Grouping for classifying gastric cancer

A grouping method, gastric cancer technology, applied in biochemical equipment and methods, medical preparations containing active ingredients, analytical materials, etc., can solve the problem of not being able to capture the complete diversity of gastric cancer subtypes, and the difficulty of deriving immortal cell lines from gastric tumors And other issues

Inactive Publication Date: 2015-07-15
AGENCY FOR SCI TECH & RES +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, this approach may not capture the full diversity of gastric cancer subtypes
Specifically, it is difficult to derive immortal cell lines from primary gastric tumors

Method used

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  • Grouping for classifying gastric cancer
  • Grouping for classifying gastric cancer
  • Grouping for classifying gastric cancer

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0208] Determining the number of gastric cancer subtypes

[0209] Fifty-six gastric adenocarcinomas were analyzed and combined with previously reported expression profiles to form a collection of 248 profiles (Gene Expression Omnibus accession numbers GSE15459 and GSE22183). All samples were analyzed on the Affymetrix U133 Plus 2.0 expression array.

[0210] Gene expression microarray data obtained from multiple sources have significant batch effects. Therefore, to ensure that batch effects or non-biological variation between sample groups arising from subtle but systematic differences in experimental manipulations do not obscure biological structures or lead to artifacts, the data were processed through ComBat to eliminate batch effect. The nascent dataset is named "SG248".

[0211] Assessment of whether biological substructures were retained after ComBat treatment was done as follows. First, consensus clustering was applied to each of the two individual batches (Singap...

Embodiment 2

[0230] Genomic Copy Number Alterations (CNAs)

[0231] 138 of the SG201 tumor samples were analyzed using the Affymetrix SNP6 DNA microarray (Affymetrix, 2009), and 98 non-malignant samples were used as reference. Using these data, the number of chromosomal segments affected by CNAs was determined for each tumor.

[0232] Preprocessing and normalization were performed using Affymetrix Genotyping Console 3.0 (Affymertrix, 2008). For each tumor sample, "Log2Ratios" (term in Affymetrix Genotyping Console) were generated for SNPs (single nucleotide polymorphisms) and copy number probe sets on the array. Cyclic binary partitioning was applied to Log2Ratios using the R package DNAcopy (http: / / www.bioconductor.org / packages / 2.3 / bioc / html / DNAcopy.html).

[0233] The segmented data were then mapped to chromosome segments using the hg18 chromosome segment locations from the UCSC Genome Browse database. The copy number of each chromosome segment was estimated as the length-weighted a...

Embodiment 3

[0243] methylation profile

[0244] The DNA methylation profiles of 139 of the SG201 tumor samples were determined. The Illumina Infinium HumanMethylation 27 array (Weisenberger et al., 2008) was used to evaluate CpG methylation levels across 26,486 orthosomes across the genome, including 94 non-malignant samples from the Singapore cohort as a reference.

[0245] The extent to which the DNA methylation profiles of each subtype differed from those of 94 non-malignant gastric tissue samples was investigated. To do this, the methylation levels of each CpG in all tumors in each subtype were compared to the methylation levels of non-malignant samples. The DNA methylation level (β value) of each probe was calculated using Illumina's Genome Studio software. Differential methylation analysis relative to 94 non-malignant samples was performed on the aggressive subtype, proliferative subtype, and metabolic subtype sample sets, respectively. CpGs with significantly different methyla...

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Abstract

There is provided a grouping for classifying a gastric cancer tumor sample obtained from a patient suffering or suspected to suffer from gastric cancer, wherein the grouping comprises an invasive subtype, a proliferative subtype and a metabolic subtype. There is also provided a predictor and methods of using the grouping.

Description

[0001] References to related applications [0002] This application claims priority from Singapore Patent Application No. 201206943-1 filed 18 September 2012, the contents of which are hereby incorporated by reference in their entirety for all purposes. technical field [0003] The present invention generally relates to biochemistry and medical applications of biochemical molecules. Background of the invention [0004] Gastric adenocarcinoma (a type of gastric cancer) is the second leading cause of cancer death worldwide, with particularly high morbidity and mortality in East Asia, Eastern Europe, and Latin America. Surgery remains the mainstay of treatment, but is only effective in the early stages. However, due to the lack of symptoms at an early stage, most patients are diagnosed with advanced disease and have a very poor prognosis. [0005] The underlying mechanisms of gastric carcinogenesis are poorly understood. Like many other cancers, gastric cancer is complex and...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): C12Q1/68G01N33/574G16B40/30G16B20/20G16B25/10
CPCG01N2800/52G01N33/57446G01N2800/7028C12Q1/6886C12Q2600/158G01N2800/56G16B20/20G16B25/10G16B40/30C12Q2600/106G16B20/00A61K31/513C12Q2600/154C12Q2600/112C12Q2600/156G16B40/00G16B25/00A61P1/00A61P35/00
Inventor 雷政登史蒂夫·罗森帕特里克·泰恩曼尼什·斯拉凡斯霍斯特·弗罗托安娜·恩戈
Owner AGENCY FOR SCI TECH & RES
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