ColoType: a forty gene signature for consensus molecular subtyping of colorectal cancer tumors using whole-genome assay or targeted RNA-sequencing

Colorectal cancer (CRC) tumors can be partitioned into four biologically distinct consensus molecular subtypes (CMS1-4) using gene expression. Evidence is accumulating that tumors in different subtypes are likely to respond differently to treatments. However, to date, there is no clinical diagnostic test for CMS subtyping. In this study, we used novel methodology in a multi-cohort training domain (n = 1,214) to develop the ColoType scores and classifier to predict CMS1-4 based on expression of 40 genes. In three validation cohorts (n = 1,744, in total) representing three distinct gene-expression measurement technologies, ColoType predicted gold-standard CMS subtypes with accuracies 0.90, 0.91, 0.88, respectively. To accommodate for potential intratumoral heterogeneity and tumors of mixed subtypes, ColoType was designed to report continuous scores measuring the prevalence of each of CMS1–4 in a tumor, in addition to specifying the most prevalent subtype. For analysis of clinical specimens, ColoType was also implemented with targeted RNA-sequencing (Illumina AmpliSeq). In a series of formalin-fixed, paraffin-embedded CRC samples (n = 49), ColoType by targeted RNA-sequencing agreed with subtypes predicted by two independent methods with accuracies 0.92, 0.82, respectively. With further validation, ColoType by targeted RNA-sequencing, may enable clinical application of CMS subtyping with widely-available and cost-effective technology

A New Way of Identifying Biomarkers in Biomedical Basic-Research Studies

A simple, nonparametric and distribution free method was developed for quick identification of the most meaningful biomarkers among a number of candidates in complex biological phenomena, especially in relatively small samples. This method is independent of rigid model forms or other link functions. It may be applied both to metric and non-metric data as well as to independent or matched parallel samples. With this method identification of the most relevant biomarkers is not based on inferential methods; therefore, its application does not require corrections of the level of significance, even in cases of thousands of variables. Hence, the introduced method is appropriate to analyze and evaluate data of complex investigations in clinical and pre-clinical basic research, such as gene or protein expressions, phenotype-genotype associations in case-control studies on the basis of thousands of genes and SNPs (single nucleotide polymorphism), search of prevalence in sleep EEG-Data, functional magnetic resonance imaging (fMRI) or others

Neutrino propagation in the Earth and emerging charged leptons with nuPyProp\texttt{nuPyProp}

Ultra-high-energy neutrinos serve as messengers of some of the highest energy astrophysical environments. Given that neutrinos are neutral and only interact via weak interactions, neutrinos can emerge from sources, traverse astronomical distances, and point back to their origins. Their weak interactions require large target volumes for neutrino detection. Using the Earth as a neutrino converter, terrestrial, sub-orbital, and satellite-based instruments are able to detect signals of neutrino-induced extensive air showers. In this paper, we describe the software code $\texttt{nuPyProp}$ that simulates tau neutrino and muon neutrino interactions in the Earth and predicts the spectrum of the $\tau$-lepton and muons that emerge. The $\texttt{nuPyProp}$ outputs are lookup tables of charged lepton exit probabilities and energies that can be used directly or as inputs to the $\texttt{nuSpaceSim}$ code designed to simulate optical and radio signals from extensive air showers induced by the emerging charged leptons. We describe the inputs to the code, demonstrate its flexibility and show selected results for $\tau$-lepton and muon exit probabilities and energy distributions. The $\texttt{nuPyProp}$ code is open source, available on github.Comment: 42 pages, 21 figures, code available at https://github.com/NuSpaceSim/nupypro

Mountain-valley view of the MoR-values corresponding to the most informative SNPs.

<p>Identifying genes with relevant differences in microarray-based expressions between HAB, NAB and LAB mice. Using the MoR method with the entropy-based stop criterion for variable selection , and among probes were proven to be very informative (relevant) in their expression profiles between HAB and NAB (black symbols over the black solid line), HAB and LAB (blue symbols over the blue solid line)and NAB and LAB (red symbols over the red solid line) mice, respectively. All relevant probes belong to the set of the genes identified and declared with laborious methods from the study investigators as top candidate genes for further investigations.</p

Reliability of the measure of relevance.

<p>Reliability of the measure of relevance for the two sample-problem expressed in sensitivity and specificity indexes. Two different distributions, uniform and bimodal, were used for evaluating reliability. Like the objectivity also the reliability property of the relevance measure goes up to excellent levels (sensitivity and specificity about 100%) with increased normalized biological differences and sample sizes.</p>a<p>Mean rates (in %) of the variables detected as relevant or significant by .</p>b<p>Mean rates (in %) of variables detected as relevant or significant by applying appropriate nonparametric tests.</p

Boxplots of the MoR values under diverse sample designs and data structures.

<p>Irrespective of data type and sample design in the two-sample problem, the measure of relevance shows similar values if the variables are very informative (Normalized Biological Difference (NBD) about ), semi-informative (NBD about ) or non-informative (NBD about ).</p

Measure of relevance for simulated and authentic data.

<p><i>(a)</i> Courses of the best MoR-values by simulating a two-sample problem with variables of different data types and sample designs. For each variable and each sample pseudo random numbers following a certain distribution (see more details in text) were drawn. The three lines correspond to situations with high-informative, semi-informative or non-informative variables among the considered variables, respectively; all other variables were non-informative. Irrespective of data type and sample design the informative variables (blue lines) were correctly identified with . As expected the semi-informative variables showed lower -values and were only partially detected as relevant. <i>(b)</i> Application of the MoR approach to sleep EEG data in order to investigate the effect of sleep deprivation on sleep behavior. subjects were examined in two nights, before and after sleep deprivation. Dataset consists of different data types (metric, ordinal, binary etc.). The MoR-values of the parameters over the solid (red) line are all greater than the sample-related cut-off criterion .</p

Example of different distributions of two random variables.

<p>Location and shape of two independent (1a, 1b, 1c) and two dependent random variables (1d and 1e).</p

Validity of new method compared to alternative methods.

<p>Validity of the measure of relevance for the two sample-problem evaluated by sensitivity and specificity. For comparisons we used the a multiple-testing-adjusted approach based on the t-test and the tree-based Random Forest approach. For informative or semi-informative metric data sensitivity showed good concordance with multiple testing without -adjustment. This was irrespective of sample design and sample size. Specificity did not show as good results as sensitivity (for more details see text). However, the specificity results were better than those obtained from t-test without multiple-testing-adjustment.</p