Promises and limitations of biomarkers

Contact lenses as novel tear fluid sampling vehicles for total RNA isolation, precipitation, and amplification

The tear fluid is a readily accessible, potential source for biomarkers of disease and could be used to monitor the ocular response to contact lens (CL) wear or ophthalmic pathologies treated by therapeutic CLs. However, the tear fluid remains largely unexplored as a biomarker source for RNA-based molecular analyses. Using a rabbit model, this study sought to determine whether RNA could be collected from commercial CLs and whether the duration of CL wear would impact RNA recovery. The results were referenced to standardized strips of filtered paper (e.g., Shirmer Strips) placed in the inferior fornix. By performing total RNA isolation, precipitation, and amplification with commercial kits and RT-PCR methods, CLs were found to have no significant differences in RNA concentration and purity compared to Schirmer Strips. The study also identified genes that could be used to normalize RNA levels between tear samples. Of the potential control genes or housekeeping genes, GAPDH was the most stable. This study, which to our knowledge has never been done before, provides a methodology for the detection of RNA and gene expression changes from tear fluid that could be used to monitor or study eye diseases.

Biosensing chips for cancer diagnosis and treatment: a new wave towards clinical innovation

Recent technological advances in nanoscience and material designing have led to the development of point-of-care devices for biomolecule sensing and cancer diagnosis. In situ and portable sensing devices for bedside, diagnosis can effectively improve the patient’s clinical outcomes and reduce the mortality rate. Detection of exosomal RNAs by immuno-biochip with increased sensitivity and specificity to diagnose cancer has raised the understanding of the tumor microenvironment and many other technology-based biosensing devices hold great promise for clinical innovations to conquer the unbeatable fort of cancer metastasis. Electrochemical biosensors are the most sensitive category of biomolecule detection sensors with significantly low concentrations down to the atomic level. In this sense, this review addresses the recent advances in cancer detection and diagnosis by developing significant biological sensing devices that are believed to have better sensing potential than existing facilities.

Investigating reproducibility and tracking provenance - A genomic workflow case study

Background

Computational bioinformatics workflows are extensively used to analyse genomics data, with different approaches available to support implementation and execution of these workflows. Reproducibility is one of the core principles for any scientific workflow and remains a challenge, which is not fully addressed. This is due to incomplete understanding of reproducibility requirements and assumptions of workflow definition approaches. Provenance information should be tracked and used to capture all these requirements supporting reusability of existing workflows.

Results

We have implemented a complex but widely deployed bioinformatics workflow using three representative approaches to workflow definition and execution. Through implementation, we identified assumptions implicit in these approaches that ultimately produce insufficient documentation of workflow requirements resulting in failed execution of the workflow. This study proposes a set of recommendations that aims to mitigate these assumptions and guides the scientific community to accomplish reproducible science, hence addressing reproducibility crisis.

Conclusions

Reproducing, adapting or even repeating a bioinformatics workflow in any environment requires substantial technical knowledge of the workflow execution environment, resolving analysis assumptions and rigorous compliance with reproducibility requirements. Towards these goals, we propose conclusive recommendations that along with an explicit declaration of workflow specification would result in enhanced reproducibility of computational genomic analyses.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1747-0) contains supplementary material, which is available to authorized users.

Meta-analysis approach as a gene selection method in class prediction: does it improve model performance? A case study in acute myeloid leukemia

BACKGROUND

Aggregating gene expression data across experiments via meta-analysis is expected to increase the precision of the effect estimates and to increase the statistical power to detect a certain fold change. This study evaluates the potential benefit of using a meta-analysis approach as a gene selection method prior to predictive modeling in gene expression data.

RESULTS

Six raw datasets from different gene expression experiments in acute myeloid leukemia (AML) and 11 different classification methods were used to build classification models to classify samples as either AML or healthy control. First, the classification models were trained on gene expression data from single experiments using conventional supervised variable selection and externally validated with the other five gene expression datasets (referred to as the individual-classification approach). Next, gene selection was performed through meta-analysis on four datasets, and predictive models were trained with the selected genes on the fifth dataset and validated on the sixth dataset. For some datasets, gene selection through meta-analysis helped classification models to achieve higher performance as compared to predictive modeling based on a single dataset; but for others, there was no major improvement. Synthetic datasets were generated from nine simulation scenarios. The effect of sample size, fold change and pairwise correlation between differentially expressed (DE) genes on the difference between MA- and individual-classification model was evaluated. The fold change and pairwise correlation significantly contributed to the difference in performance between the two methods. The gene selection via meta-analysis approach was more effective when it was conducted using a set of data with low fold change and high pairwise correlation on the DE genes.

CONCLUSION

Gene selection through meta-analysis on previously published studies potentially improves the performance of a predictive model on a given gene expression data.

Quality management of biorepositories

Biomedical investigators require high quality human tissue to support their research; thus, an important aspect of the provision of tissues by biorepositories is the assurance of high quality and consistency of processing specimens. This is best accomplished by a quality management system (QMS). This article describes the basis of a QMS program designed to aid biorepositories that want to improve their operations. In 1983, the UAB Tissue Collection and Biobanking Facility (TCBF) introduced a QMS program focused on providing solid tissues to support a wide range of research; this QMS included a quality control examination of the specific specimens provided for research. Similarly, the Division of Laboratory Sciences at the Centers for Disease Control and Prevention (CDC) introduced a QMS program for their laboratory analyses, focused primarily on bodily fluids. The authors of this article bring together the experience of the QMS programs at these two sites to facilitate the development or improvement of quality management systems of a wide range of biorepositories.

Factors Affecting the Use of Human Tissues in Biomedical Research: Implications in the Design and Operation of a Biorepository

The availability of high-quality human tissues is necessary to advance medical research. Although there are inherent and induced limitations on the use of human tissues in research, biorepositories play critical roles in minimizing the effects of such limitations. Specifically, the optimal utilization of tissues in research requires tissues to be diagnosed accurately, and the actual specimens provided to investigators must be carefully described (i.e., there must be quality control of each aliquot of the tissue provided for research, including a description of any damage to tissues). Tissues also should be collected, processed, stored, and distributed (i.e., handled) uniformly under a rigorous quality management system (QMS). Frequently, tissues are distributed to investigators by tissue banks which have collected, processed, and stored them by standard operating procedures (SOPs). Alternatively, tissues for research may be handled via SOPs that are modified to the specific requirements of investigators (i.e., using a prospective biorepository model). The primary goal of any type of biorepository should be to ensure its specimens are of high quality and are utilized appropriately in research; however, approaches may vary based on the tissues available and requested. For example, extraction of specific molecules (e.g., microRNA) to study molecular characteristics of a tissue may require less clinical annotation than tissues that are utilized to identify how the molecular expression might be used to clarify a clinical outcome of a disease or the response to a specific therapy. This review focuses on the limitations of the use of tissues in research and how the design and operations of a tissue biorepository can minimize some of these limitations.

Use of semantic workflows to enhance transparency and reproducibility in clinical omics

BACKGROUND

Recent highly publicized cases of premature patient assignment into clinical trials, resulting from non-reproducible omics analyses, have prompted many to call for a more thorough examination of translational omics and highlighted the critical need for transparency and reproducibility to ensure patient safety. The use of workflow platforms such as Galaxy and Taverna have greatly enhanced the use, transparency and reproducibility of omics analysis pipelines in the research domain and would be an invaluable tool in a clinical setting. However, the use of these workflow platforms requires deep domain expertise that, particularly within the multi-disciplinary fields of translational and clinical omics, may not always be present in a clinical setting. This lack of domain expertise may put patient safety at risk and make these workflow platforms difficult to operationalize in a clinical setting. In contrast, semantic workflows are a different class of workflow platform where resultant workflow runs are transparent, reproducible, and semantically validated. Through semantic enforcement of all datasets, analyses and user-defined rules/constraints, users are guided through each workflow run, enhancing analytical validity and patient safety.

METHODS

To evaluate the effectiveness of semantic workflows within translational and clinical omics, we have implemented a clinical omics pipeline for annotating DNA sequence variants identified through next generation sequencing using the Workflow Instance Generation and Specialization (WINGS) semantic workflow platform.

RESULTS

We found that the implementation and execution of our clinical omics pipeline in a semantic workflow helped us to meet the requirements for enhanced transparency, reproducibility and analytical validity recommended for clinical omics. We further found that many features of the WINGS platform were particularly primed to help support the critical needs of clinical omics analyses.

CONCLUSIONS

This is the first implementation and execution of a clinical omics pipeline using semantic workflows. Evaluation of this implementation provides guidance for their use in both translational and clinical settings.

Combining bioinformatics and MS-based proteomics: clinical implications

Clinical proteomics research aims at i) discovery of protein biomarkers for screening, diagnosis and prognosis of disease, ii) discovery of protein therapeutic targets for improvement of disease prevention, treatment and follow-up, and iii) development of mass spectrometry (MS)-based assays that could be implemented in clinical chemistry, microbiology or hematology laboratories. MS has been increasingly applied in clinical proteomics studies for the identification and quantification of proteins. Bioinformatics plays a key role in the exploitation of MS data in several aspects such as the generation and curation of protein sequence databases, the development of appropriate software for MS data treatment and integration with other omics data and the establishment of adequate standard files for data sharing. In this article, we discuss the main MS approaches and bioinformatics solutions that are currently applied to accomplish the objectives of clinical proteomic research.

Issues in collecting, processing and storing human tissues and associated information to support biomedical research

The availability of human tissues to support biomedical research is critical to advance translational research focused on identifying and characterizing approaches to individualized (personalized) medical care. Providing such tissues relies on three acceptable models - a tissue banking model, a prospective collection model and a combination of these two models. An unacceptable model is the "catch as catch can" model in which tissues are collected, processed and stored without goals or a plan or without standard operating procedures, i.e., portions of tissues are collected as available and processed and stored when time permits. In the tissue banking model, aliquots of tissues are collected according to SOPs. Usually specific sizes and types of tissues are collected and processed (e.g., 0.1 gm of breast cancer frozen in OCT). Using the banking model, tissues may be collected that may not be used and/or do not meet specific needs of investigators; however, at the time of an investigator request, tissues are readily available as is clinical information including clinical outcomes. In the model of prospective collection, tissues are collected based upon investigator requests including specific requirements of investigators. For example, the investigator may request that two 0.15 gm matching aliquots of breast cancer be minced while fresh, put in RPMI media with and without fetal calf serum, cooled to 4°C and shipped to the investigator on wet ice. Thus, the tissues collected prospectively meet investigator needs, all collected specimens are utilized and storage of specimens is minimized; however, investigators must wait until specimens are collected, and if needed, for clinical outcome. The operation of any tissue repository requires well trained and dedicated personnel. A quality assurance program is required which provides quality control information on the diagnosis of a specimen that is matched specifically to the specimen provided to an investigator instead of an overall diagnosis of the specimen via a surgical pathology report. This is necessary because a specific specimen may not match the diagnosis of the case due to many factors such as necrosis, unsuspected tumor invasion of apparently normal tissue, and areas of fibrosis which are mistaken grossly for tumor. Aliquots for quality control (QC) may or may not be collected at the time of collection and in some cases, QC may not occur until specimens are distributed to investigators. In establishing a tumor repository, multiple issues need to be considered. These include the available resources, long term support, space and equipment. The needs of the potential users need to be identified as to the types of tissues and services needed and the annotation expected. Other specific issues to be considered include collection of specimens potentially infected with blood borne pathogens (e.g., hepatitis B), charge back mechanisms, informatics needs and support, and investigator requirements (e.g., recognition of repository contributions in publications). In general, the repository should not perform the research of the investigators, but should provide the infrastructure necessary to support the research of the investigator. Thus, the goals of the repository must be established. Similarly, ethical and regulatory issues must be evaluated. In general, tissue repositories need ethical (e.g., IRB) and privacy (e.g., HIPAA) review. Also, safety issues need to be considered as well as how biohazards will be addressed by investigator-users. Considerations involving the transfer of specimens to other organization usually require a material transfer agreement (MTA). A MTA should address biohazards as well as indemnification. Thus, many issues must be considered and addressed in order to establish and operate successfully a biorepository.

A novel "reactomics" approach for cancer diagnostics

Non-invasive detection and monitoring of lethal diseases, such as cancer, are considered as effective factors in treatment and survival. We describe a new disease diagnostic approach, denoted “reactomics”, based upon reactions between blood sera and an array of vesicles comprising different lipids and polydiacetylene (PDA), a chromatic polymer. We show that reactions between sera and such a lipid/PDA vesicle array produce chromatic patterns which depend both upon the sera composition as well as the specific lipid constituents within the vesicles. The chromatic patterns were processed through machine-learning algorithms, and the bioinformatics analysis could distinguish both between cancer-bearing and healthy patients, respectively, as well between two types of cancers. Size-separation and enzymatic digestion experiments indicate that lipoproteins are the primary components in sera which react with the chromatic biomimetic vesicles. This colorimetric reactomics concept is highly generic, robust, and does not require a priori knowledge upon specific disease markers in sera. Therefore, it could be employed as complementary or alternative approach for disease diagnostics.

Biomarkers in the age of omics: time for a systems biology approach

Limitations to biomarker discovery are not only technical or bioinformatic but conceptual as well. In our attempt to offer a solution, we are highlighting three issues that we think are limiting progress in biomarkers discovery. First, the confusion stemming from the imposition of a pathology-type immunohistochemical marker (IHCM) concept on omics data without fully understanding the characteristics and limitations of IHCMs as applied in clinical pathology. Second, the lack of serious consideration for the scope of disease heterogeneity. Third, the refusal of the biomedical community to borrow from other biological disciplines their well established methods for dealing with heterogeneity. Therefore, real progress in biomarker discovery will be attained when we recognize that an omics biomarker cannot be assigned and validated without a priori data modeling and subtyping of the disease itself to reveal the extent of its heterogeneity, and its omics' clonal aberrations (drivers) underlying its subtypes and pathways' diversity. To further support our viewpoints, we are contributing a novel a systems biology method such as parsimony phylogenetic approach for disease modeling prior to biomarker circumscription. As an analytical approach that has been successfully used for a half of a century in other biological disciplines, parsimony phylogenetics simultaneously achieves several objectives: it provides disease modeling in a hierarchical phylogenetic classification, identifies biomarkers as the shared derived expressions or mutations--synapomorphies, constructs the omics profiles of specimens based on the most parsimonious arrangement of their heterogeneous data, and permits network profiling of affected signaling pathways as the biosignature of disease classes.

Omics-based molecular target and biomarker identification

Genomic, proteomic, and other omic-based approaches are now broadly used in biomedical research to facilitate the understanding of disease mechanisms and identification of molecular targets and biomarkers for therapeutic and diagnostic development. While the Omics technologies and bioinformatics tools for analyzing Omics data are rapidly advancing, the functional analysis and interpretation of the data remain challenging due to the inherent nature of the generally long workflows of Omics experiments. We adopt a strategy that emphasizes the use of curated knowledge resources coupled with expert-guided examination and interpretation of Omics data for the selection of potential molecular targets. We describe a downstream workflow and procedures for functional analysis that focus on biological pathways, from which molecular targets can be derived and proposed for experimental validation.

A need for basic research on fluid-based early detection biomarkers

Cancer continues to be a major cause of mortality despite decades of effort and expense. The problem reviewed here is that before many cancers are discovered they have already progressed to become drug resistant or metastatic. Biomarkers found in blood or other body fluids could supplement current clinical indicators to permit earlier detection and thereby reduce cancer mortality.

Prognostic immunophenotypic biomarker studies in diffuse large B cell lymphoma with special emphasis on rational determination of cut-off scores

A number of biomarkers, particularly proteins that contribute to prognosis in diffuse large B cell lymphoma (DLBCL), have been identified. However, translation into accepted standards to predict survival has not yet been accomplished, primarily due to contradictory reports in the literature resulting from, among other factors, arbitrary methodologies used to set cut-off values for determining positivity. Some of these problems might be resolved by application of rational statistical methods for determination of cut-off scores. Herein, we critically address issues on in situ phenotypic prognostic tumor-related biomarkers in DLBCL with a particular and practical emphasis on tools for cut-off level determination, especially receiver operating characteristic curve analysis. Moreover, we candidly illustrate the application of these tools for efficient disease-specific survival prognostication on a tissue microarray collective of 240 primary DLBCL using the common prognostic biomarkers Bcl-2, Bcl-6, CD10, FOXP1, MUM1, and Cyclin E. Comparison of the results relative to disease-specific survival unequivocally showed the superior discriminatory power of the cut-off levels calculated by receiver operating curves and the Youden's index, compared to arbitrary cut-off values from the literature, advocating fundamental application of rational methods for determination of clinically relevant prognostic biomarkers' cut-off scores.

Plasma Protein Biomarkers Correlated with the Development of Diet-Induced Type 2 Diabetes in Mice

INTRODUCTION: Early detection, assessment of disease progression, and application of an appropriate therapeutic intervention are all important for the care of patients with type 2 diabetes. Currently, however, there is no simple test for early detection of type 2 diabetes. Established diagnostic tests for the disease including oral glucose tolerance, fasting blood glucose, and hemoglobin A1c are relatively late markers where the disease has already progressed. Since blood is in direct contact with many tissues, we hypothesized that pathological tissue changes are likely to be reflected in proteomic profiles of plasma. METHODS: Mice were reared either on regular chow or a high-fat diet at weaning and several physiological responses (i.e., weight, fasting plasma glucose and insulin, and glucose tolerance) were monitored at regular time intervals. Plasma was collected at regular intervals for proteomic analysis by two-dimensional gel electrophoresis and subsequent mass spectrometry. RESULTS: Onset of hyperinsulinemia with corresponding glucose intolerance was observed in 2 weeks and fasting blood glucose levels rose significantly after 4 weeks on the high-fat diet. Many proteins were found to exist in multiple forms (isoforms). Levels of some isoforms including plasma retinol binding protein, transthyretin, Apolipoprotein A1, and kininogen showed significant changes as early as 4 weeks which coincided with the very early development of glucose intolerance. CONCLUSIONS: These results show that a proteomic approach to study the development of type 2 diabetes may uncover unknown early post-translationally modified diagnostic and/or therapeutic protein targets.