Accurate confidence aware clustering of array CGH tumor profiles.

MOTIVATION: Chromosomal aberrations tend to be characteristic for given (sub)types of cancer. Such aberrations can be detected with array comparative genomic hybridization (aCGH). Clustering aCGH tumor profiles aids in identifying chromosomal regions of interest and provides useful diagnostic information on the cancer type. An important issue here is to what extent individual aCGH tumor profiles can be reliably assigned to clusters associated with a given cancer type. RESULTS: We introduce a novel evolutionary fuzzy clustering (EFC) algorithm, which is able to deal with overlapping clusterings. Our method assesses these overlaps by using cluster membership degrees, which we use here as a confidence measure for individual samples to be assigned to a given tumor type. We first demonstrate the usefulness of our method using a synthetic aCGH dataset and subsequently show that EFC outperforms existing methods on four real datasets of aCGH tumor profiles involving four different cancer types. We also show that in general best performance is obtained using 1- Pearson correlation coefficient as a distance measure and that extra preprocessing steps, such as segmentation and calling, lead to decreased clustering performance. AVAILABILITY: The source code of the program is available from http://ibi.vu.nl/programs/efcwww

CGHnormaliter: a Bioconductor package for normalization of array CGH data with many CNAs.

SUMMARY: CGHnormaliter is a package for normalization of array comparative genomic hybridization (aCGH) data. It uses an iterative procedure that effectively eliminates the influence of imbalanced copy numbers. This leads to a more reliable assessment of copy number alterations (CNAs). CGHnormaliter is integrated in the Bioconductor environment allowing a smooth link to visualization tools and further data analysis. AVAILABILITY AND IMPLEMENTATION: The CGHnormaliter package is implemented in R and under GPL 3.0 license available at Bioconductor: http://www.bioconductor.org CONTACT: heringa@few.vu.nl

CGHnormaliter: an iterative strategy to enhance normalization of array CGH data with imbalanced aberrations.

BACKGROUND: Array comparative genomic hybridization (aCGH) is a popular technique for detection of genomic copy number imbalances. These play a critical role in the onset of various types of cancer. In the analysis of aCGH data, normalization is deemed a critical pre-processing step. In general, aCGH normalization approaches are similar to those used for gene expression data, albeit both data-types differ inherently. A particular problem with aCGH data is that imbalanced copy numbers lead to improper normalization using conventional methods. RESULTS: In this study we present a novel method, called CGHnormaliter, which addresses this issue by means of an iterative normalization procedure. First, provisory balanced copy numbers are identified and subsequently used for normalization. These two steps are then iterated to refine the normalization. We tested our method on three well-studied tumor-related aCGH datasets with experimentally confirmed copy numbers. Results were compared to a conventional normalization approach and two more recent state-of-the-art aCGH normalization strategies. Our findings show that, compared to these three methods, CGHnormaliter yields a higher specificity and precision in terms of identifying the 'true' copy numbers. CONCLUSION: We demonstrate that the normalization of aCGH data can be significantly enhanced using an iterative procedure that effectively eliminates the effect of imbalanced copy numbers. This also leads to a more reliable assessment of aberrations. An R-package containing the implementation of CGHnormaliter is available at http://www.ibi.vu.nl/programs/cghnormaliterwww.

Modeling the effects of binary mixtures on survival in time.

In general, effects of mixtures are difficult to describe, and most of the models in use are descriptive in nature and lack a strong mechanistic basis. The aim of this experiment was to develop a process-based model for the interpretation of mixture toxicity measurements, with effects of binary mixtures on survival as a starting point. The survival of Folsomia candida was monitored daily for 21 d during the exposure to six binary mixtures of cadmium, copper, lead, and zinc in a loamy sand soil. The measurements were used to develop a model to describe survival in time. The model consists of two parts: A one-compartment model that describes uptake and elimination of the compounds, and a hazard model describing survival. The model was very successful in describing the data and at finding possible interactions. The mixture of copper and lead showed a slight antagonistic effect, the other mixtures showed no interaction. The model is straightforward in its biological assumptions and does not require a mode-of-action a priori choice of the mixture that might influence the modeled interaction of the components in the mixture. The model requires measurements at intermediate time points, but runs with relatively few parameters and is robust in finding interactions. When mixture effects are considered at only one time point, care should be taken with the assignment of interactions because these may be different for different points during the time course of the experiments.

DNA copy number profiles correlate with outcome in colorectal cancer patients treated with fluoropyrimidine/antifolate-based regimens.

For decades 5-fluorouracil (5-FU) has remained the treatment of choice in the adjuvant and palliative setting of colorectal cancer (CRC). The combinations of 5-FU or its oral prodrug capecitabine with irinotecan/oxaliplatin and the novel agents bevacizumab/cetuximab increased responses. However, the overall prognosis is poor, and predictive biomarkers of cytotoxic drugs activity are missing. Pharmacogenetic studies focused on candidate determinants of drug activity/metabolism, such as thymidylate synthase or dihydropyrimidine dehydrogenase, but reported controversial results. Given the heterogeneous and complex nature of CRC, it is likely that many aberrations underlying its progression can also affect therapeutic response. Therefore, high-throughput arrays for genome-wide-DNA aberrations play a pivotal role for new markers discovery by moving from hypothesis-driven, targeted-research to unbiased screening of the whole genetic spectrum. Chromosomal aberrations are critical events in tumorigenesis, and genomic regions harbouring DNA gains/losses have been identified in 85% of CRC patients. These aberrations change the expression of many genes, which might explain the differential effects of specific chemotherapeutic agents. In particular, recent studies reported correlations between DNA copy-number profiles and response to fluoropyrimidine-based regimens, such as leucovorin-modulated-5-FU+irinotecan (FOLFIRI), capecitabine+irinotecan (CAPIRI) and pemetrexed+irinotecan (ALIRI). Genome-wide profiling by oligonucleotide-based array-comparative-genomic-hybridization (aCGH) revealed genomic loci, of which the copy-number status may serve as marker for outcome after FOLFIRI and CAPIRI. Larger randomized and prospective trials of these aCGH platforms in CRC patients treated with fluoropyrimidine-based regimens are ongoing, and will ultimately demonstrate whether these findings can be of actual value to predict clinical outcome and direct the choice of therapy.