Chemotherapy for advanced gastric cancer.

BACKGROUND: Gastric cancer currently ranks second in global cancer mortality. Most patients are either diagnosed at an advanced stage, or develop a relapse after surgery with curative intent. Apart from supportive care and palliative radiation to localized (e.g. bone) metastasis, systemic chemotherapy is the only treatment option available in this situation. OBJECTIVES: To assess the efficacy of chemotherapy versus best supportive care, combination versus single agent chemotherapy and different combination chemotherapy regimens in advanced gastric cancer. SEARCH STRATEGY: We searched the Cochrane Central Register of Controlled Trials, MEDLINE and EMBASE up to March 2009, reference lists of studies, and contacted pharmaceutical companies and national and international experts. SELECTION CRITERIA: Randomised controlled trials on systemic intravenous chemotherapy versus best supportive care, combination versus single agent chemotherapy and different combination chemotherapies in advanced gastric cancer. DATA COLLECTION AND ANALYSIS: Two authors independently extracted data. A third investigator was consulted in case of disagreements. We contacted study authors to obtain missing information. MAIN RESULTS: Thirty five trials, with a total of 5726 patients, have been included in the meta-analysis of overall survival. The comparison of chemotherapy versus best supportive care consistently demonstrated a significant benefit in overall survival in favour of the group receiving chemotherapy (hazard ratios (HR) 0.37; 95% confidence intervals (CI) 0.24 to 0.55, 184 participants). The comparison of combination versus single-agent chemotherapy provides evidence for a survival benefit in favour of combination chemotherapy (HR 0.82; 95% CI 0.74 to 0.90, 1914 participants). The price of this benefit is increased toxicity as a result of combination chemotherapy. When comparing 5-FU/cisplatin-containing combination therapy regimens with versus without anthracyclines (HR 0.77; 95% CI 0.62 to 0.95, 501 participants) and 5-FU/anthracycline-containing combinations with versus without cisplatin (HR 0.82; 95% CI 0.73 to 0.92, 1147 participants) there was a significant survival benefit for regimens including 5-FU, anthracyclines and cisplatin. Both the comparison of irinotecan versus non-irinotecan (HR 0.86; 95% CI 0.73 to 1.02, 639 participants) and docetaxel versus non-docetaxel containing regimens (HR 0.93; 95% CI 0.75 to 1.15, 805 participants) show non-significant overall survival benefits in favour of the irinotecan and docetaxel-containing regimens. AUTHORS' CONCLUSIONS: Chemotherapy significantly improves survival in comparison to best supportive care. In addition, combination chemotherapy improves survival compared to single-agent 5-FU. All patients should be tested for their HER-2 status and trastuzumab should be added to a standard fluoropyrimidine/cisplatin regimen in patients with HER-2 positive tumours. Two and three-drug regimens including 5-FU, cisplatin, with or without an anthracycline, as well as irinotecan or docetaxel-containing regimens are reasonable treatment options for HER-2 negative patients.

Comparative modular analysis of gene expression in vertebrate organs.

ABSTRACT: BACKGROUND: The degree of conservation of gene expression between homologous organs largely remains an open question. Several recent studies reported some evidence in favor of such conservation. Most studies compute organs' similarity across all orthologous genes, whereas the expression level of many genes are not informative about organ specificity. RESULTS: Here, we use a modularization algorithm to overcome this limitation through the identification of inter-species co-modules of organs and genes. We identify such co-modules using mouse and human microarray expression data. They are functionally coherent both in terms of genes and of organs from both organisms. We show that a large proportion of genes belonging to the same co-module are orthologous between mouse and human. Moreover, their zebrafish orthologs also tend to be expressed in the corresponding homologous organs. Notable exceptions to the general pattern of conservation are the testis and the olfactory bulb. Interestingly, some co-modules consist of single organs, while others combine several functionally related organs. For instance, amygdala, cerebral cortex, hypothalamus and spinal cord form a clearly discernible unit of expression, both in mouse and human. CONCLUSIONS: Our study provides a new framework for comparative analysis which will be applicable also to other sets of large-scale phenotypic data collected across different species.

Malaria haplotype frequency estimation.

We present a Bayesian approach for estimating the relative frequencies of multi-single nucleotide polymorphism (SNP) haplotypes in populations of the malaria parasite Plasmodium falciparum by using microarray SNP data from human blood samples. Each sample comes from a malaria patient and contains one or several parasite clones that may genetically differ. Samples containing multiple parasite clones with different genetic markers pose a special challenge. The situation is comparable with a polyploid organism. The data from each blood sample indicates whether the parasites in the blood carry a mutant or a wildtype allele at various selected genomic positions. If both mutant and wildtype alleles are detected at a given position in a multiply infected sample, the data indicates the presence of both alleles, but the ratio is unknown. Thus, the data only partially reveals which specific combinations of genetic markers (i.e. haplotypes across the examined SNPs) occur in distinct parasite clones. In addition, SNP data may contain errors at non-negligible rates. We use a multinomial mixture model with partially missing observations to represent this data and a Markov chain Monte Carlo method to estimate the haplotype frequencies in a population. Our approach addresses both challenges, multiple infections and data errors.

Aging preferentially affects molecular pathways implicated in development and disease of myelinating glial cells

The central and peripheral nervous systems are involved in multiple age-dependent neurological deficits that are often attributed to alterations in function of myelinating glial cells. However, the molecular events that underlie the age-related decline of glial cell function are unknown. We used Schwann cells as a model to study biological processes affected in glial cells by aging. We comprehensively profiled gene expression of the Schwann cellrich mouse sciatic nerve throughout life, from day of birth until senescence (840 days of age). We combined the aging data with the microarray transcriptional data obtained using nerves isolated from Schwann cell-specific neuropathy-inducing mutants MPZCre/+/Lpin1fE2−3/fE2−3 , MPZCre/+/ScapfE1/fE1 and Pmp22-null mice. The majority of age related transcripts were also affected in the analyzed mouse models of neuropathy (54.4%) and in development (59.5%) indicating a high level of overlapping in implicated molecular pathways. We observed that compared to peripheral nerve development, dynamically changing expression profiles in aging have opposite (anticorrelated) orientation while they copy the orientation of transcriptional changes observed in analyzed neuropathy models. Subsequent clustering and biological annotation of dynamically changing transcripts revealed that the processes most significantly deregulated in aging include inflammatory/immune response and lipid biosynthesis/metabolism. Importantly, the changes in these pathways were also observed in myelinated oligodendrocyte-rich optic nerves of aged mice, albeit with lower magnitude. This observation suggests that similar biological processes are affected in aging glial cells in central and peripheral nervous systems, however with different dynamics. Our data, which provide the first comprehensive comparison of molecular changes in glial cells in three distinct biological conditions comprising development, aging and disease, provide not only a new inside into the molecular alterations underlying neural system aging but also identify target pathways for potential therapeutic approaches to prevent or delay complications associated with age-related and inherited forms of neuropathies. *Current address: Department of Physiology, UCSF, San Francisco, CA, USA.

Aging preferentially affects molecular pathways implicated in development and disease of myelinating glial cells

The central and peripheral nervous systems are involved in multiple agedependent neurological deficits that are often attributed to alterations in function of myelinating glial cells. However, the molecular events that underlie the age-related decline of glial cell function are unknown. We used Schwann cells as a model to study biological processes affected in glial cells by aging. We comprehensively profiled gene expression of the Schwann cell-rich mouse sciatic nerve throughout life, from day of birth until senescence (840 days of age). We combined the aging data with the microarray transcriptional data obtained using nerves isolated from Schwann cell-specific neuropathy-inducing mutants MPZCre/þ/Lpin1fE2-3/fE2-3, MPZCre/þ/ScapfE1/fE1 and Pmp22-null mice. A majority of age related transcripts were also affected in the analyzed mouse models of neuropathy (54.4%) and in development (59.5%) indicating a high level of overlapping in implicated molecular pathways. We observed that compared to peripheral nerve development, dynamically changing expression profiles in aging have opposite (anticorrelated) orientation while they copy the orientation of transcriptional changes observed in analyzed neuropathy models. Subsequent clustering and biological annotation of dynamically changing transcripts revealed that the processes most significantly deregulated in aging include inflammatory/ immune response and lipid biosynthesis/metabolism. Importantly, the changes in these pathways were also observed in myelinated oligodendrocyte- rich optic nerves of aged mice, albeit with lower magnitude. This observation suggests that similar biological processes are affected in aging glial cells in central and peripheral nervous systems, however with different dynamics. Our data, which provide the first comprehensive comparison of molecular changes in glial cells in three distinct biological conditions comprising development, aging and disease, provide not only a new inside into the molecular alterations underlying neural system aging but also identify target pathways for potential therapeutical approaches to prevent or delay complications associated with age-related and inherited forms of neuropathies.

Test of four colon cancer risk-scores in formalin fixed paraffin embedded microarray gene expression data.

BACKGROUND: Prognosis prediction for resected primary colon cancer is based on the T-stage Node Metastasis (TNM) staging system. We investigated if four well-documented gene expression risk scores can improve patient stratification. METHODS: Microarray-based versions of risk-scores were applied to a large independent cohort of 688 stage II/III tumors from the PETACC-3 trial. Prognostic value for relapse-free survival (RFS), survival after relapse (SAR), and overall survival (OS) was assessed by regression analysis. To assess improvement over a reference, prognostic model was assessed with the area under curve (AUC) of receiver operating characteristic (ROC) curves. All statistical tests were two-sided, except the AUC increase. RESULTS: All four risk scores (RSs) showed a statistically significant association (single-test, P < .0167) with OS or RFS in univariate models, but with HRs below 1.38 per interquartile range. Three scores were predictors of shorter RFS, one of shorter SAR. Each RS could only marginally improve an RFS or OS model with the known factors T-stage, N-stage, and microsatellite instability (MSI) status (AUC gains < 0.025 units). The pairwise interscore discordance was never high (maximal Spearman correlation = 0.563) A combined score showed a trend to higher prognostic value and higher AUC increase for OS (HR = 1.74, 95% confidence interval [CI] = 1.44 to 2.10, P < .001, AUC from 0.6918 to 0.7321) and RFS (HR = 1.56, 95% CI = 1.33 to 1.84, P < .001, AUC from 0.6723 to 0.6945) than any single score. CONCLUSIONS: The four tested gene expression-based risk scores provide prognostic information but contribute only marginally to improving models based on established risk factors. A combination of the risk scores might provide more robust information. Predictors of RFS and SAR might need to be different.

Selecting control genes for RT-QPCR using public microarray data.

Background: Gene expression analysis has emerged as a major biological research area, with real-time quantitative reverse transcription PCR (RT-QPCR) being one of the most accurate and widely used techniques for expression profiling of selected genes. In order to obtain results that are comparable across assays, a stable normalization strategy is required. In general, the normalization of PCR measurements between different samples uses one to several control genes (e. g. housekeeping genes), from which a baseline reference level is constructed. Thus, the choice of the control genes is of utmost importance, yet there is not a generally accepted standard technique for screening a large number of candidates and identifying the best ones. Results: We propose a novel approach for scoring and ranking candidate genes for their suitability as control genes. Our approach relies on publicly available microarray data and allows the combination of multiple data sets originating from different platforms and/or representing different pathologies. The use of microarray data allows the screening of tens of thousands of genes, producing very comprehensive lists of candidates. We also provide two lists of candidate control genes: one which is breast cancer-specific and one with more general applicability. Two genes from the breast cancer list which had not been previously used as control genes are identified and validated by RT-QPCR. Open source R functions are available at http://www.isrec.isb-sib.ch/similar to vpopovic/research/ Conclusion: We proposed a new method for identifying candidate control genes for RT-QPCR which was able to rank thousands of genes according to some predefined suitability criteria and we applied it to the case of breast cancer. We also empirically showed that translating the results from microarray to PCR platform was achievable.

A robust genomic signature for the detection of colorectal cancer patients with microsatellite instability phenotype and high mutation frequency.

Microsatellite instability (MSI) occurs in 10-20% of colorectal tumours and is associated with good prognosis. Here we describe the development and validation of a genomic signature that identifies colorectal cancer patients with MSI caused by DNA mismatch repair deficiency with high accuracy. Microsatellite status for 276 stage II and III colorectal tumours has been determined. Full-genome expression data was used to identify genes that correlate with MSI status. A subset of these samples (n = 73) had sequencing data for 615 genes available. An MSI gene signature of 64 genes was developed and validated in two independent validation sets: the first consisting of frozen samples from 132 stage II patients; and the second consisting of FFPE samples from the PETACC-3 trial (n = 625). The 64-gene MSI signature identified MSI patients in the first validation set with a sensitivity of 90.3% and an overall accuracy of 84.8%, with an AUC of 0.942 (95% CI, 0.888-0.975). In the second validation, the signature also showed excellent performance, with a sensitivity 94.3% and an overall accuracy of 90.6%, with an AUC of 0.965 (95% CI, 0.943-0.988). Besides correct identification of MSI patients, the gene signature identified a group of MSI-like patients that were MSS by standard assessment but MSI by signature assessment. The MSI-signature could be linked to a deficient MMR phenotype, as both MSI and MSI-like patients showed a high mutation frequency (8.2% and 6.4% of 615 genes assayed, respectively) as compared to patients classified as MSS (1.6% mutation frequency). The MSI signature showed prognostic power in stage II patients (n = 215) with a hazard ratio of 0.252 (p = 0.0145). Patients with an MSI-like phenotype had also an improved survival when compared to MSS patients. The MSI signature was translated to a diagnostic microarray and technically and clinically validated in FFPE and frozen samples.

The new direct oral anticoagulants in special indications: rationale and preliminary data in cancer, mechanical heart valves, anti-phospholipid syndrome, and heparin-induced thrombocytopenia and beyond.

The present review will briefly summarize the interplay between coagulation and inflammation, highlighting possible effects of direct inhibition of factor Xa and thrombin beyond anticoagulation. Additionally, the rationale for the use of the new direct oral anticoagulants (DOACs) for indications such as cancer-associated venous thromboembolism (CAT), mechanical heart valves, thrombotic anti-phospholipid syndrome (APS), and heparin-induced thrombocytopenia (HIT) will be explored. Published data on patients with cancer or mechanical heart valves treated with DOAC will be discussed, as well as planned studies in APS and HIT. Although at the present time published evidence is insufficient for recommending DOAC in the above-mentioned indications, there are good arguments in favor of clinical trials investigating their efficacy in these contexts. Direct inhibition of factor Xa or thrombin may reveal interesting effects beyond anticoagulation as well.

Gene expression databases and data mining.

The DNA microarray technology has arguably caught the attention of the worldwide life science community and is now systematically supporting major discoveries in many fields of study. The majority of the initial technical challenges of conducting experiments are being resolved, only to be replaced with new informatics hurdles, including statistical analysis, data visualization, interpretation, and storage. Two systems of databases, one containing expression data and one containing annotation data are quickly becoming essential knowledge repositories of the research community. This present paper surveys several databases, which are considered "pillars" of research and important nodes in the network. This paper focuses on a generalized workflow scheme typical for microarray experiments using two examples related to cancer research. The workflow is used to reference appropriate databases and tools for each step in the process of array experimentation. Additionally, benefits and drawbacks of current array databases are addressed, and suggestions are made for their improvement.

Increased expression of urokinase-type plasminogen activator mRNA determines adverse prognosis in ErbB2-positive primary breast cancer.

PURPOSE: To evaluate and validate mRNA expression markers capable of identifying patients with ErbB2-positive breast cancer associated with distant metastasis and reduced survival. PATIENTS AND METHODS: Expression of 60 genes involved in breast cancer biology was assessed by quantitative real-time PCR (qrt-PCR) in 317 primary breast cancer patients and correlated with clinical outcome data. Results were validated subsequently using two previously published and publicly available microarray data sets with different patient populations comprising 295 and 286 breast cancer samples, respectively. RESULTS: Of the 60 genes measured by qrt-PCR, urokinase-type plasminogen activator (uPA or PLAU) mRNA expression was the most significant marker associated with distant metastasis-free survival (MFS) by univariate Cox analysis in patients with ErbB2-positive tumors and an independent factor in multivariate analysis. Subsequent validation in two microarray data sets confirmed the prognostic value of uPA in ErbB2-positive tumors by both univariate and multivariate analysis. uPA mRNA expression was not significantly associated with MFS in ErbB2-negative tumors. Kaplan-Meier analysis showed in all three study populations that patients with ErbB2-positive/uPA-positive tumors exhibited significantly reduced MFS (hazard ratios [HR], 4.3; 95% CI, 1.6 to 11.8; HR, 2.7; 95% CI, 1.2 to 6.2; and, HR, 2.8; 95% CI, 1.1 to 7.1; all P < .02) as compared with the group with ErbB2-positive/uPA-negative tumors who exhibited similar outcome to those with ErbB2-negative tumors, irrespective of uPA status. CONCLUSION: After evaluation of 898 breast cancer patients, uPA mRNA expression emerged as a powerful prognostic indicator in ErbB2-positive tumors. These results were consistent among three independent study populations assayed by different techniques, including qrt-PCR and two microarray platforms.

GLUT3 is induced during epithelial-mesenchymal transition and promotes tumor cell proliferation in non-small cell lung cancer.

BACKGROUND: Alterations in glucose metabolism and epithelial-mesenchymal transition (EMT) constitute two important characteristics of carcinoma progression toward invasive cancer. Despite an extensive characterization of each of them separately, the links between EMT and glucose metabolism of tumor cells remain elusive. Here we show that the neuronal glucose transporter GLUT3 contributes to glucose uptake and proliferation of lung tumor cells that have undergone an EMT. RESULTS: Using a panel of human non-small cell lung cancer (NSCLC) cell lines, we demonstrate that GLUT3 is strongly expressed in mesenchymal, but not epithelial cells, a finding corroborated in hepatoma cells. Furthermore, we identify that ZEB1 binds to the GLUT3 gene to activate transcription. Importantly, inhibiting GLUT3 expression reduces glucose import and the proliferation of mesenchymal lung tumor cells, whereas ectopic expression in epithelial cells sustains proliferation in low glucose. Using a large microarray data collection of human NSCLCs, we determine that GLUT3 expression correlates with EMT markers and is prognostic of poor overall survival. CONCLUSIONS: Altogether, our results reveal that GLUT3 is a transcriptional target of ZEB1 and that this glucose transporter plays an important role in lung cancer, when tumor cells loose their epithelial characteristics to become more invasive. Moreover, these findings emphasize the development of GLUT3 inhibitory drugs as a targeted therapy for the treatment of patients with poorly differentiated tumors.

Histology-driven data mining of lipid signatures from multiple imaging mass spectrometry analyses: application to human colorectal cancer liver metastasis biopsies.

Imaging mass spectrometry (IMS) represents an innovative tool in the cancer research pipeline, which is increasingly being used in clinical and pharmaceutical applications. The unique properties of the technique, especially the amount of data generated, make the handling of data from multiple IMS acquisitions challenging. This work presents a histology-driven IMS approach aiming to identify discriminant lipid signatures from the simultaneous mining of IMS data sets from multiple samples. The feasibility of the developed workflow is evaluated on a set of three human colorectal cancer liver metastasis (CRCLM) tissue sections. Lipid IMS on tissue sections was performed using MALDI-TOF/TOF MS in both negative and positive ionization modes after 1,5-diaminonaphthalene matrix deposition by sublimation. The combination of both positive and negative acquisition results was performed during data mining to simplify the process and interrogate a larger lipidome into a single analysis. To reduce the complexity of the IMS data sets, a sub data set was generated by randomly selecting a fixed number of spectra from a histologically defined region of interest, resulting in a 10-fold data reduction. Principal component analysis confirmed that the molecular selectivity of the regions of interest is maintained after data reduction. Partial least-squares and heat map analyses demonstrated a selective signature of the CRCLM, revealing lipids that are significantly up- and down-regulated in the tumor region. This comprehensive approach is thus of interest for defining disease signatures directly from IMS data sets by the use of combinatory data mining, opening novel routes of investigation for addressing the demands of the clinical setting.

The prognostic value of health-related quality-of-life data in predicting survival in glioblastoma cancer patients: results from an international randomised phase III EORTC Brain Tumour and Radiation Oncology Groups, and NCIC Clinical Trials Group study.

This is one of the few studies that have explored the value of baseline symptoms and health-related quality of life (HRQOL) in predicting survival in brain cancer patients. Baseline HRQOL scores (from the EORTC QLQ-C30 and the Brain Cancer Module (BN 20)) were examined in 490 newly diagnosed glioblastoma cancer patients for the relationship with overall survival by using Cox proportional hazards regression models. Refined techniques as the bootstrap re-sampling procedure and the computation of C-indexes and R(2)-coefficients were used to try and validate the model. Classical analysis controlled for major clinical prognostic factors selected cognitive functioning (P=0.0001), global health status (P=0.0055) and social functioning (P<0.0001) as statistically significant prognostic factors of survival. However, several issues question the validity of these findings. C-indexes and R(2)-coefficients, which are measures of the predictive ability of the models, did not exhibit major improvements when adding selected or all HRQOL scores to clinical factors. While classical techniques lead to positive results, more refined analyses suggest that baseline HRQOL scores add relatively little to clinical factors to predict survival. These results may have implications for future use of HRQOL as a prognostic factor in cancer patients.