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Classification of cancers

A cancer, brain cancer technology, applied in the field of cancer classification, can solve problems such as inability to represent molecular subtypes, limiting individual cancers, etc.

Inactive Publication Date: 2013-01-02
LIFE TECH CORP
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

As a result, molecular subtypes defined in one study may not be representative of molecular subtypes in general
Second, most analyzes to date assume that each cancer sample necessarily falls into one molecular subtype, which limits practical correlation of prognosis and treatment to a single molecular subtype that may not fully define an individual cancer

Method used

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  • Classification of cancers
  • Classification of cancers
  • Classification of cancers

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0092] Meta-analysis of gene expression in cancer

[0093] Analysis of gene expression in cancer patients was obtained from the Oncomine database at http: / / www.oncomine.org, and processed and normalized as described in Rhodes et al., Neoplasia, 2007 Feb;9(2):166-80. Data sets from the 15 most representative cancer types were analyzed. Average linkage hierarchical clustering using the Poisson correlation coefficient as a distance measure was performed on each data set. Up to 10,000 features with the largest standard deviation (but not more than 50% of the total features) were included in the analysis. To reduce the hierarchical clustering results of discrete gene expression clusters, clusters with the most features were identified with a minimum Poisson correlation coefficient of 0.5 and a minimum of 10 features (Rhodes, Neoplasia, 2007). Based on Fisher's exact test, pairwise correlation analysis was performed for each pair of clusters, the number of overlapping genes was co...

Embodiment 2

[0095] Identification of Cancer Modules

[0096] Paired cluster correlations were visualized using a network representation (Cytoscape) and modules of highly connected clusters were identified. To reduce the cluster correlation network into discrete modular groups, the edges without at least two supporting indirect associations are removed, and the nodes and edges connecting the two groups of connected clusters that are least connected if not removed are removed. Define each cancer module as a list of contiguous clusters.

[0097] For each module, representative genes are ranked based on the number of clusters of which they are members. The cancer modules identified for the 15 cancer types are shown in Tables 1-161.

Embodiment 3

[0099] Identification of cancers belonging to the cancer module using quantitative RT-PCR

[0100] Tumor biopsies are obtained from patients. use Oligo(dT) 25 mRNA Purification Kit (Invitrogen, Carlsbad, CA) purifies messenger RNA from the biopsies according to the manufacturer's instructions. Briefly, tumor cells were lysed by grinding the samples in liquid nitrogen to form crude lysates. The lysate was added to the washed Oligo(dT) 25 The beads were incubated at room temperature to allow the poly-A mRNA to anneal to the beads. The beads with bound mRNA are recovered using a magnet, washing away other cellular components. Then, the mRNA was eluted from the beads and used in RT-PCR.

[0101] Use RETRO according to the manufacturer's two-step RT-PCR protocol cDNA kit ( Austin, TX) reverse transcribed the purified mRNA. Briefly, 20-200 ng of mRNA was mixed with 10 base random primers and denatured at 85°C. The primers were then annealed to the mRNA template on ice...

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Abstract

A system for classifying a patient's cancer as belonging to one or more Cancer Modules of 1 of 15 different cancer types is provided. The Cancer Modules are useful to identify patient populations and individual patients demonstrating specific prognosis, risk of metastasis and / or recurrence, response or lack of response to drugs, and the like.

Description

Background technique [0001] Gene expression profiling can grade cancers into molecular subtypes. General approaches have been used to perform two-dimensional hierarchical clustering, identify sample groups that cluster together (i.e., molecular subtypes), and then describe the set of genes that are most relevant to the sample group. This method has two major drawbacks. First, molecular subtypes are subject to study-specific biases, such as sampling bias, tissue collection bias (eg, interstitial contamination), technique bias, tissue processing bias, and numerous others. As a result, molecular subtypes defined in a study may not be representative of molecular subtypes in general. Second, most analyses to date assume that each cancer sample must fall into a molecular subtype, which limits the practical association of prognosis and treatment with a single molecular subtype that may not fully define an individual cancer. This paper will provide a new multidimensional approach t...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): C12Q1/68
CPCC12Q2600/112C12Q2600/16C12Q1/6886C12Q2600/158C12Q2600/106C12Q2600/118A61P35/00
Inventor D·R·罗兹
Owner LIFE TECH CORP
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