Analysis of Gene Expression Using Gene Sets Discriminates Cancer Patients with and without Late Radiation Toxicity

BACKGROUND: Radiation is an effective anti-cancer therapy but leads to severe late radiation toxicity in 5%–10% of patients. Assuming that genetic susceptibility impacts this risk, we hypothesized that the cellular response of normal tissue to X-rays could discriminate patients with and without late radiation toxicity. METHODS AND FINDINGS: Prostate carcinoma patients without evidence of cancer 2 y after curative radiotherapy were recruited in the study. Blood samples of 21 patients with severe late complications from radiation and 17 patients without symptoms were collected. Stimulated peripheral lymphocytes were mock-irradiated or irradiated with 2-Gy X-rays. The 24-h radiation response was analyzed by gene expression profiling and used for classification. Classification was performed either on the expression of separate genes or, to augment the classification power, on gene sets consisting of genes grouped together based on function or cellular colocalization. X-ray irradiation altered the expression of radio-responsive genes in both groups. This response was variable across individuals, and the expression of the most significant radio-responsive genes was unlinked to radiation toxicity. The classifier based on the radiation response of separate genes correctly classified 63% of the patients. The classifier based on affected gene sets improved correct classification to 86%, although on the individual level only 21/38 (55%) patients were classified with high certainty. The majority of the discriminative genes and gene sets belonged to the ubiquitin, apoptosis, and stress signaling networks. The apoptotic response appeared more pronounced in patients that did not develop toxicity. In an independent set of 12 patients, the toxicity status of eight was predicted correctly by the gene set classifier. CONCLUSIONS: Gene expression profiling succeeded to some extent in discriminating groups of patients with and without severe late radiotherapy toxicity. Moreover, the discriminative power was enhanced by assessment of functionally or structurally related gene sets. While prediction of individual response requires improvement, this study is a step forward in predicting susceptibility to late radiation toxicity

Dissecting systems-wide data using mixture models: application to identify affected cellular processes

Abstract Background Functional analysis of data from genome-scale experiments, such as microarrays, requires an extensive selection of differentially expressed genes. Under many conditions, the proportion of differentially expressed genes is considerable, making the selection criteria a balance between the inclusion of false positives and the exclusion of false negatives. Results We developed an analytical method to determine a p-value threshold from a microarray experiment that is dependent on the quality and design of the data set. To this aim, populations of p-values are modeled as mathematical functions in which the parameters to describe these functions are estimated in an unsupervised manner. The strength of the method is exemplified by its application to a published gene expression data set of sporadic and familial breast tumors with BRCA1 or BRCA2 mutations. Conclusion We present an objective and unsupervised way to set thresholds adapted to the quality and design of the experiment. The resulting mathematical description of the data sets of genome-scale experiments enables a probabilistic approach in systems biology.</p

Genome wide molecular analysis of minimally differentiated acute myeloid leukemia

This study used single nucleotide polymorphism (SNP)-array technology to study copy number changes and to determine regions of loss of heterozygosity in minimally differentiated acute myeloid leukemia. Several chromosomal regions were found to be deleted or duplicated, and mutations in 163gene were the most frequent mutations detected

The Interactions of Proteins Representing the Gene Classifier

<div><p>Of the gene products most frequently present in the gene classifier, 33 proteins are present in the Ingenuity database. These are represented by colored symbols (green symbols indicate proteins that have higher induction after irradiation in NRs, and red symbols indicate proteins that have higher induction in ORs). The intensity of the colors indicates the difference between the groups in the magnitude of induction. The connecting proteins are represented by empty symbols. Only three of the colored proteins are not directly or indirectly linked through a connecting protein.</p> <p>Inset: The degree distribution of the proteins in the sub-network. For each protein we calculated the number of interactions in the total human interaction network (protein degree, <i>z</i>) and plotted it against the proportion of each protein degree, <i>P</i>(<i>z</i>). Vertical blue and black lines indicate the average protein degree, showing that the classifier proteins (blue) and the connecting proteins (black) represent two separate populations (<i>p</i> < 0.001, Wilcoxon test).</p></div

Improved Patient Classification Using Functionally Related Gene Sets

<div><p>(A) Gene classification (in red) and gene set classification (in blue), following the strategy of Michiels et al. [<a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.0030422#pmed-0030422-b025" target="_blank">25</a>], with 95% confidence intervals for the test of proportions. The minimal misclassification rate was 37% ± 2% with gene classification and 14% ± 2% with gene set classification.</p> <p>(B and C) The certainty of microarray classification for each patient was calculated based on (B) genes or (C) functionally related gene sets. The certainty was calculated at the training set size of 32 patients (red arrow in [A]).</p> <p>(D and E) Contingency tables summarizing the concordance between the physician and microarray classifications using (D) genes and (E) gene sets. Numbers of patients classified with certainty (cases where the tolerance limit does not include zero) are in parentheses.</p></div

The Principle of Gene Set Classification

<p>Example of gene set analysis consisting of four gene sets, three patients, and eight genes. (1) Definition of the gene sets consisting of 2–3 genes (purple squares). Gene sets 1–3 are partially overlapping. The dendrogram shows the relationship between the gene sets identified using kappa statistics. (2) Heat map of the gene expression response per patient. (3) Pairwise correlations of all gene responses between the patients. Assuming that patient 1 and patient 3 represent different classes, patient 2 would correlate slightly better with patient 1 than with patient 3. (4) For each patient, the gene responses were combined for every gene set and visualized in a heat map. (5) Pairwise correlations of all gene sets between the patients, showing improvement in correct classification of patient 2.</p

Validation of the Gene Set Classification with an Independent Patient Set

<div><p>(A) Contingency table of the physician and microarray classification of 12 additional patients. The 72 most discriminating gene sets in the training set were used to predict responder status. Numbers of patients classified with certainty are in parentheses.</p> <p>(B) A principal components analysis plot of the two principal components separating the NRs (green) from the ORs (red). Circles represent the 38 patients of the original training set, and triangles represent the 12 patients of the independent validation set.</p></div

Expression Profiles of Classifying Gene Sets

<p>Heat map of <i>r</i> values of 72 gene sets that were present in more than 20% of the 500 repeated assessments with 34 patients in the training set of the classifier. These discriminating gene sets were used in a supervised two-dimensional hierarchical clustering of NRs (green) and ORs (red) based on the <i>r</i> values The threshold for being affected was set at |β₂| = 0.4.</p