Cell Line Derived 5-FU and Irinotecan Drug-Sensitivity Profiles Evaluated in Adjuvant Colon Cancer Trial Data.

Purpose: This study evaluates whether gene signatures for chemosensitivity for irinotecan and 5-fluorouracil (5-FU) derived from in vitro grown cancer cell lines can predict clinical sensitivity to these drugs.

Methods: To test if an irinotecan signature and a SN-38 signature could identify patients who benefitted from the addition of irinotecan to 5-FU, we used gene expression profiles based on cell lines and clinical tumor material. These profiles were applied to expression data obtained from pretreatment formalin fixed paraffin embedded (FFPE) tumor tissue from 636 stage III colon cancer patients enrolled in the PETACC-3 prospective randomized clinical trial.

The consensus molecular subtypes of colorectal cancer.

Colorectal cancer (CRC) is a frequently lethal disease with heterogeneous outcomes and drug responses. To resolve inconsistencies among the reported gene expression-based CRC classifications and facilitate clinical translation, we formed an international consortium dedicated to large-scale data sharing and analytics across expert groups. We show marked interconnectivity between six independent classification systems coalescing into four consensus molecular subtypes (CMSs) with distinguishing features: CMS1 (microsatellite instability immune, 14%), hypermutated, microsatellite unstable and strong immune activation; CMS2 (canonical, 37%), epithelial, marked WNT and MYC signaling activation; CMS3 (metabolic, 13%), epithelial and evident metabolic dysregulation; and CMS4 (mesenchymal, 23%), prominent transforming growth factor-β activation, stromal invasion and angiogenesis.

Batch effect confounding leads to strong bias in performance estimates obtained by cross-validation.

Background: With the large amount of biological data that is currently publicly available, many investigators combine multiple data sets to increase the sample size and potentially also the power of their analyses. However, technical differences ("batch effects") as well as differences in sample composition between the data sets may significantly affect the ability to draw generalizable conclusions from such studies.

Focus: The current study focuses on the construction of classifiers, and the use of cross-validation to estimate their performance.

Statistical approach to protein quantification.

A major goal in proteomics is the comprehensive and accurate description of a proteome. This task includes not only the identification of proteins in a sample, but also the accurate quantification of their abundance. Although mass spectrometry typically provides information on peptide identity and abundance in a sample, it does not directly measure the concentration of the corresponding proteins.

Molecular dynamics simulations shed light on the enthalpic and entropic driving forces that govern the sequence specific recognition between netropsin and DNA.

With the aim to gain a better understanding of the various driving forces that govern sequence specific DNA minor groove binding, we performed a thermodynamic analysis of netropsin binding to an AT-containing and to a set of six mixed AT/GC-containing binding sequences in the DNA minor groove. The relative binding free energies obtained using molecular dynamics simulations and free energy calculations show significant variations with the binding sequence. While the introduction of a GC base pair in the middle or close to the middle of the binding site is unfavorable for netropsin binding, a GC base pair at the end of the binding site appears to have no negative influence on the binding.

Protein and gene model inference based on statistical modeling in k-partite graphs.

One of the major goals of proteomics is the comprehensive and accurate description of a proteome. Shotgun proteomics, the method of choice for the analysis of complex protein mixtures, requires that experimentally observed peptides are mapped back to the proteins they were derived from. This process is also known as protein inference.