Fabrication of Multilayer Microfluidic Arrays for Passive, Efficient DNA Trapping and Profiling.

Trace amounts of cell-free DNA containing cancer-specific biomarkers can be found in blood plasma. Detection of these biomarkers holds tremendous potential for applications such as noninvasive cancer diagnostics and therapeutic monitoring. However, such DNA molecules are extremely rare, and a typical patient blood sample may only contain a few copies. Here we describe the fabrication and operation of a microfluidic device to efficiently trap single DNA molecules into chambers for detection of tumor-specific biomarkers through a passive, geometric manipulation strategy.

Multiplex Digital Methylation-Specific PCR for Noninvasive Screening of Lung Cancer.

There remains tremendous interest in developing liquid biopsy assays for detection of cancer-specific alterations, such as mutations and DNA methylation, in cell-free DNA (cfDNA) obtained through noninvasive blood draws. However, liquid biopsy analysis is often challenging due to exceedingly low fractions of circulating tumor DNA (ctDNA), necessitating the use of extended tumor biomarker panels. While multiplexed PCR strategies provide advantages such as higher throughput, their implementation is often hindered by challenges such as primer-dimers and PCR competition. Alternatively, digital PCR (dPCR) approaches generally offer superior performance, but with constrained multiplexing capability. This paper describes development and validation of the first multiplex digital methylation-specific PCR (mdMSP) platform for simultaneous analysis of four methylation biomarkers for liquid-biopsy-based detection of non-small cell lung cancer (NSCLC). mdMSP employs a microfluidic device containing four independent, but identical modules, housing a total of 40 160 nanowells. Analytical validation of the mdMSP platform demonstrates multiplex detection at analytical specificities as low as 0.0005%. The clinical utility of mdMSP is also demonstrated in a cohort of 72 clinical samples of low-volume liquid biopsy specimens from patients with computed tomography (CT)-scan indeterminant pulmonary nodules, exhibiting superior clinical performance when compared to traditional MSP assays for noninvasive detection of early-stage NSCLC.

Facile Coupling of Droplet Magnetofluidic-Enabled Automated Sample Preparation for Digital Nucleic Acid Amplification Testing and Analysis.

Digital nucleic acid amplification testing (dNAAT) and analysis techniques, such as digital polymerase chain reaction (PCR), have become useful clinical diagnostic tools. However, nucleic acid (NA) sample preparation preceding dNAAT is generally laborious and performed manually, thus creating the need for a simple sample preparation technique and a facile coupling strategy for dNAAT. Therefore, we demonstrate a simple workflow which automates magnetic bead-based extraction of NAs with a one-step transfer to dNAAT. Specifically, we leverage droplet magnetofluidics (DM) to automate the movement of magnetic beads between small volumes of reagents commonly employed for NA extraction and purification. Importantly, the buffer typically used to elute the NAs off the magnetic beads is replaced by a carefully selected PCR solution, enabling direct transfer from sample preparation to dNAAT. Moreover, we demonstrate the potential for multiplexing using a digital high-resolution melt (dHRM) after the digital PCR (dPCR). The utility of this workflow is demonstrated with duplexed detection of bacteria in a sample imitating a coinfection. We first purify the bacterial DNA into a PCR solution using our DM-based sample preparation. We then transfer the purified bacterial DNA to our microfluidic nanoarray to amplify 16S rRNA using dPCR and then perform dHRM to identify the two bacterial species.

Nanoarray Digital Polymerase Chain Reaction with High-Resolution Melt for Enabling Broad Bacteria Identification and Pheno-Molecular Antimicrobial Susceptibility Test.

Toward combating infectious diseases caused by pathogenic bacteria, there remains an unmet need for diagnostic tools that can broadly identify the causative bacteria and determine their antimicrobial susceptibilities from complex and even polymicrobial samples in a timely manner. To address this need, a microfluidic and machine-learning-based platform that performs broad bacteria identification (ID) and rapid yet reliable antimicrobial susceptibility testing (AST) is developed. Specifically, this platform builds on "pheno-molecular AST", a strategy that transforms nucleic acid amplification tests (NAATs) into phenotypic AST through quantitative detection of bacterial genomic replication, and utilizes digital polymerase chain reaction (PCR) and digital high-resolution melt (HRM) to quantify and identify bacterial DNA molecules. Bacterial species are identified using integrated experiment-machine learning algorithm via HRM profiles. Digital DNA quantification allows for rapid growth measurement that reflects susceptibility profiles of each bacterial species within only 30 min of antibiotic exposure. As a demonstration, multiple bacterial species and their susceptibility profiles in a spiked-in polymicrobial urine specimen were correctly identified with a total turnaround time of ∼4 h. With further development and clinical validation, this platform holds the potential for improving clinical diagnostics and enabling targeted antibiotic treatments.

Highly Efficient Real-Time Droplet Analysis Platform for High-Throughput Interrogation of DNA Sequences by Melt.

Droplet microfluidic platforms have greatly enhanced the throughput and sensitivity of single-molecule and single-cell analyses. However, real-time analyses of individual droplets remain challenging. Most droplet microfluidic platforms have fundamental drawbacks that undermine their utility toward applications that rely on real-time monitoring to identify rare variants, such as bacterial persistence, drug discovery, antibody production, epigenetic biomarker analyses, etc. We present a platform for high-density droplet trapping and real-time analysis with 100% loading and trapping efficiency at a packing density of 110,000 droplets per in2. To demonstrate real-time analysis capabilities, we perform digital PCR and parallelized digital high-resolution melt curve acquisition on droplets to discriminate methylation levels of a tumor suppressor gene, CDO1, on a molecule-by-molecule basis. We hope that this platform, which is compatible with a large range of droplet sizes and generation technologies, may facilitate high-throughput real-time analyses on a molecule-by-molecule or cell-by-cell basis of heterogeneous populations.

Multilayer microfluidic array for highly efficient sample loading and digital melt analysis of DNA methylation.

Liquid biopsies contain a treasure of genetic and epigenetic biomarkers that contain information for the detection and monitoring of human disease. DNA methylation is an epigenetic modification that is critical to determining cellular phenotype and often becomes altered in many disease states. In cancer, aberrant DNA methylation contributes to carcinogenesis and can profoundly affect tumor evolution, metastatic potential, and resistance to therapeutic intervention. However, current technologies are not well-suited for quantitative assessment of DNA methylation heterogeneity, especially in challenging samples such as liquid biopsies with low DNA input and high background. We present a multilayer microfluidic device for quantitative analysis of DNA methylation by digital PCR and high resolution melt (HRM). The multilayer design facilitates high-density array digitization aimed at maximizing sample loading efficiency. The platform achieves highly parallelized digital PCR-HRM-based discrimination of rare heterogeneous DNA methylation as low as 0.0001% methylated/unmethylated molecules of a classic tumor suppressor gene, CDKN2A (p14ARF).

Facile profiling of molecular heterogeneity by microfluidic digital melt.

This work presents a digital microfluidic platform called HYPER-Melt (high-density profiling and enumeration by melt) for highly parallelized copy-by-copy DNA molecular profiling. HYPER-Melt provides a facile means of detecting and assessing sequence variations of thousands of individual DNA molecules through digitization in a nanowell microchip array, allowing amplification and interrogation of individual template molecules by detecting HRM fluorescence changes due to sequence-dependent denaturation. As a model application, HYPER-Melt is used here for the detection and assessment of intermolecular heterogeneity of DNA methylation within the promoters of classical tumor suppressor genes. The capabilities of this platform are validated through serial dilutions of mixed epialleles, with demonstrated detection limits as low as 1 methylated variant in 2 million unmethylated templates (0.00005%) of a classic tumor suppressor gene, CDKN2A (p14ARF). The clinical potential of the platform is demonstrated using a digital assay for NDRG4, a tumor suppressor gene that is commonly methylated in colorectal cancer, in liquid biopsies of healthy and colorectal cancer patients. Overall, the platform provides the depth of information, simplicity of use, and single-molecule sensitivity necessary for rapid assessment of intermolecular variation contributing to genetic and epigenetic heterogeneity for challenging applications in embryogenesis, carcinogenesis, and rare biomarker detection.

A Position Statement on Population Data Science: The Science of Data about People.

Information is increasingly digital, creating opportunities to respond to pressing issues about human populations using linked datasets that are large, complex, and diverse. The potential social and individual benefits that can come from data-intensive science are large, but raise challenges of balancing individual privacy and the public good, building appropriate socio-technical systems to support data-intensive science, and determining whether defining a new field of inquiry might help move those collective interests and activities forward. A combination of expert engagement, literature review, and iterative conversations led to our conclusion that defining the field of Population Data Science (challenge 3) will help address the other two challenges as well. We define Population Data Science succinctly as the science of data about people and note that it is related to but distinct from the fields of data science and informatics. A broader definition names four characteristics of: data use for positive impact on citizens and society; bringing together and analyzing data from multiple sources; finding population-level insights; and developing safe, privacy-sensitive and ethical infrastructure to support research. One implication of these characteristics is that few people possess all of the requisite knowledge and skills of Population Data Science, so this is by nature a multi-disciplinary field. Other implications include the need to advance various aspects of science, such as data linkage technology, various forms of analytics, and methods of public engagement. These implications are the beginnings of a research agenda for Population Data Science, which if approached as a collective field, can catalyze significant advances in our understanding of trends in society, health, and human behavior.

Assessing privacy risks in population health publications using a checklist-based approach.

OBJECTIVE: Recent growth in the number of population health researchers accessing detailed datasets, either on their own computers or through virtual data centers, has the potential to increase privacy risks. In response, a checklist for identifying and reducing privacy risks in population health analysis outputs has been proposed for use by researchers themselves. In this study we explore the usability and reliability of such an approach by investigating whether different users identify the same privacy risks on applying the checklist to a sample of publications. METHODS: The checklist was applied to a sample of 100 academic population health publications distributed among 5 readers. Cohen's κ was used to measure interrater agreement. RESULTS: Of the 566 instances of statistical output types found in the 100 publications, the most frequently occurring were counts, summary statistics, plots, and model outputs. Application of the checklist identified 128 outputs (22.6%) with potential privacy concerns. Most of these were associated with the reporting of small counts. Among these identified outputs, the readers found no substantial actual privacy concerns when context was taken into account. Interrater agreement for identifying potential privacy concerns was generally good. CONCLUSION: This study has demonstrated that a checklist can be a reliable tool to assist researchers with anonymizing analysis outputs in population health research. This further suggests that such an approach may have the potential to be developed into a broadly applicable standard providing consistent confidentiality protection across multiple analyses of the same data.

Anonymization for outputs of population health and health services research conducted via an online data center.

OBJECTIVE: Online data centers (ODCs) are becoming increasingly popular for making health-related data available for research. Such centers provide good privacy protection during analysis by trusted researchers, but privacy concerns may still remain if the system outputs are not sufficiently anonymized. In this article, we propose a method for anonymizing analysis outputs from ODCs for publication in academic literature. METHODS: We use as a model system the Secure Unified Research Environment, an online computing system that allows researchers to access and analyze linked health-related data for approved studies in Australia. This model system suggests realistic assumptions for an ODC that, together with literature and practice reviews, inform our solution design. RESULTS: We propose a two-step approach to anonymizing analysis outputs from an ODC. A data preparation stage requires data custodians to apply some basic treatments to the dataset before making it available. A subsequent output anonymization stage requires researchers to use a checklist at the point of downloading analysis output. The checklist assists researchers with highlighting potential privacy concerns, then applying appropriate anonymization treatments. CONCLUSION: The checklist can be used more broadly in health care research, not just in ODCs. Ease of online publication as well as encouragement from journals to submit supplementary material are likely to increase both the volume and detail of analysis results publicly available, which in turn will increase the need for approaches such as the one suggested in this paper.

Individual privacy versus public good: protecting confidentiality in health research.

Health and medical data are increasingly being generated, collected, and stored in electronic form in healthcare facilities and administrative agencies. Such data hold a wealth of information vital to effective health policy development and evaluation, as well as to enhanced clinical care through evidence-based practice and safety and quality monitoring. These initiatives are aimed at improving individuals' health and well-being. Nevertheless, analyses of health data archives must be conducted in such a way that individuals' privacy is not compromised. One important aspect of protecting individuals' privacy is protecting the confidentiality of their data. It is the purpose of this paper to provide a review of a number of approaches to reducing disclosure risk when making data available for research, and to present a taxonomy for such approaches. Some of these methods are widely used, whereas others are still in development. It is important to have a range of methods available because there is also a range of data-use scenarios, and it is important to be able to choose between methods suited to differing scenarios. In practice, it is necessary to find a balance between allowing the use of health and medical data for research and protecting confidentiality. This balance is often presented as a trade-off between disclosure risk and data utility, because methods that reduce disclosure risk, in general, also reduce data utility.

Cost-effectiveness of an advance notification letter to increase colorectal cancer screening.

OBJECTIVES: The aim of this study is to evaluate the cost-effectiveness of a patient-direct mailed advance notification letter on participants of a National Bowel Cancer Screening Program (NBCSP) in Australia, which was launched in August 2006 and offers free fecal occult blood testing to all Australians turning 50, 55, or 65 years of age in any given year. METHODS: This study followed a hypothetical cohort of 50-year-old, 55-year-old, and 65-year-old patients undergoing fecal occult blood test (FOBT) screening through a decision analytic Markov model. The intervention compared two strategies: (i) advance letter, NBCSP, and FOBT compared with (ii) NBCSP and FOBT. The main outcome measures were life-years gained (LYG), quality-adjusted life-years (QALYs) gained and incremental cost-effectiveness ratio. RESULTS: An advance notification screening letter would yield an additional 54 per 100,000 colorectal cancer deaths avoided compared with no letter. The estimated cost-effectiveness was $3,976 per LYG and $6,976 per QALY gained. CONCLUSIONS: An advance notification letter in the NBCSP may have a significant impact on LYG and cancer deaths avoided. It is cost-effective and offers a feasible strategy that could be rolled out across other screening program at an acceptable cost.

Data linkage infrastructure for cross-jurisdictional health-related research in Australia.

BACKGROUND: The Centre for Data Linkage (CDL) has been established to enable national and cross-jurisdictional health-related research in Australia. It has been funded through the Population Health Research Network (PHRN), a national initiative established under the National Collaborative Research Infrastructure Strategy (NCRIS). This paper describes the development of the processes and methodology required to create cross-jurisdictional research infrastructure and enable aggregation of State and Territory linkages into a single linkage "map". METHODS: The CDL has implemented a linkage model which incorporates best practice in data linkage and adheres to data integration principles set down by the Australian Government. Working closely with data custodians and State-based data linkage facilities, the CDL has designed and implemented a linkage system to enable research at national or cross-jurisdictional level. A secure operational environment has also been established with strong governance arrangements to maximise privacy and the confidentiality of data. RESULTS: The development and implementation of a cross-jurisdictional linkage model overcomes a number of challenges associated with the federated nature of health data collections in Australia. The infrastructure expands Australia's data linkage capability and provides opportunities for population-level research. The CDL linkage model, infrastructure architecture and governance arrangements are presented. The quality and capability of the new infrastructure is demonstrated through the conduct of data linkage for the first PHRN Proof of Concept Collaboration project, where more than 25 million records were successfully linked to a very high quality. CONCLUSIONS: This infrastructure provides researchers and policy-makers with the ability to undertake linkage-based research that extends across jurisdictional boundaries. It represents an advance in Australia's national data linkage capabilities and sets the scene for stronger government-research collaboration.

Application of logic models in a large scientific research program.

It is the purpose of this article to discuss the development and application of a logic model in the context of a large scientific research program within the Commonwealth Scientific and Industrial Research Organisation (CSIRO). CSIRO is Australia's national science agency and is a publicly funded part of Australia's innovation system. It conducts mission-driven scientific research focussed on delivering results with relevance and impact for Australia, where impact is defined and measured in economic, environmental and social terms at the national level. The Australian Government has recently signalled an increasing emphasis on performance assessment and evaluation, which in the CSIRO context implies an increasing emphasis on ensuring and demonstrating the impact of its research programs. CSIRO continues to develop and improve its approaches to impact planning and evaluation, including conducting a trial of a program logic approach in the CSIRO Preventative Health National Research Flagship. During the trial, improvements were observed in clarity of the research goals and path to impact, as well as in alignment of science and support function activities with national challenge goals. Further benefits were observed in terms of communication of the goals and expected impact of CSIRO's research programs both within CSIRO and externally. The key lesson learned was that significant value was achieved through the process itself, as well as the outcome. Recommendations based on the CSIRO trial may be of interest to managers of scientific research considering developing similar logic models for their research projects. The CSIRO experience has shown that there are significant benefits to be gained, especially if the project participants have a major role in the process of developing the logic model.

Privacy and the use of health data for research.

OBJECTIVE: We reviewed resources for researchers interested in privacy issues surrounding secondary use of health data for research. These included applicable privacy regulations and available information on privacy perception in Australia. The review is timely because the current Australian Population Health Research Network infrastructure investments are likely to attract new researchers to the field. DATA SOURCES: We used Australian federal, state and territory regulations and programs, polls and surveys, public speeches and academic literature, and some international resources. DATA SYNTHESIS: We identify four themes (de-identification, consent, bias and participation) emerging as areas of concern from the review, and discuss issues relevant to these themes. We provide arguments that excessive privacy regulation has a negative effect on public health research. CONCLUSIONS: There is little evidence of privacy complaints or breaches in health research, but significant concerns about consent and de-identification appear to persist in the community. New researchers need to take account of privacy regulation and may wish to take account of privacy perception when designing study and consent processes.

Mining unexpected temporal associations: applications in detecting adverse drug reactions.

In various real-world applications, it is very useful mining unanticipated episodes where certain event patterns unexpectedly lead to outcomes, e.g., taking two medicines together sometimes causing an adverse reaction. These unanticipated episodes are usually unexpected and infrequent, which makes existing data mining techniques, mainly designed to find frequent patterns, ineffective. In this paper, we propose unexpected temporal association rules (UTARs) to describe them. To handle the unexpectedness, we introduce a new interestingness measure, residual-leverage, and develop a novel case-based exclusion technique for its calculation. Combining it with an event-oriented data preparation technique to handle the infrequency, we develop a new algorithm MUTARC to find pairwise UTARs. The MUTARC is applied to generate adverse drug reaction (ADR) signals from real-world healthcare administrative databases. It reliably shortlists not only six known ADRs, but also another ADR, flucloxacillin possibly causing hepatitis, which our algorithm designers and experiment runners have not known before the experiments. The MUTARC performs much more effectively than existing techniques. This paper clearly illustrates the great potential along the new direction of ADR signal generation from healthcare administrative databases.

Remote access methods for exploratory data analysis and statistical modelling: Privacy-Preserving Analytics.

This paper is concerned with the challenge of enabling the use of confidential or private data for research and policy analysis, while protecting confidentiality and privacy by reducing the risk of disclosure of sensitive information. Traditional solutions to the problem of reducing disclosure risk include releasing de-identified data and modifying data before release. In this paper we discuss the alternative approach of using a remote analysis server which does not enable any data release, but instead is designed to deliver useful results of user-specified statistical analyses with a low risk of disclosure. The techniques described in this paper enable a user to conduct a wide range of methods in exploratory data analysis, regression and survival analysis, while at the same time reducing the risk that the user can read or infer any individual record attribute value. We illustrate our methods with examples from biostatistics using publicly available data. We have implemented our techniques into a software demonstrator called Privacy-Preserving Analytics (PPA), via a web-based interface to the R software. We believe that PPA may provide an effective balance between the competing goals of providing useful information and reducing disclosure risk in some situations.