http://www.biomedcentral.com/bmcmedgenomics/ The editor's pick of recent articles published by BMC Medical Genomics 2012-05-11T00:00:00Z Background: The efficacy of chemotherapy regimens in breast cancer patients is variable andunpredictable. Whether individual patients either achieve long-term remission or sufferrecurrence after therapy may be dictated by intrinsic properties of their breast tumorsincluding genetic lesions and consequent aberrant transcriptional programs. Global geneexpression profiling provides a powerful tool to identify such tumor-intrinsic transcriptionalprograms, whose analyses provide insight into the underlying biology of individual patienttumors. For example, multi-gene expression signatures have been identified that can predictthe likelihood of disease reccurrence, and thus guide patient prognosis. Whereas suchprognostic signatures are being introduced in the clinical setting, similar signatures thatpredict sensitivity or resistance to chemotherapy are not currently clinically available. Methods: We used gene expression profiling to identify genes that were co-expressed with genes whosetranscripts encode the protein targets of commonly used chemotherapeutic agents. Results: Here, we present target based expression indices that predict breast tumor response toanthracycline and taxane based chemotherapy. Indeed, these signatures were independentlypredictive of chemotherapy response after adjusting for standard clinic-pathological variablessuch as age, grade, and estrogen receptor status in a cohort of 488 breast cancer patientstreated with adriamycin and taxotere/taxol. Conclusions: Importantly, our findings suggest the practicality of developing target based indices thatpredict response to therapeutics, as well as highlight the possibility of using gene signaturesto guide the use of chemotherapy during treatment of breast cancer patients. http://www.biomedcentral.com/1755-8794/5/16 Robin M Hallett Gregory Pond John A Hassell BMC Medical Genomics 2012, 5:16 2012-05-11T00:00:00Z 10.1186/1755-8794-5-16 Predicting chemotherapy response Two new gene expression signatures can predict how breast cancer tumors will respond to two of the common kinds of chemotherapy, highlighting the potential of target-based gene expression indices to predict patient response to drug therapy. /content/figures/1755-8794-5-16-toc.gif BMC Medical Genomics 1755-8794 5 16 2012-05-11T00:00:00Z PDF Background: Infant birth weight is a complex quantitative trait associated with both neonatal and long-term health outcomes. Numerous studies have been published in which candidate genes (IGF1, IGF2, IGF2R, IGF binding proteins, PHLDA2 and PLAGL1) have been associated with birth weight, but these studies are difficult to reproduce in man and large cohort studies are needed due to the large inter individual variance in transcription levels. Also, very little of the trait variance is explained. We decided to identify additional candidates without regard for what is known about the genes. We hypothesize that DNA methylation differences between individuals can serve as markers of gene "expression potential" at growth related genes throughout development and that these differences may correlate with birth weight better than single time point measures of gene expression. Methods: We performed DNA methylation and transcript profiling on cord blood and placenta from newborns. We then used novel computational approaches to identify genes correlated with birth weight. Results: We identified 23 genes whose methylation levels explain 70-87% of the variance in birth weight. Six of these (ANGPT4, APOE, CDK2, GRB10, OSBPL5 and REG1B) are associated with growth phenotypes in human or mouse models. Gene expression profiling explained a much smaller fraction of variance in birth weight than did DNA methylation. We further show that two genes, the transcriptional repressor MSX1 and the growth factor receptor adaptor protein GRB10, are correlated with transcriptional control of at least seven genes reported to be involved in fetal or placental growth, suggesting that we have identified important networks in growth control. GRB10 methylation is also correlated with genes involved in reactive oxygen species signaling, stress signaling and oxygen sensing and more recent data implicate GRB10 in insulin signaling. Conclusions: Single time point measurements of gene expression may reflect many factors unrelated to birth weight, while inter-individual differences in DNA methylation may represent a "molecular fossil record" of differences in birth weight-related gene expression. Finding these "unexpected" pathways may tell us something about the long-term association between low birth weight and adult disease, as well as which genes may be susceptible to environmental effects. These findings increase our understanding of the molecular mechanisms involved in human development and disease progression. http://www.biomedcentral.com/1755-8794/5/10 Nahid Turan Mohamed F Ghalwash Sunita Katari Christos Coutifaris Zoran Obradovic Carmen Sapienza BMC Medical Genomics 2012, 5:10 2012-04-12T00:00:00Z 10.1186/1755-8794-5-10 Identification of birth weight-related genes Profiling of inter-individual DNA methylation differences in neonates has identified 23 genes that account for 70-87% of variance in birth weight, whilst traditional gene expression profiling identified a far smaller proportion. /content/figures/1755-8794-5-10-toc.gif BMC Medical Genomics 1755-8794 5 10 2012-04-12T00:00:00Z XML Background: Aberrant DNA methylation leads to loss of heterozygosity (LOH) or loss of imprinting (LOI) as the first hit during human carcinogenesis. Recently we developed a new high-throughput, high-resolution DNA methylation analysis method, bisulphite PCR-Luminex (BPL), using sperm DNA and demonstrated the effectiveness of this novel approach in rapidly identifying methylation errors. Results: In the current study, we applied the BPL method to the analysis of DNA methylation for identification of prognostic panels of DNA methylation cancer biomarkers of imprinted genes. We found that the BPL method precisely quantified the methylation status of specific DNA regions in somatic cells. We found a higher frequency of LOI than LOH. LOI at IGF2, PEG1 and H19 were frequent alterations, with a tendency to show a more hypermethylated state. We detected changes in DNA methylation as an early event in ovarian cancer. The degree of LOI (LOH) was associated with altered DNA methylation at IGF2/H19 and PEG1. Conclusions: The relative ease of BPL method provides a practical method for use within a clinical setting. We suggest that DNA methylation of H19 and PEG1 differentially methylated regions (DMRs) may provide novel biomarkers useful for screening, diagnosis and, potentially, for improving the clinical management of women with human ovarian cancer. http://www.biomedcentral.com/1755-8794/5/8 Hitoshi Hiura Hiroaki Okae Hisato Kobayash Naoko Miyauchi Fumi Sato Akiko Sato Fumihiko Suzuki Satoru Nagase Junichi Sugawara Kunihiko Nakai Nobuo Yaegashi Takahiro Arima BMC Medical Genomics 2012, 5:8 2012-03-26T00:00:00Z 10.1186/1755-8794-5-8 BPL method detects ovarian cancer biomarkers Aberrant patterns of methylation are found in samples from patients with ovarian cancer using the new bisulphate PCR-Luminex (BPL) method, highlighting H19 and PEG1 as potential biomarkers for the disease. /content/figures/1755-8794-5-8-toc.gif BMC Medical Genomics 1755-8794 5 8 2012-03-26T00:00:00Z XML Background: Diagnosing subclinical atherosclerosis is often difficult since patients are asymptomatic. In order to alleviate this limitation, we have developed a molecular prediction technique for predicting patients with atherogenic risks using multi-gene expression biomarkers on leukocytes. Methods: We first discovered 356 expression biomarkers which showed significant differential expression between genome-wide microarray data of monocytes from patients with familial hyperlipidemia and increased risk of atherosclerosis compared to normal controls. These biomarkers were further triaged with 56 biomarkers known to be directly related to atherogenic risks. We also applied a COXEN algorithm to identify concordantly expressed biomarkers between monocytes and each of three different cell types of leukocytes. We then developed a multi-gene predictor using all or three subsets of these 56 biomarkers on the monocyte patient data. These predictors were then applied to multiple independent patient sets from three cell types of leukocytes (macrophages, circulating T cells, or whole white blood cells) to predict patients with atherogenic risks. Results: When the 56 predictor was applied to the three patient sets from different cell types of leukocytes, all significantly stratified patients with atherogenic risks from healthy people in these independent cohorts. Concordantly expressed biomarkers identified by the COXEN algorithm provided slightly better prediction results. Conclusion: These results demonstrated the potential of molecular prediction of atherogenic risks across different cell types of leukocytes. http://www.biomedcentral.com/1755-8794/5/2 Feng Cheng Ellen C Keeley Jae K Lee BMC Medical Genomics 2012, 5:2 2012-01-13T00:00:00Z 10.1186/1755-8794-5-2 Predicting early atherosclerosis An new set of 56 gene expression biomarkers could be used to predict early atherosclerosis from leukocytes before the symptoms of heart disease have developed, and this test can be further refined to identify very high risk patients. /content/figures/1755-8794-5-2-toc.gif BMC Medical Genomics 1755-8794 5 2 2012-01-13T00:00:00Z XML