Biomedical subjects

Jaakko Astola

Publications and source records attributed to Jaakko Astola.

4 recordsLinked to original sources

PubMed2004

Effects of Herceptin treatment on global gene expression patterns in HER2-amplified and nonamplified breast cancer cell lines.

Herceptin is a humanized monoclonal antibody targeted against the extracellular domain of the HER2 oncogene, which is amplified and overexpressed in 10-34% of breast cancers. Herceptin therapy provides effective treatment in HER2-positive metastatic breast cancer, although a favorable treatment response is not achieved in all cases. Here, we show that Herceptin treatment induces a dose-dependent growth reduction in breast cancer cell lines with HER2 amplification, whereas nonamplified cell lines are practically resistant. Time-course analysis of global gene expression patterns in amplified and nonamplified cell lines indicated a major change in transcript levels between 24 and 48 h of Herceptin treatment. A step-wise gene selection algorithm revealed a set of 439 genes whose temporal expression profiles differed most between the amplified and nonamplified cell lines. The discriminatory power of these genes was confirmed by both hierarchical clustering and self-organizing map analyses. In the amplified cell lines, the Herceptin treatment induced the expression of several genes involved in RNA processing and DNA repair, while cell adhesion mediators and known oncogenes, such as c-FOS and c-KIT, were downregulated. These results provide additional clues to the downstream effects of blocking the HER2 pathway in breast cancer and may provide new targets for more effective treatment.

Antibodies, Monoclonal↗

PubMed2003

A novel strategy for microarray quality control using Bayesian networks.

MOTIVATION: High-throughput microarray technologies enable measurements of the expression levels of thousands of genes in parallel. However, microarray printing, hybridization and washing may create substantial variability in the quality of the data. As erroneous measurements may have a drastic impact on the results by disturbing the normalization schemes and by introducing expression patterns that lead to incorrect conclusions, it is crucial to discard low quality observations in the early phases of a microarray experiment. A typical microarray experiment consists of tens of thousands of spots on a microarray, making manual extraction of poor quality spots impossible. Thus, there is a need for a reliable and general microarray spot quality control strategy. RESULTS: We suggest a novel strategy for spot quality control by using Bayesian networks, which contain many appealing properties in the spot quality control context. We illustrate how a non-linear least squares based Gaussian fitting procedure can be used in order to extract features for a spot on a microarray. The features we used in this study are: spot intensity, size of the spot, roundness of the spot, alignment error, background intensity, background noise, and bleeding. We conclude that Bayesian networks are a reliable and useful model for microarray spot quality assessment. SUPPLEMENTARY INFORMATION: http://sigwww.cs.tut.fi/TICSP/SpotQuality/.

Algorithms↗

PubMed2003

The role of certain Post classes in Boolean network models of genetic networks.

A topic of great interest and debate concerns the source of order and remarkable robustness observed in genetic regulatory networks. The study of the generic properties of Boolean networks has proven to be useful for gaining insight into such phenomena. The main focus, as regards ordered behavior in networks, has been on canalizing functions, internal homogeneity or bias, and network connectivity. Here we examine the role that certain classes of Boolean functions that are closed under composition play in the emergence of order in Boolean networks. The closure property implies that any gene at any number of steps in the future is guaranteed to be governed by a function from the same class. By means of Derrida curves on random Boolean networks and percolation simulations on square lattices, we demonstrate that networks constructed from functions belonging to these classes have a tendency toward ordered behavior. Thus they are not overly sensitive to initial conditions, and damage does not readily spread throughout the network. In addition, the considered classes are significantly larger than the class of canalizing functions as the connectivity increases. The functions in these classes exhibit the same kind of preference toward biased functions as do canalizing functions, meaning that functions from this class are likely to be biased. Finally, functions from this class have a natural way of ensuring robustness against noise and perturbations, thus representing plausible evolutionarily selected candidates for regulatory rules in genetic networks.

Computational Biology↗

PubMed2003

Data extraction from composite oligonucleotide microarrays.

Microarray or DNA chip technology is revolutionizing biology by empowering researchers in the collection of broad-scope gene information. It is well known that microarray-based measurements exhibit a substantial amount of variability due to a number of possible sources, ranging from hybridization conditions to image capture and analysis. In order to make reliable inferences and carry out quantitative analysis with microarray data, it is generally advisable to have more than one measurement of each gene. The availability of both between-array and within-array replicate measurements is essential for this purpose. Although statistical considerations call for increasing the number of replicates of both types, the latter is particularly challenging in practice due to a number of limiting factors, especially for in-house spotting facilities. We propose a novel approach to design so-called composite microarrays, which allow more replicates to be obtained without increasing the number of printed spots.

Gene Expression Profiling↗