Detection and characterization of pancreatic and biliary tract cancers using cell-free DNA fragmentomics

BACKGROUND

Plasma cell-free DNA (cfDNA) fragmentomics has demonstrated significant differentiation power between cancer patients and healthy individuals, but little is known in pancreatic and biliary tract cancers. The aim of this study is to characterize the cfDNA fragmentomics in biliopancreatic cancers and develop an accurate method for cancer detection.

METHODS

One hundred forty-seven patients with biliopancreatic cancers and 71 non-cancer volunteers were enrolled, including 55 patients with cholangiocarcinoma, 30 with gallbladder cancer, and 62 with pancreatic cancer. Low-coverage whole-genome sequencing (median coverage: 2.9 ×) was performed on plasma cfDNA. Three cfDNA fragmentomic features, including fragment size, end motif and nucleosome footprint, were subjected to construct a stacked machine learning model for cancer detection. Integration of carbohydrate antigen 19-9 (CA19-9) was explored to improve model performance.

RESULTS

The stacked model presented robust performance for cancer detection (area under curve (AUC) of 0.978 in the training cohort, and AUC of 0.941 in the validation cohort), and remained consistent even when using extremely low-coverage sequencing depth of 0.5 × (AUC: 0.905). Besides, our method could also help differentiate biliopancreatic cancer subtypes. By integrating the stacked model and CA19-9 to generate the final detection model, a high accuracy in distinguishing biliopancreatic cancers from non-cancer samples with an AUC of 0.995 was achieved.

CONCLUSIONS

Our model demonstrated ultrasensitivity of plasma cfDNA fragementomics in detecting biliopancreatic cancers, fulfilling the unmet accuracy of widely-used serum biomarker CA19-9, and provided an affordable way for accurate noninvasive biliopancreatic cancer screening in clinical practice.

DendroX: multi-level multi-cluster selection in dendrograms

BACKGROUND

Cluster heatmaps are widely used in biology and other fields to uncover clustering patterns in data matrices. Most cluster heatmap packages provide utility functions to divide the dendrograms at a certain level to obtain clusters, but it is often difficult to locate the appropriate cut in the dendrogram to obtain the clusters seen in the heatmap or computed by a statistical method. Multiple cuts are required if the clusters locate at different levels in the dendrogram.

RESULTS

We developed DendroX, a web app that provides interactive visualization of a dendrogram where users can divide the dendrogram at any level and in any number of clusters and pass the labels of the identified clusters for functional analysis. Helper functions are provided to extract linkage matrices from cluster heatmap objects in R or Python to serve as input to the app. A graphic user interface was also developed to help prepare input files for DendroX from data matrices stored in delimited text files. The app is scalable and has been tested on dendrograms with tens of thousands of leaf nodes. As a case study, we clustered the gene expression signatures of 297 bioactive chemical compounds in the LINCS L1000 dataset and visualized them in DendroX. Seventeen biologically meaningful clusters were identified based on the structure of the dendrogram and the expression patterns in the heatmap. We found that one of the clusters consisting of mostly naturally occurring compounds is not previously reported and has its members sharing broad anticancer, anti-inflammatory and antioxidant activities.

CONCLUSIONS

DendroX solves the problem of matching visually and computationally determined clusters in a cluster heatmap and helps users navigate among different parts of a dendrogram. The identification of a cluster of naturally occurring compounds with shared bioactivities implicates a convergence of biological effects through divergent mechanisms.

Efficacy of Nimotuzumab plus Concurrent Chemo-Radiotherapy for Unresectable Esophageal Cancer: A Real-World Study

PURPOSE/OBJECTIVE(S)

The esophageal cancer ranked 7th in the morbidity of malignant cancer and the 6th contributed to carcinoma deaths. Most patients are diagnosed of advanced stage at first visiting. The 5-year survival rate of unresectable esophageal cancer is about 20% after the standard treatment of concurrent chemo-radiotherapy. Nimotuzumab, a humanized anti-EGFR antibody, has shown good efficacy and low toxicity in epithelial tumors. This two-center, real-world study evaluated the efficacy and safety of nimotuzumab combined with concurrent chemoradiotherapy in unresectable esophageal squamous cell carcinoma (ESCC).

MATERIALS/METHODS

Totally 503 eligible unresectable ESCC patients from Jan 2014 to Dec 2020 were included. 1:2 nearest neighbor propensity score matching (PSM) was performed to match the Nimo group (nimotuzumab plus concurrent chemo-radiotherapy) and CRT group (concurrent chemo-radiotherapy), and the covariates included age, gender, tumor location, lesion length, TNM stage, clinical stage, and radiotherapy dose. The primary endpoint was overall survival (OS). The secondary endpoints were progression-free survival (PFS), objective response rate (ORR), and disease control rate (DCR).

RESULTS

A total of 61 patients were in Nimo group which received nimotuzumab (200 mg/w, 4-6 weeks) combined with concurrent chemo-radiotherapy (chemotherapy: S-1/FP/TP/DP for 2-4 cycles; radiotherapy: 2DRT,3D-CRT or IMRT, 50-70 Gy in 25-35 fractions) and 107 patients in CRT group only received concurrent chemo-radiotherapy. The baseline characteristics were well balanced between the two groups. The efficacy of Nimo group was better than that of CRT group. The ORR was 85.2% vs. 71.0%, (P=0.037), the DCR was 98.4% vs. 91.6%, (P>0.05). The median PFS was 28.07 months vs. 19.54 months, and the 1-, 3- and 5-year PFS rates were 78.2% vs. 72.9%, 37.5% vs. 28.3%, and 29.1% vs. 21.3%, respectively (HR: 0.6860, 95% CI: 0.4902-0.9600, P=0.034). The median OS was 34.93 months vs. 24.30 months and the 1-, 3- and 5-year OS rates were 88.5% vs. 81.3%, 46.8% vs. 35.2% and 37.4% vs. 28.0%, respectively (HR: 0.6701, 95% CI: 0.4792-0.9372, P=0.024). The adverse events including radiation esophagitis, radiation pneumonitis, bone marrow suppression, nausea, vomiting, and rash were no significantly different between the two groups (P>0.05).

CONCLUSION

Nimotuzumab combined with concurrent chemo-radiotherapy improved the ORR, and prolonged PFS and OS in unresectable ESCC patients with a good tolerance.

Comut-viz: efficiently creating and browsing comutation plots online

BACKGROUND

Comutation plot is a widely used visualization method to deliver a global view of the mutation landscape of large-scale genomic studies. Current tools for creating comutation plot are either offline packages that require coding or online web servers with varied features. When a package is used, it often requires repetitive runs of code to adjust a single feature that might only be a few clicks in a web app. But web apps mostly have limited capacity for customization and cannot handle very large genomic files.

RESULTS

To improve on existing tools, we identified features that are most frequently adjusted in creating a plot and incorporate them in Comut-viz that interactively filters and visualizes mutation data as downloadable plots. It includes colored labels for numeric metadata, a preloaded palette for changing colors and two input boxes for adjusting width and height. It accepts standard mutation annotation format (MAF) files as input and can handle large MAF files with more than 200 k rows. As a front-end only app, Comut-viz guarantees privacy of user data and no latency in the analysis.

CONCLUSIONS

Comut-viz is a highly responsive and extensible web app to make comutation plots. It provides customization for frequently adjusted features and accepts large genomic files as input. It is suitable for genomic studies with more than a thousand samples.

Single-cell RNA-seq reveals ectopic and aberrant lung-resident cell populations in idiopathic pulmonary fibrosis

We provide a single-cell atlas of idiopathic pulmonary fibrosis (IPF), a fatal interstitial lung disease, by profiling 312,928 cells from 32 IPF, 28 smoker and nonsmoker controls, and 18 chronic obstructive pulmonary disease (COPD) lungs. Among epithelial cells enriched in IPF, we identify a previously unidentified population of aberrant basaloid cells that coexpress basal epithelial, mesenchymal, senescence, and developmental markers and are located at the edge of myofibroblast foci in the IPF lung. Among vascular endothelial cells, we identify an ectopically expanded cell population transcriptomically identical to bronchial restricted vascular endothelial cells in IPF. We confirm the presence of both populations by immunohistochemistry and independent datasets. Among stromal cells, we identify IPF myofibroblasts and invasive fibroblasts with partially overlapping cells in control and COPD lungs. Last, we confirm previous findings of profibrotic macrophage populations in the IPF lung. Our comprehensive catalog reveals the complexity and diversity of aberrant cellular populations in IPF.

[Comparison of gemstone spectral curve and CT value of gastric cancer with different pathological types and differentiation degrees]

Objective: To investigate the differences of gemstone spectral curve and CT value of gastric cancer with different pathological types and differentiation degrees. Methods: 91 cases of preoperative gemstone CT images with gastric cancer were collected, including 24 cases of mucinous carcinoma, 67 cases of non-mucinous carcinoma, 16 cases of signet ring cell carcinoma, 8 cases of mucinous adenocarcinoma, 32 cases of moderately differentiated adenocarcinoma and 35 cases of poorly differentiated adenocarcinoma. Gemstone CT spectral imaging was performed preoperatively, and the spectral curve of the lesion in venous phase was obtained by using GSI Viewer software, the slope of the curve was calculated, and 11 monoenergetic CT values of 40~140 keV (10 keV interval) were measured. The gemstone spectral curves and CT values of gastric cancer with different pathological types and differentiation degrees are compared. Results: The curve slopes of non-mucinous carcinoma, signet ring cell carcinoma and poorly differentiated adenocarcinoma were -1.92±0.53, -1.73±0.37 and -2.14±0.54, respectively. The absolute values were higher than those of mucinous carcinoma (-1.45±0.54), mucinous adenocarcinoma (-0.90±0.34) and moderately differentiated adenocarcinoma (-1.67±0.41), and the differences were all statistically significant (P<0.05). There were significant differences in monoenergetic CT values between mucinous and non-mucinous carcinomas at 40-140 keV (all P<0.05). The former was lower than the latter in different degrees, and the lower the energy, the greater the difference was. There were significant differences in monoenergetic CT values between signet ring cell carcinoma and mucinous adenocarcinoma at 40-100 keV (all P<0.05); monoenergetic CT values between poorly differentiated adenocarcinoma and moderately differentiated adenocarcinoma at 40-90 keV showed statistically significant differences (P<0.05). Conclusions: Gastric cancer with different pathological types and differentiation degrees have their characteristic spectral curves in venous phase, and the monoenergetic CT values are significantly different at low energy. The spectral curve of gemstone CT may be helpful to evaluate the pathological type and differentiation degree of gastric cancer before operation.

LncRNA MALAT1 facilities high glucose induced endothelial to mesenchymal transition and fibrosis via targeting miR-145/ZEB2 axis

OBJECTIVE

Diabetic nephropathy (DN) is one of the most common complications of diabetes mellitus (DM), but the pathophysiology of DN is complex and not fully understood. Renal tubal epithelial-mesenchymal transition (EMT) has been shown to be the critical mechanism of glomerulosclerosis and tubulointerstitial fibrosis. However, the precise mechanisms underlying EMT are not clear. MALAT1 was found induced by hyperglycemia in kidney but whether MALAT1 is involved in renal tubal EMT remains unknown. The objective of our study is to explore the role of MALAT1 in hyperglycemia-induced EMT and fibrosis.

PATIENTS AND METHODS

We used db/db mouse and high glucose (HG)-stimulated HK-2 cells as in vivo and in vitro model of DN, respectively. qRT-PCR was used to measure levels of MALAT1 and miR-145. In addition, we validated interactions of MALAT1-miR-145 and miR-145-ZEB2 by dual luciferase reporter assays. Western blot was used to examine expressions of proteins involved in EMT and fibrosis.

RESULTS

MALAT1 was upregulated while miR-145 was downregulated in renal tissues of db/db mice. Consistently, hyperglycemia significantly increased the level of MALAT1 but decreased miR-145 expression in a time-dependent manner in HK-2 cells. Furthermore, miR-145 binds to both MALAT1 and ZEB2. Knockdown MALAT1 or ZEB2 inhibited HG-induced EMT and fibrosis, similar to miR-145 overexpression.

CONCLUSIONS

Our study is the first to show that MALAT1 and miR-145 regulate HG-induced EMT and fibrosis. Mechanistically, MALAT1 functions as a sponge RNA for miR-145 to derepress the expression of target gene ZEB2, thereby inducing EMT and fibrosis. These results provide a novel potential target for DN therapy in the future.

[Related factors on secondary drug resistance in HIV infected persons receiving antiretroviral therapy in Shandong province: a case-control study]

Objective: To explore the causes of secondary drug resistance among HIV infected persons who were receiving antiretroviral therapy in Shandong province, and provide evidence for the improvement of antiretroviral therapy strategy. Methods: A case-control study was designed with 1∶2 matching on case and control groups. Household and face-to-face interview were conducted in October, 2015. All the study subjects were screened from both the drug resistant database of antiretroviral therapy of Shandong provincial laboratory and national comprehensive HIV/AIDS database in Shandong. The sample size was estimated as 330 cases including 110 drug resistant and 220 non-drug resistant cases. Subjects were people living with HIV/AIDS (PLWHA) aged 15 or older and received antiretroviral therapy for more than 6 months with records of virus load (VL). Subjects who presented VL above 1 000 copies/ml would receive drug resistance testing. Subjects who were confirmed resistant to with secondary drug, were selected as case group, the rest subjects with non-secondary drug resistance would form the control group. EpiData 3.1 software and SPSS 22.0 software were used to establish a database. Related influencing factors were analyzed with non- conditional stepwise logistic regression model. Results: A total of 288 cases were enrolled, including 103 in the case and 185 cases in the control groups, with average age as (37.62±1.06) years and (37.90±0.74) years old, respectively. Most of them were male, married/cohabitant, with education level of junior/senior high school or below and under Han nationality. Results from the multivariate logistic regression model showed that ORs (95%CI) of receiving antiretroviral therapy for 1-3 years, or more than 3 years were equal to 8.80 (3.69-21.00), 3.00 (1.20-7.53), compared with receiving antiretroviral therapy less than one year, respectively. OR (95%CI) of Among the PLWHA that with missing rate above 25.0% on medication, the OR appeared as 15.41(4.59-51.71), compared with not missing medication. OR (95%CI) among those who took the medicine themselves was 0.22 (0.07-0.74). Conclusions: Factors as duration of treatment, missing rate on medication and taking medicine by oneself were of influence on secondary drug resistance. Other factors as duration on antiretroviral therapy longer than 1 year, missing rate above 25.0% on medication, were related to the risk on secondary drug resistance. However, if the medicine was taken by oneself, it served as a protective factor for secondary drug resistance. It is necessary to strengthen the intervention and health education programs related to antiretroviral therapy.

Comparative Analysis of Diffusional Kurtosis Imaging, Diffusion Tensor Imaging, and Diffusion-Weighted Imaging in Grading and Assessing Cellular Proliferation of Meningiomas

BACKGROUND AND PURPOSE

An accurate evaluation of the World Health Organization grade and cellular proliferation is particularly important in meningiomas. Our aim was to prospectively evaluate and compare diffusional kurtosis imaging, DTI, and DWI metrics in determining the grade and cellular proliferation of meningiomas.

MATERIALS AND METHODS

Ninety-six consecutive patients with histopathologically confirmed meningiomas were included in this study. Mean kurtosis, radial kurtosis, axial kurtosis, fractional anisotropy, mean diffusivity, and ADC were semiautomatically obtained in the solid components of tumors. Each normalized diffusion value was compared between high-grade meningiomas and low-grade meningiomas using the Mann-Whitney U test. Receiver operating characteristic, multiple logistic regression, and Pearson correlation analysis were used for statistical evaluations.

RESULTS

Diffusional kurtosis imaging metrics (mean kurtosis, radial kurtosis, and axial kurtosis) were significantly higher in high-grade meningiomas than in low-grade meningiomas (P ≤ .001). Mean diffusivity and ADC were significantly lower in high-grade meningiomas than in low-grade meningiomas (P = .003 and .002). Mean kurtosis had significantly greater area the under curve values than mean diffusivity and fractional anisotropy in differentiating high-grade meningiomas from low-grade meningiomas (P = .038 and .002). Mean kurtosis was the only variable that could be used to independently differentiate high-grade meningiomas and low-grade meningiomas (P < .001). Significant correlations were found between the Ki-67 labeling index and kurtosis metrics (P < .001), as well as for mean diffusivity and ADC (P = .004, and .007).

CONCLUSIONS

Compared with other diffusion metrics, mean kurtosis may serve as an optimal parameter for evaluating and predicting the meningioma grade. Moreover, diffusion metrics may potentially reflect cellular proliferation.

[Herpes simplex virus-2 infection and related factors among female drug abusers in the women's compulsory drug rehabilitation center of Shandong province]

Objective: This study aimed to understand the herpes simplex virus-2 (HSV-2) infection and related factors among female drug abusers in the women's compulsory drug rehabilitation center of Shandong province and to provide reference for the prevention and control of HSV-2 in these settings. Methods: We screened all of 451 female drug abusers in the women's compulsory drug rehabilitation centers in of Shandong province and conducted a study using both questionnaire investigation and serological tests for HSV-2, HIV and syphilis. We also used EpiData 3.1 software to establish a database and SPSS 20.0 software to conduct the χ(2) test and multivariate logistic regression analysis. Results: A total of 451 female drug abusers were under study. We noticed that the rates for HSV-2 infection, HIV infection and syphilis infection appeared as 72.1% (325/451), 2.2% (10/451) and 33.5% (151/451) respectively. Results from univariate analysis showed that factors as: awareness on AIDS, having temporary sex partner after using the drug, having multiple sex partners after using the drug, providing commercial services or having temporary sex practice before being detained, with syphilis infection etc., were associated with HSV-2 infection. Data from the multivariate analysis showed that the OR (95%CI) value of HSV-2 infection was 2.90 (1.19-7.06) for those who providing commercial service, when comparing to those who did not. Compared to those who did not suffer from syphilis infection, the OR (95%CI) value of HSV-2 infection for those with syphilis infection was 2.75 (1.63-4.63). Conclusions: The rate of HSV-2 infection was high in the women's compulsory drug rehabilitation center of Shandong province. We should enhance measures and promote condom use to prevent from HSV-2 and other sexually transmitted diseases among them.

A new locus regulating MICALL2 expression was identified for association with executive inhibition in children with attention deficit hyperactivity disorder

Impaired executive inhibition is a core deficit of attention deficit hyperactivity disorder (ADHD), which is a common childhood-onset psychiatric disorder with high heritability. In this study, we performed a two-stage genome-wide association study of executive inhibition in ADHD in Han Chinese. We used the Stroop color-word interference test to evaluate executive inhibition. After quality control, 780 samples with phenotype and covariate data were included in the discovery stage, whereas 922 samples were included in the replication stage. We identified one new significant locus at 7p22.3 for the Stroop word interference time (rs11514810, P=3.42E-09 for discovery, P=0.01176 for replication and combined P=5.249E-09). Regulatory feature analysis and expression quantitative trait loci (eQTL) data showed that this locus contributes to MICALL2 expression in the human brain. Most genes in the network interacting with MICALL2 were associated with psychiatric disorders. Furthermore, hyperactive-impulsive-like behavior was induced by reducing the expression of the zebrafish gene that is homologous to MICALL2, which could be rescued by tomoxetine (atomoxetine), a clinical medication for ADHD. Our results suggested that MICALL2 is a new susceptibility gene for executive inhibition deficiency related to hyperactive-impulsive behavior in ADHD, further emphasizing the possible role of neurodevelopmental genes in the pathogenic mechanism of ADHD.

Pharmacogenomics study of thiazide diuretics and QT interval in multi-ethnic populations: the cohorts for heart and aging research in genomic epidemiology

Thiazide diuretics, commonly used antihypertensives, may cause QT interval (QT) prolongation, a risk factor for highly fatal and difficult to predict ventricular arrhythmias. We examined whether common single-nucleotide polymorphisms (SNPs) modified the association between thiazide use and QT or its component parts (QRS interval, JT interval) by performing ancestry-specific, trans-ethnic and cross-phenotype genome-wide analyses of European (66%), African American (15%) and Hispanic (19%) populations (N=78 199), leveraging longitudinal data, incorporating corrected standard errors to account for underestimation of interaction estimate variances and evaluating evidence for pathway enrichment. Although no loci achieved genome-wide significance (P<5 × 10-8), we found suggestive evidence (P<5 × 10-6) for SNPs modifying the thiazide-QT association at 22 loci, including ion transport loci (for example, NELL1, KCNQ3). The biologic plausibility of our suggestive results and simulations demonstrating modest power to detect interaction effects at genome-wide significant levels indicate that larger studies and innovative statistical methods are warranted in future efforts evaluating thiazide-SNP interactions.

Cell-specific prediction and application of drug-induced gene expression profiles

Gene expression profiling of in vitro drug perturbations is useful for many biomedical discovery applications including drug repurposing and elucidation of drug mechanisms. However, limited data availability across cell types has hindered our capacity to leverage or explore the cell-specificity of these perturbations. While recent efforts have generated a large number of drug perturbation profiles across a variety of human cell types, many gaps remain in this combinatorial drug-cell space. Hence, we asked whether it is possible to fill these gaps by predicting cell-specific drug perturbation profiles using available expression data from related conditions--i.e. from other drugs and cell types. We developed a computational framework that first arranges existing profiles into a three-dimensional array (or tensor) indexed by drugs, genes, and cell types, and then uses either local (nearest-neighbors) or global (tensor completion) information to predict unmeasured profiles. We evaluate prediction accuracy using a variety of metrics, and find that the two methods have complementary performance, each superior in different regions in the drug-cell space. Predictions achieve correlations of 0.68 with true values, and maintain accurate differentially expressed genes (AUC 0.81). Finally, we demonstrate that the predicted profiles add value for making downstream associations with drug targets and therapeutic classes.

Association between water fluoride and the level of children's intelligence: a dose-response meta-analysis

OBJECTIVES

Higher fluoride concentrations in water have inconsistently been associated with the levels of intelligence in children. The following study summarizes the available evidence regarding the strength of association between fluoridated water and children's intelligence.

STUDY DESIGN

Meta-analysis.

METHODS

PubMed, Embase, and Cochrane Library databases were systematically analyzed from November 2016. Observational studies that have reported on intelligence levels in relation to high and low water fluoride contents, with 95% confidence intervals (CIs) were included. Further, the results were pooled using inverse variance methods. The correlation between water fluoride concentration and intelligence level was assessed by a dose-response meta-analysis.

RESULTS

Twenty-six studies reporting data on 7258 children were included. The summary results indicated that high water fluoride exposure was associated with lower intelligence levels (standardized mean difference : -0.52; 95% CI: -0.62 to -0.42; P < 0.001). The findings from subgroup analyses were consistent with those from overall analysis. The dose-response meta-analysis suggested a significant association between water fluoride dosage and intelligence (P < 0.001), while increased water fluoride exposure was associated with reduced intelligence levels.

CONCLUSIONS

Greater exposure to high levels of fluoride in water was significantly associated with reduced levels of intelligence in children. Therefore, water quality and exposure to fluoride in water should be controlled in areas with high fluoride levels in water.

Comparative efficacy and toxicity of induction chemotherapy with concurrent stereotactic body radiotherapy and stereotactic body radiotherapy with subsequent chemotherapy in patients with clinical stage T1-3N0M0 non-small cell lung carcinoma

PURPOSE

We compared the clinical efficacy and toxicity of stereotactic body radiotherapy with induction chemotherapy and concurrent radiochemotherapy vs stereotactic body radiotherapy with subsequent chemotherapy in patients with clinical stage T1-3N0M0 non-small cell lung carcinoma.

METHODS

We retrospectively analyzed 38 patients with c-stage T1-3N0M0 non-small cell lung carcinoma who received stereotactic body radiotherapy. All patients received six cycles of chemotherapy. Fifteen of the patients were treated with three cycles of induction chemotherapy, one cycle of concurrent radiochemotherapy, and then two cycles of consolidation chemotherapy, while 23 patients received Sequential Radiotherapy/Chemotherapy.

RESULTS

Patients in the induction chemotherapy group experienced a longer duration of esophagitis (median 2 vs 0, range 0-6 vs 0-3.6 weeks, p = 0.04). We divided the patients into two groups based on their median pre-treatment tumor volume (cm³): >32.11 and ≤32.11. The tumor response rate in patients with larger tumor volume was substantially higher in the induction chemotherapy group than in the Sequential Radiotherapy/Chemotherapy group (66.67 vs 40%). Among patients with pre-treatment tumor volume (cm³) >32.11, the median local progression-free survival (LPFS) in the induction chemotherapy group and Sequential Radiotherapy/Chemotherapy group was 18 months (range 7-72 months) and 11 months (range 6-53 months), respectively. There was a statistically significant difference between the two groups (p = 0.006).

CONCLUSIONS

Simultaneous SBRT and chemotherapy can result in a longer duration of esophagitis. However, for patients with large tumor volume, ICT combined with concurrent radiochemotherapy may result in better local tumor response as well as longer LPFS and progression-free survival. To better elucidate the best treatment, further clinical trials are needed.

Activation of tumor suppressor protein PP2A inhibits KRAS-driven tumor growth

Targeted cancer therapies, which act on specific cancer-associated molecular targets, are predominantly inhibitors of oncogenic kinases. While these drugs have achieved some clinical success, the inactivation of kinase signaling via stimulation of endogenous phosphatases has received minimal attention as an alternative targeted approach. Here, we have demonstrated that activation of the tumor suppressor protein phosphatase 2A (PP2A), a negative regulator of multiple oncogenic signaling proteins, is a promising therapeutic approach for the treatment of cancers. Our group previously developed a series of orally bioavailable small molecule activators of PP2A, termed SMAPs. We now report that SMAP treatment inhibited the growth of KRAS-mutant lung cancers in mouse xenografts and transgenic models. Mechanistically, we found that SMAPs act by binding to the PP2A Aα scaffold subunit to drive conformational changes in PP2A. These results show that PP2A can be activated in cancer cells to inhibit proliferation. Our strategy of reactivating endogenous PP2A may be applicable to the treatment of other diseases and represents an advancement toward the development of small molecule activators of tumor suppressor proteins.

Abstract IA05: New methods and better theory for pre-clinical cancer pharmacogenomics

Drug dose-response measurements are the cornerstone of pre-clinical assessments of new and existing therapeutics. As a means to identify biomarkers of drug response, high-throughput studies have attempted to relate drug sensitivity across hundreds of cell lines and hundreds of drugs to inhibition of mitogensis and induction of apoptosis. And, by analogy with antibiotic susceptibility testing, it is increasingly possible to screen primary human tumor cells as a means to personalize therapy for individual patients. I will describe new ways of performing such studies that promise to avoid common confounders.

The metrics usually used to parameterize drug response (IC50, Emax, or AUC) are based on assessing the cell count of a treated condition relative to an untreated control. All of these metrics suffer from a fundamental flaw: cell lines divide at very different rates and those that undergo more divisions over the course of an assay are scored as more sensitive than cell lines with fewer divisions, even if their inherent drug sensitivities are identical. We developed a new method to parameterize drug response, the growth rate inhibition (GR) metrics, which is based on the ratio of growth rates under treatment conditions in relation to an untreated control. GR metrics are independent of cell growth over the course of the experiment and thus enable us to accurately compare cell lines with varying growth rates or experimental conditions that can alter growth rates.

I will describe the use of GR metrics to analyze six breast cancer cell lines treated with ~100 small molecule inhibitors at six doses. We focused on inhibitors targeting key signaling nodes such as PI3K, AKT, or MAPK, as well as receptor tyrosine kinases (RTKs) and cyclin-dependent kinases (CDKs), and many of the drugs studied are currently in clinical trials. We measured the cell's transcriptional response at 3 hours and 24 hours using the L1000 transcriptional profiling assay and scored phenotypes by imaging at 3 days. We find that some drugs, particularly those targeting cell cycle kinases and chaperones, elicit near identical responses in all cell lines both transcriptionally and phenotypically. Kinase inhibitors, on the other hand, predominantly elicit cell type-specific responses at the molecular and phenotypic levels. Interestingly, a subset of signaling kinase inhibitors induces significant change at the transcriptional level without affecting cell growth. Analysis of the transcriptional profiles under these conditions allows us to identify synergistic drug combinations, which act by blocking adaptive drug resistance. Thus, inexpensive, high-throughput transcript profiling can uncover subtle mechanisms of drug sensitivity and resistance and guide the design of effective drug combinations.

Our work shows the value of using drug response metrics that are independent of growth rate. This applies to high-throughput profiling of cell lines, but also to patient-derived tumor and normal cells. GR metrics are also useful whenever cells are manipulated in ways that can affect division times (e.g. by varying the microenvironment or introducing genetic changes). We expect GR metrics to improve pre-clinical pharmacology and precision therapeutic assays employing patient-derived tumor cells.

Citation Format: Mario Niepel, Marc Hafner, Qiaonan Duan, Aravind Subramanian, Avi Ma'ayan, Peter K. Sorger. New methods and better theory for pre-clinical cancer pharmacogenomics. [abstract]. In: Proceedings of the AACR Special Conference on Engineering and Physical Sciences in Oncology; 2016 Jun 25-28; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2017;77(2 Suppl):Abstract nr IA05.

Extraction and analysis of signatures from the Gene Expression Omnibus by the crowd

Gene expression data are accumulating exponentially in public repositories. Reanalysis and integration of themed collections from these studies may provide new insights, but requires further human curation. Here we report a crowdsourcing project to annotate and reanalyse a large number of gene expression profiles from Gene Expression Omnibus (GEO). Through a massive open online course on Coursera, over 70 participants from over 25 countries identify and annotate 2,460 single-gene perturbation signatures, 839 disease versus normal signatures, and 906 drug perturbation signatures. All these signatures are unique and are manually validated for quality. Global analysis of these signatures confirms known associations and identifies novel associations between genes, diseases and drugs. The manually curated signatures are used as a training set to develop classifiers for extracting similar signatures from the entire GEO repository. We develop a web portal to serve these signatures for query, download and visualization.

L1000CDS2: LINCS L1000 characteristic direction signatures search engine

The library of integrated network-based cellular signatures (LINCS) L1000 data set currently comprises of over a million gene expression profiles of chemically perturbed human cell lines. Through unique several intrinsic and extrinsic benchmarking schemes, we demonstrate that processing the L1000 data with the characteristic direction (CD) method significantly improves signal to noise compared with the MODZ method currently used to compute L1000 signatures. The CD processed L1000 signatures are served through a state-of-the-art web-based search engine application called L1000CDS². The L1000CDS² search engine provides prioritization of thousands of small-molecule signatures, and their pairwise combinations, predicted to either mimic or reverse an input gene expression signature using two methods. The L1000CDS² search engine also predicts drug targets for all the small molecules profiled by the L1000 assay that we processed. Targets are predicted by computing the cosine similarity between the L1000 small-molecule signatures and a large collection of signatures extracted from the gene expression omnibus (GEO) for single-gene perturbations in mammalian cells. We applied L1000CDS² to prioritize small molecules that are predicted to reverse expression in 670 disease signatures also extracted from GEO, and prioritized small molecules that can mimic expression of 22 endogenous ligand signatures profiled by the L1000 assay. As a case study, to further demonstrate the utility of L1000CDS², we collected expression signatures from human cells infected with Ebola virus at 30, 60 and 120 min. Querying these signatures with L1000CDS² we identified kenpaullone, a GSK3B/CDK2 inhibitor that we show, in subsequent experiments, has a dose-dependent efficacy in inhibiting Ebola infection in vitro without causing cellular toxicity in human cell lines. In summary, the L1000CDS² tool can be applied in many biological and biomedical settings, while improving the extraction of knowledge from the LINCS L1000 resource.

Abstract 788: Transcriptional landscape of drug response guides the design of potent and synergistic drug combinations

Transcriptional profiling of drug-treated cells yields high dimensional response signatures that allow drugs to be compared with each other. For example, the Connectivity Map collects signatures that are aggregated across multiple cell types. However, most therapeutic drugs are effective only against a subset of disease genotypes, particularly in the case of anti-cancer drugs. Here we ask how transcriptional signatures vary across cell lines and dose and correlate these signatures to the phenotypic response (growth inhibition). Using these cell line specific signatures, we inferred which signaling pathways are perturbed by specific kinase inhibitors and identified synergistic drug combinations.

We treated 6 breast cancer cell lines with more than 100 targeted inhibitors at 6 doses and measured their transcriptional response at 2 time points. We focused on inhibitors targeting key the PI3K and MAPK signaling pathways, as well as receptor tyrosine kinases (RTKs) and cyclin-dependent kinases (CDKs); many of them are currently studied in clinical trials. We identified that ∼40% of the perturbations induce a significant difference in their gene expression profile. Clustering revealed the signatures are time point specific. Some clusters contain perturbations from multiple cell lines, like CDK inhibitors that down regulate genes related to the cell cycle in all six lines. In contrast, clusters comprising inhibitors of the PI3K and MAPK pathways are specific to each cell line and pathway. The perturbations induced by RTK and non-RTK inhibitors cluster with either the PI3K or the MAPK inhibitors depending on the cell line. Thus, the transcriptional response allows us to identify differences in pathway usage between cell lines, in particular to which pathway RTKs signal predominantly.

We found that the significance of the transcriptional signature is not necessarily related to growth inhibition. In particular, some inhibitors have little effect on growth, yet induce a significant transcriptional signature. The most striking case is the inhibition of MEK and EGFR in BT20 that induces strong transcriptional and biochemical responses but inhibits growth by only ∼20%. Based on the transcriptional signature we inferred and validated experimentally that FoxO, which is generally regulated by the PI3K pathway, is partially activated by MEK or EGFR inhibition. This suggests that EGFR and PI3K inhibitors act synergistically in BT20, which we validated experimentally both at the level of FoxO activation and growth inhibition. We validated the most promising drug pair by treating xenografts.

We have shown how we can use measurements of expression signatures and cellular phenotypes following single drug perturbations to identify drug combinations that are synergistic in individual cell lines. This approach is a step toward the rational design of co-drugging strategies with differential effect and larger therapeutic windows.

Citation Format: Marc Hafner, Mario Niepel, Qiaonan Duan, Evan Paull, Josh Stuart, Aravind Subramanian, Avi Ma’ayan, Peter K. Sorger. Transcriptional landscape of drug response guides the design of potent and synergistic drug combinations. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 788.

Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.

Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.

Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.

RNA-Seq analysis for the potential targets and molecular mechanisms of 17 β-estradiol in squamous cell lung carcinoma

The efficacy of 17 β-estradiol (E2) was valid in some cancers, while its effects on squamous cell lung carcinoma (SCLC) were still unclear. The aim of our study was to investigate the potential targets and molecular mechanisms of E2 in SCLC cells.Two RNA libraries from human lung carcinoma cells (SK-MES-1) with and without E2 treatment were constructed and sequenced. The differentially expressed genes (DEGs) between cells with or without E2 treatment were identified by cuffdiff software. Hierarchical Clustering Analysis (HCA) was performed for displaying gene expression changes and classification. Furthermore, enrichment analyses of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology Biological Process (GO BP) terms were performed through DAVID. The protein-protein interaction (PPI) network was constructed through STRING. Additionally, differentially expressed lncRNAs were also selected by cuffdiff software.Total 129 DEGs including 58 up- and 71 down- regulated genes were obtained. Cancer-related pathways including small cell lung cancer, hypertrophic cardiomyopathy (HCM) and pathways in cancer and biological processes including regulation of phosphorus metabolic process, protein localization and nucleus organization were enriched. The PPI network with 113 nodes and 312 edges was constructed. CASP3, ITGA2, COL4A6, PML and CDC25B were identified as hub nodes which had more interactions with others in the PPI network. Furthermore, eight up-regulated and ten down-regulated lncRNAs were selected.CASP3, ITGA2 and Lnc-DLK1-4:31 (one of down-regulated lncRNAs) might play pivotal roles in E2 treated SCNC cells by influencing cell apoptosis, angiogenesis and cell invasion respectively.

Genetic parameters and genetic correlations among triacylglycerol and phospholipid fractions in Angus cattle

The objective of this study was to estimate genetic parameters for intramuscular fatty acids from triacylglycerol (TAG) and phospholipid (PL) fractions in beef LM tissue. Longissimus muscle samples were obtained from 1,833 Angus cattle to determine the intramuscular fatty acid composition for 31 lipids and lipid classes from TAG and PL fractions and were classified by structure into saturated (SFA), monounsaturated (MUFA), polyunsaturated (PUFA), omega-3 (n-3), and omega-6 (n-6) fatty acids. An atherogenic index (AI) was also determined as a measure of the unsaturated fatty acid to SFA ratio. Restricted maximum likelihood methods combined with pedigree data were used to estimate variance components with the WOMBAT software package. Heritability estimates ranged from 0.00 to 0.63 for the major classes of fatty acids. Heritability estimates differed between the TAG and PL fractions, with higher estimates for TAG up to 0.64 and lower estimates for PL that ranged from 0.00 to 0.14. Phenotypic and genetic correlations among individual fatty acids were determined for the TAG fraction as well as among carcass traits, including rib eye area, numerical marbling score, yield grade, ether fat, and Warner-Bratzler shear force value. Strong negative or positive genetic correlations were observed among individual fatty acids in the TAG fraction, which ranged from -0.99 to 0.97 ( < 0.05). Moderate correlations between carcass traits and fatty acids from the TAG fraction ranged from -0.43 to 0.32 ( < 0.05). These results indicate that fatty acids prominent in beef tissues show significant genetic variation as well as genetic relationships with carcass traits.

Abstract B20: Transcriptional landscape of drug response guides the design of specific and potent drug combinations

Understanding responses to targeted agents is a key step toward the design of new therapeutic strategies that improve clinical cancer care. Here, we profiled the effects of a collection of kinase inhibitors using the L1000 transcriptional assay and combined these results with both phenotypic and biochemical response measurements to gain a more complete understanding of drug response. Using algorithms that reconstruct which signaling pathways are perturbed by specific kinase inhibitors, we identified potentially synergistic drug combinations and validated them experimentally.

We treated six breast cancer cell lines with more than 100 targeted inhibitors at six doses and measured their transcriptional response at two time points. We focused on inhibitors targeting key the PI3K and MAPK signaling pathways, as well as receptor tyrosine kinases (RTKs) and cyclin-dependent kinases (CDKs); many of them are currently studied in clinical trials. We identified that 37% of the perturbations induce a significant difference in their gene expression profile based on the characteristic direction of the response. Clustering of signatures revealed they are time point specific: 3 hour signatures differ from the 24 hour ones. Some clusters contain perturbations from multiple cell lines, like CDK inhibitors that down regulate genes related to the cell cycle in all six lines. In contrast, clusters comprising inhibitors of the PI3K/AKT and MAPK pathways are specific to each cell line and pathway. The perturbations induced by RTK and non-RTK inhibitors cluster with either the PI3K or the MAPK inhibitors depending on the cell line. Thus, the transcriptional response allow us to identify differences in pathway usage between cell lines, in particular which RTK signals predominantly to the PI3K or the MAPK pathway.

When we related transcriptional response to the growth inhibition after three days, we found that the strength of the transcriptional signature is not necessarily related to growth inhibition. In particular, we identified cases where inhibitors have little effect on growth, yet induce a significant transcriptional signature. The most striking case is the inhibition of MEK and EGFR in BT-20 that induces strong transcriptional and biochemical responses but only 20-30% of growth inhibition. Based on the transcriptional signature we inferred and validated experimentally that FoxO, which is generally regulated by the PI3K pathway, is partially activated following MEK or EGFR inhibition. This suggests that EGFR inhibitors and PI3K inhibitors act synergistically in BT-20, which we validated experimentally both at the level of FoxO activation and growth inhibition. We are currently verifying the most promising drug pair in xenografts.

We have shown how we can use measurements of expression signatures and cellular phenotypes following single drug perturbations to identify drug combinations that are potent and specific to individual cell lines. This approach is a step toward the rational design of co-drugging strategies with differential effect and larger therapeutic windows.

Citation Format: Marc Hafner, Mario Niepel, Qiaonan Duan, Evan Paull, Josh Stuart, Aravind Subramanian, Avi Ma'ayan, Peter Sorger. Transcriptional landscape of drug response guides the design of specific and potent drug combinations. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr B20.

Sire breed effect on beef longissimus mineral concentrations and their relationships with carcass and palatability traits

The objective of this study was to evaluate sire breed effect on mineral concentration in beef longissimus thoracis (LT) and investigate the correlations between beef mineral concentrations and carcass and palatability traits. Steer progeny (N=246) from the Germplasm Evaluation project-Cycle VIII were used in this study. In addition to carcass traits, LT was evaluated for mineral concentrations, Warner-Bratzler shear force, and palatability traits. A mixed linear model estimated breed effects on mineral concentrations. No significant sire breed (P≥0.43) or dam breed (P≥0.20) effects were identified for mineral concentrations. Pearson correlation coefficients were calculated among mineral concentrations, carcass, and sensory traits. Zinc concentration was positively correlated (P≤0.05) with total iron (r=0.14), heme iron (r=0.13), and magnesium (r=0.19). Significant (P<0.05) correlations were identified between non-heme or heme iron and most traits in this study. Magnesium concentration was correlated with all carcass and palatability traits.

Lean Big Data integration in systems biology and systems pharmacology

Data sets from recent large-scale projects can be integrated into one unified puzzle that can provide new insights into how drugs and genetic perturbations applied to human cells are linked to whole-organism phenotypes. Data that report how drugs affect the phenotype of human cell lines and how drugs induce changes in gene and protein expression in human cell lines can be combined with knowledge about human disease, side effects induced by drugs, and mouse phenotypes. Such data integration efforts can be achieved through the conversion of data from the various resources into single-node-type networks, gene-set libraries, or multipartite graphs. This approach can lead us to the identification of more relationships between genes, drugs, and phenotypes as well as benchmark computational and experimental methods. Overall, this lean 'Big Data' integration strategy will bring us closer toward the goal of realizing personalized medicine.

Drug/Cell-line Browser: interactive canvas visualization of cancer drug/cell-line viability assay datasets

SUMMARY

Recently, several high profile studies collected cell viability data from panels of cancer cell lines treated with many drugs applied at different concentrations. Such drug sensitivity data for cancer cell lines provide suggestive treatments for different types and subtypes of cancer. Visualization of these datasets can reveal patterns that may not be obvious by examining the data without such efforts. Here we introduce Drug/Cell-line Browser (DCB), an online interactive HTML5 data visualization tool for interacting with three of the recently published datasets of cancer cell lines/drug-viability studies. DCB uses clustering and canvas visualization of the drugs and the cell lines, as well as a bar graph that summarizes drug effectiveness for the tissue of origin or the cancer subtypes for single or multiple drugs. DCB can help in understanding drug response patterns and prioritizing drug/cancer cell line interactions by tissue of origin or cancer subtype.

AVAILABILITY AND IMPLEMENTATION

DCB is an open source Web-based tool that is freely available at: http://www.maayanlab.net/LINCS/DCB CONTACT: avi.maayan@mssm.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Analysis of genetic relationships and identification of lily cultivars based on inter-simple sequence repeat markers

Inter-simple sequence repeat (ISSR) markers were used to discriminate 62 lily cultivars of 5 hybrid series. Eight ISSR primers generated 104 bands in total, which all showed 100% polymorphism, and an average of 13 bands were amplified by each primer. Two software packages, POPGENE 1.32 and NTSYSpc 2.1, were used to analyze the data matrix. Our results showed that the observed number of alleles (NA), effective number of alleles (NE), Nei's genetic diversity (H), and Shannon's information index (I) were 1.9630, 1.4179, 0.2606, and 0.4080, respectively. The highest genetic similarity (0.9601) was observed between the Oriental x Trumpet and Oriental lilies, which indicated that the two hybrids had a close genetic relationship. An unweighted pair-group method with arithmetic means dendrogram showed that the 62 lily cultivars clustered into two discrete groups. The first group included the Oriental and OT cultivars, while the Asiatic, LA, and Longiflorum lilies were placed in the second cluster. The distribution of individuals in the principal component analysis was consistent with the clustering of the dendrogram. Fingerprints of all lily cultivars built from 8 primers could be separated completely. This study confirmed the effect and efficiency of ISSR identification in lily cultivars.

LINCS Canvas Browser: interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures

For the Library of Integrated Network-based Cellular Signatures (LINCS) project many gene expression signatures using the L1000 technology have been produced. The L1000 technology is a cost-effective method to profile gene expression in large scale. LINCS Canvas Browser (LCB) is an interactive HTML5 web-based software application that facilitates querying, browsing and interrogating many of the currently available LINCS L1000 data. LCB implements two compacted layered canvases, one to visualize clustered L1000 expression data, and the other to display enrichment analysis results using 30 different gene set libraries. Clicking on an experimental condition highlights gene-sets enriched for the differentially expressed genes from the selected experiment. A search interface allows users to input gene lists and query them against over 100 000 conditions to find the top matching experiments. The tool integrates many resources for an unprecedented potential for new discoveries in systems biology and systems pharmacology. The LCB application is available at http://www.maayanlab.net/LINCS/LCB. Customized versions will be made part of the http://lincscloud.org and http://lincs.hms.harvard.edu websites.

The characteristic direction: a geometrical approach to identify differentially expressed genes

Background

Identifying differentially expressed genes (DEG) is a fundamental step in studies that perform genome wide expression profiling. Typically, DEG are identified by univariate approaches such as Significance Analysis of Microarrays (SAM) or Linear Models for Microarray Data (LIMMA) for processing cDNA microarrays, and differential gene expression analysis based on the negative binomial distribution (DESeq) or Empirical analysis of Digital Gene Expression data in R (edgeR) for RNA-seq profiling.

Results

Here we present a new geometrical multivariate approach to identify DEG called the Characteristic Direction. We demonstrate that the Characteristic Direction method is significantly more sensitive than existing methods for identifying DEG in the context of transcription factor (TF) and drug perturbation responses over a large number of microarray experiments. We also benchmarked the Characteristic Direction method using synthetic data, as well as RNA-Seq data. A large collection of microarray expression data from TF perturbations (73 experiments) and drug perturbations (130 experiments) extracted from the Gene Expression Omnibus (GEO), as well as an RNA-Seq study that profiled genome-wide gene expression and STAT3 DNA binding in two subtypes of diffuse large B-cell Lymphoma, were used for benchmarking the method using real data. ChIP-Seq data identifying DNA binding sites of the perturbed TFs, as well as known drug targets of the perturbing drugs, were used as prior knowledge silver-standard for validation. In all cases the Characteristic Direction DEG calling method outperformed other methods. We find that when drugs are applied to cells in various contexts, the proteins that interact with the drug-targets are differentially expressed and more of the corresponding genes are discovered by the Characteristic Direction method. In addition, we show that the Characteristic Direction conceptualization can be used to perform improved gene set enrichment analyses when compared with the gene-set enrichment analysis (GSEA) and the hypergeometric test.

Conclusions

The application of the Characteristic Direction method may shed new light on relevant biological mechanisms that would have remained undiscovered by the current state-of-the-art DEG methods. The method is freely accessible via various open source code implementations using four popular programming languages: R, Python, MATLAB and Mathematica, all available at: http://www.maayanlab.net/CD.

Polymorphisms in calpastatin and mu-calpain genes are associated with beef iron content

The objective of this study was to assess the association of markers in the calpastatin and mu-calpain loci with iron in beef cattle muscle. The population consisted of 259 cross-bred steers from Beefmaster, Brangus, Bonsmara, Romosinuano, Hereford and Angus sires. Total iron and heme iron concentrations were measured. Markers in the calpastatin (referred to as CAST) and mu-calpain (referred to as CAPN4751) genes were used to assess their association with iron levels. The mean and standard error for iron and heme iron content in the population was 35.6 ± 1.3 μg and 27.1 ± 1.4 μg respectively. Significant associations (P < 0.01) of markers were observed for both iron and heme iron content. For CAST, animals with the CC genotype had higher levels of iron and heme iron in longissimus dorsi muscle. For CAPN4751, individuals with the TT genotype had higher concentrations of iron and heme iron than did animals with the CC and CT genotypes. Genotypes known to be associated with tougher meat were associated with higher levels of iron concentration.

Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool

BACKGROUND

System-wide profiling of genes and proteins in mammalian cells produce lists of differentially expressed genes/proteins that need to be further analyzed for their collective functions in order to extract new knowledge. Once unbiased lists of genes or proteins are generated from such experiments, these lists are used as input for computing enrichment with existing lists created from prior knowledge organized into gene-set libraries. While many enrichment analysis tools and gene-set libraries databases have been developed, there is still room for improvement.

RESULTS

Here, we present Enrichr, an integrative web-based and mobile software application that includes new gene-set libraries, an alternative approach to rank enriched terms, and various interactive visualization approaches to display enrichment results using the JavaScript library, Data Driven Documents (D3). The software can also be embedded into any tool that performs gene list analysis. We applied Enrichr to analyze nine cancer cell lines by comparing their enrichment signatures to the enrichment signatures of matched normal tissues. We observed a common pattern of up regulation of the polycomb group PRC2 and enrichment for the histone mark H3K27me3 in many cancer cell lines, as well as alterations in Toll-like receptor and interlukin signaling in K562 cells when compared with normal myeloid CD33+ cells. Such analyses provide global visualization of critical differences between normal tissues and cancer cell lines but can be applied to many other scenarios.

CONCLUSIONS

Enrichr is an easy to use intuitive enrichment analysis web-based tool providing various types of visualization summaries of collective functions of gene lists. Enrichr is open source and freely available online at: http://amp.pharm.mssm.edu/Enrichr.

Genetic parameters for concentrations of minerals in longissimus muscle and their associations with palatability traits in Angus cattle

The objective of this study was to estimate genetic parameters for concentrations of minerals in LM and to evaluate their associations with beef palatability traits. Samples of LM from 2,285 Angus cattle were obtained and fabricated into steaks for analysis of mineral concentrations and for trained sensory panel assessments. Nine minerals, including calcium, copper, iron, magnesium, manganese, phosphorus, potassium, sodium, and zinc, were quantified. Restricted maximum likelihood procedures were used to obtain estimates of variance and covariance components under a multiple-trait animal model. Estimates of heritability for mineral concentrations in LM varied from 0.01 to 0.54. Iron and sodium were highly and moderately heritable, respectively, whereas the other minerals were lowly heritable except for calcium, copper, and manganese, which exhibited no genetic variation. Strong positive genetic correlations existed between iron and zinc (0.49, P < 0.05), between magnesium and phosphorus (0.88, P < 0.05), between magnesium and sodium (0.68, P < 0.05), and between phosphorus and potassium (0.69, P < 0.05). Overall tenderness assessed by trained sensory panelists was positively associated with manganese, potassium, and sodium and negatively associated with phosphorus and zinc concentrations (P < 0.05). Juiciness assessed by trained sensory panelists was negatively associated with magnesium and positively associated with manganese and sodium concentrations (P < 0.05). Livery or metallic flavor was not associated with any of the minerals (P > 0.05). Beefy flavor was positively associated with calcium, iron, and zinc and negatively associated with sodium concentration, whereas a painty or fishy flavor was positively associated with sodium and negatively associated with calcium and potassium concentrations (P < 0.05). Beef is a major contributor of iron and zinc in the human diet, and these results demonstrate sufficient genetic variation for these traits to be improved through marker-assisted selection programs without compromising beef palatability.