Purpose: Global gene expression profiling has been widely used in lung cancer research to identify clinically relevant molecular subtypes as well as to predict prognosis and therapy response. So far, the value of these multigene signatures in clinical practice is unclear, and the biologic importance of individual genes is difficult to assess, as the published signatures virtually do not overlap.Experimental Design: Here, we describe a novel single institute cohort, including 196 non-small lung cancers (NSCLC) with clinical information and long-term follow-up. Gene expression array data were used as a training set to screen for single genes with prognostic impact. The top 450 probe sets identified using a univariate Cox regression model (significance level P < 0.01) were tested in a meta-analysis including five publicly available independent lung cancer cohorts (n ¼ 860).Results: The meta-analysis revealed 14 genes that were significantly associated with survival (P < 0.001) with a false discovery rate <1%. The prognostic impact of one of these genes, the cell adhesion molecule 1 (CADM1), was confirmed by use of immunohistochemistry on tissue microarrays from 2 independent NSCLC cohorts, altogether including 617 NSCLC samples. Low CADM1 protein expression was significantly associated with shorter survival, with particular influence in the adenocarcinoma patient subgroup.Conclusions: Using a novel NSCLC cohort together with a meta-analysis validation approach, we have identified a set of single genes with independent prognostic impact. One of these genes, CADM1, was further established as an immunohistochemical marker with a potential application in clinical diagnostics.

Information on design principles governing transcriptome changes upon transition from safe to hazardous drug concentrations or from tolerated to cytotoxic drug levels are important for the application of toxicogenomics data in developmental toxicology. Here, we tested the effect of eight concentrations of valproic acid (VPA; 25–1000 μM) in an assay that recapitulates the development of human embryonic stem cells to neuroectoderm. Cells were exposed to the drug during the entire differentiation process, and the number of differentially regulated genes increased continuously over the concentration range from zero to about 3000. We identified overrepresented transcription factor binding sites (TFBS) as well as superordinate cell biological processes, and we developed a gene ontology (GO) activation profiler, as well as a two-dimensional teratogenicity index. Analysis of the transcriptome data set by the above biostatistical and systems biology approaches yielded the following insights: (i) tolerated (≤25 μM), deregulated/teratogenic (150–550 μM), and cytotoxic (≥800 μM) concentrations could be differentiated. (ii) Biological signatures related to the mode of action of VPA, such as protein acetylation, developmental changes, and cell migration, emerged from the teratogenic concentrations range. (iii) Cytotoxicity was not accompanied by signatures of newly emerging canonical cell death/stress indicators, but by catabolism and decreased expression of cell cycle associated genes. (iv) Most, but not all of the GO groups and TFBS seen at the highest concentrations were already overrepresented at 350–450 μM. (v) The teratogenicity index reflected this behavior, and thus differed strongly from cytotoxicity. Our findings suggest the use of the highest noncytotoxic drug concentration for gene array toxicogenomics studies, as higher concentrations possibly yield wrong information on the mode of action, and lower drug levels result in decreased gene expression changes and thus a reduced power of the study.

Analysis of transcriptome changes has become an established method to characterize the reaction of cells to toxicants. Such experiments are mostly performed at compound concentrations close to the cytotoxicity threshold. At present, little information is available on concentration-dependent features of transcriptome changes, in particular, at the transition from noncytotoxic concentrations to conditions that are associated with cell death. Thus, it is unclear in how far cell death confounds the results of transcriptome studies. To explore this gap of knowledge, we treated pluripotent stem cells differentiating to human neuroepithelial cells (UKN1 assay) for short periods (48 h) with increasing concentrations of valproic acid (VPA) and methyl mercury (MeHg), two compounds with vastly different modes of action. We developed various visualization tools to describe cellular responses, and the overall response was classified as "tolerance" (minor transcriptome changes), "functional adaptation" (moderate/strong transcriptome responses, but no cytotoxicity), and "degeneration". The latter two conditions were compared, using various statistical approaches. We identified (i) genes regulated at cytotoxic, but not at noncytotoxic, concentrations and (ii) KEGG pathways, gene ontology term groups, and superordinate biological processes that were only regulated at cytotoxic concentrations. The consensus markers and processes found after 48 h treatment were then overlaid with those found after prolonged (6 days) treatment. The study highlights the importance of careful concentration selection and of controlling viability for transcriptome studies. Moreover, it allowed identification of 39 candidate "biomarkers of cytotoxicity". These could serve to provide alerts that data sets of interest may have been affected by cell death in the model system studied.

Our data identified some effects of the long-term treatment on total HIV DNA levels and highlighted the partial influence of comorbidities and coinfections. Total HIV DNA monitoring contributed to therapy response estimates and HIV reservoir quantification. The results suggest that HIV DNA monitoring during ART might be useful as a persistence marker in both HIV-monoinfected patients and those with comorbidities and coinfections.