Getting personalized cancer genome analysis into the clinic: the challenges in bioinformatics

Abstract

Progress in genomics has raised expectations in many fields, and particularly in personalized cancer research. The new technologies available make it possible to combine information about potential disease markers, altered function and accessible drug targets, which, coupled with pathological and medical information, will help produce more appropriate clinical decisions. The accessibility of such experimental techniques makes it all the more necessary to improve and adapt computational strategies to the new challenges. This review focuses on the critical issues associated with the standard pipeline, which includes: DNA sequencing analysis; analysis of mutations in coding regions; the study of genome rearrangements; extrapolating information on mutations to the functional and signaling level; and predicting the effects of therapies using mouse tumor models. We describe the possibilities, limitations and future challenges of current bioinformatics strategies for each of these issues. Furthermore, we emphasize the need for the collaboration between the bioinformaticians who implement the software and use the data resources, the computational biologists who develop the analytical methods, and the clinicians, the systems' end users and those ultimately responsible for taking medical decisions. Finally, the different steps in cancer genome analysis are illustrated through examples of applications in cancer genome analysis.

Figure 1

Scheme of a comprehensive bioinformatics pipeline to analyze personalized genomic information. The five steps in the pipeline are shown in the top row, with the main methods that have so far been developed for each step the middle and outstanding problems in the bottom row. (1) Revision of genomic information. In this rapidly developing area methods and software are continuously changing to match the improvements in sequencing technologies. (2) Analysis of the consequences of specific mutations and genomic alterations. The analysis needs go from the area of point mutation prediction in proteins to the much more challenging area of prediction of mutations in non-coding regions, including promoter regions and TF binding sites. Other genetic alterations important in cancer must also be taken into consideration, such as copy number variation, modification of splice sites and altered splicing patterns. (3) Mapping of gene/protein variants at the network level. At this point, the relationships between individual components (genes and proteins) are analyzed in terms of their involvement in gene control networks, protein interaction maps and signaling/metabolic pathways. It is clearly necessary to develop a network analysis infrastructure and analysis methods capable of extracting information from heterogeneous data sources. (4) Translation of the information into potential drugs or treatments. The pharmacogenomic analysis of the information is essential to identify potential drugs or treatments. The analysis at this level integrates genomic information with that obtained from databases linking drugs and potential targets, combining it with data on clinical trials drawn from text or web sources. Toxicogenomics information adds an interesting dimension that enables additional exploration of the data. (5) Finally, it is essential to make the information extracted by the systems accessible to the end users in adequate conditions, including geneticists, biomedical scientists and clinicians.

Figure 2

Screenshots representing the basic information provided by the wKinMut system for analyzing a set of point mutations in protein kinases [147,148]. The panels present: (a) general information about the protein kinase imported from various databases; (b) information about the possible consequences of the mutations extracted from annotated databases, each linked to the original source; (c) predictions of the consequences of the mutations in terms of the principal features of the corresponding protein kinase, including the results of the kinase-specific system KinMut [110] (Table 2); (d) an alignment of related sequences, including information about conserved and variable positions; (e) the position of the mutations in the corresponding protein structure (when available); (f) sentences related to the specific mutations from [77]; (g) information about the function and interactions of the protein kinase extracted from PubMed with the iHOP system [149,150]. A detailed description of the wKinMut system can be found in [147] and in the documentation of the web site [148].

Figure 3

An interface (CONTEXTS) that we have developed for the analysis of cancer genome studies at the level of biological networks [122,151]. The upper panel shows the menus for selecting specific cancer studies, databases for pathway analysis (or set of annotations) and the level of confidence required for the relationships. From the user's requests, the system identifies the pathways or functional classes common to the different cancer studies, and the interface allows the corresponding information to be retrieved. The graph represent various cancer studies (those selected in the 'tumor types' panel are represented by red circles) using the pathways extracted from the Reactome database [152] as the background (the reference selected in the 'Annotation databases' panel and represented by small triangles). For the selected lung cancer study, the 'Lung tumor mutated genes' panel provides a link to the related genes indicating the database (source) from where the information was extracted. The lower panel represents the information on the pathways selected by the user ('innate immunity signaling') as directly provided by the Reactome database.