights were within 10% of the mean vehicle control group. Test compounds were administered either orally (10 ml of corn oil / kg body weight) or by intra-peritoneal injection (5 ml of saline / kg body weight). Animals were dosed once daily starting on day 0, and necropsied 24 hrs after the last dose following an overnight fast on day 5 (n=5) and day 28 (n=10). An equivalent number o...

example 3

Derivation of a Predictive Renal Tubule Injury Signature

[0138] Overview

[0139] The support vector machine algorithm was trained to classify experimentally confirmed nephrotoxicants from non-nephrotoxicants using the data acquired in Examples 1 and 2 above. A linear classifier (i.e., gene signature) was derived using kidney expression profiles from rats treated with 15 nephrotoxicants that induce renal tubular injury after 4 weeks of daily dosing, and 49 non-nephrotoxicants known not to induce renal tubular injury under subchronic dosing conditions.

[0140] Gene Signature Derivation

[0141] To derive the gene signature, a three-step process of data reduction, signature generation and cross-validation of the predictive signature was used. A total of 7478 gene probes from the total of 10,000 on the CodeLink™ RU1 microarray were pre-selected based on having less than 5% missing values (e.g., invalid measurement or below signal threshold) in either the positive or negative class of the tr...

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Abstract

The invention discloses reagent sets and gene signatures for predicting onset of renal tubule injury in a subject. The invention also provides a necessary set of 186 genes useful for generating signatures of varying size and performance capable of predicting onset of renal tubule injury. The invention also provides methods, apparatuses and reagents useful for predicting future renal tubule injury based on expression levels of genes in the signatures. In one particular embodiment the invention provides a method for predict whether a compound will induce renal tubule injury using gene expression data from sub-acute treatments.

Description

CROSS REFERENCE TO RELATED APPLICATIONS [0001] This application is a continuation-in-part of U.S. patent application Ser. No. 11 / 184,272, filed on Jul. 18, 2005, which claims priority from U.S. Provisional Application No. 60 / 589,409, filed Jul. 19, 2004, each of which is hereby incorporated by reference herein in its entirety.FIELD OF THE INVENTION [0002] This invention relates to reagent sets and gene signatures useful for predicting the onset of renal tubule injury (RTI) in a subject. The invention also provides methods, apparatuses and kits useful for predicting occurrence of renal tubule injury based on expression levels of genes in the signatures. In one embodiment the invention provides a method for predicting whether a compound will induce renal tubule injury using gene expression data from sub-acute treatments. BACKGROUND OF THE INVENTION [0003] Renal tubule injury (also referred to herein as, “tubular nephrosis”) is a common drug-induced toxicity that includes degenerative ...

Claims

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Application Information

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Patent Type & Authority Applications(United States)
IPC IPC(8): C12Q1/68G01N33/53C12M1/34
CPCG01N33/6893G01N2500/00G01N2800/347
Inventor NATSOULIS, GEORGESFIELDEN, MARKJARNAGIN, KURTKOLAJA, KYLE
Owner ENTELOS INC
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