Anduril 2: Upgraded large-scale data integration framework

aSummary: Anduril is an analysis and integration framework that facilitates the design, use, parallelization and reproducibility of bioinformatics workflows. Anduril has been upgraded to use Scala for pipeline construction, which simplifies software maintenance, and facilitates design of complex pipelines. Additionally, Anduril's bioinformatics repository has been expanded with multiple components, and tutorial pipelines, for next-generation sequencing data analysis.Peer reviewe

New insights into the role of age and carcinoembryonic antigen in the prognosis of colorectal cancer

The aim of this study was to verify through relative survival (an estimate of cancer-specific survival) the true prognostic factors of colorectal cancer. The study involved 506 patients who underwent locally radical resection. All the clinical, histological and laboratory parameters were prognostically analysed for both overall and relative survival. This latter was calculated from the expected survival of the general population with identical age, sex and calendar years of observation. Univariate and multivariate analyses were applied to the proportional hazards model. Liver metastases, age, lymph node involvement and depth of bowel wall involvement were independent prognosticators of both overall and relative survival, whereas carcinoembryonic antigen (CEA) was predictive only of relative survival. Increasing age was unfavourably related to overall survival, but mildly protective with regard to relative survival. Three out of the five prognostic factors identified are the cornerstones of the current staging systems, and were confirmed as adequate by the analysis of relative survival. The results regarding age explain the conflicting findings so far obtained from studies considering overall survival only and advise against the adoption of absolute age limits in therapeutic protocols. Moreover, the prechemotherapy CEA level showed a high clinical value

Large-scale data integration framework provides a comprehensive view on glioblastoma multiforme

Background: Coordinated efforts to collect large-scale data sets provide a basis for systems level understanding of complex diseases. In order to translate these fragmented and heterogeneous data sets into knowledge and medical benefits, advanced computational methods for data analysis, integration and visualization are needed.Methods: We introduce a novel data integration framework, Anduril, for translating fragmented large-scale data into testable predictions. The Anduril framework allows rapid integration of heterogeneous data with state-of-the-art computational methods and existing knowledge in bio-databases. Anduril automatically generates thorough summary reports and a website that shows the most relevant features of each gene at a glance, allows sorting of data based on different parameters, and provides direct links to more detailed data on genes, transcripts or genomic regions. Anduril is open-source; all methods and documentation are freely available.Results: We have integrated multidimensional molecular and clinical data from 338 subjects having glioblastoma multiforme, one of the deadliest and most poorly understood cancers, using Anduril. The central objective of our approach is to identify genetic loci and genes that have significant survival effect. Our results suggest several novel genetic alterations linked to glioblastoma multiforme progression and, more specifically, reveal Moesin as a novel glioblastoma multiforme-associated gene that has a strong survival effect and whose depletion in vitro significantly inhibited cell proliferation. All analysis results are available as a comprehensive website.Conclusions: Our results demonstrate that integrated analysis and visualization of multidimensional and heterogeneous data by Anduril enables drawing conclusions on functional consequences of large-scale molecular data. Many of the identified genetic loci and genes having significant survival effect have not been reported earlier in the context of glioblastoma multiforme. Thus, in addition to generally applicable novel methodology, our results provide several glioblastoma multiforme candidate genes for further studies. Anduril is available at http://csbi.ltdk.helsinki.fi/anduril/ The glioblastoma multiforme analysis results are available at http://csbi.ltdk.helsinki.fi/anduril/tcga-gbm

Tumor marker utility and prognostic relevance of cathepsin B, cathepsin L, urokinase-type plasminogen activator, plasminogen activator inhibitor type-1, CEA and CA 19-9 in colorectal cancer

<p>Abstract</p> <p>Background</p> <p>Cathepsin B and L (CATB, CATL), urokinase-type plasminogen activator (uPA) and its inhibitor PAI-1 play an important role in colorectal cancer invasion. The tumor marker utility and prognostic relevance of these proteases have not been evaluated in the same experimental setting and compared with that of CEA and CA-19-9.</p> <p>Methods</p> <p>Protease, CEA and CA 19-9 serum or plasma levels were determined in 56 patients with colorectal cancer, 25 patients with ulcerative colitis, 26 patients with colorectal adenomas and 35 tumor-free control patients. Protease, CEA, CA 19-9 levels have been determined by ELISA and electrochemiluminescence immunoassay, respectively; their sensitivity, specificity, diagnostic accuracy have been calculated and correlated with clinicopathological staging.</p> <p>Results</p> <p>The protease antigen levels were significantly higher in colorectal cancer compared with other groups. Sensitivity of PAI-1 (94%), CATB (82%), uPA (69%), CATL (41%) were higher than those of CEA or CA 19-9 (30% and 18%, respectively). PAI-1, CATB and uPA demonstrated a better accuracy than CEA or CA 19-9. A combination of PAI-1 with CATB or uPA exhibited the highest sensitivity value (98%). High CATB, PAI-1, CEA and CA 19-9 levels correlated with advanced Dukes stages. CATB (<it>P </it>= 0.0004), CATL (<it>P </it>= 0.02), PAI-1 (<it>P </it>= 0.01) and CA 19-9 (<it>P </it>= 0.004) had a significant prognostic impact. PAI-1 (<it>P </it>= 0.001), CATB (<it>P </it>= 0.04) and CA 19-9 (<it>P </it>= 0.02) proved as independent prognostic variables.</p> <p>Conclusion</p> <p>At the time of clinical detection proteases are more sensitive indicators for colorectal cancer than the commonly used tumor markers. Determinations of CATB, CATL and PAI-1 have a major prognostic impact in patients with colorectal cancer.</p

The multiplex bead array approach to identifying serum biomarkers associated with breast cancer

Introduction Breast cancer is the most common type of cancer seen in women in western countries. Thus, diagnostic modalities sensitive to early-stage breast cancer are needed. Antibody-based array platforms of a data-driven type, which are expected to facilitate more rapid and sensitive detection of novel biomarkers, have emerged as a direct, rapid means for profiling cancer-specific signatures using small samples. In line with this concept, our group constructed an antibody bead array panel for 35 analytes that were selected during the discovery step. This study was aimed at testing the performance of this 35-plex array panel in profiling signatures specific for primary non-metastatic breast cancer and validating its diagnostic utility in this independent population. Methods Thirty-five analytes were selected from more than 50 markers through screening steps using a serum bank consisting of 4,500 samples from various types of cancer. An antibody-bead array of 35 markers was constructed using the Luminex (TM) bead array platform. A study population consisting of 98 breast cancer patients and 96 normal subjects was analysed using this panel. Multivariate classification algorithms were used to find discriminating biomarkers and validated with another independent population of 90 breast cancer and 79 healthy controls. Results Serum concentrations of epidermal growth factor, soluble CD40-ligand and proapolipoprotein A1 were increased in breast cancer patients. High-molecular-weight-kininogen, apolipoprotein A1, soluble vascular cell adhesion molecule-1, plasminogen activator inhibitor-1, vitamin-D binding protein and vitronectin were decreased in the cancer group. Multivariate classification algorithms distinguished breast cancer patients from the normal population with high accuracy (91.8% with random forest, 91.5% with support vector machine, 87.6% with linear discriminant analysis). Combinatorial markers also detected breast cancer at an early stage with greater sensitivity. Conclusions The current study demonstrated the usefulness of the antibody-bead array approach in finding signatures specific for primary non-metastatic breast cancer and illustrated the potential for early, high sensitivity detection of breast cancer. Further validation is required before array-based technology is used routinely for early detection of breast cancer.Kenny HA, 2008, J CLIN INVEST, V118, P1367, DOI 10.1172/JCI33775Shah FD, 2008, INTEGR CANCER THER, V7, P33, DOI 10.1177/1534735407313883Carlsson A, 2008, EUR J CANCER, V44, P472, DOI 10.1016/j.ejca.2007.11.025Nolen BM, 2008, BREAST CANCER RES, V10, DOI 10.1186/bcr2096Brogren H, 2008, THROMB RES, V122, P271, DOI 10.1016/j.thromres.2008.04.008Varki A, 2007, BLOOD, V110, P1723, DOI 10.1182/blood-2006-10-053736Madsen CD, 2007, J CELL BIOL, V177, P927, DOI 10.1083/jcb.200612058Levenson VV, 2007, BBA-GEN SUBJECTS, V1770, P847, DOI 10.1016/j.bbagen.2007.01.017VAZQUEZMARTIN A, 2007, EUR J CANCER, V43, P1117GARCIA M, 2007, GLOBAL CANC FACTS FIMoore LE, 2006, CANCER EPIDEM BIOMAR, V15, P1641, DOI 10.1158/1055-9965.EPI-05-0980Borrebaeck CAK, 2006, EXPERT OPIN BIOL TH, V6, P833, DOI 10.1517/14712598.6.8.833Zannis VI, 2006, J MOL MED-JMM, V84, P276, DOI 10.1007/s00109-005-0030-4Jemal A, 2006, CA-CANCER J CLIN, V56, P106Silva HC, 2006, NEOPLASMA, V53, P538Chahed K, 2005, INT J ONCOL, V27, P1425Jain KK, 2005, EXPERT OPIN PHARMACO, V6, P1463, DOI 10.1517/14656566.6.9.1463Abe O, 2005, LANCET, V365, P1687Paradis V, 2005, HEPATOLOGY, V41, P40, DOI 10.1002/hep.20505Molina R, 2005, TUMOR BIOL, V26, P281, DOI 10.1159/000089260Furberg AS, 2005, CANCER EPIDEM BIOMAR, V14, P33Benoy IH, 2004, CLIN CANCER RES, V10, P7157Song JS, 2004, BLOOD, V104, P2065, DOI 10.1182/blood-2004-02-0449Schairer C, 2004, J NATL CANCER I, V96, P1311, DOI 10.1093/jnci/djh253Hellman K, 2004, BRIT J CANCER, V91, P319, DOI 10.1038/sj.bjc.6601944Roselli M, 2004, CLIN CANCER RES, V10, P610Zhou AW, 2003, NAT STRUCT BIOL, V10, P541, DOI 10.1038/nsb943Hapke S, 2003, BIOL CHEM, V384, P1073Miller JC, 2003, PROTEOMICS, V3, P56Amirkhosravi A, 2002, BLOOD COAGUL FIBRIN, V13, P505Bonello N, 2002, HUM REPROD, V17, P2272Li JN, 2002, CLIN CHEM, V48, P1296Louhimo J, 2002, ANTICANCER RES, V22, P1759Knezevic V, 2001, PROTEOMICS, V1, P1271Di Micco P, 2001, DIGEST LIVER DIS, V33, P546Ferrigno D, 2001, EUR RESPIR J, V17, P667Webb DJ, 2001, J CELL BIOL, V152, P741Gion M, 2001, EUR J CANCER, V37, P355Schonbeck U, 2001, CELL MOL LIFE SCI, V58, P4Blackwell K, 2000, J CLIN ONCOL, V18, P600Carriero MV, 1999, CANCER RES, V59, P5307Antman K, 1999, JAMA-J AM MED ASSOC, V281, P1470Loskutoff DJ, 1999, APMIS, V107, P54Molina R, 1998, BREAST CANCER RES TR, V51, P109Bajou K, 1998, NAT MED, V4, P923Chan DW, 1997, J CLIN ONCOL, V15, P2322Chu KC, 1996, J NATL CANCER I, V88, P1571vanDalen A, 1996, ANTICANCER RES, V16, P2345Yamamoto N, 1996, CANCER RES, V56, P2827KOCH AE, 1995, NATURE, V376, P517HADDAD JG, 1995, J STEROID BIOCHEM, V53, P579FOEKENS JA, 1994, J CLIN ONCOL, V12, P1648GEARING AJH, 1993, IMMUNOL TODAY, V14, P506HUTCHENS TW, 1993, RAPID COMMUN MASS SP, V7, P576DECLERCK PJ, 1992, J BIOL CHEM, V267, P11693GABRIJELCIC D, 1992, AGENTS ACTIONS S, V38, P350BIEGLMAYER C, 1991, TUMOR BIOL, V12, P138DNISTRIAN AM, 1991, TUMOR BIOL, V12, P82VANDALEN A, 1990, TUMOR BIOL, V11, P189KARAS M, 1988, ANAL CHEM, V60, P2299, DOI 10.1021/ac00171a028LERNER WA, 1983, INT J CANCER, V31, P463WESTGARD JO, 1981, CLIN CHEM, V27, P493TROUSSEAU A, 1865, CLIN MED HOTEL DIEU, V3, P654*R PROJ, R PROJ STAT COMP1