Practical guidance for applying the ADNEX model from the IOTA group to discriminate between different subtypes of adnexal tumors.

All gynecologists are faced with ovarian tumors on a regular basis, and the accurate preoperative diagnosis of these masses is important because appropriate management depends on the type of tumor. Recently, the International Ovarian Tumor Analysis (IOTA) consortium published the Assessment of Different NEoplasias in the adneXa (ADNEX) model, the first risk model that differentiates between benign and four types of malignant ovarian tumors: borderline, stage I cancer, stage II-IV cancer, and secondary metastatic cancer. This approach is novel compared to existing tools that only differentiate between benign and malignant tumors, and therefore questions may arise on how ADNEX can be used in clinical practice. In the present paper, we first provide an in-depth discussion about the predictors used in ADNEX and the ability for risk prediction with different tumor histologies. Furthermore, we formulate suggestions about the selection and interpretation of risk cut-offs for patient stratification and choice of appropriate clinical management. This is illustrated with a few example patients. We cannot propose a generally applicable algorithm with fixed cut-offs, because (as with any risk model) this depends on the specific clinical setting in which the model will be used. Nevertheless, this paper provides a guidance on how the ADNEX model may be adopted into clinical practice

Cancer-related electronic support groups as navigation-aids: Overcoming geographic barriers

Cancer-related electronic support groups (ESGs) may be regarded as a complement to face-to-face groups when the latter are available, and as an alternative when they are not. Advantages over face-to-face groups include an absence of barriers imposed by geographic location, opportunities for anonymity that permit sensitive issues to be discussed, and opportunities to find peers online. ESGs can be especially valuable as navigation aids for those trying to find a way through the healthcare system and as a guide to the cancer journey. Outcome indicators that could be used to evaluate the quality of ESGs as navigation aids need to be developed and tested. Conceptual models for the navigator role, such as the Facilitating Navigator Model, are appropriate for ESGs designed specifically for research purposes. A Shared or Tacit Model may be more appropriate for unmoderated ESGs. Both conceptual models raise issues in Internet research ethics that need to be addres

Drosophila as a model system to study nonautonomous mechanisms affecting tumour growth and cell death

The study of cancer has represented a central focus in medical research for over a century. The great complexity and constant evolution of the pathology require the use of multiple research model systems and interdisciplinary approaches. This is necessary in order to achieve a comprehensive understanding into the mechanisms driving disease initiation and progression, to aid the development of appropriate therapies. In recent decades, the fruit fly Drosophila melanogaster and its associated powerful genetic tools have become a very attractive model system to study tumour-intrinsic and non-tumour-derived processes that mediate tumour development in vivo. In this review, we will summarize recent work on Drosophila as a model system to study cancer biology. We will focus on the interactions between tumours and their microenvironment, including extrinsic mechanisms affecting tumour growth and how tumours impact systemic host physiology

Modeling association between DNA copy number and gene expression with constrained piecewise linear regression splines

DNA copy number and mRNA expression are widely used data types in cancer studies, which combined provide more insight than separately. Whereas in existing literature the form of the relationship between these two types of markers is fixed a priori, in this paper we model their association. We employ piecewise linear regression splines (PLRS), which combine good interpretation with sufficient flexibility to identify any plausible type of relationship. The specification of the model leads to estimation and model selection in a constrained, nonstandard setting. We provide methodology for testing the effect of DNA on mRNA and choosing the appropriate model. Furthermore, we present a novel approach to obtain reliable confidence bands for constrained PLRS, which incorporates model uncertainty. The procedures are applied to colorectal and breast cancer data. Common assumptions are found to be potentially misleading for biologically relevant genes. More flexible models may bring more insight in the interaction between the two markers.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS605 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Mapping Functions in Health-Related Quality of Life: Mapping From Two Cancer-Specific Health-Related Quality-of-Life Instruments to EQ-5D-3L.

BACKGROUND: Clinical trials in cancer frequently include cancer-specific measures of health but not preference-based measures such as the EQ-5D that are suitable for economic evaluation. Mapping functions have been developed to predict EQ-5D values from these measures, but there is considerable uncertainty about the most appropriate model to use, and many existing models are poor at predicting EQ-5D values. This study aims to investigate a range of potential models to develop mapping functions from 2 widely used cancer-specific measures (FACT-G and EORTC-QLQ-C30) and to identify the best model. METHODS: Mapping models are fitted to predict EQ-5D-3L values using ordinary least squares (OLS), tobit, 2-part models, splining, and to EQ-5D item-level responses using response mapping from the FACT-G and QLQ-C30. A variety of model specifications are estimated. Model performance and predictive ability are compared. Analysis is based on 530 patients with various cancers for the FACT-G and 771 patients with multiple myeloma, breast cancer, and lung cancer for the QLQ-C30. RESULTS: For FACT-G, OLS models most accurately predict mean EQ-5D values with the best predicting model using FACT-G items with similar results using tobit. Response mapping has low predictive ability. In contrast, for the QLQ-C30, response mapping has the most accurate predictions using QLQ-C30 dimensions. The QLQ-C30 has better predicted EQ-5D values across the range of possible values; however, few respondents in the FACT-G data set have low EQ-5D values, which reduces the accuracy at the severe end. CONCLUSIONS: OLS and tobit mapping functions perform well for both instruments. Response mapping gives the best model predictions for QLQ-C30. The generalizability of the FACT-G mapping function is limited to populations in moderate to good health

Structure and function prediction of human homologue hABH5 of _E. coli_ ALKB5 using in silico approach

Newly discovered human homologues of ALKB protein have shown the activity of DNA damaging drugs, used for cancer therapy. Little is known about the structure and function of hABH5, one of the members of this superfamily. Therefore, in the present study we intend to predict its structure and function using various bioinformatics tools. Modeling was done with modeler 9v7 to predict the 3D structure of the hABH5 protein. 3-D model of hABH5, ALKBH5.B99990005.pdb was predicted and evaluated. Validation results showed 96.8% residues in favor and an additional allowed region of the Ramachandran plot. Ligand binding residues prediction showed four ligand clusters, having 25 ligands in cluster 1. Importantly, conserved pattern of Pro158-X-Asp160-Xn-His266 in the functional domain was detected. DNA and RNA binding sites were also predicted in the model. The predicted and validated model of human homologue hABH5 resulting from this study may unveil the mechanism of DNA damage repair in humans and accelerate research on designing appropriate inhibitors, aiding in chemotherapy and cancer related diseases

Structure and function prediction of human homologue hABH5 of _E. coli_ ALKB5 using in silico approach

Newly discovered human homologues of ALKB protein have shown the activity of DNA damaging drugs, used for cancer therapy. Little is known about the structure and function of hABH5, one of the members of this superfamily. Therefore, in the present study we intend to predict its structure and function using various bioinformatics tools. Modeling was done with modeler 9v7 to predict the 3D structure of the hABH5 protein. 3-D model of hABH5, ALKBH5.B99990005.pdb was predicted and evaluated. Validation results showed 96.8% residues in favor and an additional allowed region of the Ramachandran plot. Ligand binding residues prediction showed four ligand clusters, having 25 ligands in cluster 1. Importantly, conserved pattern of Pro158-X-Asp160-Xn-His266 in the functional domain was detected. DNA and RNA binding sites were also predicted in the model. The predicted and validated model of human homologue hABH5 resulting from this study may unveil the mechanism of DNA damage repair in humans and accelerate research on designing appropriate inhibitors, aiding in chemotherapy and cancer related diseases

Molecular modelling and Function Prediction of hABH7, human homologue of _E. coli_ ALKB7

Human homologues of ALKB protein have shown the prime role in DNA damaging drugs, used for cancer therapy. Little is known about structure and function of hABH7, one of the members of this superfamily. Therefore, in the present study we intend to predict its structure and function using various bioinformatics tools. Modeling was done with modeller 9v7 to predict the 3D structure of the hABH7 protein. The tertiary structure model of hABH7, ALKBH7.B99990002.pdb was predicted and evaluated. Validation results showed 97.8% residues in favored and additional allowed regions of Ramachandran plots. Ligand binding residues prediction showed four ligand clusters, having 25 ligands in cluster 1. Importantly, presence of a Phe120-Gly121-Gly122 conserved pattern in the functional domain was detected. In the predicted structural model of hABH7, amino acid residues, Arginine at 57, 58, 59 and 60 along with tyrosine at 61 were predicted in RNA binding sites of the model. The predicted and validated model of human homologue hABH7 resulting from this study may unveil the mechanism of DNA damage repair in humans and accelerate the research on designing appropriate inhibitors aiding in chemotherapy and cancer related diseases