189 research outputs found
Correction: LGR5 regulates pro-survival MEK/ERK and proliferative Wnt/β-catenin signalling in neuroblastoma
No full textA Correction on: LGR5 regulates pro-survival MEK/ERK and proliferative Wnt/ß-catenin signalling in neuroblastoma Gabriella Cunha Vieira, S. Chockalingam, Zsombor Melegh, Alexander Greenhough, Sally Malik, Marianna Szemes, Ji Hyun Park, Abderrahmane Kaidi, Li Zhou, Daniel Catchpoole, Rhys Morgan, David O. Bates, Peter J. Gabb and Karim Malik Original article: Oncotarget. 2015; 6:40053-67. DOI: 10.18632/oncotarget.5548. The originally Figure 5 contains duplicate total-ERK panels. The proper Figure 5 is attached. The authors sincerely apologize for this error
Visual communication and trust in the health domain
No full textVisual communication and user trust are always challenges in the health domain where conservativeness, precision, and domain knowledge can outweigh the validity of the outcomes in the analytical methods and processes. It is crucial to provide better awareness and understanding to domain experts, model developers, and even patients who might be conscious of their condition and how a treatment or a diagnosis is decided for them. This chapter contributes a discussion on trust and its issues in health data-driven science and how trust should be associated with analytical and computational processes, which are enhanced by visualisation and interaction. We also provide brief guidance on the models and methods for improving interpretability and trust in the health domain
Visualisation for explainable machine learning in biomedical data analysis
No full textThis chapter covers innovations in biomedical data mining and interpretations, especially using visualisations in interpretable machine learning for biomedical data analysis. Visualisations are important in presenting artificial intelligence models and validating the machine learning results. There are more new and complex machine learning methods that have been created to assist decision-making in recent years in the medical domain. Most of them are treated as “black boxes”, as the training and prediction processes are hidden in complicated mathematical theories. Visualisation is a way to reveal the process and help a human understand the cause of a decision. Knowing the “why” for the prediction results and “how” the model works can improve users’ trust in artificial intelligence results. The chapter introduces different visualisations used in interpreting supervised and unsupervised machine learning models for biomedical data. We also provide discussions and future work on using visualisations in interpreting data mining results in the medical domain
Correction: LGR5 regulates pro-survival MEK/ERK and proliferative Wnt/ß-catenin signalling in neuroblastoma
No full textA Correction on: LGR5 regulates pro-survival MEK/ERK and proliferative Wnt/ß-catenin signalling in neuroblastoma Gabriella Cunha Vieira, S. Chockalingam, Zsombor Melegh, Alexander Greenhough, Sally Malik, Marianna Szemes, Ji Hyun Park, Abderrahmane Kaidi, Li Zhou, Daniel Catchpoole, Rhys Morgan, David O. Bates, Peter J. Gabb and Karim Malik Original article: Oncotarget. 2015; 6:40053-67. DOI: 10.18632/oncotarget.5548. The originally Figure 5 contains duplicate total-ERK panels. The proper Figure 5 is attached. The authors sincerely apologize for this error
Feature-ranking methods for RNA sequencing data
No full textRibonucleic acid sequencing (RNA-Seq) is a technique that is used a lot to study and evaluate gene expression patterns and find genes that are expressed differently in different biological situations. Numerous computational algorithms for analysing RNA-seq data have been developed that categorise them per features in many pre-defined classifications. Feature-ranking techniques have emerged as a powerful tool for analysing RNA sequencing data, enabling the identification of the most relevant genes that are associated with specific phenotypes or biological processes. In this chapter, we give an overview of different ways to rank features and how they can be used to analyse data from RNA sequencing. We also compare how well different methods work using benchmark datasets and talk about the difficulties of combining multiple data sources and figuring out what the results mean. Last, we talk about possible future directions for the development and use of feature-ranking techniques. These include the use of deep learning techniques, the use of single-cell sequencing data, and the development of methods for figuring out how genes interact with each other. We evaluate selected features by optimising parameters and identifying a higher-performing classifier. The accuracy, recall, false-positive rate (FPR), and precision are used to analyse the comparison. The chapter aims to provide a comprehensive guide for researchers who want to use feature-ranking techniques to analyse RNA sequencing data and gain insights into the underlying biology
Frequent epigenetic inactivation of KIBRA, an upstream member of the Salvador/Warts/Hippo (SWH) tumor suppressor network, is associated with specific genetic event in B-cell acute lymphocytic leukemia
No full textThe WW-domain containing protein KIBRA has recently been identified as a new member of the Salvador/Warts/Hippo (SWH) pathway in Drosophila and is shown to act as a tumor suppressor gene in Drosophila. This pathway is conserved in humans and members of the pathway have been shown to act as tumor suppressor genes in mammalian systems. We determined the methylation status of the 5' CpG island associated with the KIBRA gene in human cancers. In a large panel of cancer cell lines representing common epithelial cancers KIBRA was unmethylated. But in pediatric acute lymphocytic leukemia (ALL) cell lines KIBRA showed frequent hypermethylation and silencing of gene expression, which could be reversed by treatment with 5-aza-2'-deoxycytidine. In ALL patient samples KIBRA was methylated in 70% B-ALL but was methylated in <20% T-ALL leukemia (p = 0.0019). In B-ALL KIBRA methylation was associated with ETV6/RUNX1 {t(12;21) (p13;q22)} chromosomal translocation (p = 0.0082) phenotype, suggesting that KIBRA may play an important role in t(12;21) leukemogenesis. In ALL paired samples at diagnosis and remission KIBRA methylation was seen in diagnostic but not in any of the remission samples accompanied by loss of KIBRA expression in disease state compared to patients in remission. Hence KIBRA methylation occurs frequently in B-cell acute lymphocytic leukemia but not in epithelial cancers and is linked to specific genetic event in B-ALL
Sequestration of AS-DACA into Acidic Compartments of the Membrane Trafficking System as a Mechanism of Drug Resistance in Rhabdomyosarcoma
No full textThe accumulation of weakly basic drugs into acidic organelles has recently been described as a contributor to resistance in childhood cancer rhabdomyosarcoma (RMS) cell lines with differential sensitivity to a novel topoisomerase II inhibitor, AS-DACA. The current study aims to explore the contribution of the endocytic pathway to AS-DACA sequestration in RMS cell lines. A 24-fold differential in AS-DACA cytotoxicity was detected between the RMS lines RD and Rh30. The effect of inhibitors of the endocytic pathway on AS-DACA sensitivity in RMS cell lines, coupled with the variations of endosomal marker expression, indicated the late endosomal/lysosomal compartment was implicated by confounding lines of evidence. Higher expression levels of Lysosomal-Associated Membrane Protein-1 (LAMP1) in the resistant RMS cell line, RD, provided correlations between the increased amount and activity of these compartments to AS-DACA resistance. The late endosomal inhibitor 3-methyladenine increased AS-DACA sensitivity solely in RD leading to the reduction of AS-DACA in membrane trafficking organelles. Acidification inhibitors did not produce an increase in AS-DACA sensitivity nor reduce its sequestration, indicating that the pH partitioning of weakly basic drugs into acidic compartments does not likely contribute to the AS-DACA sequestering resistance mechanism evident in RMS cells
Methotrexate-related central neurotoxicity: clinical characteristics, risk factors and genome-wide association study in children treated for acute lymphoblastic leukemia
No full textSymptomatic methotrexate-related central neurotoxicity (MTX neurotoxicity) is a severe toxicity experienced during acute lymphoblastic leukemia (ALL) therapy with potential long-term neurologic complications. Risk factors and long-term outcomes require further study. We conducted a systematic, retrospective review of 1,251 consecutive Australian children enrolled on Berlin-Frankfurt-Münster or Children's Oncology Group-based protocols between 1998-2013. Clinical risk predictors for MTX neurotoxicity were analyzed using regression. A genome-wide association study (GWAS) was performed on 48 cases and 537 controls. The incidence of MTX neurotoxicity was 7.6% (n=95 of 1,251), at a median of 4 months from ALL diagnosis and 8 days after intravenous or intrathecal MTX. Grade 3 elevation of serum aspartate aminotransferase (P=0.005, odds ratio 2.31 [range, 1.28–4.16]) in induction/consolidation was associated with MTX neurotoxicity, after accounting for the only established risk factor, age ≥10 years. Cumulative incidence of CNS relapse was increased in children where intrathecal MTX was omitted following symptomatic MTX neurotoxicity (n=48) compared to where intrathecal MTX was continued throughout therapy (n=1,174) (P=0.047). Five-year central nervous system relapse-free survival was 89.2±4.6% when intrathecal MTX was ceased compared to 95.4±0.6% when intrathecal MTX was continued. Recurrence of MTX neurotoxicity was low (12.9%) for patients whose intrathecal MTX was continued after their first episode. The GWAS identified single-nucletide polymorphism associated with MTX neurotoxicity near genes regulating neuronal growth, neuronal differentiation and cytoskeletal organization (P<1x10-6). In conclusion, increased serum aspartate aminotransferase and age ≥10 years at diagnosis were independent risk factors for MTX neurotoxicity. Our data do not support cessation of intrathecal MTX after a first MTX neurotoxicity event.Marion K. Mateos, Glenn M Marshall, Pasquale M. Barbaro, Michael C.J. Quinn, Carly George, Chelsea Mayoh, Rosemary Sutton, Tamas Revesz, Jodie E Giles, Draga Barbaric, Frank Alvaro, Françoise Mechinaud, Daniel Catchpoole, John A. Lawson, Georgia Chenevix-Trench, Stuart MacGregor, Rishi S.Kotecha, Luciano Dalla-Pozza, and Toby N. Traha
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