Two way clustering of Microarray Data using a Hybrid Approach

The Microarray technique is rather powerful, as it allows to test up thousands of genes at a time, but this produces an overwhelming set of data files containing huge amounts of data, which is quite difficult to pre-process, separate, classify and correlate for interesting conclusions to be extracted. Modern machine learning, data mining and clustering techniques based on information theory, are needed to read and interpret the information contents buried in those large data sets. Independent Component Analysis method can be used to correct the data affected by corruption processes or to filter the uncorrectable one and then clustering methods can group similar genes or classify samples. In this paper a hybrid approach is used to obtain a two way unsupervised clustering for a corrected microarray data.

Knowledge-based analysis of microarray gene expression data by using support vector machines

We introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge of gene function to identify unknown genes of similar function from expression data. SVMs avoid several problems associated with unsupervised clustering methods, such as hierarchical clustering and self-organizing maps. SVMs have many mathematical features that make them attractive for gene expression analysis, including their flexibility in choosing a similarity function, sparseness of solution when dealing with large data sets, the ability to handle large feature spaces, and the ability to identify outliers. We test several SVMs that use different similarity metrics, as well as some other supervised learning methods, and find that the SVMs best identify sets of genes with a common function using expression data. Finally, we use SVMs to predict functional roles for uncharacterized yeast ORFs based on their expression data.

Statistical modeling of large microarray data sets to identify stimulus-response profiles

A statistical modeling approach is proposed for use in searching large microarray data sets for genes that have a transcriptional response to a stimulus. The approach is unrestricted with respect to the timing, magnitude or duration of the response, or the overall abundance of the transcript. The statistical model makes an accommodation for systematic heterogeneity in expression levels. Corresponding data analyses provide gene-specific information, and the approach provides a means for evaluating the statistical significance of such information. To illustrate this strategy we have derived a model to depict the profile expected for a periodically transcribed gene and used it to look for budding yeast transcripts that adhere to this profile. Using objective criteria, this method identifies 81% of the known periodic transcripts and 1,088 genes, which show significant periodicity in at least one of the three data sets analyzed. However, only one-quarter of these genes show significant oscillations in at least two data sets and can be classified as periodic with high confidence. The method provides estimates of the mean activation and deactivation times, induced and basal expression levels, and statistical measures of the precision of these estimates for each periodic transcript.

Dissection moléculaire de la sénescence cellulaire induite par le stress et la thérapie dans le cancer de l’ovaire et son impact sur la réponse des patientes

Le cancer de l’ovaire (COv) est le cancer gynécologique le plus létal chez la femme et les traitements existants, chirurgie et chimiothérapie, ont peu évolué au cours des dernières décennies. Nous proposons que la compréhension des différents destins cellulaires tels que la sénescence que peuvent choisir les cellules du cancer de l’ovaire en réponse à la chimiothérapie pourrait conduire à de nouvelles opportunités thérapeutiques. La sénescence cellulaire a été largement associée à l’activité de la protéine TP53, qui est mutée dans plus de 90% des cas de cancer de l’ovaire séreux de haut grade (COv-SHG), la forme la plus commune de la maladie. Dans nos travaux, à partir d’échantillons dérivés de patientes, nous montrons que les cultures primaires du cancer de l’ovaire séreux de haut grade exposées au stress ou à des drogues utilisées en chimiothérapie entrent en senescence grâce à l’activité d’un isoforme du gène CDKN2A (p16INK4A). Dans ces cellules, nous avons évalué les caractéristiques fondamentales de la sénescence cellulaire tels que les altérations morphologiques, l’activité béta galactosidase associée à la sénescence, les dommages à l’ADN, l’arrêt du cycle cellulaire et le phénotype sécrétoire associé à la sénescence. En utilisant des micromatrices tissulaires construites à partir d’échantillons humains de COv-SHG pré- et post-chimiothérapie, accompagnées de leurs données cliniques, nous avons quantifié des marqueurs de sénescence incluant une diminution de la prolifération cellulaire quelques semaines après chimiothérapie. De façon intéressante, l’expression de p16INK4A dans les échantillons de COv-SHG prétraitement corrèle avec une survie prolongée des patientes suite au traitement. Ceci suggère ainsi pour la première fois un impact biologique bénéfique pour la présence de cellules cancéreuses qui sont capable d’activer la sénescence, particulièrement pour le traitement du cancer de l’ovaire. Dans le but de complémenter les thérapies actuelles avec des approches de manipulation pharmacologique de la sénescence, nos résultats suggèrent qu’il serait important de déterminer l’impact positif ou négatif de la sénescence induite par la thérapie sur la progression de la maladie et la survie, pour chaque type de cancer de façon indépendante.

Using BRFSS data for small area analysis of cancer screening behaviors /

"June 1995."

Use of multiple biomarkers for a molecular diagnosis of prostate cancer

The identification of biomarkers capable of providing a reliable molecular diagnostic test for prostate cancer (PCa) is highly desirabie clinically. We describe here 4 biomarkers, UDP-N-Acetyl-alpha-D-galactosamine transferase (GalNAc-T3; not previously associated with PCa), PSMA, Hepsin and DD3/PCA3, which, in combination, distinguish prostate cancer from benign prostate hyperplasia (BPH). GalNAc-T3 was identified as overexpressed in PCa tissues by microarray analysis, confirmed by quantitative real-time PCR and shown immunohistochemically to be localised to prostate epithelial cells with higher expression in malignant cells. Real-time quantitative PCR analysis across 21 PCa and 34 BPH tissues showed 4.6-fold overexpression of GalNAc-T3 (p = 0.005). The noncoding mRNA (DD3/PCA3) was overexpressed 140-fold (p = 0.007) in the cancer samples compared to BPH tissues. Hepsin was overexpressed 21-fold (p = 0.049, whereas the overexpression for PSMA was 66-fold (p = 0.047). When the gene expression data for these 4 biomarkers was combined in a logistic regression model, a predictive index was obtained that distinguished 100% of the PCa samples from all of the BPH samples. Therefore, combining these genes in a real-time PCR assay represents a powerful new approach to diagnosing PCa by molecular profiling. (c) 2005 Wiley-Liss, Inc.

Role of smoking in global and regional cancer epidemiology: current patterns and data needs

Although smoking is widely recognized as a major cause of cancer, there is little information on how it contributes to the global and regional burden of cancers in combination with other risk factors that affect background cancer mortality patterns. We used data from the American Cancer Society's Cancer Prevention Study II (CPS-II) and the WHO and IARC cancer mortality databases to estimate deaths from 8 clusters of site-specific cancers caused by smoking, for 14 epidemiologic subregions of the world, by age and sex. We used lung cancer mortality as an indirect marker for accumulated smoking hazard. CPS-II hazards were adjusted for important covariates. In the year 2000, an estimated 1.42 (95% CI 1.27-1.57) million cancer deaths in the world, 21% of total global cancer deaths, were caused by smoking. Of these, 1.18 million deaths were among men and 0.24 million among women; 625,000 (95% CI 485,000-749,000) smoking-caused cancer deaths occurred in the developing world and 794,000 (95% CI 749,000-840,000) in industrialized regions. Lung cancer accounted for 60% of smoking-attributable cancer mortality, followed by cancers of the upper aerodigestive tract (20%). Based on available data, more than one in every 5 cancer deaths in the world in the year 2000 were caused by smoking, making it possibly the single largest preventable cause of cancer mortality. There was significant variability across regions in the role of smoking as a cause of the different site-specific cancers. This variability illustrates the importance of coupling research and surveillance of smoking with that for other risk factors for more effective cancer prevention. (C) 2005 Wiley-Liss, Inc.

Repeated occurrence of basal cell carcinoma of the skin and multifailure survival analysis: Follow-up data from the nambour skin cancer prevention trial

The aim of this study was to apply multifailure survival methods to analyze time to multiple occurrences of basal cell carcinoma (BCC). Data from 4.5 years of follow-up in a randomized controlled trial, the Nambour Skin Cancer Prevention Trial (1992-1996), to evaluate skin cancer prevention were used to assess the influence of sunscreen application on the time to first BCC and the time to subsequent BCCs. Three different approaches of time to ordered multiple events were applied and compared: the Andersen-Gill, Wei-Lin-Weissfeld, and Prentice-Williams-Peterson models. Robust variance estimation approaches were used for all multifailure survival models. Sunscreen treatment was not associated with time to first occurrence of a BCC (hazard ratio = 1.04, 95% confidence interval: 0.79, 1.45). Time to subsequent BCC tumors using the Andersen-Gill model resulted in a lower estimated hazard among the daily sunscreen application group, although statistical significance was not reached (hazard ratio = 0.82, 95% confidence interval: 0.59, 1.15). Similarly, both the Wei-Lin-Weissfeld marginal-hazards and the Prentice-Williams-Peterson gap-time models revealed trends toward a lower risk of subsequent BCC tumors among the sunscreen intervention group. These results demonstrate the importance of conducting multiple-event analysis for recurring events, as risk factors for a single event may differ from those where repeated events are considered.

Skin cancer clinics in Australia: workload profile and performance indicators from an analysis of billing data

Objective: To describe the workload profile in a network of Australian skin cancer clinics. Design and setting: Analysis of billing data for the first 6 months of 2005 in a primary-care skin cancer clinic network, consisting of seven clinics and staffed by 20 doctors, located in the Northern Territory, Queensland and New South Wales. Main outcome measures: Consultation to biopsy ratio (CBR); biopsy to treatment ratio (BTR); number of benign naevi excised per melanoma (number needed to treat [NNT]). Results: Of 69780 billed activities, 34 622 (49.6%) were consultations, 19 358 (27.7%) biopsies, 8055 (11.5%) surgical excisions, 2804 (4.0%) additional surgical repairs, 1613 (2.3%) non-surgical treatments of cancers and 3328 (4.8%) treatments of premalignant or non-malignant lesions. A total of 6438 cancers were treated (116 melanomas by excision, 4709 non-melanoma skin cancers [NMSCs] by excision, and 1613 NMSCs non-surgically); 5251 (65.2%) surgical wounds were repaired by direct suture, 2651 (32.9%) by a flap (of which 44.8% were simple flaps), 42 (0.5%) by wedge excision and 111 (1.4%) by grafts. The CBR was 1.79, the BTR was 3.1 and the NNT was 28.6. Conclusions: In this network of Australian skin cancer clinics, one in three biopsies identified a skin cancer (BTR, 3.1), and about 29 benign lesions were excised per melanoma (NNT, 28.6). The estimated NNT was similar to that reported previously in general practice. More data are needed on health outcomes, including effectiveness of treatment and surgical repair.

Microarray-based comparative genomic hybridisation of breast cancer patients receiving neoadjuvant chemotherapy

We analysed the molecular genetic profiles of breast cancer samples before and after neoadjuvant chemotherapy with combination doxorubicin and cyclophosphamide (AC). DNA was obtained from microdissected frozen breast core biopsies from 44 patients before chemotherapy. Additional samples were obtained before the second course of chemotherapy (D21) and after the completion of the treatment (surgical specimens) in 17 and 21 patients, respectively. Microarray-based comparative genome hybridisation was performed using a platform containing approx5800 bacterial artificial chromosome clones (genome-wide resolution: 0.9 Mb). Analysis of the 44 pretreatment biopsies revealed that losses of 4p, 4q, 5q, 12q13.11–12q13.12, 17p11.2 and 17q11.2; and gains of 1p, 2p, 7q, 9p, 11q, 19p and 19q were significantly associated with oestrogen receptor negativity. 16q21–q22.1 losses were associated with lobular and 8q24 gains with ductal types. Losses of 5q33.3–q4 and 18p11.31 and gains of 6p25.1–p25.2 and Xp11.4 were associated with HER2 amplification. No correlations between DNA copy number changes and clinical response to AC were found. Microarray-based comparative genome hybridisation analysis of matched pretreatment and D21 biopsies failed to identify statistically significant differences, whereas a comparison between matched pretreatment and surgical samples revealed a statistically significant acquired copy number gain on 11p15.2–11p15.5. The modest chemotherapy-driven genomic changes, despite profound loss of cell numbers, suggest that there is little therapeutic selection of resistant non-modal cell lineages.

MILVA:An interactive tool for the exploration of multidimensional microarray data

Clustering techniques such as k-means and hierarchical clustering are commonly used to analyze DNA microarray derived gene expression data. However, the interactions between processes underlying the cell activity suggest that the complexity of the microarray data structure may not be fully represented with discrete clustering methods.

Lack of effect of lowering LDL cholesterol on cancer: meta-analysis of individual data from 175,000 people in 27 randomised trials of statin therapy

Background: Statin therapy reduces the risk of occlusive vascular events, but uncertainty remains about potential effects on cancer. We sought to provide a detailed assessment of any effects on cancer of lowering LDL cholesterol (LDL-C) with a statin using individual patient records from 175,000 patients in 27 large-scale statin trials. Methods and Findings: Individual records of 134,537 participants in 22 randomised trials of statin versus control (median duration 4.8 years) and 39,612 participants in 5 trials of more intensive versus less intensive statin therapy (median duration 5.1 years) were obtained. Reducing LDL-C with a statin for about 5 years had no effect on newly diagnosed cancer or on death from such cancers in either the trials of statin versus control (cancer incidence: 3755 [1.4% per year [py]] versus 3738 [1.4% py], RR 1.00 [95% CI 0.96-1.05]; cancer mortality: 1365 [0.5% py] versus 1358 [0.5% py], RR 1.00 [95% CI 0.93-1.08]) or in the trials of more versus less statin (cancer incidence: 1466 [1.6% py] vs 1472 [1.6% py], RR 1.00 [95% CI 0.93-1.07]; cancer mortality: 447 [0.5% py] versus 481 [0.5% py], RR 0.93 [95% CI 0.82-1.06]). Moreover, there was no evidence of any effect of reducing LDL-C with statin therapy on cancer incidence or mortality at any of 23 individual categories of sites, with increasing years of treatment, for any individual statin, or in any given subgroup. In particular, among individuals with low baseline LDL-C (<2 mmol/L), there was no evidence that further LDL-C reduction (from about 1.7 to 1.3 mmol/L) increased cancer risk (381 [1.6% py] versus 408 [1.7% py]; RR 0.92 [99% CI 0.76-1.10]). Conclusions: In 27 randomised trials, a median of five years of statin therapy had no effect on the incidence of, or mortality from, any type of cancer (or the aggregate of all cancer).