ProKinO: an ontology for integrative analysis of protein kinases in cancer

Background

Protein kinases are a large and diverse family of enzymes that are genomically altered in many human cancers. Targeted cancer genome sequencing efforts have unveiled the mutational profiles of protein kinase genes from many different cancer types. While mutational data on protein kinases is currently catalogued in various databases, integration of mutation data with other forms of data on protein kinases such as sequence, structure, function and pathway is necessary to identify and characterize key cancer causing mutations. Integrative analysis of protein kinase data, however, is a challenge because of the disparate nature of protein kinase data sources and data formats.

Results

Here, we describe ProKinO, a protein kinase-specific ontology, which provides a controlled vocabulary of terms, their hierarchy, and relationships unifying sequence, structure, function, mutation and pathway information on protein kinases. The conceptual representation of such diverse forms of information in one place not only allows rapid discovery of significant information related to a specific protein kinase, but also enables large-scale integrative analysis of protein kinase data in ways not possible through other kinase-specific resources. We have performed several integrative analyses of ProKinO data and, as an example, found that a large number of somatic mutations (∼288 distinct mutations) associated with the haematopoietic neoplasm cancer type map to only 8 kinases in the human kinome. This is in contrast to glioma, where the mutations are spread over 82 distinct kinases. We also provide examples of how ontology-based data analysis can be used to generate testable hypotheses regarding cancer mutations.

Conclusion

We present an integrated framework for large-scale integrative analysis of protein kinase data. Navigation and analysis of ontology data can be performed using the ontology browser available at: http://vulcan.cs.uga.edu/prokino.     

Distinguishing between driver and passenger mutations in individual cancer genomes by network enrichment analysis

Background

In somatic cancer genomes, delineating genuine driver mutations against a background of multiple passenger events is a challenging task. The difficulty of determining function from sequence data and the low frequency of mutations are increasingly hindering the search for novel, less common cancer drivers. The accumulation of extensive amounts of data on somatic point and copy number alterations necessitates the development of systematic methods for driver mutation analysis.

Results

We introduce a framework for detecting driver mutations via functional network analysis, which is applied to individual genomes and does not require pooling multiple samples. It probabilistically evaluates 1) functional network links between different mutations in the same genome and 2) links between individual mutations and known cancer pathways. In addition, it can employ correlations of mutation patterns in pairs of genes. The method was used to analyze genomic alterations in two TCGA datasets, one for glioblastoma multiforme and another for ovarian carcinoma, which were generated using different approaches to mutation profiling. The proportions of drivers among the reported de novo point mutations in these cancers were estimated to be 57.8% and 16.8%, respectively. The both sets also included extended chromosomal regions with synchronous duplications or losses of multiple genes. We identified putative copy number driver events within many such segments. Finally, we summarized seemingly disparate mutations and discovered a functional network of collagen modifications in the glioblastoma. In order to select the most efficient network for use with this method, we used a novel, ROC curve-based procedure for benchmarking different network versions by their ability to recover pathway membership.

Conclusions

The results of our network-based procedure were in good agreement with published gold standard sets of cancer genes and were shown to complement and expand frequency-based driver analyses. On the other hand, three sequence-based methods applied to the same data yielded poor agreement with each other and with our results. We review the difference in driver proportions discovered by different sequencing approaches and discuss the functional roles of novel driver mutations. The software used in this work and the global network of functional couplings are publicly available at http://research.scilifelab.se/andrej_alexeyenko/downloads.html.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-308) contains supplementary material, which is available to authorized users.     

In vivo response to methotrexate forecasts outcome of acute lymphoblastic leukemia and has a distinct gene expression profile

Background

Childhood acute lymphoblastic leukemia (ALL) is the most common cancer in children, and can now be cured in approximately 80% of patients. Nevertheless, drug resistance is the major cause of treatment failure in children with ALL. The drug methotrexate (MTX), which is widely used to treat many human cancers, is used in essentially all treatment protocols worldwide for newly diagnosed ALL. Although MTX has been extensively studied for many years, relatively little is known about mechanisms of de novo resistance in primary cancer cells, including leukemia cells. This lack of knowledge is due in part to the fact that existing in vitro methods are not sufficiently reliable to permit assessment of MTX resistance in primary ALL cells. Therefore, we measured the in vivo antileukemic effects of MTX and identified genes whose expression differed significantly in patients with a good versus poor response to MTX.

Methods and Findings

We utilized measures of decreased circulating leukemia cells of 293 newly diagnosed children after initial “up-front” in vivo MTX treatment (1 g/m²) to elucidate interpatient differences in the antileukemic effects of MTX. To identify genomic determinants of these effects, we performed a genome-wide assessment of gene expression in primary ALL cells from 161 of these newly diagnosed children (1–18 y). We identified 48 genes and two cDNA clones whose expression was significantly related to the reduction of circulating leukemia cells after initial in vivo treatment with MTX. This finding was validated in an independent cohort of children with ALL. Furthermore, this measure of initial MTX in vivo response and the associated gene expression pattern were predictive of long-term disease-free survival (p < 0.001, p = 0.02).

Conclusions

Together, these data provide new insights into the genomic basis of MTX resistance and interpatient differences in MTX response, pointing to new strategies to overcome MTX resistance in childhood ALL.Trial registrations: Total XV, Therapy for Newly Diagnosed Patients With Acute Lymphoblastic Leukemia, http://www.ClinicalTrials.gov ({"type":"clinical-trial","attrs":{"text":"NCT00137111","term_id":"NCT00137111"}}NCT00137111); Total XIIIBH, Phase III Randomized Study of Antimetabolite-Based Induction plus High-Dose MTX Consolidation for Newly Diagnosed Pediatric Acute Lymphocytic Leukemia at Intermediate or High Risk of Treatment Failure (NCI-T93-0101D); Total XIIIBL, Phase III Randomized Study of Antimetabolite-Based Induction plus High-Dose MTX Consolidation for Newly Diagnosed Pediatric Acute Lymphocytic Leukemia at Lower Risk of Treatment Failure (NCI-T93-0103D).     

Should Informed Consent for Cancer Treatment Include a Discussion about Hospital Outcome Disparities?

Background to the debate: Several studies have found disparities in the outcome of medical procedures across different hospitals—better outcomes have been associated with higher procedure volume. An Institute of Medicine workshop found such a “volume–outcome relationship” for two types of cancer surgery: resection of the pancreas and esophagus (http://www.iom.edu/?id=31508). This debate examines whether physicians have an ethical obligation to inform patients of hospital outcome disparities for these cancers.     

EMu: probabilistic inference of mutational processes and their localization in the cancer genome

The spectrum of mutations discovered in cancer genomes can be explained by the activity of a few elementary mutational processes. We present a novel probabilistic method, EMu, to infer the mutational signatures of these processes from a collection of sequenced tumors. EMu naturally incorporates the tumor-specific opportunity for different mutation types according to sequence composition. Applying EMu to breast cancer data, we derive detailed maps of the activity of each process, both genome-wide and within specific local regions of the genome. Our work provides new opportunities to study the mutational processes underlying cancer development. EMu is available at http://www.sanger.ac.uk/resources/software/emu/.     

Production of Tissue Microarrays, Immunohistochemistry Staining and Digitalization Within the Human Protein Atlas

The tissue microarray (TMA) technology provides the means for high-throughput analysis of multiple tissues and cells. The technique is used within the Human Protein Atlas project for global analysis of protein expression patterns in normal human tissues, cancer and cell lines. Here we present the assembly of 1 mm cores, retrieved from microscopically selected representative tissues, into a single recipient TMA block. The number and size of cores in a TMA block can be varied from approximately forty 2 mm cores to hundreds of 0.6 mm cores. The advantage of using TMA technology is that large amount of data can rapidly be obtained using a single immunostaining protocol to avoid experimental variability. Importantly, only limited amount of scarce tissue is needed, which allows for the analysis of large patient cohorts ^{1 2}. Approximately 250 consecutive sections (4 μm thick) can be cut from a TMA block and used for immunohistochemical staining to determine specific protein expression patterns for 250 different antibodies. In the Human Protein Atlas project, antibodies are generated towards all human proteins and used to acquire corresponding protein profiles in both normal human tissues from 144 individuals and cancer tissues from 216 different patients, representing the 20 most common forms of human cancer. Immunohistochemically stained TMA sections on glass slides are scanned to create high-resolution images from which pathologists can interpret and annotate the outcome of immunohistochemistry. Images together with corresponding pathology-based annotation data are made publically available for the research community through the Human Protein Atlas portal (www.proteinatlas.org) (Figure 1) ^{3 4}. The Human Protein Atlas provides a map showing the distribution and relative abundance of proteins in the human body. The current version contains over 11 million images with protein expression data for 12.238 unique proteins, corresponding to more than 61% of all proteins encoded by the human genome.     

Gene characterization index: assessing the depth of gene annotation

Background

We introduce the Gene Characterization Index, a bioinformatics method for scoring the extent to which a protein-encoding gene is functionally described. Inherently a reflection of human perception, the Gene Characterization Index is applied for assessing the characterization status of individual genes, thus serving the advancement of both genome annotation and applied genomics research by rapid and unbiased identification of groups of uncharacterized genes for diverse applications such as directed functional studies and delineation of novel drug targets.

Methodology/Principal Findings

The scoring procedure is based on a global survey of researchers, who assigned characterization scores from 1 (poor) to 10 (extensive) for a sample of genes based on major online resources. By evaluating the survey as training data, we developed a bioinformatics procedure to assign gene characterization scores to all genes in the human genome. We analyzed snapshots of functional genome annotation over a period of 6 years to assess temporal changes reflected by the increase of the average Gene Characterization Index. Applying the Gene Characterization Index to genes within pharmaceutically relevant classes, we confirmed known drug targets as high-scoring genes and revealed potentially interesting novel targets with low characterization indexes. Removing known drug targets and genes linked to sequence-related patent filings from the entirety of indexed genes, we identified sets of low-scoring genes particularly suited for further experimental investigation.

Conclusions/Significance

The Gene Characterization Index is intended to serve as a tool to the scientific community and granting agencies for focusing resources and efforts on unexplored areas of the genome. The Gene Characterization Index is available from http://cisreg.ca/gci/.     

Non-contiguous finished genome sequence and description of Anaerococcus vaginalis

We report the properties of a draft genome sequence of the bacterium Anaerococcus vaginalis strain PH9, a species within the Anaerococcus genus. This strain, whose genome is described here, was isolated from the fecal flora of a 26-year-old woman suffering from morbid obesity. A. vaginalis is an obligate anaerobic coccus. Here we describe the features of this organism, together with the complete genome sequence and annotation. The 2,048,125-bp long (one chromosome but no plasmid) and contains 2,095 protein-coding and 38 RNA genes, including three rRNA genes.Key words: Anaerococcus vaginalis, genome