Structural similarity assessment for drug sensitivity prediction in cancer

<p>Abstract</p> <p>Background</p> <p>The ability to predict drug sensitivity in cancer is one of the exciting promises of pharmacogenomic research. Several groups have demonstrated the ability to predict drug sensitivity by integrating chemo-sensitivity data and associated gene expression measurements from large anti-cancer drug screens such as NCI-60. The general approach is based on comparing gene expression measurements from sensitive and resistant cancer cell lines and deriving drug sensitivity profiles consisting of lists of genes whose expression is predictive of response to a drug. Importantly, it has been shown that such profiles are generic and can be applied to cancer cell lines that are not part of the anti-cancer screen. However, one limitation is that the profiles can not be generated for untested drugs (i.e., drugs that are not part of an anti-cancer drug screen). In this work, we propose using an existing drug sensitivity profile for drug A as a substitute for an untested drug B given high structural similarities between drugs A and B.</p> <p>Results</p> <p>We first show that structural similarity between pairs of compounds in the NCI-60 dataset highly correlates with the similarity between their activities across the cancer cell lines. This result shows that structurally similar drugs can be expected to have a similar effect on cancer cell lines. We next set out to test our hypothesis that we can use existing drug sensitivity profiles as substitute profiles for untested drugs. In a cross-validation experiment, we found that the use of substitute profiles is possible without a significant loss of prediction accuracy if the substitute profile was generated from a compound with high structural similarity to the untested compound.</p> <p>Conclusion</p> <p>Anti-cancer drug screens are a valuable resource for generating omics-based drug sensitivity profiles. We show that it is possible to extend the usefulness of existing screens to untested drugs by deriving substitute sensitivity profiles from structurally similar drugs part of the screen.</p

An Anti-Human ICAM-1 Antibody Inhibits Rhinovirus-Induced Exacerbations of Lung Inflammation

Human rhinoviruses (HRV) cause the majority of common colds and acute exacerbations of asthma and chronic obstructive pulmonary disease (COPD). Effective therapies are urgently needed, but no licensed treatments or vaccines currently exist. Of the 100 identified serotypes, ∼90% bind domain 1 of human intercellular adhesion molecule-1 (ICAM-1) as their cellular receptor, making this an attractive target for development of therapies; however, ICAM-1 domain 1 is also required for host defence and regulation of cell trafficking, principally via its major ligand LFA-1. Using a mouse anti-human ICAM-1 antibody (14C11) that specifically binds domain 1 of human ICAM-1, we show that 14C11 administered topically or systemically prevented entry of two major groups of rhinoviruses, HRV16 and HRV14, and reduced cellular inflammation, pro-inflammatory cytokine induction and virus load in vivo. 14C11 also reduced cellular inflammation and Th2 cytokine/chemokine production in a model of major group HRV-induced asthma exacerbation. Interestingly, 14C11 did not prevent cell adhesion via human ICAM-1/LFA-1 interactions in vitro, suggesting the epitope targeted by 14C11 was specific for viral entry. Thus a human ICAM-1 domain-1-specific antibody can prevent major group HRV entry and induction of airway inflammation in vivo

Pilot evaluation of a walking school bus program in a low-income, urban community

<p>Abstract</p> <p>Background</p> <p>To evaluate the impact of a walking school bus (WSB) program on student transport in a low-income, urban neighborhood.</p> <p>Methods</p> <p>The design was a controlled, quasi-experimental trial with consecutive cross-sectional assessments. The setting was three urban, socioeconomically disadvantaged, public elementary schools (1 intervention vs. 2 controls) in Seattle, Washington, USA. Participants were ethnically diverse students in kindergarten-5^thgrade (aged 5–11 years). The intervention was a WSB program consisting of a part-time WSB coordinator and parent volunteers. Students' method of transportation to school was assessed by a classroom survey at baseline and one-year follow-up. The Pearson Chi-squared test compared students transported to school at the intervention versus control schools at each time point. Due to multiple testing, we calculated adjusted p-values using the Ryan-Holm stepdown Bonferroni procedure. McNemar's test was used to examine the change from baseline to 12-month follow-up for walking versus all other forms of school transport at the intervention or control schools.</p> <p>Results</p> <p>At baseline, the proportions of students (n = 653) walking to the intervention (20% +/- 2%) or control schools (15% +/- 2%) did not differ (<it>p </it>= 0.39). At 12-month follow up, higher proportions of students (n = 643, <it>p </it>= 0.001)) walked to the intervention (25% +/- 2%) versus the control schools (7% +/- 1%). No significant changes were noted in the proportion of students riding in a car or taking the school bus at baseline or 12-month follow up (all <it>p </it>> 0.05). Comparing baseline to 12-month follow up, the numbers of students who walked to the intervention school increased while the numbers of students who used the other forms of transport did not change (<it>p </it>< 0.0001). In contrast, the numbers of students who walked to the control schools decreased while the numbers of students who used the other forms of transport did not change (<it>p </it>< 0.0001).</p> <p>Conclusion</p> <p>A WSB program is a promising intervention among urban, low-income elementary school students that may promote favorable changes toward active transport to school.</p> <p>Trial Registration</p> <p>ClinicalTrials.gov NCT00402701</p

Classification of heterogeneous microarray data by maximum entropy kernel

<p>Abstract</p> <p>Background</p> <p>There is a large amount of microarray data accumulating in public databases, providing various data waiting to be analyzed jointly. Powerful kernel-based methods are commonly used in microarray analyses with support vector machines (SVMs) to approach a wide range of classification problems. However, the standard vectorial data kernel family (linear, RBF, etc.) that takes vectorial data as input, often fails in prediction if the data come from different platforms or laboratories, due to the low gene overlaps or consistencies between the different datasets.</p> <p>Results</p> <p>We introduce a new type of kernel called maximum entropy (ME) kernel, which has no pre-defined function but is generated by kernel entropy maximization with sample distance matrices as constraints, into the field of SVM classification of microarray data. We assessed the performance of the ME kernel with three different data: heterogeneous kidney carcinoma, noise-introduced leukemia, and heterogeneous oral cavity carcinoma metastasis data. The results clearly show that the ME kernel is very robust for heterogeneous data containing missing values and high-noise, and gives higher prediction accuracies than the standard kernels, namely, linear, polynomial and RBF.</p> <p>Conclusion</p> <p>The results demonstrate its utility in effectively analyzing promiscuous microarray data of rare specimens, e.g., minor diseases or species, that present difficulty in compiling homogeneous data in a single laboratory.</p

Microarray evidence of glutaminyl cyclase gene expression in melanoma: implications for tumor antigen specific immunotherapy

BACKGROUND: In recent years encouraging progress has been made in developing vaccine treatments for cancer, particularly with melanoma. However, the overall rate of clinically significant results has remained low. The present research used microarray datasets from previous investigations to examine gene expression patterns in cancer cell lines with the goal of better understanding the tumor microenvironment. METHODS: Principal Components Analyses with Promax rotational transformations were carried out with 90 cancer cell lines from 3 microarray datasets, which had been made available on the internet as supplementary information from prior publications. RESULTS: In each of the analyses a well defined melanoma component was identified that contained a gene coding for the enzyme, glutaminyl cyclase, which was as highly expressed as genes from a variety of well established biomarkers for melanoma, such as MAGE-3 and MART-1, which have frequently been used in clinical trials of melanoma vaccines. CONCLUSION: Since glutaminyl cyclase converts glutamine and glutamic acid into a pyroglutamic form, it may interfere with the tumor destructive process of vaccines using peptides having glutamine or glutamic acid at their N-terminals. Finding ways of inhibiting the activity of glutaminyl cyclase in the tumor microenvironment may help to increase the effectiveness of some melanoma vaccines

Validity of instruments to assess students' travel and pedestrian safety

<p>Abstract</p> <p>Background</p> <p>Safe Routes to School (SRTS) programs are designed to make walking and bicycling to school safe and accessible for children. Despite their growing popularity, few validated measures exist for assessing important outcomes such as type of student transport or pedestrian safety behaviors. This research validated the SRTS school travel survey and a pedestrian safety behavior checklist.</p> <p>Methods</p> <p>Fourth grade students completed a brief written survey on how they got to school that day with set responses. Test-retest reliability was obtained 3-4 hours apart. Convergent validity of the SRTS travel survey was assessed by comparison to parents' report. For the measure of pedestrian safety behavior, 10 research assistants observed 29 students at a school intersection for completion of 8 selected pedestrian safety behaviors. Reliability was determined in two ways: correlations between the research assistants' ratings to that of the Principal Investigator (PI) and intraclass correlations (ICC) across research assistant ratings.</p> <p>Results</p> <p>The SRTS travel survey had high test-retest reliability (κ = 0.97, n = 96, p < 0.001) and convergent validity (κ = 0.87, n = 81, p < 0.001). The pedestrian safety behavior checklist had moderate reliability across research assistants' ratings (ICC = 0.48) and moderate correlation with the PI (r = 0.55, p =< 0.01). When two raters simultaneously used the instrument, the ICC increased to 0.65. Overall percent agreement (91%), sensitivity (85%) and specificity (83%) were acceptable.</p> <p>Conclusions</p> <p>These validated instruments can be used to assess SRTS programs. The pedestrian safety behavior checklist may benefit from further formative work.</p

Using Expression and Genotype to Predict Drug Response in Yeast

Personalized, or genomic, medicine entails tailoring pharmacological therapies according to individual genetic variation at genomic loci encoding proteins in drug-response pathways. It has been previously shown that steady-state mRNA expression can be used to predict the drug response (i.e., sensitivity or resistance) of non-genotyped mammalian cancer cell lines to chemotherapeutic agents. In a real-world setting, clinicians would have access to both steady-state expression levels of patient tissue(s) and a patient's genotypic profile, and yet the predictive power of transcripts versus markers is not well understood. We have previously shown that a collection of genotyped and expression-profiled yeast strains can provide a model for personalized medicine. Here we compare the predictive power of 6,229 steady-state mRNA transcript levels and 2,894 genotyped markers using a pattern recognition algorithm. We were able to predict with over 70% accuracy the drug sensitivity of 104 individual genotyped yeast strains derived from a cross between a laboratory strain and a wild isolate. We observe that, independently of drug mechanism of action, both transcripts and markers can accurately predict drug response. Marker-based prediction is usually more accurate than transcript-based prediction, likely reflecting the genetic determination of gene expression in this cross

An Integrated Approach to the Prediction of Chemotherapeutic Response in Patients with Breast Cancer

BACKGROUND: A major challenge in oncology is the selection of the most effective chemotherapeutic agents for individual patients, while the administration of ineffective chemotherapy increases mortality and decreases quality of life in cancer patients. This emphasizes the need to evaluate every patient's probability of responding to each chemotherapeutic agent and limiting the agents used to those most likely to be effective. METHODS AND RESULTS: Using gene expression data on the NCI-60 and corresponding drug sensitivity, mRNA and microRNA profiles were developed representing sensitivity to individual chemotherapeutic agents. The mRNA signatures were tested in an independent cohort of 133 breast cancer patients treated with the TFAC (paclitaxel, 5-fluorouracil, adriamycin, and cyclophosphamide) chemotherapy regimen. To further dissect the biology of resistance, we applied signatures of oncogenic pathway activation and performed hierarchical clustering. We then used mRNA signatures of chemotherapy sensitivity to identify alternative therapeutics for patients resistant to TFAC. Profiles from mRNA and microRNA expression data represent distinct biologic mechanisms of resistance to common cytotoxic agents. The individual mRNA signatures were validated in an independent dataset of breast tumors (P = 0.002, NPV = 82%). When the accuracy of the signatures was analyzed based on molecular variables, the predictive ability was found to be greater in basal-like than non basal-like patients (P = 0.03 and P = 0.06). Samples from patients with co-activated Myc and E2F represented the cohort with the lowest percentage (8%) of responders. Using mRNA signatures of sensitivity to other cytotoxic agents, we predict that TFAC non-responders are more likely to be sensitive to docetaxel (P = 0.04), representing a viable alternative therapy. CONCLUSIONS: Our results suggest that the optimal strategy for chemotherapy sensitivity prediction integrates molecular variables such as ER and HER2 status with corresponding microRNA and mRNA expression profiles. Importantly, we also present evidence to support the concept that analysis of molecular variables can present a rational strategy to identifying alternative therapeutic opportunities

Role of Transferrin Receptor and the ABC Transporters ABCB6 and ABCB7 for Resistance and Differentiation of Tumor Cells towards Artesunate

The anti-malarial artesunate also exerts profound anti-cancer activity. The susceptibility of tumor cells to artesunate can be enhanced by ferrous iron. The transferrin receptor (TfR) is involved in iron uptake by internalization of transferrin and is over-expressed in rapidly growing tumors. The ATP-binding cassette (ABC) transporters ABCB6 and ABCB7 are also involved in iron homeostasis. To investigate whether these proteins play a role for sensitivity towards artesunate, Oncotest's 36 cell line panel was treated with artesunate or artesunate plus iron(II) glycine sulfate (Ferrosanol®). The majority of cell lines showed increased inhibition rates, for the combination of artesunate plus iron(II) glycine sulfate compared to artesunate alone. However, in 11 out of the 36 cell lines the combination treatment was not superior. Cell lines with high TfR expression significantly correlated with high degrees of modulation indicating that high TfR expressing tumor cells would be more efficiently inhibited by this combination treatment than low TfR expressing ones. Furthermore, we found a significant relationship between cellular response to artesunate and TfR expression in 55 cell lines of the National Cancer Institute (NCI), USA. A significant correlation was also found for ABCB6, but not for ABCB7 in the NCI panel. Artesunate treatment of human CCRF-CEM leukemia and MCF7 breast cancer cells induced ABCB6 expression but repressed ABCB7 expression. Finally, artesunate inhibited proliferation and differentiation of mouse erythroleukemia (MEL) cells. Down-regulation of ABCB6 by antisense oligonucleotides inhibited differentiation of MEL cells indicating that artesunate and ABCB6 may cooperate. In conclusion, our results indicate that ferrous iron improves the activity of artesunate in some but not all tumor cell lines. Several factors involved in iron homeostasis such as TfR and ABCB6 may contribute to this effect

Multiclass classification of microarray data samples with a reduced number of genes

<p>Abstract</p> <p>Background</p> <p>Multiclass classification of microarray data samples with a reduced number of genes is a rich and challenging problem in Bioinformatics research. The problem gets harder as the number of classes is increased. In addition, the performance of most classifiers is tightly linked to the effectiveness of mandatory gene selection methods. Critical to gene selection is the availability of estimates about the maximum number of genes that can be handled by any classification algorithm. Lack of such estimates may lead to either computationally demanding explorations of a search space with thousands of dimensions or classification models based on gene sets of unrestricted size. In the former case, unbiased but possibly overfitted classification models may arise. In the latter case, biased classification models unable to support statistically significant findings may be obtained.</p> <p>Results</p> <p>A novel bound on the maximum number of genes that can be handled by binary classifiers in binary mediated multiclass classification algorithms of microarray data samples is presented. The bound suggests that high-dimensional binary output domains might favor the existence of accurate and sparse binary mediated multiclass classifiers for microarray data samples.</p> <p>Conclusions</p> <p>A comprehensive experimental work shows that the bound is indeed useful to induce accurate and sparse multiclass classifiers for microarray data samples.</p