A critical evaluation of network and pathway based classifiers for outcome prediction in breast cancer

Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single gene classifiers, the gain in performance is difficult to assess. This stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance. Here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches. Contrary to previous claims, we find that composite feature classifiers do not outperform simple single gene classifiers. We investigate the effect of (1) the number of selected features; (2) the specific gene set from which features are selected; (3) the size of the training set and (4) the heterogeneity of the data set on the performance of composite feature and single gene classifiers. Strikingly, we find that randomization of secondary data sources, which destroys all biological information in these sources, does not result in a deterioration in performance of composite feature classifiers. Finally, we show that when a proper correction for gene set size is performed, the stability of single gene sets is similar to the stability of composite feature sets. Based on these results there is currently no reason to prefer prognostic classifiers based on composite features over single gene classifiers for predicting outcome in breast cancer

Knowledge driven decomposition of tumor expression profiles

<p>Abstract</p> <p>Background</p> <p>Tumors have been hypothesized to be the result of a mixture of oncogenic events, some of which will be reflected in the gene expression of the tumor. Based on this hypothesis a variety of data-driven methods have been employed to decompose tumor expression profiles into component profiles, hypothetically linked to these events. Interpretation of the resulting data-driven components is often done by post-hoc comparison to, for instance, functional groupings of genes into gene sets. None of the data-driven methods allow the incorporation of that type of knowledge directly into the decomposition.</p> <p>Results</p> <p>We present a linear model which uses knowledge driven, pre-defined components to perform the decomposition. We solve this decomposition model in a constrained linear least squares fashion. From a variety of options, a lasso-based solution to the model performs best in linking single gene perturbation data to mouse data. Moreover, we show the decomposition of expression profiles from human breast cancer samples into single gene perturbation profiles and gene sets that are linked to the hallmarks of cancer. For these breast cancer samples we were able to discern several links between clinical parameters, and the decomposition weights, providing new insights into the biology of these tumors. Lastly, we show that the order in which the Lasso regularization shrinks the weights, unveils consensus patterns within clinical subgroups of the breast cancer samples.</p> <p>Conclusion</p> <p>The proposed lasso-based constrained least squares decomposition provides a stable and relevant relation between samples and knowledge-based components, and is thus a viable alternative to data-driven methods. In addition, the consensus order of component importance within clinical subgroups provides a better molecular characterization of the subtypes.</p

Specific genomic aberrations in primary colorectal cancer are associated with liver metastases

Background: Accurate staging of colorectal cancer (CRC) with clinicopathological parameters is important for predicting prognosis and guiding treatment but provides no information about organ site of metastases. Patterns of genomic aberrations in primary colorectal tumors may reveal a chromosomal signature for organ specific metastases. Methods: Array Comparative Genomic Hybridization (aCGH) was employed to asses DNA copy number changes in primary colorectal tumors of three distinctive patient groups. This included formalin-fixed, paraffin-embedded tissue of patients who developed liver metastases (LM; n = 36), metastases (PM; n = 37) and a group that remained metastases-free (M0; n = 25). A novel statistical method for identifying recurrent copy number changes, KC-SMART, was used to find specific locations of genomic aberrations specific for various groups. We created a classifier for organ specific metastases based on the aCGH data using Prediction Analysis for Microarrays (PAM). Results: Specifically in the tumors of primary CRC patients who subsequently developed liver metastasis, KC-SMART analysis identified genomic aberrations on chromosome 20q. LM-PAM, a shrunken centroids classifier for liver metastases occurrence, was able to distinguish the LM group from the other groups (M0&PM) with 80% accuracy (78% sensitivity and 86% specificity). The classification is predominantly based on chromosome 20q aberrations. Conclusion: Liver specific CRC metastases may be predicted with a high accuracy based on specific genomic aberrations in the primary CRC tumor. The ability to predict the site of metastases is important for improvement of personalized patient management.MediamaticsElectrical Engineering, Mathematics and Computer Scienc

Assessments Related to the Physical, Affective and Cognitive Domains of Physical Literacy Amongst Children Aged 7–11.9 Years: A Systematic Review

Background Over the past decade, there has been increased interest amongst researchers, practitioners and policymakers in physical literacy for children and young people and the assessment of the concept within physical education (PE). This systematic review aimed to identify tools to assess physical literacy and its physical, cognitive and affective domains within children aged 7–11.9 years, and to examine the measurement properties, feasibility and elements of physical literacy assessed within each tool. Methods Six databases (EBSCO host platform, MEDLINE, PsycINFO, Scopus, Education Research Complete, SPORTDiscus) were searched up to 10th September 2020. Studies were included if they sampled children aged between 7 and 11.9 years, employed field-based assessments of physical literacy and/or related affective, physical or cognitive domains, reported measurement properties (quantitative) or theoretical development (qualitative), and were published in English in peer-reviewed journals. The methodological quality and measurement properties of studies and assessment tools were appraised using the COnsensus-based Standards for the selection of health Measurement INstruments risk of bias checklist. The feasibility of each assessment was considered using a utility matrix and elements of physical literacy element were recorded using a descriptive checklist. Results The search strategy resulted in a total of 11467 initial results. After full text screening, 11 studies (3 assessments) related to explicit physical literacy assessments. Forty-four studies (32 assessments) were relevant to the affective domain, 31 studies (15 assessments) were relevant to the physical domain and 2 studies (2 assessments) were included within the cognitive domain. Methodological quality and reporting of measurement properties within the included studies were mixed. The Canadian Assessment of Physical Literacy-2 and the Passport For Life had evidence of acceptable measurement properties from studies of very good methodological quality and assessed a wide range of physical literacy elements. Feasibility results indicated that many tools would be suitable for a primary PE setting, though some require a level of expertise to administer and score that would require training. Conclusions This review has identified a number of existing assessments that could be useful in a physical literacy assessment approach within PE and provides further information to empower researchers and practitioners to make informed decisions when selecting the most appropriate assessment for their needs, purpose and context. The review indicates that researchers and tool developers should aim to improve the methodological quality and reporting of measurement properties of assessments to better inform the field. Trial registration PROSPERO: CRD4201706221

An expression profile for diagnosis of lymph node metastases from primary head and neck squamous cell carcinomas.

Item does not contain fulltextMetastasis is the process by which cancers spread to distinct sites in the body. It is the principal cause of death in individuals suffering from cancer. For some types of cancer, early detection of metastasis at lymph nodes close to the site of the primary tumor is pivotal for appropriate treatment. Because it can be difficult to detect lymph node metastases reliably, many individuals currently receive inappropriate treatment. We show here that DNA microarray gene-expression profiling can detect lymph node metastases for primary head and neck squamous cell carcinomas that arise in the oral cavity and oropharynx. The predictor, established with an 82-tumor training set, outperforms current clinical diagnosis when independently validated. The 102 predictor genes offer unique insights into the processes underlying metastasis. The results show that the metastatic state can be deciphered from the primary tumor gene-expression pattern and that treatment can be substantially improved