Effect of sesquiterpene lactone coronopilin on leukaemia cell population growth, cell type-specific induction of apoptosis and mitotic catastrophe.

OBJECTIVES: The aim of this study was to investigate anti-leukaemic potential of coronopilin, a sesquiterpene lactone from Ambrosia arborescens, and to characterize mechanism(s) underlying its activity. MATERIALS AND METHODS: The study was conducted on Jurkat and U937, two leukaemia-derived cell lines. Apoptosis and impairment of cell cycle progression were evaluated by flow cytometry and by microscopic analysis. Changes in protein expression and activation were evaluated by western blot analysis. Coronopilin-tubulin covalent adducts were demonstrated by mass spectrometry. RESULTS: Coronopilin inhibited (IC(50) ≤ 20 µm) leukaemia cell population growth, but displayed poor cytotoxicity to normal white blood cells. On Jurkat cells, coronopilin exerted cell population growth inhibition activity, mainly by triggering caspase-dependent apoptosis. Conversely, in U937 cells, coronopilin's primary response was a robust arrest in G(2) /M. Marked increase in mitotic index and presence of activated cyclin B1/Cdk1 complex, phosphorylated histone H3 at Ser10, and hyperpolymerized tubulin indicated that cells accumulated in mitosis. Prolonged mitotic arrest ultimately resulted in U937 mitotic catastrophe, and dying cells exhibited the features of non-caspase-dependent death. CONCLUSIONS: This study demonstrated that coronopilin efficiently inhibited leukaemia cell population growth by triggering cell type-specific responses. Moreover, coronopilin-mediated cell population expansion inhibition was specific to neoplastic cells, as normal white blood cell viability was not significantly affected. Thus, coronopilin may represent an interesting new chemical scaffold upon which to develop new anti-leukaemic agents.

On the statistical assessment of classifiers using DNA microarray data.

BACKGROUND: In this paper we present a method for the statistical assessment of cancer predictors which make use of gene expression profiles. The methodology is applied to a new data set of microarray gene expression data collected in Casa Sollievo della Sofferenza Hospital, Foggia--Italy. The data set is made up of normal (22) and tumor (25) specimens extracted from 25 patients affected by colon cancer. We propose to give answers to some questions which are relevant for the automatic diagnosis of cancer such as: Is the size of the available data set sufficient to build accurate classifiers? What is the statistical significance of the associated error rates? In what ways can accuracy be considered dependant on the adopted classification scheme? How many genes are correlated with the pathology and how many are sufficient for an accurate colon cancer classification? The method we propose answers these questions whilst avoiding the potential pitfalls hidden in the analysis and interpretation of microarray data. RESULTS: We estimate the generalization error, evaluated through the Leave-K-Out Cross Validation error, for three different classification schemes by varying the number of training examples and the number of the genes used. The statistical significance of the error rate is measured by using a permutation test. We provide a statistical analysis in terms of the frequencies of the genes involved in the classification. Using the whole set of genes, we found that the Weighted Voting Algorithm (WVA) classifier learns the distinction between normal and tumor specimens with 25 training examples, providing e = 21% (p = 0.045) as an error rate. This remains constant even when the number of examples increases. Moreover, Regularized Least Squares (RLS) and Support Vector Machines (SVM) classifiers can learn with only 15 training examples, with an error rate of e = 19% (p = 0.035) and e = 18% (p = 0.037) respectively. Moreover, the error rate decreases as the training set size increases, reaching its best performances with 35 training examples. In this case, RLS and SVM have error rates of e = 14% (p = 0.027) and e = 11% (p = 0.019). Concerning the number of genes, we found about 6000 genes (p < 0.05) correlated with the pathology, resulting from the signal-to-noise statistic. Moreover the performances of RLS and SVM classifiers do not change when 74% of genes is used. They progressively reduce up to e = 16% (p < 0.05) when only 2 genes are employed. The biological relevance of a set of genes determined by our statistical analysis and the major roles they play in colorectal tumorigenesis is discussed. CONCLUSIONS: The method proposed provides statistically significant answers to precise questions relevant for the diagnosis and prognosis of cancer. We found that, with as few as 15 examples, it is possible to train statistically significant classifiers for colon cancer diagnosis. As for the definition of the number of genes sufficient for a reliable classification of colon cancer, our results suggest that it depends on the accuracy required.

Cytokine gene polymorphisms in gastric cancer patients from two Italian areas at high and low cancer prevalence.

Polymorphisms of interleukin-1beta (IL-1beta), IL-1 receptor antagonist (IL1-RN), and tumor necrosis factor-alpha (TNF-alpha) genes are supposed to be key determinants of gastric cancer risk. Our aim was to study the association between these polymorphisms and gastric cancer in two areas characterized by high (Pavia/Bologna, North Italy) and low (San Giovanni Rotondo, South Italy) gastric cancer prevalence. Genomic DNA was obtained from 216 healthy donors and 98 gastric cancer patients from Pavia and Bologna, and 146 healthy donors and 86 gastric cancer patients from San Giovanni Rotondo. Two SNP in IL-1beta (-511 C/T) and TNF-alpha (-308 G/A) as well as the VNTR polymorphism of IL-1RN locus were studied. A significant linkage disequilibrium was found between IL-1beta -511 and IL-1RN. Genotype and allele frequencies at the IL-1beta, IL-1RN, and TNF-alpha loci in gastric cancer cases were not significantly different from controls. An epistatic effect between IL-1beta -511 and IL-1RN was found with the IL-1beta -511C/IL-1RN*2 haplotype conferring a significant protection against the intestinal-type of gastric cancer in the Southern population. In conclusion, IL-1beta, IL1-RN, and TNF-alpha genotypes are not associated with gastric cancer in Italian patients. An epistatic interrelationship between IL-1beta -511 and IL-1RN confers protection against gastric cancer in low-risk Italian population.