Discretization of Gene Expression Data Unmasks Molecular Subgroups Recurring in Different Human Cancer Types.

Despite the individually different molecular alterations in tumors, the malignancy associated biological traits are strikingly similar. Results of a previous study using renal cell carcinoma (RCC) as a model pointed towards cancer-related features, which could be visualized as three groups by microarray based gene expression analysis. In this study, we used a mathematic model to verify the presence of these groups in RCC as well as in other cancer types. We developed an algorithm for gene-expression deviation profiling for analyzing gene expression data of a total of 8397 patients with 13 different cancer types and normal tissues. We revealed three common Cancer Transcriptomic Profiles (CTPs) which recurred in all investigated tumors. Additionally, CTPs remained robust regardless of the functions or numbers of genes analyzed. CTPs may represent common genetic fingerprints, which potentially reflect the closely related biological traits of human cancers

Graphical illustration of CTPs and patient classification.

<p>Shown is a graphical overview on the nature of generated RCC-CTPs as well as their comparison with the CTP of one patient with a different cancer type. The deviation from the mean expression for all probe sets and their relative correlation to each other (continuous curves) define the RCC-CTP target profiles. For CTP assignment, the CTP profile of an individual patient with another cancer type (dashed curve) is compared to the target RCC-CTPs.</p

CTP profiles of the different cancer types using randomly picked tumor suppressor or oncogenes.

<p>Shown are best descriptors of gene expression deviations from the mean of randomly selected tumor suppressor genes <b>(A)</b> and oncogenes <b>(B)</b> encompassing different cancer types. For a detailed overview illustrating all expression deviations including genes showing no deviations from the mean, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0161514#pone.0161514.s004" target="_blank">S4</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0161514#pone.0161514.s005" target="_blank">S5</a> Tables. <b>Red:</b> relative gene expression deviation is lower than the mean expression, <b>Green:</b> relative gene expression deviation is higher than the mean expression. CTPs differ by the overall relative gene expression deviations. The entirety of expression deviations from the mean of genes, not that of one single gene is relevant for affiliating one distinct tumor to CTP-A, -B or -C.</p

Workflow for generating RCC-CTPs.

<p><b>A</b>. Determination of the gene expression value (log₂) of gene probe set 1 in all RCC samples tested and generation of the mean value for probe set 1 of the entire RCC tumor cohort (exemplified values are shown). <b>B.</b> Subtraction of the mean value from true expression value for probe set 1 of each patient. <b>C.</b> Distribution of the remaining deviation values from mean for probe set 1. <b>D.</b> Annotation of remaining deviation values from mean for probe set 1 as 1, -1 or 0 depending on their localization in the distribution. <b>E.</b> Steps A to D are performed for all probe sets of the gene expression microarray. <b>F.</b> Individual CTP profile for each patient given by a vector covering all expression values. <b>G.</b> Grouping of RCC patients into CTP-A, -B or -C according to Beleut <i>et al</i>. 2012; Determination of the CTP mean values of all gene probe sets for patient group A, group B and group C and generation of the final RCC CTP A, -B and -C target vectors. <b>H.</b> Gene expression data from other cancer types calculated according to steps A to F and generation of patient-specific CTP-vectors. <b>I.</b> Correlation of RCC CTP-A, -B and -C target vectors from step G with patient-specific CTP of other cancer types derived from step H. (BC) breast cancer patients; (LC) lung cancer patients, (CC) colon cancer patients. <b>J.</b> Assigning a patient or control to tumor subgroup according to the CTP with the highest correlation.</p

Visualization of CTPs by <i>de Finetti</i> like mapping.

<p><b>A.</b><i>De Finetti</i> like diagram illustrating RCC classification of GSE19949 into the three CTP groups as previously defined [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0161514#pone.0161514.ref022" target="_blank">22</a>]. Each dot represents one patient, colors code for the distinct groups, as indicated. <b>B.</b> <i>De Finetti</i> like diagram illustrating the sub-classification of an independent ccRCC dataset (GSE22541) according to the Beleut <i>et al</i>. 2012 rules. The color code per patient defines its CTP assignment as identified in A. <b>C.</b> Control <i>de Finetti</i> like diagram, in which Self Organizing Maps (SOM) have been applied in classifying GSE22541 ccRCC primary tumors for comparison. The color code per patient defines the “CTP” to which the respective tumor would belong according to SOM. <b>D.</b> <i>De Finetti</i> like diagram illustrating the classification of normal renal tissues of GSE53757 according to identified CTPs. Note the weak correlation of individual samples with profile vectors, leading to mostly a central clustering of analyzed samples. <b>E.</b> <i>De Finetti</i> like mapping of a breast cancer dataset (GSE12093) and <b>F.</b> a Lymphoma dataset (GSE34771) according to RCC-CTPs. Note the increased scattering of individual patients in E and F when compared to control.</p