Abstract
In this article, we address decision support for cancer by exploiting clinical data and identifying mutations on tumour suppressor genes. The goal is to perform data integration between medicine and molecular biology by developing a framework where clinical and genomic features are appropriately combined in order to handle cancer diseases. The constitution of such a decision support system is based on (a) cancer clinical data and (b) biological information that is derived from genomic sources. Through this integration, real time conclusions can be drawn for early diagnosis, staging and more effective cancer treatment.
Key Terms in this Chapter
Mutation: A change in the genetic material (usually DNA or RNA) of a living being. Mutations can happen for a lot of different reasons. They can happen because of errors during cell division, because of radiation, chemicals, and so forth.
Cancer Staging: Knowledge of the extent of the cancer at the time of diagnosis. It is based on three components: the size or depth of penetration of the tumour (T), the involvement of lymph nodes (N), and the presence or absence of metastases (M).
Tumour Suppressor Gene: A gene that reduces the probability that a cell in a multicellular organism will turn into a tumor cell. A mutation or deletion of such a gene will increase the probability of the formation of a tumor.
Data Integration: The problem of combining data residing at different sources and providing the user with a unified view of these data. This important problem emerges in several scientific domains, for example, combining results from different bioinformatics repositories.
Single Nucleotide Polymorphism (SNP): A DNA sequence variation occurring when a single nucleotide—A, T, C, or G—in the genome differs between members of a species or between paired chromosomes in an individual.
Cluster Analysis: The task of decomposing or partitioning a dataset into groups so that the points in one group are similar to each other and are as different as possible from the points in other groups.
Clinical Decision Support Systems (CDSS): Entities that intend to support clinical personnel in medical decision-making tasks. In more technical terms, CDSSs are active knowledge systems that use two or more items of patient data to generate case-specific advice.
Data Mining: The analysis of observational datasets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owner.