Background: Previous studies on bladder cancer have shown nodal involvement to be an independent indicator of prognosis and survival. This study aimed at developing an objective method for detection of nodal metastasis from molecular profiles of primary urothelial carcinoma tissues.

Despite important advances in microarray-based molecular classification of tumors, its application in clinical settings remains formidable. This is in part due to the limitation of current analysis programs in discovering robust biomarkers and developing classifiers with a practical set of genes. Genetic programming (GP) is a type of machine learning technique that uses evolutionary algorithm to simulate natural selection as well as population dynamics, hence leading to simple and comprehensible classifiers. Here we applied GP to cancer expression profiling data to select feature genes and build molecular classifiers by mathematical integration of these genes. Analysis of thousands of GP classifiers generated for a prostate cancer data set revealed repetitive use of a set of highly discriminative feature genes, many of which are known to be disease associated. GP classifiers often comprise five or less genes and successfully predict cancer types and subtypes. More importantly, GP classifiers generated in one study are able to predict samples from an independent study, which may have used different microarray platforms. In addition, GP yielded classification accuracy better than or similar to conventional classification methods. Furthermore, the mathematical expression of GP classifiers provides insights into relationships between classifier genes. Taken together, our results demonstrate that GP may be valuable for generating effective classifiers containing a practical set of genes for diagnostic/prognostic cancer classification.

Using primary nonmuscle invasive urothelial carcinoma from initial occurrences genetic programming identified transcripts in reproducible fashion, which were predictive of recurrence. These findings could potentially impact nonmuscle invasive urothelial carcinoma management.

BACKGROUND: One in 4 patients with lymph node-negative, invasive colorectal carcinoma (CRC) develops recurrent disease after undergoing curative surgery, and most die of advanced disease. Predicting which patients will develop a recurrence is a significantly growing, unmet medical need. METHODS: Archival formalin-fixed, paraffin-embedded (FFPE) primary adenocarcinoma tissues obtained at surgery were retrieved from 74 patients with CRC (15 with stage I disease and 59 with stage II disease) for Training/Test Sets. In addition, FFPE tissues were retrieved from 49 patients with stage I CRC and 215 patients with stage II colon cancer for an External Validation (EV) Set (n = 264) from 18 hospitals in 4 countries. No patients had received neoadjuvant/adjuvant therapy. Proprietary genetic programming analysis of expression profiles for 225 prespecified tumor genes was used to create a 36-month recurrence risk signature. RESULTS: Using reverse transcriptase-polymerase chain reaction, a 5-gene rule correctly classified 62 of 92 recurrent patients and 87 of 172 nonrecurrent patients in the EV Set (sensitivity, 0.67; specificity, 0.51). “High-risk” patients had a greater probability of 36-month recurrence (42%) than “low-risk” patients (26%; hazard ratio, 1.80; 95% confidence interval, 1.19-2.71; P = .007; Cox regression) independent of T-classification, the number of lymph nodes examined, histologic grade/subtype, anatomic location, age, sex, or race. The rule outperformed (P = .021) current National Comprehensive Cancer Network Guidelines (hazard ratio, 0.897). The same rule also differentiated the risk of recurrence (hazard ratio, 1.63; P = .031) in a subset of patients from the EV Set who had stage I/II colon cancer only (n = 251). CONCLUSIONS: To the authors' knowledge, the 5-gene rule (OncoDefender-CRC) is the first molecular prognostic that has been validated in both stage I CRC and stage II colon cancer. It outperforms standard clinicopathologic prognostic criteria and obviates the need to retrieve ≥12 lymph nodes for accurate prognostication. It identifies those patients most likely to develop recurrent disease within 3 years after curative surgery and, thus, those most likely to benefit from adjuvant treatment. Cancer 2012. © 2012 American Cancer Society.

BackgroundDiscrimination between clinical and environmental strains within many bacterial species is currently underexplored. Genomic analyses have clearly shown the enormous variability in genome composition between different strains of a bacterial species. In this study we have used Legionella pneumophila, the causative agent of Legionnaire's disease, to search for genomic markers related to pathogenicity. During a large surveillance study in The Netherlands well-characterized patient-derived strains and environmental strains were collected. We have used a mixed-genome microarray to perform comparative-genome analysis of 257 strains from this collection.ResultsMicroarray analysis indicated that 480 DNA markers (out of in total 3360 markers) showed clear variation in presence between individual strains and these were therefore selected for further analysis. Unsupervised statistical analysis of these markers showed the enormous genomic variation within the species but did not show any correlation with a pathogenic phenotype. We therefore used supervised statistical analysis to identify discriminating markers. Genetic programming was used both to identify predictive markers and to define their interrelationships. A model consisting of five markers was developed that together correctly predicted 100% of the clinical strains and 69% of the environmental strains.ConclusionsA novel approach for identifying predictive markers enabling discrimination between clinical and environmental isolates of L. pneumophila is presented. Out of over 3000 possible markers, five were selected that together enabled correct prediction of all the clinical strains included in this study. This novel approach for identifying predictive markers can be applied to all bacterial species, allowing for better discrimination between strains well equipped to cause human disease and relatively harmless strains.