Background Neoadjuvant chemotherapy prior to cystectomy confers a survival benefit in bladder cancer. Yet, it has not been widely adopted, since most patients do not benefit and we are currently unable to predict those that do. As the most important predictor of recurrence following cystectomy is pathologically positive nodes, we propose that tools defining this stage would be useful in selecting patients for neoadjuvant chemotherapy. Methods We developed a gene expression model (GEM) predictive of pathological node status for use on primary tumor tissue from clinically node negative (cN0) patients. From a subset of transcripts detected faithfully by microarrays from both paired frozen and formalin fixed tissues (N=32 pairs), we developed both the GEM and cutoffs identifying patient strata with elevated risk of nodal involvement using two separate training cohorts (N=90 and N=66). We then evaluated the GEM and cutoffs to predict node positive disease in tissues from a Phase III trial cohort (AUO-AB-05/95, N=185). Findings A 20-gene GEM was developed and exhibited an AUC=0·67 [95%CI 0·59–0·75], for prediction of nodal disease at cystectomy in AUO-AB-05/95. The cutoff system identified subjects with high (RR 1·74, [1·03–2·93]) and low (RR 0·70, [0·51–0·96]) relative risk of node positive disease. Multivariate logistic regression demonstrated the GEM predictor was independent of age, gender, pathologic stage, and lymphovascular space invasion (P=0·019). Interpretations Selecting patients for neoadjuvant chemotherapy based on risk of node positive disease has the potential to benefit high-risk patients while sparing others toxicity and delay to cystectomy. Funding U.S. National Cancer Institute (R01CA143971)

High-throughput screening (HTS) strategies and protocols have undergone significant development in the last decade. It is now possible to screen hundreds of thousands of compounds, each exploring multiple biological phenotypes and parameters, against various cell lines or model systems in a single setting. However, given the vast amount of data such studies generate, the fact that they use multiple reagents, and are often technician-intensive, questions have been raised about the variability, reliability and reproducibility of HTS results. Assessments of the impact of the multiple factors in HTS studies could arguably lead to more compelling insights into the robustness of the results of a particular screen, as well as the overall quality of the study. We leveraged classical, yet highly flexible, analysis of variance (ANOVA)-based linear models to explore how different factors contribute to the variation observed in a screening study of four different melanoma cell lines and 120 drugs over nine dosages studied in two independent academic laboratories. We find that factors such as plate effects, appropriate dosing ranges, and to a lesser extent, the laboratory performing the screen, are significant predictors of variation in drug responses across the cell lines. Further, we show that when sources of variation are quantified and controlled for, they contextualize claims of inconsistencies and reveal the overall quality of the HTS studies performed at each participating laboratory. In the context of the broader screening study, we show that our analysis can also elucidate the robust effects of drugs, even those within specific cell lines.

Genes do not work in isolation, but rather as part of networks that have many feedback and redundancy mechanisms. Studying the properties of genetic networks and how individual genes contribute to overall network functions can provide insight into genetically-mediated disease processes. Most analytical techniques assume a network topology based on normal state networks. However, gene perturbations often lead to the rewiring of relevant networks and impact relationships among other genes. We apply a suite of analysis methodologies to assess the degree of transcriptional network rewiring observed in different sets of melanoma cell lines using whole genome gene expression microarray profiles. We assess evidence for network rewiring in melanoma patient tumor samples using RNA-sequence data available from The Cancer Genome Atlas. We make a distinction between “unsupervised” and “supervised” network-based methods and contrast their use in identifying consistent differences in networks between subsets of cell lines and tumor samples. We find that different genes play more central roles within subsets of genes within a broader network and hence are likely to be better drug targets in a disease state. Ultimately, we argue that our results have important implications for understanding the molecular pathology of melanoma as well as the choice of treatments to combat that pathology.