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Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis
Published on: June 27, 2020
KAOS: a new automated computational method for the identification of overexpressed genes
Angelo Nuzzo1,2, Giovanni Carapezza1, Sebastiano Di Bella1
1Business Unit Oncology, Nerviano Medical Sciences srl, Nerviano, MI, 20014, Italy.
Background:
Kinase over-expression and activation as a consequence of gene amplification or gene fusion events is a well-known mechanism of tumorigenesis. The search for novel rearrangements of kinases or other druggable genes may contribute to understanding the biology of cancerogenesis, as well as lead to the identification of new candidate targets for drug discovery. However this requires the ability to query large datasets to identify rare events occurring in very small fractions (1-3 %) of different tumor subtypes. This task is different from what is normally done by conventional tools that are able to find genes differentially expressed between two experimental conditions.
Results:
We propose a computational method aimed at the automatic identification of genes which are selectively over-expressed in a very small fraction of samples within a specific tissue. The method does not require a healthy counterpart or a reference sample for the analysis and can be therefore applied also to transcriptional data generated from cell lines. In our implementation the tool can use gene-expression data from microarray experiments, as well as data generated by RNASeq technologies.
Conclusions:
The method was implemented as a publicly available, user-friendly tool called KAOS (Kinase Automatic Outliers Search). The tool enables the automatic execution of iterative searches for the identification of extreme outliers and for the graphical visualization of the results. Filters can be applied to select the most significant outliers. The performance of the tool was evaluated using a synthetic dataset and compared to state-of-the-art tools. KAOS performs particularly well in detecting genes that are overexpressed in few samples or when an extreme outlier stands out on a high variable expression background. To validate the method on real case studies, we used publicly available tumor cell line microarray data, and we were able to identify genes which are known to be overexpressed in specific samples, as well as novel ones.
Insights
We developed KAOS, a tool to find rare gene over-expression in cancer samples. This aids in discovering new drug targets by identifying outlier genes in tumor subtypes.
Area of Science:
- Computational biology
- Genomics
- Cancer research
Background:
- Kinase over-expression drives tumorigenesis through gene amplification or fusion.
- Identifying rare genetic events (1-3%) in tumor subtypes is crucial for cancer biology and drug discovery.
- Conventional tools struggle to detect these rare events, unlike differential expression analysis.
Purpose of the Study:
- To propose a computational method for automatically identifying genes selectively over-expressed in a small fraction of tissue samples.
- To develop a tool that does not require a healthy counterpart, enabling analysis of cell line data.
- To enable the use of both microarray and RNASeq gene-expression data.
Main Methods:
- Developed a computational method for automatic identification of outlier gene expression.
- Implemented the method as a user-friendly tool named KAOS (Kinase Automatic Outliers Search).
- Utilized iterative searches and graphical visualization with filters for significant outlier selection.
Main Results:
- KAOS effectively detects genes overexpressed in a small subset of samples, even against high background variability.
- The tool was validated on synthetic datasets, outperforming state-of-the-art methods.
- Real-world case studies using public tumor cell line data identified known and novel overexpressed genes.
Conclusions:
- KAOS is a publicly available tool for identifying rare gene over-expression events in cancer.
- The method aids in understanding cancerogenesis and discovering new therapeutic targets.
- KAOS demonstrates strong performance in detecting subtle, yet significant, gene expression outliers.
