Validating a gene expression signature of invasive ductal carcinoma of the breast and detecting key genes using neural networks
Validating a gene expression signature of invasive ductal carcinoma of the breast and detecting key genes using neural networks
Samarasinghe, Sandhya ; Kulasiri, Don
Samarasinghe, Sandhya
Kulasiri, Don
Date
2009-07
Type
Conference Contribution - published
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Abstract
Breast cancer is one of the leading causes of death in women in the world. It is a complex disease
with challenges to accurate diagnosis due to cancer subtypes that are difficult to distinguish. The most
common subtype is Invasive Ductal Carcinoma (IDC), a cancer in ductal cells that line the milk ducts in the
breast. In depth understanding of the genetic basis of IDC can help treat it more effectively.
Microarray based gene expression analysis is making new grounds in accurate diagnosis of diseases
including cancer. Microarray experiments are designed to measure the expression levels of thousands of
genes in various cells/tissues of interest and they are analysed to decipher a small set of genes that constitutes
the gene signature of a particular disease. The few studies on breast cancer gene expression compare cancer
subtypes but very few have compared gene expression between matched cancer and healthy tissues in the
breast (Turashvili et al., 2007). The few studies that have compared different subtypes have little agreement
on the gene signatures (Turashvili, 2007; Zhao et al., 2004, Sorlie, et al., 2001). Therefore, it is highly
beneficial to further assess the validity of genes identified as differentially expressed, in order to boost
confidence in the usefulness of the genes in various medical applications including diagnosis, prognosis and
drug development. In this study, the validity of differentially expressed genes pertaining to a carefully
conducted experiment on breast tissues affected by Invasive Ductal Carcinoma (IDL) and matched healthy
tissues is conducted using neural networks and statistical methods. The data was obtained from NCBI
database and deposited by Turashvili et al (2007) from their experiments on breast cancer. The original
authors extracted a 326 gene signature for IDC using statistical methods. In our study, the ability of this gene
set to discriminate the disease state from healthy state is investigated and validated using two sets of
independent datasets.
Our visual and qualitative exploration using Self organizing maps (SOM) followed by statistical tests
indicated that the validation data supported 80% of the original gene signature. Another SOM results
declared that the original gene set is able to classify patients as being healthy or having IDC. Original gene
set was optimally clustered into two classes based on correlation of expression patterns of genes by SOM
/Ward clustering. The two classes and genes in them were supported by 60% of the validation data. As an
alternative, PCA was used to determine genes with correlated expressions in the original gene signature and 4
PCs accounted for 86% of the variation in the data with the first 2 PCs accounting for around 70%. Top most
important 100 genes in PC1 and PC2 provided 52% support for the two SOM classes with PC1 dominating
class 1 and PC2, class 2. Genes that were validated by independent data in the two SOM classes were used
in conjunction with PC1 and PC2 to extract highly influential genes from the top 6%, 18% and 57% of the
original genes represented by PC1 and PC2. These key genes may prove to be the most crucial in identifying
ductal tumor from healthy tissues. Four new genes were among key genes that may shed more light onto the
disease mechanism. The key genes as well as overall set of validated genes may provide further support to
understand or refine genetic networks that these genes are part of in the next stage of our study.
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